Systems, Decision and Control in Energy II (Studies in Systems, Decision and Control, 346) 3030691888, 9783030691882

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Studies in Systems, Decision and Control 346

Artur Zaporozhets Volodymyr Artemchuk   Editors

Systems, Decision and Control in Energy II

Studies in Systems, Decision and Control Volume 346

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control–quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/13304

Artur Zaporozhets · Volodymyr Artemchuk Editors

Systems, Decision and Control in Energy II

Editors Artur Zaporozhets Department of Monitoring and Optimization of Thermophysical Processes Institute of Engineering Thermophysics of NAS of Ukraine Kyiv, Ukraine

Volodymyr Artemchuk Department of Mathematical and Econometric Modeling Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine Kyiv, Ukraine

ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems, Decision and Control ISBN 978-3-030-69188-2 ISBN 978-3-030-69189-9 (eBook) https://doi.org/10.1007/978-3-030-69189-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Prospects for the Development of Energy in the World and Ukraine: A Novel View

Abstract The main priorities in the development of the energy industry both in Ukraine and in the world are considered. The analysis of the use of different energy sources in the world and in Ukraine, in particular gas, coal, nuclear energy, oil, and renewable energy sources, is carried out. The results of research by scientists in the energy industry, which are based in the Department of Physical and Technical Problems of Energy of the National Academy of Sciences of Ukraine, are presented. The main results are divided into four categories: electrical engineering, heat power engineering, cybersecurity and computer science, environmental safety. A special contribution to the creation of this book belongs to young scientists of the National Academy of Sciences of Ukraine.

Priority Areas of Energy Development Energy is the basis for ensuring industrial production, the provision of services in the transport sector, the functioning of housing and communal services, the creation of favorable living conditions for the population, and energy is an industry, which largely determines the level of socio-economic development of the country. An important priority for the development of the energy sector in the long term should be the minimization of risks: economic, social, environmental, which should make the development of energy and the entire economy of any country sustainable. Today’s global energy market is hard to compare with the past, even within 5–10 years. The global striving for energy independence, environmental friendliness, global price fluctuations irrevocably change the usual relationship in the energy market. Renewable sources are no longer alternative. They are no longer the privilege of developed countries. Currently, many developing countries spend more on renewable energy sources than developed ones. A striking example in this matter is the experience of Ukraine in attracting foreign investment for the development of infrastructure for renewable energy sources.

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In the world, the environmental factor in the energy sector is becoming an increasingly important priority for the formation of a new economy, modernization, technological renewal, which determines the need for a transition to sustainable development and green energy, structural and technological modernization, and transformation of the energy sector. However, there are still dialogues between the countries about what kind of energy should be—green, nuclear, or fossil. On the one hand, the positive role of green energy is obvious—its use does not harm the environment, it is renewable. On the other hand, it also has disadvantages: high cost, generation instability. This is enough to compare its advantages with more economical nuclear power, which is cheaper, but at the same time is considered dangerous. Undoubtedly, each country chooses its own unique path, taking into account its own resources and experience. The Baltic countries had a rather difficult time getting rid of their energy “unity” with the Russian Federation, while individual territories of Canada and the United States of America, on the contrary, cooperate more closely within the same energy system. France continues to develop nuclear energy, and Germany has embarked on a green energy course. For Ukraine, the practice of using certain energy sources by other countries can become an invaluable experience.

Energy Sources in the World and Ukraine According to the State Statistics Service, for 2018, the distribution of energy consumption in Ukraine is shown in Fig. 1. Despite the fact that the level of introduction of renewable energy sources is gradually increasing, according to this indicator, Ukraine is still far from the EU (Fig. 2). However, global energy trends are not obvious enough. And the issues of using various types of energy resources in Ukraine and the world remains relevant.

Renewable Energy Sources According to analytical materials from businessviews.com.ua, renewable energy is the most dynamically developing energy sector in the world. According to the forecasts of many experts, in 2050, this type of energy should take first place in the energy supply system. Renewable energy sources are now developing much faster than nuclear or oil energy in their time. In Ukraine, the development of renewable energy is also very progressing. But some problems are already clearly visible. Firstly, the tariff for renewable energy is so high that the country cannot afford to buy the volumes of energy that are currently being produced. In addition, in the near

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Fig. 1 Distribution of consumption of energy resources in Ukraine in 2018

Fig. 2 Distribution between various sources of electricity generation in Ukraine and the EU for 2019

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future, this will lead to an increase in electricity tariffs. The Ministry of Energy of Ukraine is currently planning a reform to gradually reduce the green tariff. Secondly, renewable energy sources strongly destabilize our energy system. Our power grids are outdated and cannot accept the volumes of electricity that are being produced. In addition, Ukraine cannot store electricity in large quantities, so at the moment exactly as much electricity is produced as it is consumed. An increase in the share of renewable energy sources in the country’s energy balance will lead to the shutdown of nuclear power plants or thermal power plants.

Gas Gas is the second most dynamic source of energy in the world after renewable energy sources. By 2050, the increase in gas consumption in the world by more than half (58%) will be provided by domestic production. Liquefied gas will account for 28% of consumption in the countries of the world. The least demand will be for pipelines. In advanced economies, gas will displace oil, coal, and nuclear power. Gas is relatively easy to produce and store, is one of the most environmentally friendly fuels with low emissions, and its price tends to decrease. Ukraine’s gas problems are primarily related to its ineffective use. Gas production is also underdeveloped in Ukraine. Of the 30 billion cubic meters of gas that we consume, 20–22 billion cubic meters have to be imported. According to many experts’ opinions, the best option for Ukraine is to develop its own gas production. However, the relatively long return on investment in drilling the well is currently slowing production rates.

Nuclear Energy The role of nuclear energy in the world as a whole remains constant, but its balance is changing. Developed countries reduce their share in the total energy balance while developing countries increase it. This is primarily due to the fact that nuclear power plants (NPPs) in developed countries were built a long time ago, but now they are already outdated. Therefore, they are gradually being decommissioned. And the construction of new NPPs is quite expensive and takes a long time. China currently occupies a leading position among countries in the world in the development of nuclear energy. In Ukraine, the share of nuclear power in electricity generation is more than 50%, and all the plants were built in Soviet times. 75Seventy-five percent of them will must be closed in the period 2030–40 due to the expiration of the service life. And there are currently no plans to replace them. It is possible that their service life will be extended again.

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Oil Oil consumption in the world as a whole remains quite stable. This is mainly due to the growing demand for plastics and space products, as well as through the development of air travel. Transportation does not increase oil consumption due to the growth of the electric vehicle industry and biofuel production, as well as the energy efficiency of vehicles. Oil production in Ukraine at present does not meet the own needs of the population. Ukraine imports over 50% of oil. The total amount of imported gasoline is difficult to calculate since the “gray” market for oil products is highly developed. Own production is also poorly developed. As with gas production, oil wells have a long payback period.

Coal The total consumption of coal in the world is not falling; however, in advanced economies, it is gradually abandoning its use. Some countries generally implement an absolute ban on its use. So, France plans to close all coal power plants in 2021, Sweden—in 2022, Great Britain, Austria, and Italy—until 2026. The use of coal is growing at the expense of India. This country accounts for more than 50% of the increase in coal consumption among developing countries. China is the largest consumer of coal. But even China is starting to reduce its use of coal, preferring renewable energy sources. Coal provides 70% of the capacity of thermal power plants, producing about 30% of electricity in Ukraine. Compared to the European Union (EU), Ukraine has very low standards, and therefore large volumes of emissions of harmful substances. Ukraine lacks funds to carry out a general ecological modernization of heat-generating facilities. At the same time, Ukraine also cannot currently provide itself with coal completely, therefore, part of it is also imported. However, Ukraine must gradually abandon the use of coal. This is one of the conditions for Ukraine’s integration into the EU.

Review of Current Scientific Research The materials presented in the monograph “Systems, Decision and Control in Energy II” represent an integrated approach of Ukrainian scientists to the development of the energy industry both in Ukraine and the world as a whole. The authors’ teams of the chapters of the book united the best scientific institutions into the universities of Ukraine, in particular Institute of Engineering Thermophysics of NAS of Ukraine (Kyiv), .G.E. Pukhov Institute for Modelling in Energy Engineering of NAS

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of Ukraine (Kyiv), Institute of Electrodynamics of the NAS of Ukraine (Kyiv), Institute of General Energy of NAS of Ukraine (Kyiv), State Institution “The Institute of Environmental Geochemistry of NAS of Ukraine” (Kyiv), National Aviation University (Kyiv), National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnical Institute,” V.N. Karazin Kharkiv National University (Kharkiv), Admiral Makarov National University of Shipbuilding (Mykolaiv), Kyiv International University (Kyiv), National University of Civil Defence of Ukraine (Kharkiv), National Pedagogical Dragomanov University (Kyiv), Interregional Academy of Personnel Management (Kyiv), Polissia National University (Zhytomyr), State University Zhytomyr Polytechnic (Zhytomyr), Institute of Telecommunications and Global Information Space of the NAS of Ukraine (Kyiv), NPC “UKRENERGO” (Kyiv). The content of the monograph is divided into four subsections: electrical engineering, heat power engineering, cybersecurity and computer science, environmental safety.

Electrical Engineering In subsection 1, the authors pay attention to contemporary issues related to the development of the electric power industry, electrical engineering, the physics of electrical phenomena, and renewable energy sources (such as solar energy and wind energy). In particular, chapter “Corona Discharge Problem in Extra High Voltage Transmission Line” [1] is devoted to the corona discharge problem in the extra high-voltage transmission line. Corona discharge is a phenomenon that occurs on extra highvoltage transmission lines. Because of corona, there is much loss that occurs in the transmission system. Due to the large error in determining the type of weather along the overhead route, empirical methods for calculating corona losses are unacceptable for the efficient operation of electrical systems. The developed system allows determining the components of the total power loss in extra high voltage power lines, primarily the power loss per corona in real time, which will allow to optimize the operating mode in order to reduce these losses and achieve energy savings. Chapter “Efficiency Increase of Open Phase Modes in Bulk Electrical Networks” [2] considered to efficiency increasing of open-phase modes in bulk electrical networks. The chapter proposes a method for determining the reduction in line capacity in open-phase mode (single-phase automatic reclosing). On the basis of the proposed method, the reduction of the throughput for the actually operated EHV lines is analyzed. Through the use of modern reactive power compensation devices, the ineffectiveness of the use of controlled shunt reactors and STATCOM for the purpose of increasing the line capacity during the single-phase auto reclosure pause was determined. Chapter “Nonlinear Mathematical Model of Optimal Solar Photovoltaic Station Design” [3] focused on the nonlinear mathematical model of optimal solar photovoltaic station design. To assess the minimum production cost of electricity generated by solar photovoltaic station, a simple nonlinear mathematical model of optimal solar

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photovoltaic station design is proposed. The examples of calculations made with the model demonstrate that essential changes in the production cost of electricity could be achieved depending only on the equipment structure of the solar photovoltaic station which depends on its geographical location. Simulation of efficiency enhancement of electric power generation by wind turbines in wind cadaster various zones presented in chapter “Simulation of Efficiency Enhancement of Electric Power Generation by Wind Turbines in Wind Cadaster Various Zones” [4]. A new mathematical model was pioneered for calculating a capacity factor with regard to the operation time period in the zone of strong winds and also a new model of multiple linear regression for defining the dependence of the operation duration in the zone of strong winds upon the hub height, and rated wind speed of power characteristic of the wind turbine. The said models enable the calculation of annual production of electric power based only on the three passport parameters (rating, hub height and rated wind speed of the wind turbine) without reference to power curve and wind cadaster of the expected construction site of the wind power station. Comparative analysis and recommendations for use of frequency regulation technologies in integrated power systems with a large share of wind power plants are given in chapter “Comparative Analysis and Recommendations for the Use of Frequency Regulation Technologies in Integrated Power Systems with a Large Share of Wind Power Plants” [5]. With the use of the author’s model, which takes into account these nonlinear constraints, a series of calculations was performed to identify the possibilities of using each of the known classes of power plants and electrical units for automatic frequency control in the integrated power systems (IPSs) with powerful wind power plants (coal, gas-oil power plants, gas piston units, battery energy storage systems (BESSs)). Variants for each of the technologies where the frequency in the IPS met the requirements of the European power system ENTSO-E and the IPS of Ukraine were considered. It was found that for IPS, which are comparable to IPS of Ukraine in terms of the combination of operating conditions, only BESSs are available for use as a frequency regulator in terms of technological and economic factors. Chapter “Increasing Vertical Resolution in Electrometry of Oil and Gas Wells” [6] considered new software implementation of effective methods for well electrometry modeling. For electrical logging, it is based on the original approach of using the integrated current method. That made it possible to quickly and accurately solve the problem of determining the influence of the location of the return electrode in the electrical logging probes at a finite distance on the vertical resolution of the method. For induction logging, the proposed method allows not only to quickly and accurately solve the direct problem. These methods allow not to change the type and number of equations in the system of linear algebraic equations during modeling of cuts changes, but to change only values of system coefficients. Proposed methods were implemented in software, tested, and successfully used for modeling in geophysical research for complicated well cuts. These approaches have made it possible to improve the accuracy of solving the iterative inverse electrometry problem. It uses the result of solving the direct problem at each iteration step.

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Heat Power Engineering Subsection 2 is devoted to modern problems in heat power engineering and considers modern means and methods that increase the efficiency and reliability of the functioning of heat power facilities. In chapter “Optimization Models of Industrial Furnaces and Methods for Obtaining Their Numerical Solution” [7], optimization models of industrial furnaces and methods for obtaining their numerical solution are considered. The chapter describes two mathematical optimization models for research and improving the efficiency of modern industrial and muffle furnaces on an electrical basis. The first mathematical model involves finding the temperatures of internal spot heaters, the location of which is known in advance. The second mathematical model assumes finding the locations of the furnace spot heaters, the temperatures of which are already known. This chapter presents a general nonlinear case of this mathematical model. The numerical solution of the optimization models is obtained using high-speed optimization methods that are derived from the classical Newton’s method. Also, a comparative analysis of the work of the methods by the number of calls to the procedure for solving the direct problem of heat conduction is given. Development of a system for diagnosing heat power equipment based on IEEE 802.11s is presented in chapter “Development of a System for Diagnosing Heat Power Equipment Based on IEEE 802.11s” [8]. The possibility of using a mesh network for diagnosing heat power equipment is considered. A comparison is made between traditional Ethernet and IEEE 802.11s based networks. A working prototype of a measuring module of a system for diagnosing energy equipment, which is an element of a mesh network, has been developed. A functional diagram of the relationship of the measuring modules of the equipment diagnostic system has been constructed. Aspects of the installation and management of communications, energy saving in IEEE 802.11s are considered. Modeling the coal industry technological development considering environmental restrictions is presented in chapter “Modeling the Coal Industry Technological Development Considering Environmental Restrictions” [9]. The purpose of the article is to develop mathematical models and tools for optimizing the technological development of coal mining based on the use of environmental and economic indicators of the efficiency of coal production advanced technologies, taking into account the capture and utilization of coalbed methane. A model of production type has been constructed, based on production activities of coal mining and taking into account the formation of coalbed methane. The software implementation of the model made it possible to determine the effective technologies and the optimum complete set of treatment complexes for re-equipment of state mines for achieving maximum production and ensuring environmental safety. The volume of coalbed methane emissions reduction for the period up to 2040, as well as the volume of solid waste generated in the process of coal production, have been determined. Comparative efficiency assessment of using biofuels in heat supply systems by levelized cost of heat into account environmental taxes is considered in chapter

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“Comparative Efficiency Assessment of Using Biofuels in Heat Supply Systems by Levelized Cost of Heat into Account Environmental Taxes” [10]. The authors show that “Levelized Cost of Heat” (LCOH) indicator can change significantly over time due to changes in fuel prices, environmental tax rates, etc., so the corresponding risks when investing in various heating projects must be taken into account. This work is aimed at revealing the essence of these risks, as well as to determine the change in LCOH on the example of boilers with a capacity of 100, 500, and 1000 kW, which burn the following fuels: natural gas, anthracite, wood chips, wood pellets, sunflower husk pellets, straw briquettes, wood, peat briquettes. Thirteen variants of LCOH calculations were made for boilers with a capacity of 100 kW, 10—for boilers of 500 kW, and 9—for boilers of 1000 kW (a total of 32 variants) for the prices of 2016 and 2020 in Ukraine. In addition, it should be noted that the authors summarized the specific emissions of pollutants (kg/t of fuel) and emissions of pollutants for fuel (g/GJ) for the above fuels. Among the pollutants, the following NOx , SOx , CO2 , and PM10 were taken into account.

The Cybersecurity and Computer Science Subsection 3 is devoted to issues of cybersecurity of critical facilities, in particular energy facilities, as well as the development of computer science and the introduction of modern information and measurement systems in the energy sector. Requirements for taxonomy of cyber threats of critical infrastructure facilities and analysis of existing approaches are presented in chapter “Requirements for a Taxonomy of Cyber Threats of Critical Infrastructure Facilities and an Analysis of Existing Approaches” [11]. This chapter presents a unified approach to the systematization of cyber threats. The essence of this approach is to eliminate uncertainties regarding the description of cyber threats. Given the possible risks of information security and cybersecurity of information systems and process control systems at critical infrastructure facilities, a study of known cyberattacks has been conducted. Based on the experience of the world community in the field of cybersecurity and information technology, the classification of methods of intrusion and types of attacks on information systems. Using a formalized approach with the help of drawings and diagrams, the vectors of attacks, the structure of the attack, and the development of the information security incident are presented. The results of this study can be useful for assessing information security risks, in particular in determining the criticality of the organization’s assets, in creating information security management systems, and developing appropriate technical regulations to ensure the continuity of business processes of the organization. Chapter “A New Task Scheduling Algorithm for GRID Systems with Non-alienable Resources” [12] considered new task scheduling algorithm for GRID systems with non-alienable resources. The analysis of existing algorithms of planning of GRID systems which are constructed on the basis of not alienable resources is carried out. The most used planning algorithms are listed. This chapter describes a

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new approach to solving the problem of scheduling task flows on processes or GRID nodes of a system with non-alienable resources. The proposed approach is universal and allows to plan tasks for both static and dynamic systems. A new scheduling algorithm, developed on its basis, is also presented. Concept of the architecture and creation of SIEM system in critical infrastructure is viewed in chapter “A Concept of the Architecture and Creation for SIEM System in Critical Infrastructure” [13]. In this chapter the analysis of the existing models, systems, and methods for cyber threats detection in critical infrastructure was carried out for their disadvantages defining. A model of cloud service has been developed; it allows to ensure the security of cloud service and conduct appropriate simulations. An improved method for cyber threats detection has been developed, it allows to detect cyber threats in cloud services and classify them. The developed method was experimentally investigated using NSL-KDD dataset. It was proved the correctness of its work and the possibility of application in cloud services as well as increase efficiency of cloud system security by 48.02%. Cloud service model has been developed which can be used to build cloud services based on the various architectures. Also, the structural scheme of the main concept of the architecture and creation for SIEM was constructed, that can be used in energy and other sectors of critical infrastructure. The application of virtual and augmented reality at NPPs is considered in chapter “Application of Virtual and Augmented Reality at Nuclear Power Plants” [14]. The research examined publications on positive aspects of virtual and augmented reality technologies application for various industries, in particular for nuclear energy. Practical examples of the application of virtual and augmented reality technologies for the operation of NPPs are described. As a result of the study, it is determined that currently virtual and augmented reality technologies for NPPs are used in the following areas: modeling of various nuclear energy processes; operation, repair, and maintenance of equipment; presentation of activities, construction of the station; staff training and education. Use of virtual and augmented reality technologies for NPPs confirms its economic efficiency through the reduction of cost and time costs of staff travel to facilities; elimination of design errors before the beginning of the stage of construction and installation works; increasing the level of industrial safety; improving the management of NPPs. Theoretical aspects of the application of material measure in measurements are summarized in chapter “Application of Material Measure in Measurements: Theoretical Aspects” [15]. The issues of using material measures to assess the results of measurement of physical quantities are considered. The concept of matching the material and probability measures with the aim of forming a unified approach to the assessment of the result and characteristics of measurement uncertainty is proposed. An example of using a set of material and probability measures in hardware and software modules of information-measuring systems is shown.

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The Environmental Safety Subsection 4 deals with the problems of rational use of natural resources, accounting for emissions of harmful substances, environmental issues at energy facilities, as well as the development of a methodology for environmental safety. Innovative developments to solve major aspects of environmental and radiation safety of Ukraine are considered in chapter “Innovative Developments to Solve Major Aspects of Environmental and Radiation Safety of Ukraine” [16]. This study presents innovative developments that were created by the scientific team of the Department of Nuclear, Radiation and Technogenic-Ecological Safety of the State Institution “The Institute of Environmental Geochemistry of the National Academy of Sciences of Ukraine.” The results of scientific research and development in the following areas are presented. Among them are a plasma-chemical unit to clean nuclear power plants wastewaters; a system of operative radiation control (high speed) on the basis of the aircraft; a system of radiation control and identification of ionizing radiation on the basis of a solid-state detector with high metrological and operational characteristics; identifier-spectrometer based on a solid-state detector for nuclear fuel cycle facilities and others. Chapter “Ecological Situation of Post-mining Regions in Ukraine” [17] is analyzed the ecological situation of post-mining regions in Ukraine. The chapter compares the protective potential of the geological environment (GE) of the Chernobyl accident zone and the Donbas region, as well as performs an expert assessment of the environmental protection potential of the post-mining in the leading coal mining areas (MA) of Ukraine. It is shown that a new model of mineral resources use, structure of geological exploration works, scientific substantiation of maximum allowable changes of GE at the closing of “old” and formation of new MA, improvement of environmental monitoring based on GIS technologies and remote sensing of the Earth is needed. Development of teaching methodology in the field of environmental monitoring of atmosphere is considered in chapter “Development of Teaching Methodology in the Field of Environmental Monitoring of Atmosphere” [18]. The article substantiates the importance of conducting environmental monitoring of the surface layer of the atmosphere using specialized software-modeling systems. Information software is described for the tasks of monitoring and controlling the ecological status of urbanized areas (AISEEM system), which was developed by the authors. The features of the special course for training future specialists in the field of ecology and environmental protection using specialized software and modeling systems are considered. Chapter “A Transdisciplinary Analytical System for Supporting the Environmental Researches” [19] considered a transdisciplinary analytical system to support environmental researches. The main aspects of man-caused impact and efficiency of contact and remote methods of data acquisition on the ecological situation within the framework of environmental monitoring were analyzed. The method of calculation

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of multi-parametric dynamic correlation is developed for the analysis and interpretation of ecological datasets. Mathematical framework of the proposed approach is Pearson correlation coefficient calculating. The proposed method is implemented in the form of information technology and tested on the example of a long-term array data analysis on water pollution in Ukraine. Test locations for method implementation are situated in the river basin of Ustia, Rivne region, and river basin of Prut, Chernivtsi region. The proposed approach and developed software for its use can become a part of the analytical system for support of environmental researches. The concept of transdisciplinary integration of information about environmental situation and their scientifically substantiated interpretation in problems of ecological management on the basis of geographic information technologies were offered. The conceptual approach to the development of software tools for analysis and synthesis of geophysical monitoring systems models are showed in chapter “A Conceptual Approach to the Development of Software Tools for the Analysis and Synthesis of Geophysical Monitoring Systems Models” [20]. The main purpose of this study is the presentation of the conceptual approach to the development of software tools for the analysis and synthesis of a geophysical monitoring systems model. To achieve this goal, two approaches are proposed to describe the interaction “humanity—the environment.” The first approach is based on the use of “game principles.” At the heart of the second approach, the environment is considered as a system characterized by fluctuations under the influence of anthropogenic load. The present chapter substantiates the model of the geophysical monitoring system based on the Lagrange equations for an oscillatory system with two or more degrees of freedom in the “factor space,” and the model based on “game principles.” Estimated efficiency of biogenic elements removal from wastewater in the ideal displacement photobioreactor is carried out in chapter “Estimated Efficiency of Biogenic Elements Removal from Waste Water in the Ideal Displacement Photobioreactor” [21]. Surface water bodies’ eutrophication problem caused by their pollution by nutrients coming together with wastewater is considered in the chapter. It is proposed to use a technological scheme at municipal and other sewage treatment plants, which provides for the organization of additional wastewater treatment by using them as a medium for the cultivation of energy microalgae. The use of the absorption process of nutrients by microalgae organisms due to the metabolism of the latter is proposed. To implement the process, the use of closed photobioreactors of ideal displacement is proposed. To design such photobioreactors, nutrient removal efficiency calculating model, namely nitrogen and phosphorus compounds from wastewater during the operation of such a photobioreactor, is proposed.

Conclusions Energy consumption in Ukraine is characterized by an imperfect structure, namely, the largest share of energy sources falls on fossil fuels, and the share of renewable energy sources remains significantly lower than in Europe and the world as a whole.

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The level of energy intensity of Gross Domestic Product in Ukraine remains one of the highest in the world. In addition, the volume of traditional fuel and energy resources in the country is rather limited. All this has a negative impact on the competitiveness of the domestic economy. However, Ukraine has good enough potential to correct all the shortcomings of the energy sector and form a good energy balance, which will be formed on economically advantageous indicators and high environmental standards. Ukrainian scientists, including young scientists of the Department of Physical and Technical Problems of Energy of the National Academy of Sciences of Ukraine, work in various branches of energy and are able to generate results not only on a regional but also on a global scale. This statement is supported by this book. Artur Zaporozhets Volodymyr Artemchuk

References 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

Ievgev, O., Zaitsev, Ie., Kuchanskyy, V.: Corona discharge problem in extra high voltage transmission line. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Kuchanskyy, V., Malakhatka, D., Blinov, I.: Efficiency Increasing of Open Phase Modes in Bulk Electrical Networks. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Bilenko, M., Buratynskyi, I., Leshchenko, I., Nechaieva, T., Shulzhenko, S.: Nonlinear Mathematical Model of Optimal Solar Photovoltaic Station Design. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Podgurenko, V., Kutsan, Yu., Getmanets, O., Terekhov, V.: Simulation of Efficiency Enhancement of Electric Power Generation by Wind Turbines in Wind Cadaster Various Zones. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Zgurovets, O., Kulyk, M.: Comparative analysis and recommendations for use of frequency regulation technologies in integrated power systems with a large share of wind power plants. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Myrontsov, M., Karpenko, O., Trofymchuk, O., Okhariev, V., Anpilova, Ye.: Increasing vertical resolution in electrometry of oil and gas wells. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Zaporozhets, A., Khaidurov, V. Tsiupii, T.: Optimization models of industrial furnaces and methods for obtaining their numerical solution. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Zaporozhets, A., Babak, V., Sverdlova, A., Isaienko, V., Babikova, K.: Development of a system for diagnosing heat power equipment based on IEEE 802.11s. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Makarov, V., Kaplin, M., Bilan, T., Perov, M.: Modeling the coal industry technological development considering environmental restrictions. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Bogoslavska, O., Stanytsina, V., Artemchuk, V., Garmata, O., Lavrinenko, V.: Comparative Efficiency Assessment of Using Biofuels in Heat Supply Systems by Levelized Cost of Heat into Account Environmental Taxes. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021

xviii 11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

Prospects for the Development of Energy in the World and Ukraine: A … Komarov, M., Davydiuk, A., Onyskova, A., Tkachenko, V., Honchar, S.: Critical Infrastructure Facilities and Analysis of Existing Approaches. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Uzdenov, T.: New Task Scheduling Algorithm for GRID Systems with Non - Alienable Resources. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Berdibayev, R., Gnatyuk, S., Yevchenko, Ya., Kishchenko, V.: Concept of the Architecture and Creation for SIEM System in Critical Infrastructure. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Popov, O., Iatsyshyn, A., Sokolov, D., Dement, M., Neklonskyi, I., Yelizarov, A.: Application of Virtual and Augmented Reality at Nuclear Power Plants. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Babak, V., Zaporozhets, A., Kuts, Yu., Scherbak, L., Eremenko, V.: Application of material measure in measurements: theoretical aspects. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Zabulonov, Yu., Popov, O., Burtniak, V., Iatsyshyn, A., Kovach, V.: Innovative developments to solve major aspects of environmental and radiation safety of Ukraine. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Trofymchuk, O., Yakovliev, Ye., Anpilova, Ye., Myrontsov, M., Okhariev, V.: Ecological situation of post-mining regions in Ukraine. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Iatsyshyn, A., Artemchuk, V., Zaporozhets, A., Popov, O., Kovach, V., Taraduda, D.: Development of teaching methodology in the field of environmental monitoring of atmosphere. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Trofymchuk, O., Myrontsov, M., Okhariev, V., Anpilova, Ye., Trysnyuk, V.: Transdisciplinary analytical system for support the environmental researches. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Maevsky, O., Artemchuk, V., Brodsky, Yu., Makarenko, L., Shpylovyi, Yu.: The Conceptual Approach to the Development of Software Tools for Analysis and Synthesis of Geophysical Monitoring Systems Models. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021 Shamanskyi, S., Boichenko, S., Pavliukh, L.: Estimated efficiency of biogenic elements removal from waste water in the ideal displacement photobioreactor. In: Systems, Decision and Control in Energy II. Studies in Systems, Decision and Control, 2021

Contents

Electrical Engineering Corona Discharge Problem in Extra High Voltage Transmission Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ievgev O. Zaitsev and Vladislav V. Kuchanskyy

3

Efficiency Increase of Open Phase Modes in Bulk Electrical Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vladislav V. Kuchanskyy, Denys O. Malakhatka, and Ihor Blinov

31

Nonlinear Mathematical Model of Optimal Solar Photovoltaic Station Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maryna Bilenko, Ihor Buratynskyi, Iryna Leshchenko, Tetiana Nechaieva, and Sergii Shulzhenko Simulation of Efficiency Enhancement of Electric Power Generation by Wind Turbines in Wind Cadaster Various Zones . . . . . . . . Volodymyr Podgurenko, Yulii Kutsan, Oleg Getmanets, and Volodymyr Terekhov Comparative Analysis and Recommendations for the Use of Frequency Regulation Technologies in Integrated Power Systems with a Large Share of Wind Power Plants . . . . . . . . . . . . . . . . . . . . Oleksandr Zgurovets and Mykhailo Kulyk

49

63

81

Increasing Vertical Resolution in Electrometry of Oil and Gas Wells . . . 101 Mykyta Myrontsov, Oleksiy Karpenko, Oleksandr Trofymchuk, Viacheslav Okhariev, and Yevheniia Anpilova Heat Power Engineering Optimization Models of Industrial Furnaces and Methods for Obtaining Their Numerical Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Artur Zaporozhets, Vladyslav Khaidurov, and Tamara Tsiupii

xix

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Contents

Development of a System for Diagnosing Heat Power Equipment Based on IEEE 802.11s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Artur Zaporozhets, Vitaliy Babak, Anastasia Sverdlova, Volodymyr Isaienko, and Kateryna Babikova Modeling the Coal Industry Technological Development Considering Environmental Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Vitaliy Makarov, Mykola Kaplin, Tetiana Bilan, and Mykola Perov Comparative Efficiency Assessment of Using Biofuels in Heat Supply Systems by Levelized Cost of Heat into Account Environmental Taxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Olga Bogoslavska, Valentyna Stanytsina, Volodymyr Artemchuk, Oleksandr Garmata, and Viktoriia Lavrinenko Cybersecurity and Computer Science Requirements for a Taxonomy of Cyber Threats of Critical Infrastructure Facilities and an Analysis of Existing Approaches . . . . . . . 189 Maksym Komarov, Andrii Davydiuk, Alla Onyskova, Volodymyr Tkachenko, and Serhii Honchar A New Task Scheduling Algorithm for GRID Systems with Non-alienable Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Taras Uzdenov A Concept of the Architecture and Creation for SIEM System in Critical Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Rat Berdibayev, Sergiy Gnatyuk, Yaroslav Yevchenko, and Vitaliy Kishchenko Application of Virtual and Augmented Reality at Nuclear Power Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Oleksandr Popov, Anna Iatsyshyn, Dmytro Sokolov, Maksym Dement, Ihor Neklonskyi, and Alexander Yelizarov Application of Material Measure in Measurements: Theoretical Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Vitaliy Babak, Artur Zaporozhets, Yurii Kuts, Leonid Scherbak, and Volodymyr Eremenko Environmental Safety Innovative Developments to Solve Major Aspects of Environmental and Radiation Safety of Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Yurii Zabulonov, Oleksandr Popov, Volodymyr Burtniak, Andrii Iatsyshyn, Valeriia Kovach, and Anna Iatsyshyn

Contents

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Ecological Situation of Post-mining Regions in Ukraine . . . . . . . . . . . . . . . 293 Oleksandr Trofymchuk, Yevhenii Yakovliev, Yevheniia Anpilova, Mykyta Myrontsov, and Viacheslav Okhariev Development of Teaching Methodology in the Field of Environmental Monitoring of Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . 307 Andrii Iatsyshyn, Volodymyr Artemchuk, Artur Zaporozhets, Oleksandr Popov, Valeriia Kovach, and Dmytro Taraduda A Transdisciplinary Analytical System for Supporting the Environmental Researches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Oleksandr Trofymchuk, Mykyta Myrontsov, Viacheslav Okhariev, Yevheniia Anpilova, and Vasyl Trysnyuk A Conceptual Approach to the Development of Software Tools for the Analysis and Synthesis of Geophysical Monitoring Systems Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Oleksandr Maevsky, Volodymyr Artemchuk, Yuri Brodsky, Lesia Makarenko, and Yurii Shpylovyi Estimated Efficiency of Biogenic Elements Removal from Waste Water in the Ideal Displacement Photobioreactor . . . . . . . . . . . . . . . . . . . . . 347 Sergii Shamanskyi, Sergii Boichenko, and Lesia Pavliukh

Electrical Engineering

Corona Discharge Problem in Extra High Voltage Transmission Line Ievgev O. Zaitsev

and Vladislav V. Kuchanskyy

Abstract Despite the great successes made both in our country and abroad, and the existing needs of industry in means and methods of measurement, the electric field of industrial frequency was not covered by methods, and by means of measurement, means of calibration and verification, as well as standards governing measurement techniques. Existing methods and means of measuring electric field strength are not suitable for measuring with satisfactory accuracy the fields of industrial frequency near and on the surface of electrical equipment due to the introduction of significant distortions in the electric field. This suggests the need to provide industry, science, ecology and other aspects of human activity with the necessary knowledge to create tools and methods for measuring the electric field intensity of industrial frequency. As a consequence of the above, the relevance of the topic of the thesis follows from the requirement for the further development of the theory, methods and means of measuring electric field intensity at an industrial frequency, which ensure the reliability and satisfactory accuracy of measurements both near electrical equipment and in the free space of power lines. A significant increase in the power, range and voltage of the lines, which reflects the main trend in the development of electric power transmission technology in recent decades, has put forward a number of new scientific and technical problems and has demanded a more perfect solution to the problems previously considered with respect to lines of lower voltage classes. Such tasks, the solution of which determines to a large extent both the cost of the structure and the economical operation of power lines of three-phase currents of extra high voltage, include the task of engineering methods for calculating various manifestations of the corona on wires. Corona discharge is the phenomenon which occurs on extra high voltage transmission lines. Because of corona there is much loss occurs in the transmission system. We can also identify corona discharge on lines by means of blue color spark and hissing sound. A corona discharge is an electrical discharge. Corona has also a typical sound called hissing sound, by which we can identify corona I. O. Zaitsev (B) · V. V. Kuchanskyy The Institute of Electrodynamics of the National Academy of Sciences of Ukraine, Kyiv, Ukraine e-mail: [email protected] V. V. Kuchanskyy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_1

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I. O. Zaitsev and V. V. Kuchanskyy

discharge. Corona discharges generally occur in high voltage transmission system. It is very harmful for transmission system, because as it is loss it losses energy flowing in the system. Corona takes place in a high voltage transmission line when neutral atom or molecule in a strong electric field is ionized. Happening so positive ion and free electron created around the conductor and the conduction takes place through air between conductors of high voltage transmission line. It causes the power loss in line, audible noise, electromagnetic interference, purple glow, and ozone production and insulation damage of the insulating medium. Keywords Corona discharge · Crucial electrical density · Electrical power losses · Optical information measuring systems · Control accuracy

1 Introduction During the transmission of electrical energy, losses occur in each element of the electrical network. To study the components of losses in various elements of the network and assess the need to carry out one or another measure aimed at reducing losses, an analysis of the structure of electricity losses is performed. The division of losses into components can be carried out according to different criteria: type of losses (active, reactive), nature (constant, variable), voltage classes, groups of elements, production units, etc. Power loss is the power consumed by network elements during energy transfer or simply when the network is energized. They are divided into active and reactive power losses, conditionally constant and conditionally variable losses, as well as losses in lines, transformers and other network elements. Conditional-variable (load) losses are losses that arise in the longitudinal branches of the equivalent circuit of the network elements. These losses are proportional to the square of the load current, which varies greatly over time. Therefore, load losses are also subject to large changes. Conditionally constant is the loss that occurs in the transverse branches of the equivalent circuit. These losses can be considered approximately proportional to the square of the voltage. They depend little on the load current and arise even in its absence, that is, at idle speed. Therefore, they are also called no-load losses. Since the voltage in the network changes little over time, the no-load losses remain almost constant. The question of accounting for corona power losses stands somewhat apart. Corona conductivity is a complex function of voltage and weather conditions. Since, however, in lines with split wires, corona losses, as a rule, amount to no more than 20% of the total active power losses, it is quite permissible to take into account the corona in design and operating calculations in the form of a constant distributed active conductivity of the lines, and in some cases be limited to taking into account corona losses in the total balance of active capacities.

Corona Discharge Problem in Extra High Voltage Transmission Line

5

Effective transmission line management requires complete and accurate information about the mode parameters that characterize the current state. The parameters of the overhead power line, active resistance, active and capacitive conductivity of the line, power losses for heating the wires and the corona in real conditions vary depending on the ambient temperature and meteorological conditions of the line route. Wire resistance is a function of current density, ambient temperature, wind speed and precipitation intensity. In this regard, the operational modeling of the parameters of the airline mode, taking into account the real operating conditions is of great importance. Effective transmission line management requires complete and accurate information about the mode parameters that characterize the current state. The parameters of the overhead power line, active resistance, active and capacitive conductivity of the line, power losses for heating the wires and the corona in real conditions vary depending on the ambient temperature and meteorological conditions of the line route. Wire resistance is a function of current density, ambient temperature, and wind speed and precipitation intensity. In this regard, the operational modeling of the parameters of the airline mode, taking into account the real operating conditions is of great importance. In the tasks of calculation, identification of electrical parameters, operational control and optimization of modes of the ultrahigh voltage transmission line there is a problem of choosing a calculation model corresponding to the accuracy of the original data, optimal control of line modes by voltage and reactive power; prompt refinement of line model parameters; technical and economic analysis of the operation of the overhead power line, in the exchange of electrical energy between power systems. In the tasks of calculation, identification of electrical parameters, operational control and optimization of modes of the ultrahigh voltage transmission line there is a problem of choosing a calculation model corresponding to the accuracy of the original data. optimal control of line modes by voltage and reactive power; prompt refinement of line model parameters; technical and economic analysis of the operation of the overhead power line, in the exchange of electrical energy between power systems.

2 The Functions of Extra High Voltage Transmission Lines in Integrated Electrical Power Systems Power transmission extra high voltage (EHV) lines is customary to attribute power transmission with a rated voltage of 500 and 750 kV, is now widely used in many countries. In most industrialized countries, there are bulk grids of these voltage classes that form energy systems on a national scale [1–6]. The most important connections between the power systems of different states are also created using voltages of these classes. The specific features of the EHV lines—significant capacitance, overvoltage, the corona effects [7, 8], powerful electric fields, etc. necessitated the use of new methods for calculating the parameters and modes [5, 6].

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An increase in electricity consumption, an increase in the unit capacity of power units and installed capacity of thermal, nuclear and hydraulic power plants and an increase in the transmission distance of energy characteristic of modern energy industry have led to the fact that the majority of units are connected to the power system using high power transfer capacity lines. The list of EHV lines is listed on Table 1. Significant power transfer capability of transmission lines can be achieved by using an EHV [6], which can significantly reduce the number of parallel circuits of power lines and thus reduce the width of the corridor for power lines. Under these conditions, the correct choice of the rated voltage provides a reduction in the initial costs and the costs of operating power transmissions. Therefore, the creation of modern large energy systems is reliable and economical task. The bulk electrical power system is shown on Fig. 1. The analysis of materials on the creation and operating experience of these power transmissions shows that technically the problem of electric power transmission using EHV has not been satisfactorily solved. Despite some differences in the line designs and parameters of the main electrical equipment [6], a number of fundamental decisions specific to the power lines of these classes have developed and have become Table 1 The list of EHV lines of Integrated electrical power system №

The name of the line

1

Khmelnitsky NPP—Rzeszow (Poland)

396

2

South Ukrainian NPP—Isaccea (Romania)

403

3

Kievska Substation—Rivne NPP

353

4

Zaporizhzhya NPP—Kakhovka Substation

190

5

Substation Western Ukraine—Albertirsa (Hungary)

479

6

Zaporizhzhya NPP—Dniprovska Substatio

137

7

Zaporizhzhya NPP—Dniprovska Substation

211

8

Substation West-Ukrainian—Rivne NPP

282

9

Substation Pivdennodonbaska—Substation Donbass

220

10

Zaporizhzhya NPP—Substation Pivdennodonbaska

150

11

Substation Dniprovska—South Ukrainian NPP

333

12

Substation Zaporizhzhya-KuTES substation

323

13

Substation Western Ukraine-PS Vinnytsia

361

14

CHAES—Khmelnitsky NPP

136

15

Substation West Ukrainian-Substation Vinnitsa

177

16

Substation North Ukrainian-Kursk NPP

228

17

Vinnitsa—South Ukrainian NPP

304

18

Substation West Ukrainian—Khmelnitsky NPP

190

19

Substation Vinnitsa-South Ukrainian NPP

126

Length, km

Corona Discharge Problem in Extra High Voltage Transmission Line 4хАS-400

4хАS-400

362

418

3х3х110 3х3х333

2х3х110 2х3х333

7 750 кВ

3х3х110 2х3х333 330 кВ

Fig. 1 Bulk electrical power system

generally accepted, regarding phase design [9, 10], reactive power compensation [8], coordination of insulation, lightning protection devices, relay protection, etc. To ensure the stability of the parallel operation of the generators connected to the ends of the power supply system, especially by EHV line, it is necessary to reduce the longitudinal reactance of the line. This is achieved by splitting the phase of the linear wires [6, 9, 10] into several parallel wires located at the vertices of the polygon, the number and dimensions of the sides of which are determined by the specific requirements of the project. The splitting of the wires provides an increase in the equivalent phase radius without increasing the mass of the wires. This design of the phase is also rational from the point of view of attenuating the effects of the corona [7], since it provides a decrease in the electric field strength on the surface of the wires. In all over the world, a huge number of research works which are dedicated to solving practical problem related to the creation of power lines of extra high voltage with high power transfer capacity. This allowed EHV technology to take a leading place in the world in the field of long-distance power transmission and provided priority in resolving a number of the most complex problems of the construction and operation of extra high voltage power lines. The practical results of this work are significant. The Integrated Energy System has been created one of the largest energy associations in the world with as a single centralized dispatch control. The parallel operation of Ukraine with the integrated energy system of Europe of the member countries is provided by power transmission EHV lines [11]. The bulk transmission lines of rated voltage 750 kV are the main system-forming lines in the power system of Ukraine and deliver electricity from powerful units of nuclear power plants (NPPs), as well as the necessary exchange between parts of

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the power system. In addition, their development and efficient operation is the main prerequisite for ensuring the integration of Ukraine’s energy system into the European energy system. The failure of the EHV transmission line or the equipment that ensures their connection to the power system is a serious system accident. It can cause the system to break down into parts with a shortage and excess of generating capacity, which will require disconnection of consumers in scarce regions and shutdown of power plants in surplus. It is clear that such an abnormal mode of the main electric network will be far from optimal and will have significant deviations in quality and efficiency, so preventing the failure of transmission lines is an important scientific and practical task not only in terms of uninterrupted power supply. Based on the principles of the system approach, the power system can be represented by a set of grids for different purposes and rated voltages, which form certain hierarchical levels for energy flows. The distribution of energy flows between networks is associated with the manifestation of the fundamental principle of least action, which in electrical engineering is realized through Kirchhoff’s laws [6, 8, 10]. Therefore, with the natural distribution of energy flows between networks, its losses will be the smallest. But when using sinusoidal alternating current, this conclusion is valid for full power. At the same time, the economic regime with minimal losses of active power, which are of interest to us in assessing the efficiency of energy transport, is established only in the conditional scheme of active resistance. Studies have shown that the natural mode is significantly (1.4–1.5 times) inferior to the economic in terms of losses, and there is an overload of lower voltage networks with inadequate energy transport flows, which reduces the capacity of the power system as a whole [6, 11]. There is almost always a corona discharge on the wires of EHV transmission lines. Coronation of wires at operating voltage leads to energy loss and the appearance of field interference to communications, and therefore during operation, this phenomenon should be limited [6, 7]. At the same time, when overvoltages, the corona reduce the multiplicity of overvoltages [8, 11], so it can be considered as a positive factor. In general, the problem of taking into account the effect of the corona on the multiplicity of overvoltages is complicated by nonlinear relationships between the parameters of the regime, external factors and the characteristics of the discharge. Characteristics of the category determine the parameters of the corona model. These values vary widely and depend on external factors, in particular, weather conditions along the route line [8]. In the value of the parameters of the corona model are calculated by the values of the factors of influence given in [8], and throughout the modeling process, they are considered unchanged. The corona leads to additional losses of electricity on overhead lines. A corona is a type of self-sustained discharge in air that occurs on electrodes of small radius at certain electric field strength on them. External manifestations of the corona discharge on the line wires are characteristic crackling and luminescence in separate places on the surface of the wires [12–15]. Justification of the engineering method for calculating power and energy losses on the corona on the wires of power lines is the main ultimate goal of this chapter. At

Corona Discharge Problem in Extra High Voltage Transmission Line

9

the same time, not only the traditional question of estimating average annual losses is clarified, but new aspects of the problem are posed and developed in terms of the probability of occurrence of various levels of losses and statistical laws of their distribution, which corresponds to modern trends in the technical and economic approach to solving engineering problems. A sufficiently complete confidence in the correctness of the solutions found can take place only when these solutions are obtained on the basis of distinct ideas about the physical mechanism of the AC corona process, on the one hand, and on the other, if these solutions are tested in practice. This approach to solving the main problem led to the development of methods and research on the physical mechanism of the corona. This last task has practical applications. The second practical side of the approach to solving the main problem required the creation of a research method and the actual research of the corona on the wires of power lines directly in the conditions of their operation. We are talking about lengthy and systematic measurements of power and energy losses per corona, the results of which will establish the laws of the distribution of losses over time and obtain reliable data necessary when designing such expensive structures as modern power lines. In this work, we propose a set of scientifically based technical solutions that allows you to create methods and means of measuring the electric field in free space and near electrical equipment, and closes the knowledge gap in this area, which is of great social and economic importance.

3 The Effects of Corona Discharge on Bulk Electrical Power Systems Against the background of changes in the economic mechanism in countries in the energy sector, changes in economic relations, the problem of reducing energy losses in electric networks has not only not lost its relevance, but, on the contrary, has advanced to one of the tasks of ensuring the financial stability of energy supplying organizations [4, 5]. The experience of exploitation power systems shows that with long-term operation of overhead power lines, the loss of power and electricity in them even at a constant load increases. One of the reasons is a change in the configuration of individual sections of the power transmission line and, therefore, changes in the values of equivalent resistances. Of the main possible reasons for the change in the longitudinal resistance of the power lines [4, 5]. Loss of electricity in networks is one of the most important indicators of the operation of an electric grid company. They reflect the technical condition and quality of operation of electric networks, the level of moral and technical aging, the perfection of information collection and accounting systems, in other words, the efficiency of the entire enterprise’s activity, and also directly affect its economy. Reducing

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I. O. Zaitsev and V. V. Kuchanskyy

energy losses in networks for various purposes is one of the main directions of the implementation of energy-saving policies in the country and in the world. In turn, technical losses are the sum of the three components of losses in the lines and equipment of electric networks: – losses depending on the load of the electric network (load losses); – losses depending on weather conditions (losses per corona). The performed analysis [15–19] shows that the noted specific power losses per corona are practically independent of the physical parameters of the phase conductive conductors, since, firstly, the radius of the phase wire is involved in the formula for determining losses under the sign of the logarithm and its dynamics (for example, corrosion) not palpable; secondly, phase-conductor splitting is used in networks with a voltage of 110 kV and more and an equivalent radius is involved in the calculations [9], which virtually eliminates the possibility of taking into account the corrosion changes in the physical parameters of overhead line conductors. The accuracy of determining the loss of power in electric networks, which has technical, economic and social significance, depends on the accuracy of the grid simulation, as well as on the applied mathematical model of the established mode. The determination of the corona loss in overhead power lines from the point of view of accuracy is problematic, since they depend on a number of specific factors (meteorological conditions on the line route, electric field, geometric parameters, location of phase wires of power lines, etc.). Studies were performed to clarify the methodology for calculating power and energy losses on the corona on wires in flat conditions. In [13, 14], the rationale and derivation of the equation of active power loss per corona based on the integral characteristics of the corona and the results of probe studies of the motion of a space charge in the corona field of an alternating current are presented. According to [16], in the region of the common corona, the dependence of power losses per wire on voltage and power line parameters is expressed by the formula. A corona discharge is an electrical discharge which is created on by ionization of a fluid such as surrounding a conductor that is electrically charged. Corona discharges generally occur in high and extra voltage system. Basically the corona discharge observed at the surface of a conductor is due to formation of electron avalanches which occur when the intensity of the electric field at the conductor surface exceeds a certain critical value. In addition to the irregularities caused by the top wire, there are always surface damages (scratches, burrs), dirt (traces of grease, particles of organic and inorganic origin) on the overhead lines and atmospheric deposits (raindrops, dew, snow, frost, ice, frost). This leads to a local increase in electric field strength and, as a result, to the appearance of a local corona at individual points on the wire. The characteristics of radio interference and losses at the local corona mainly depend on the electric field strength on the wire, its diameter, surface condition, meteorological conditions and are subject to significant scatter. The power loss per corona depends on the voltage of the overhead lines, the design of the phase (number of wires per phase, brand, wire diameter, splitting pitch), the

Corona Discharge Problem in Extra High Voltage Transmission Line

Restriction of the use of complex shunt reactor switching system Increasing the static and dynamic stability of the grid

Reducing technological power losses

Improving technical and economic indicators of electric power systems and AC transmissions

Reduction use of generators in power plants as regulated sources of reactive power

FACTS (Controlled shunt reactors and STATCOM)

Voltage regulation and voltage fluctuations

Suppression resonance overvoltages

11 compensation of excess charging power of power lines

Improving performance characteristics of the network Optimization of modes bulk electrical power systems

Limitation of transients overvoltages

Increasing of power capability

Fig. 2 Tasks that the SMART GRID concept allows to solve in bulk electric networks through controlled reactive power devices

distance between the phases, the load current. Long-term studies have shown that corona losses are significantly dependent on weather conditions: good weather, dry snow, rain (wet snow), high humidity, etc. Losses on the corona in dry snow, rain (wet snow) in comparison with losses on the corona in good weather increases by 3–40 times. Since the levels of corona loss on the overhead line reach significant values, it is advisable to consider methods and means that contribute to their reduction. One of the reasons for hindering the optimization of power system operation is the loss of corona wires due to their significant values and stationary random nature of origin and development. In the works it was found that the loss of the corona significantly affects the means to reduce the total loss. That is, the corona of wires reduces the efficiency of controlled shunt reactors Fig. 2, which would increase the efficiency and reliability of the bulk electrical grids in many areas. Planning the modes taking into account losses on the corona encounters considerable difficulties due to the fact that the weather is probabilistic in nature, as well as the conditions for passing each line. In order to carry out operational control of the voltage in the network, it is necessary to have current and forecast values of losses in the wires and on the corona, in addition, it is necessary to take into account the conditions of heating and cooling of the wires of the overhead line. So, in calm weather in the sun, the temperature of the wire can increase by more than 20° C with respect to air temperature. Therefore, the planning of regimes taking into account losses on the corona is currently only being worked out. An analysis of a large number of works performed in our country and abroad on the assessment, measurement, calculation and analysis of corona losses allows us to conclude that the formulas given in literature for calculating corona losses give an approximate estimate of them, direct measurements of these losses on existing lines require the use of specialized equipment and measuring devices, are associated with high costs of material resources and organizational efforts. Tension is an important fundamental force characteristic of electrical pole. In this regard, the means of measuring the intensity of low-frequency electronic devices fell out of the range of devices for measuring electrical quantities. This situation is especially typical for measuring instruments for industrial frequency electric drives on the surface of high-voltage insulators and near power lines.

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In contrast to load losses, the losses on the corona are of a different nature and are determined by the features of the air insulation of power lines. Of a special nature are measures aimed at reducing losses on the corona, the basis of which are various methods of regulating the operating voltage of the network. Obviously, in market conditions, the compensation of costs for energy losses due to the corona should be formed according to different rules than for technological losses. In contrast to the load component of the loss of electricity, losses on the corona require further research and improvement of accounting methods. The main weather factors decisively affecting the amount of electricity losses do not have clear boundaries for identification, which reduces the reliability of the calculation methods. In market conditions, the task of assessing actual corona losses at the pace of the process becomes relevant. Today, for its solution in the grids of 750 kV there is a necessary information base: all network objects are equipped with means for measuring mode parameters and devices, automation systems for the commercial accounting of electricity are actively introduced. Telecommunication information about the parameters of the mode at the ends of each power transmission line and for each autotransformer constantly arrives at the system. Under these conditions, the technique for determining corona losses is obvious. The total power loss in the power transmission line is determined from the telemetry of the active power or according to the data on the energy consumption at the ends of the power lines for the adopted time interval. Losses depending on the load can be determined by calculation using a shaped equivalent circuit using the parameters of telemetry. Losses per corona are defined as the difference between the measured total losses in the branch and the calculated load losses. The algorithm for measuring power losses per corona is based on the use of telemetric information about the operating parameters of the line issued by the system. According to the algorithm for calculation used the values of the current power losses, which are determined by subtracting the active power P2 at its end from the active power P1 entering the line at its end, and this compensates for the systematic and random error of the loss measurement. Against the background of changes in the economic mechanism in countries in the energy sector, changes in economic relations, the problem of reducing energy losses in electric networks has not only not lost its relevance, but, on the contrary, has advanced to one of the tasks of ensuring the financial stability of energy supplying organizations [4, 5]. The experience of exploitation power systems shows that with long-term operation of overhead power lines, the loss of power and electricity in them even at a constant load increases. One of the reasons is a change in the configuration of individual sections of the power transmission line and, therefore, changes in the values of equivalent resistances. Of the main possible reasons for the change in the longitudinal resistance of the power lines [4, 5]. Loss of electricity in networks is one of the most important indicators of the operation of an electric grid company. They reflect the technical condition and quality of operation of electric networks, the level of moral and technical aging, the perfection of information collection and accounting systems, in other words, the efficiency

Corona Discharge Problem in Extra High Voltage Transmission Line

13

of the entire enterprise’s activity, and also directly affect its economy. Reducing energy losses in networks for various purposes is one of the main directions of the implementation of energy-saving policies in the country and in the world. In turn, technical losses are the sum of the three components of losses in the lines and equipment of electric networks: – losses depending on the load of the electric network (load losses); – losses depending on weather conditions (losses per corona). The performed analysis [15–19] shows that the noted specific power losses per corona are practically independent of the physical parameters of the phase conductive conductors, since, firstly, the radius of the phase wire is involved in the formula for determining losses under the sign of the logarithm and its dynamics (for example, corrosion) not palpable; secondly, phase-conductor splitting is used in networks with a voltage of 110 kV and more and an equivalent radius is involved in the calculations [9], which virtually eliminates the possibility of taking into account the corrosion changes in the physical parameters of overhead line conductors. The accuracy of determining the loss of power in electric networks, which has technical, economic and social significance, depends on the accuracy of the grid simulation, as well as on the applied mathematical model of the established mode. The determination of the corona loss in overhead power lines from the point of view of accuracy is problematic, since they depend on a number of specific factors (meteorological conditions on the line route, electric field, geometric parameters, location of phase wires of power lines, etc.). Studies were performed to clarify the methodology for calculating power and energy losses on the corona on wires in flat conditions. In [13, 14], the rationale and derivation of the equation of active power loss per corona based on the integral characteristics of the corona and the results of probe studies of the motion of a space charge in the corona field of an alternating current are presented. According to [16], in the region of the common corona, the dependence of power losses per wire on voltage and power line parameters is expressed by the formula. A corona discharge is an electrical discharge which is created on by ionization of a fluid such as surrounding a conductor that is electrically charged. Corona discharges generally occur in high and extra voltage system. Basically the corona discharge observed at the surface of a conductor is due to formation of electron avalanches which occur when the intensity of the electric field at the conductor surface exceeds a certain critical value. In addition to the irregularities caused by the top wire, there are always surface damages (scratches, burrs), dirt (traces of grease, particles of organic and inorganic origin) on the overhead lines and atmospheric deposits (raindrops, dew, snow, frost, ice, frost). This leads to a local increase in electric field strength and, as a result, to the appearance of a local corona at individual points on the wire. The characteristics of radio interference and losses at the local corona mainly depend on the electric field strength on the wire, its diameter, surface condition, meteorological conditions and are subject to significant scatter.

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I. O. Zaitsev and V. V. Kuchanskyy

The power loss per corona depends on the voltage of the overhead lines, the design of the phase (number of wires per phase, brand, wire diameter, splitting pitch), the distance between the phases, the load current. Long-term studies have shown that corona losses are significantly dependent on weather conditions: good weather, dry snow, rain (wet snow), high humidity, etc. Losses on the corona in dry snow, rain (wet snow) in comparison with losses on the corona in good weather increases by 3–40 times. Since the levels of corona loss on the overhead line reach significant values, it is advisable to consider methods and means that contribute to their reduction. One of the reasons for hindering the optimization of power system operation is the loss of corona wires due to their significant values and stationary random nature of origin and development. In the works it was found that the loss of the corona significantly affects the means to reduce the total loss. That is, the corona of wires reduces the efficiency of controlled shunt reactors Fig. 2, which would increase the efficiency and reliability of the bulk electrical grids in many areas. Planning the modes taking into account losses on the corona encounters considerable difficulties due to the fact that the weather is probabilistic in nature, as well as the conditions for passing each line. In order to carry out operational control of the voltage in the network, it is necessary to have current and forecast values of losses in the wires and on the corona, in addition, it is necessary to take into account the conditions of heating and cooling of the wires of the overhead line. So, in calm weather in the sun, the temperature of the wire can increase by more than 20 °C with respect to air temperature. Therefore, the planning of regimes taking into account losses on the corona is currently only being worked out. An analysis of a large number of works performed in our country and abroad on the assessment, measurement, calculation and analysis of corona losses allows us to conclude that the formulas given in literature for calculating corona losses give an approximate estimate of them, direct measurements of these losses on existing lines require the use of specialized equipment and measuring devices, are associated with high costs of material resources and organizational efforts. Tension is an important fundamental force characteristic of electrical pole. In this regard, the means of measuring the intensity of low-frequency electronic devices fell out of the range of devices for measuring electrical quantities. This situation is especially typical for measuring instruments for industrial frequency electric drives on the surface of high-voltage insulators and near power lines. In contrast to load losses, the losses on the corona are of a different nature and are determined by the features of the air insulation of power lines. Of a special nature are measures aimed at reducing losses on the corona, the basis of which are various methods of regulating the operating voltage of the network. Obviously, in market conditions, the compensation of costs for energy losses due to the corona should be formed according to different rules than for technological losses. In contrast to the load component of the loss of electricity, losses on the corona require further research and improvement of accounting methods. The main weather factors decisively affecting the amount of electricity losses do not have clear boundaries for identification, which reduces the reliability of the calculation methods.

Corona Discharge Problem in Extra High Voltage Transmission Line

15

In market conditions, the task of assessing actual corona losses at the pace of the process becomes relevant. Today, for its solution in the grids of 750 kV there is a necessary information base: all network objects are equipped with means for measuring mode parameters and devices, automation systems for the commercial accounting of electricity are actively introduced. Telecommunication information about the parameters of the mode at the ends of each power transmission line and for each autotransformer constantly arrives at the system. Under these conditions, the technique for determining corona losses is obvious. The total power loss in the power transmission line is determined from the telemetry of the active power or according to the data on the energy consumption at the ends of the power lines for the adopted time interval. Losses depending on the load can be determined by calculation using a shaped equivalent circuit using the parameters of telemetry. Losses per corona are defined as the difference between the measured total losses in the branch and the calculated load losses. The algorithm for measuring power losses per corona is based on the use of telemetric information about the operating parameters of the line issued by the system. According to the algorithm for calculation used the values of the current power losses, which are determined by subtracting the active power P2 at its end from the active power P1 entering the line at its end, and this compensates for the systematic and random error of the loss measurement.

4 Calculation of Corona Discharge Power Losses in the Power Transmission Lines Dependences of the field strength of the middle phase on the split phase structure were investigated. Analysis of the results of the study indicates that the electric field strength on the surface of the wires decreases while increasing the distance between the wires of the adjacent phases of the extra voltage transmission line and reducing the splitting step. At the same time, the value of the electric field strength corresponding to the emergence of a stable corona changes. The corona occurs at some initial tension E 0 , this value is relatively constant, since if the voltage increases the volume charge of the corona increases, which reduces the tension on the surface of the wire. The critical tension on the surface of the wire E 0 , at which the corona takes the form of an independent discharge, can be determined by the empirical Peek formula [20]: E 0 = 3.03 · 10

−3

  0.298 0.298 1+ √ ·δ·m· 1+ √ r0 · δ r0 · δ

(1)

0,386 where r0 —single wire radius of phase, cm; m—wire smoothness factor; δ = 273+t — average annual relative air density, p—pressure, mm Hg; t—air temperature.

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I. O. Zaitsev and V. V. Kuchanskyy

The value of the average electric field strength on the surface of the split-phase wire is determined similarly to single wires by the formula [20]: E av = 1.47

U N Cav n · r0

(2)

where U N —rated voltage of the line; Dav —average geometric distance between phases; n—number of sub-conductor; req —equivalent wire radius when splitting the phase into sub-conductors; C0 —average linear capacitance of the wire, pF/km. For an approximate calculation of the electric field strength of a three-phase transposed line, it is sufficient to find the average linear capacitance of the wire, without taking into account the effect of the earth, according to the expression [20]: Cav =

24  , lg Dreqav

(3)

where Dav —average geometric distance between phases; req —equivalent wire radius when splitting the phase into sub-conductors. The maximum electric field strength of the split wires is determined by the coefficient of transition and from the average electric field strength to the maximum by the expression [20]: E = k · E av

(4)

An equivalent strength of the field [20]: E eq = (E + E av )/2

(5)

There are many formulas for determining the power loss per corona in power lines, as well as the total power loss. For example corona losses that can be calculated using the empirical Mayr formula [12]: P = nk f r02 E eq (E 0 − E eq )(2, 3

1350 − 1) · 10−5 f r0

(6)

where f —frequency 50 Hz. The power dissipated in the system due to corona discharges is called corona loss. Accurate estimation of corona loss is difficult because of its variable nature. It has been found that the corona loss under fair weather condition is less than under foul weather conditions. The corona loss under appropriate weather conditions is given below by the Peek’s formula [21]

Corona Discharge Problem in Extra High Voltage Transmission Line

P Cor ona =

244 ( f + 25)(E n − E 0 )2 δ



17

req −5 10 Dav

(7)

Peek’s formula is applicable for decided visual corona. This formula the gives the inaccurate result when the losses are low, and En/Eo is less than 1.8. It is superseded by Peterson’s formula given below [21]: E n2  10−5 P Cor ona = 2.1 f F  Dav log10 req

(8)

The specific value of the average annual active power loss per corona for the three-phase high-voltage power line is the sum of the phase losses for each group of weather conditions [20]:  0c = n · r02 ψg.w.

3  k=1

g.w.k + ψd.s.

3  k=1

d.s.k + ψr.

3  k=1

r.k + ψ f r.

3 

 f r.k

k=1

(9) where c.w.k , s.s.k , r.k ,  f r.k —generalized power losses to the corona for good weather, dry snow, rain and frost for k- th phase of the line; ψg.w. ψd.s. ψr. ψ f r. —the probability of the appropriate weather group. Determine the total power loss in the line can be obtained by the difference in power measurements at the ends of the line. In this case, the problem arises of the separation of technological losses and power losses per corona. For this, the formula for determining the load (technological) and corona losses in the power line was derived: P = G · (B 2 · A1 − 6 · (A2 + A3) + G 2 · (R 2 + X ) + 8) · U 2   G · A1 P 2 + Q2 + + ((A3 · (3 · A2 − 4(A3 − 4)) +R · 2 U2   A42 + G 2 · X 2 A4 · A2 − A4 · A3 − 7 · G · A2 − )· P + − −15 · G · X · Q (10) 2 2

where R—active resistance Ohm; X —inductive resistance Ohm; G—active conductivity, Sm; B—reactive conductivity, cm; U —rated voltage, kV; P—MWatt active power, Q—reactive power, MWAr. For the sake of convenience, additional coefficients have been introduced. A = R 2 + X 2 ;A2 = B X ;A3 = G R A4 = B R. In turn, formulas for determining technological power losses were derived: P T ech =

R · (X · B 2 · Q 2 · U 2 − R · B 2 · P · U 2 − 2 · B · Q · U 2 + 2 · P 2 + 2 · Q 2 ) 2U 2

(11)

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I. O. Zaitsev and V. V. Kuchanskyy

Table 2 Comparison of the results of known equations, proposed equations, and experimental data Defined parameters

Mayr formula

Suggested equations

Experimental data

Peek’s formula

Peterson’s formula

Total power losses, MWatt P

–

66.96

67.5

–

–

Technological losses, MWatt P T ech

–

13.56

12.2

–

–

Corona losses, MWatt P Cor ona

50.2

53.37

54.3

45.5

48.9

Expression of determination of corona power losses: P Cor ona =

G · (2 · B 2 · U 4 · X 2 − 7 · B · Q · U 2 · X 2 2U 2 2 4 G · (2 · G · U · X − 12 · B · U 4 · X ) + 2U 2 2 4 G · (2 · G · U · X 2 − G · P · U 2 · X 2 + P 2 · X 2 + Q 2 · X 2 ) + 2U 2 2 2 G · (30 · Q · U · X − 16 · U 4 ) + (12) 2U 2

Analysis of overall active power losses P T ech and P Cor ona in the typical modes of operation was performed for transmission lines with the following parameters: l = 400, km – length of the line, U = 750, kV – rated voltage of the line. Construction of the wire 4xAS-400/93, is characterized by the next values r 0 = 0.071 Ohm/km; x0 = 0.284 Ohm/km; g0 = 22.35 · 10−9 Sm/km; b0 = 4.10 · 10−6 Sm/km. The active power is P = 1000, M W att and the reactive power is Q = 400, M W ar . So the losses which are calculated by Eqs. (6–9) in such parameters of the line will be P = 66, 96M W att, P T ech = 13, 56M W att and P Cor ona = 53, 37M W att. To determine the accuracy of the calculation using the derived formulas, the formula Mayr (6), Peek (7), Peterson (87) and measuring the difference in the values of the active power at the ends of the power line, a comparative analysis was carried out. Comparison of the results of known equations, proposed equations and experimental data are summarized in Table 2.

5 Measuring Energy Losses with Optical Sensing Elements The system software (primary data acquisition and processing module) consists of the following parts: data processing software for measurement module, a microcontroller

Corona Discharge Problem in Extra High Voltage Transmission Line

19

configuration module for information-measuring channel, a primary data processing module. The specially designed software is used for the control system for data collection, processing, and analysis. This software is used for the operation control of primary sensor of corona discharge power losses measurement systems, to organize other hardware and software modules, for the primary process implementation, data collection and data transmission from the transducers to designed devices for the secondary processing, for the statistical data analysis, storage and control data display in a simple form for the operator. Operations of calculating the value of corona discharge power losses special software solution is used. In turn, obtained at the work of the module of mathematical processing and module of automatic control of the state of the electrical equipment node is transferred to the data storage organization module for database management based on the history of measurements. In this case, it is possible to create knowledge bases with diagnostic features, which depend on the value of the physical parameter that is control of the certain state of the power equipment. To implement the measurement algorithm it was necessary to solve the following tasks were solved: 1. 2. 3. 4.

5.

Consideration of the components of electricity losses in the electrical power system and existing methods for their calculation. Analysis of the sensitivity of the method when measuring small values of electric energy losses on the corona (at high humidity) and in wires in real time. The study of methodological and instrumental errors embedded in the software package algorithms for determining load losses and corona losses in real time. Analysis of the effect of reducing electricity losses on the corona and in the wires of the power lines when regulating voltage according to data on current values of electricity losses. Determination of the maximum voltage control range and the possible effect of saving power losses on a unit 500 and 750 kV.

The considered algorithm is used in the measurement systems software package, designed to estimate active, reactive power, voltages and linear losses, and allows you to quickly monitor the level of corona loss to quickly take steps to reduce them. The measurement systems are also designed for: • the definition of the class of corona discharge power losses in the power transmission lines; • selection of diagnostic signals available for measurement, and control points on the object under study; • development of a mathematical model of the diagnostic object, the analysis of which allows substantiating possible diagnostic parameters; • development of algorithms for obtaining numerical values of selected diagnostic parameters; • construction of decisive rules for identifying and classifying defects; creation of means implementing certain steps of the diagnostic process from the selected measurement and diagnostic signals before making diagnostic solutions.

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I. O. Zaitsev and V. V. Kuchanskyy

The problem of contactless measurements of electric field strength is rather urgent. Such measurements are necessary for the remote monitoring of losses in overhead power lines [29], including transmission line parameters such as discharge to the corona. In this case, the means of losses in overhead power lines discharge are in a rather harsh environment of the operating environment, which is characterized by a significant intensity of electromagnetic fields, high temperature and humidity and ozone content. In this case, the actual task is to increase the noise immunity of the monitoring and measurement of the losses in overhead power lines. The solution of this problem with the use of traditional structures discussed above today requires the use of sophisticated measuring equipment directly on the transmission line, so there is a need to implement measuring converters with the application of fundamentally new solutions, methods and methods of construction. Perspective way to design of measurement system it’s to use optical measurement (OM) system [26–30]. This system usually consisting of optical sensing element for measurement and analyzer unit of parameters of line they are completely inert to the effects of the fields of the internal environment of the powerful generators, although they have a higher cost of means being placed in the “safe zone” where there is no effect of the transmission line operating environment. An essential feature of OM is their important and significant perspective in application, especially since, in addition to noise immunity, they operate in powerful electromagnetic fields; they also have considerable measurement distance (up to hundreds of meters), as well as high potential accuracy and measurement speed through the use of high-speed optical and resolution optical elements, despite the fact that the technology for the production of primary measurement converters for OM is currently sufficiently high complicated and expensive, requires the use of special technological and metrological equipment. So, in this case one of the necessary conditions it is that the optical sensing element or primary transducer of the sensor introduced a minimum of distortion in the structure of the field distribution, be passive (not requiring availability power supply in the measurement area), miniature and provided galvanic discharge between the measurement area and the equipment. These conditions are satisfied by fiber-optic sensors (FOS) based on the linear Pockels electro-optic effect. In this case, as sensitive elements can be used ferroelectric crystals of 3 m class. On Fig. 3 shown one of channel of system design for corona discharge power losses measurement systems in [22] with optical sensor network in the transmission lines of the high- and extra-high voltage transmission lines. The principle of operation of optical measuring systems is based on electro-optical modulation of a light wave due to the conversion of linearly polarized light to elliptically polarize. In this case, the longitudinal Pockels effect is used when the electric field is directed into the crystal along an axis parallel to the direction of light propagation. To determine the function of converting of the one optical sensor (Fig. 2) from system of optical sensor network, let us consider the simplest case of using the specified design for optical fiber meters with amplitude modulation of the information

Corona Discharge Problem in Extra High Voltage Transmission Line

21

Fig. 3 The block diagram of the block diagram of corona discharge power losses optical measurement systems

signal. In this case, to determine the sensor conversion functions L(F), take advantage of the dependence of optical radiation in the sensor on the value of the attenuation of the optical light in the Pokels optical cell. So, L(F) calculated as L() = 0 · K

(13)

where F0 —the initial power of the laser radiation at the output of the fiber; K— coefficient taking into account value of the attenuation of the optical light in the Pokels optical cell and account the amount of laser radiation incident on the photo detector. On Fig. 3 are shown construction of the sensitive element of the optical sensor of the electric field. To evaluate the value K it is necessary to determine the light emission power of a semiconductor laser F, which, at the output of the ferroelectric crystal, enters the end of the fiber optic bundle with a core diameter of one fiber 2a, can be determined by the following expression:

 K Di f f ≈

SO F L 21

2 ·

1 ··χ π

(14)

where L1—distance to the controlled object; χ—transmittance of light radiation, which depends on the power of the corona; SOF—the total area of the illuminated ends of the receiving cable of the fibers; 2a = 25–100 μm—diameter of fiber core; F—the light flux at the output of the crystal [23]. Expression (13) is true if the angular size of the laser spot at the exit of the crystal is less than the viewing angle of the receiving device [23].

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I. O. Zaitsev and V. V. Kuchanskyy

6 Experimental Data from Measurement System with Optical Sensing Elements The development of methods for calculating, analyzing and standardizing energy losses during the operation of extra high voltage power lines, as well as the selection of economically feasible ways to reduce them, has been ongoing for over 40 years. But to date, a methodology for determining loss standards has not been established. Not even the principles of rationing are defined. Scientific developments and publications in this area are characterized by a wide range of opinions (Fig. 4). The choice of methods and the construction of measuring instruments Fig. 5 are largely determined by the physical effect or phenomenon that underlies the construction of the measuring system. Therefore, the subsequent development of new scientifically based technical solutions should be accompanied by the identification of physical effects suitable for constructing means for measuring electric field strength and the establishment of general laws governing the interaction of these tools with field sources. This, in turn, will allow us to formulate measurement methods and develop both structural diagrams and the design of the system and measuring devices as a whole. Structurally, the system is an electro-optical element placed in a housing that provides alignment of the device. The measuring prototype as use modulator that shown at Fig. 4 can usually operate continuously nearly 300 h, it’s design resistant to mechanical and climatic influences. All this allows use it’s for practical applications for measuring strength of electrical pole. The developed system is based on the electro-optical effect, in which a linearly polarized light flux is phase modulated and passed through an electro-optical medium, for example usually using Pokels or Kerr cells. In this case, the amplitude of the optical phase modulation is chosen equal to or greater than the maximum rotation of the plane of polarization caused by the measured electric field. By analyzing the transmitted light, the difference in the output electrical signals is formed and the duration of the positive and negative values of the difference signal recorded in each period of the optical phase manipulation is recorded. By their duration they judge the intensity, and by the ratio of the durations of the direction of the measured electric field.

From Sensor system & analyzer unit

Fiber optic

Polarizer

Ferroelectric crystals

Fiber optic harness

To Sensor system & analyzer unit

Light signal source (laser) Analyzer

casing External electric field

Fig. 4 Principle of the work of the optical sensing element

Photo detector

Corona Discharge Problem in Extra High Voltage Transmission Line

23

Fig. 5 Prototype of measuring the electric field strength of a corona discharge

Regularly conducted patent searches over the past thirty years for scientific, technical and patent literature (for countries: USA, Great Britain, China, France, Japan, Germany, Poland, the Czech Republic and Slovakia) showed that there is a very a limited selection of methods (methods) suitable for measuring variable fields of power lines and electrical installations [24–33]. The work carried out on the application of electro-optical methods is due to the need to measure the electric field vector in installations for various purposes. The coefficient taking into account value of the attenuation of the optical light in the Pokels optical cell calculated as the intensity F of the light flux at the output of the crystal by next expression  ⎤ 2π 3 sin( · l) · n 0 r41 · E i ×⎥ 1 ⎢1 + λ   = 0 ⎣ ⎦ 2 × sin(2ϕ −  · l) ⎡



(15)

where 0 − light intensity at the input of the crystal; λ− wavelength of monochromatic light; n 0 − refractive index;E i − electric field strength in crystal; − optical activity of the electro-optical crystal; l− length of the crystal; r41 − electro-optical coefficient. Get (15) and value A0 expressions for transformation function on the out of system (12), can be written as

L(P) = 0 ·

1 ·χ · π



SO F L 21

2

 ⎤ sin( · l) 2π 3 ×⎥ ⎢ 1 + λ · n 0 r41 · E i  ·⎣ ⎦ × sin(2ϕ −  · l) ⎡



(16)

Using turn (15), it is possible to obtain the power value at the measurement points by the system in high and ultra-high voltage power lines. Given the obtained values,

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I. O. Zaitsev and V. V. Kuchanskyy

the system calculates power losses. The measurement results allow one to analytically determine energy losses (load losses, losses on the corona with leakage currents) in real time, which allows optimizing the operation mode of the overhead line in order to reduce these losses and achieve significant energy savings. To reduce the calculation error by formulas (15)–(16), is proposed the use of the multi-frequency phase method [32–43]. In this case, a signal with a different frequency passes sequentially through the crystal. The result is a single harmonic signal with some amplitude and phase shift values, which have a complex functional dependence, which is described by expression (16) and can be analytically form can be written as |A 1 |e− jϕ 1 = |A1 |e− jϕ1 + |A2 |e− jϕ2 + · · · + |An |e− jϕn

(17)

where |A 1 |–the amplitude of the total signal at the 1st frequency; ϕ 1 –phase shift of the total signal at the 1st frequency; |A1 |, | A2 |, . . . , |An |–amplitudes of signals went through the crystal in the 1st frequency; ϕ1 , ϕ2 , . . . , ϕn –phase shifts of signals went through the crystal in the 1st frequency. As the frequency of the probing signal changes linearly, the phase shifts of the signals went through the crystal also change linearly. Therefore, the total signals in some frequency range can be described by the system of equations: ⎧ |A 1 |e− jϕ 1 = |A1 |e− jϕ1 + |A2 |e− jϕ2 + · · · + |An |e− jϕn ⎪ ⎪ ⎪ ⎪ ⎨ |A |e− jϕ 2 = |A |e− j2ϕ1 + |A |e− j2ϕ2 + · · · + |A |e− j2ϕn 2 1 2 n ⎪ · · · ⎪ ⎪ ⎪ ⎩ |A m |e− jϕ2m = |A1 |e− jmϕ1 + |A2 |e− jmϕ2 + · · · + |An |e− jmϕn

(18)

where |A 1 |, | A 2 |, . . . , |A m |—the amplitude of the total signal at the 1st frequency; ϕ 1 , ϕ 2 , · · · , ϕ m—is the phase shift of the total signal at the 1st frequency. It is suggested to write down two systems of equations. One system of Eqs. (18) we write the second system of equations in the same way (16) but do not shift by one frequency but not by m frequencies. The left of each equation records the vectors of the total signal reflected by all the objects. On the right is the sum of the signal vectors at different frequencies passed through the crystal. Entering the next designations: b˙1 = |A 1 |e− jϕ 1 , , b˙m = |A m |e− jϕ m a˙ 1 = |A1 |, . . . , a˙ n = |An | c˙1 = e− jϕ1 , . . . , c˙n = e− jϕn .

Corona Discharge Problem in Extra High Voltage Transmission Line

25

and r˙1 = c˙1 · c˙2 · c˙3 , r˙2 = c˙1 · c˙2 + c˙1 · c˙3 + c˙2 · c˙3 , r˙1 = c˙1 + c˙2 + c˙3

(19)

and make mathematical transformation, we get the neat system 

r˙1 ·b˙1 −˙r2 ·b˙2 +˙r3 ·b˙3 =b˙4 r˙1 ·b˙2 −˙r2 ·b˙3 +˙r3 ·b˙4 =b˙5 r˙1 ·b˙3 −˙r2 ·b˙4 +˙r3 ·b˙5 =b˙6

(20)

Expressions (18) are the coefficients of the cubic equation according to the theorem of Viet. Thus using the solution of the system of Eqs. (20) r˙1 , r˙2 , r˙3 then can write the next cubic equation: c˙3 + r˙3 c˙2 + r˙2 c˙ + r˙1 = 0

(21)

The solution of cubic Eq. (18) gives the value of a single vector of a signal passing through a crystal. ϕ1 = arg(cˆ1 ), ϕ2 = arg(cˆ2 ), . . . ϕi = arg(cˆi )

(22)

In this case, the value of the expression (21) will be calculated taking into account (22), which allows increasing the accuracy of measurement of the information component in the corona discharge power losses optical measurement systems. In Fig. 6 shown installation the system on both wires and insulators. Also in laboratory conditions, an electrical circuit was developed for an experimental setup for studying a corona discharge, as well as testing an electro-optical light modulator for measuring field strength. The electrical circuit of the experimental installation is shown in Fig. 7. On Fig. 7 AT—autotransformer; T—high voltage transformer; Rp—protective resistance; R1—R2—high-voltage ohmic separator; C1–C2—high-voltage capacitive separator; N is an oscilloscope; C is the capacity to understand the voltpendant characteristic; R is the resistance to characterize the current–voltage and voltage-current characteristics; C—metal cylinder; W—wire. Using a test bench Fig. 8 and the system of Fig. 5, the results were obtained, which are shown in Fig. 8.

7 Conclusion Due to the large error in determining the type of weather along the overhead route, empirical methods for calculating corona losses are unacceptable for the efficient operation of electrical systems. The developed system allows us to determine the components of the total power loss in extra high voltage power lines, primarily the

26

I. O. Zaitsev and V. V. Kuchanskyy 1

1

2

2

2,5

6

29

7

2 3

18

18

3

32,5

3

15,6

15,6

Fig. 6 750 kV transmission line tower. 1-points of attachment of lightning protection cables; 2points of fastening of a garland of insulators; point of installation remote measuring system; 3-points installation of sensor of the measurement system

Fig. 7 The electrical circuit of the experimental installation

Corona Discharge Problem in Extra High Voltage Transmission Line

27

Fig. 8 Electrical field strength

power loss per corona in real time, which will allow us to optimize the operating mode in order to reduce these losses and achieve energy savings. Experimental studies of the developed measuring instruments have confirmed the correctness of the theoretical provisions and the suitability of the devices for industrial operation. This allows you to organize their serial production. The developed methods and means of measurement make it possible to control the quality of manufacture of high-voltage products, obtain information on the quality of high-voltage equipment that has been in operation (non-destructive testing), and have a tool for the experimental analysis of electric heights in scientific research, in ecology, safety, technological processes and other areas. Thus, the work performed allowed us to bring the problem of development and design of means for measuring electric field strength with electro-inductive spherical sensors to their logical conclusion, which consists in the possibility of designing, like sensors, the same means of measuring electric field strength. For operative estimation of total losses of active power of an airline and allocation of a component of losses on a crown the specialized measuring system with use of multipurpose sensors of personal computers for storage in memory of the synchronized measurements of current parameters of the mode at the ends of the line is offered.

References 1. Desai, I., Jamnani, J.G., Takalkar, S.M.: Reliability improvement of Extra High Voltage substation. In: Proceedings of the International Conference on Engineering, Ahmedabad, Gujarat, 2011, pp. 1–6. https://doi.org/10.1109/NUiConE.2011.6153301 2. Sili, Y., Gang, L., PeiRen, W., Duifeng, Y., Xiangxiang, G., Yuan, Y.: Discussion on the problem about capacitive current switching of EHV and UHV AC circuit breaker. In: 2017 4th International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST), Xían, 2017, pp. 771–777. https://doi.org/10.1109/ICEPE-ST.2017.8188955

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3. Jie, Z.W.: Research on overvoltage suppression in extra/ultra high voltage under different operation mode. In: 2014 IEEE PES T&D Conference and Exposition, Chicago, IL, 2014, pp. 1–4. https://doi.org/10.1109/TDC.2014.6863363 4. Hunko, I., Kuchanskyi, V., Nesterko, A., Rubanenko, O.: Modes of electrical systems and grids with renewable energy sources - LAMBERT Academic Publishing, 2019, 184 p. ISBN 978-613-9-88956-3 5. Hunko, I.O., Kuchanskyy, V.V., Nesterko, A.B.: Engineering sciences: development prospects in countries of Europe at the beginning of the third millennium: Collective monograph, vol 2, 492 p, 2018, Riga: Izdevniec¯ıba “Baltija Publishing”. ISBN 978-9934-571-63-3 6. Dias, R., Lima, A., Portela, C., Aredes, M.: Extra long-distance bulk power transmission. IEEE Trans. Power Deliv. 26(3), 1440–1448 (2011). https://doi.org/10.1109/TPWRD.2011.2126610 7. Kuznetsov, V., Tugay, Y., Kuchanskyy, V.: Influence of corona discharge on the internal ovevoltages in highway electrical networks. Tech. Electrodyn. 6, 55–60 (2017). 8. Kuchanskyy, V.: Application of controlled shunt reactors for suppression abnormal resonance overvoltages in assymetric modes. In: 2019 IEEE 6th International Conference on Energy Smart Systems (ESS), Kyiv, Ukraine, 2019, pp. 122–125 9. Kuchanskyi, V.V.: Phase structure influence estimation of the extra high voltage line on abnormal resonance overvoltages. Pratsi Instytutu Electrodynamiky 52, 22–27 (2019) 10. Kuznetsov, Y.T., Kuchanskyy, V.V.: Investigation of transposition EHV transmission lines on abnormal overvoltages. Tech. Electrodyn. 6, 51–56 (2013) 11. Tugay, Y.I.: The resonance overvoltages in EHV network. In: IEEE International Conference on Electrical Power Quality and Utilization, 2009, Lodz. Issue 1. pp. 14–18 12. Tamazov, A.I.: Losses on the corona in high-voltage overhead power lines. Moscow, Sputnik+, 2016. 571 p. ISBN 978-5-9973-3869-5 13. Pan, W., Li, Y., Chen, X.: Calculation method of corona loss in EHV/UHV system based on distributed parameter characteristic. IEE J. Trans. Elec. Electron. Eng. 14, 730–734 (2019). https://doi.org/10.1002/tee.22859 14. Jiang, X., Chen, J., Shu, L., Hu, J., Zhang, Z., Wang, S.: Studying corona onset characteristics after rime ice accumulation on energized stranded conductors. In IEEE Transactions on Dielectrics and Electrical Insulation, 2013, vol. 20, no. 5, pp. 1799–1807. https://doi.org/10. 1109/TDEI.2013.6633711 15. Lu, F.-C., You, S.-H., Liu, Y.-P., Wan, Q.-F., Zhao, Z.-B.: AC conductors’ corona-loss calculation and analysis in corona cage. IEEE Trans. Power Del. 27(2), 877–885 (2012). https://doi. org/10.1109/TPWRD.2012.2183681 16. Hernandez-Guiteras, J., Casals-Torrens, P.: Determination of the corona inception voltage in an extra high voltage substation connector. In IEEE Transactions on Dielectrics and Electrical Insulation, 2013, vol. 20, no. 1, pp. 82–88. https://doi.org/10.1109/TDEI.2013.6451344 17. Zaitsev, I.O., Levytskyi, A., Kromplyas, B., Panchyk, M., Bereznychenko, V.: Study influence industrial frequency magnetic field on capacitive pressing sensor for large turbogenerator core clamping system. In: Proceedings of the 2019 IEEE Ukraine International Conference On Electrical And Computer Engineering (UKRCON-2019), 2–6 July, 2019 Lviv, Ukraine, pp. 566–569. https://doi.org/10.1109/UKRCON.2019.8879949 18. Li, Z.-X., Fan, J.-B., Yin, Y., Chen, G.: Numerical calculation of the negative onset corona voltage of high-voltage direct current bare overhead transmission conductors. IET Gener. Transm. Distrib. 4, 1009–1015 (2010) 19. Bian X., Yu, D., Meng, X., MacAlpine, M., Wang, L., Guan, Z. et al.: Corona-generated space charge effects on electric field distribution for an indoor corona cage and a monopolar test line. IEEE Trans. Dielectr. Electr. Insul. 18, 1767–1778 (2011) 20. Suleimanov, V.N., Katsadze, T.L.: Electric networks and systems—K.: NTUU “KPI”, p. 504 (2007). ISBN 978-966-622-246-9 21. Tonmitr, K., Ratanabuntha, T.: Comparison of power loss due to corona phenomena model with peek’s formula in high voltage 115 kV and 230 kV System. Procedia Comp. Sci. 86, 385–388 (2016). https://doi.org/10.1016/j.procs.2016.05.037

Corona Discharge Problem in Extra High Voltage Transmission Line

29

22. Blinov, I., Zaitsev, I.O., Kuchanskyy, V.V.: Problems, methods and means of monitoring power losses in overhead transmission lines. In: Systems, Decision and Control in Energy I: Collective monograph. Springer, 2020, pp. 123–136. https://doi.org/10.l007/978-3-030-48583-2_8 23. Kuchanskyy, V., Zaitsev, I.O.: Corona discharge power losses measurement systems in high- and extra-high voltage transmissions lines. In: Proceedings of the IEEE 2020 IEEE 7th International Conference on Energy Smart Systems (2020 IEEE ESS), Ukraine (2020), pp. 48–53. https:// doi.org/10.1109/ESS50319.2020.9160088 24. Sahu, S., Swain. S.D., Ray, P.K., Puhan, P.S.: Modeling of transmission line with estimation of corona loss i-manager’s. J. Power Sys. Eng. 7(1), 43–50 (2019). https://doi.org/10.26634/ jps.7.1.16537 25. Guoying, W., Haiwen, Y., Jianxun, L.: Fiber-based remote control device and engineering application. In: 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, Shenyang, 2013, pp. 1544–1547 26. Wang P., Zhang, G.: The measurement method for corona discharge current under high-voltage environment. IEEE Trans. Instrument. Meas. 57(8), 1786–1790 (2008) 27. Wang, P., Zhang, G., Zhou, J., Gu, C.: Optical micro-current transducer for the measurement of corona discharge current under high voltage environment. In: 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007, Warsaw, 2007, pp. 1–3 28. Liao, R., Liu, K., Liu, H., Wu, F.: Measurement of space charge in negative corona on a small corona cage. IEEE Trans. Dielectr. Elec. Insul. 23(4), 2344–2352 (2016) 29. Mihailovic, P., Petricevic, S., Stojkovic, Z., Radunovic, J.: Development of a portable fiber optic current sensor for power systems monitoring. IEEE Trans. Instrum. Meas. 53(1), 24–30 (2004) 30. Yin, F., Farzaneh, M., Jiang, X.: Laboratory investigation of AC corona loss and corona onset voltage on a conductor under icing conditions. IEEE Trans. Dielectr. Elec. Insul. 23(3), 1862– 1871 (2016). https://doi.org/10.1109/TDEI.2016.005626 31. Wang, P., Zhang, G.: The measurement method for corona discharge current under high-voltage environment. IEEE Trans. Instrum. Meas. 57(8), 1786–1790 (2008) 32. Levytskyi, A.S., Zaitsev, I.O., Kobzar, K.O.: Measuring the stroke of cone disk springs in power accumulators of the turbogenerator stator core using a capacitive sensor. Devices Methods Meas. 9(2), 121–129 (2018) (In Russ.). https://doi.org/10.21122/2220-9506-2018-9-2-121-129 33. Zaitsev, I.O., Levytskyi, A.S., Kromplyas, B.A.: Hybrid capacitive sensor for hydro- and turbo generator monitoring system. In: Proceedings of the International Conference on Modern Electrical and Energy System (MEES-17). Kremenchuk, Ukraine, pp. 288–291 (2017). Last accessed 15–17 Nov 2017. https://doi.org/10.1109/MEES.2017.8248913 34. Zaitsev, I.O., Kononenko, A.G., Masyurenko, Yu.A., Nizhensky, A.D. et al.: Specific errors of phase-frequency laser distance meters. Tech. Electrodyn. 2009(3), 50–54 (2009) 35. Braginets, I.A., Zaitsev, I.O., Kononenko, A.G., Masyurenko, Yu.A., Nizhensky, A.D.: Phase-frequency laser rangefinders with the triangular law of frequency modulation. Tech. Electrodyn. 2009(6), 70–74 (2009) 36. Zaitsev, I.O.: Error analysis of frequency-phase measuring systems taking into account the characteristics of multiplying devices. Tech. Electrodyn. 2013(6), 82–87 (2013) 37. Zaitsev, I.O., Levytskyi, A.S., Kromplyas, B.A.: Capacitive distance sensor with coplanar electrodes for large turbogenerator core clamping system. In: Proceedings of the 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), April 16–18, 2019, Kiev, Ukraine, pp. 644–647 38. Zaitsev, I.O.: A study of synthesizers for multi frequency phase range finder system with using LABVIEW. Tech. Electrodyn. 2014(2), 84–88 (2014) 39. Zaitsev, I.O.: Analysis error of FMCW measuring systems considering the characteristics the mixers devices. Tech. Electrodyn. 2013(6), 82–87 (2013) 40. Okosi, T., Okamoto, K., Otsu, M., Nishihara, X., Kyuma, K., Hatate, K.: Fiber optic sensors. Leningrad “Energoatomizdat” 256 (1990) 41. Zaitsev, E.O.: A study of synthesizers for multi frequency phase range finder system with using LABVIEW. Tekhnichna elektrodynamika. 2014(2), 84–88 (2014)

30

I. O. Zaitsev and V. V. Kuchanskyy

42. Burkov, V.D., Mamedov, A.M., Potapov, V.T., Potapov, T.V., Udalov, M.E.: Fiber-optic sensor of electric field strength. Vestnik MGUL. Lesnoy vestnik. 2008(4), 130–132 ( 2008) 43. Yakushenko, Y.T.: Theory and calculation of optoelectronic devices. Mech. Eng. 360 (1989)

Efficiency Increase of Open Phase Modes in Bulk Electrical Networks Vladislav V. Kuchanskyy , Denys O. Malakhatka , and Ihor Blinov

Abstract The current state of power engineering in industrialized countries is characterized by the following trends: the continuing growth of loads determined by the development of the economies of these countries; high density of electrical networks of various voltage classes; unification of electrical networks of various companies into single national energy systems; the introduction of market relations in the electric power industry, which does not always cause the exchange of power and energy between individual energy companies, electric power systems and large regions, which is not always provided for by the dispatch schedule; major accidents usually occurring due to the weakness of individual intersystem connections and leading to blackouts in large regions. Therefore, the issues of increasing the throughput and controllability of electrical networks through the use of special technical means are currently very relevant. These funds should ensure the solution of the following tasks: increasing the capacity of individual lines or the network as a whole; control of flows of active and reactive power for individual connections or sections of the network, which makes it possible to reduce losses, meet requirements for the balance of reactive power and voltage level in nodes, and increase the static stability of the system; impact on special modes of electrical networks associated with switching on or off network elements, self-excitation and self-swinging of generators in order to improve the characteristics of these modes; balancing network parameters in various modes, as well as limiting short-circuit currents; increasing the reliability of the system through

V. V. Kuchanskyy (B) · D. O. Malakhatka · I. Blinov The Institute of Electrodynamics of the National Academy of Sciences of Ukraine, Kyiv, Ukraine e-mail: [email protected] D. O. Malakhatka e-mail: [email protected] I. Blinov e-mail: [email protected] I. Blinov National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_2

31

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V. V. Kuchanskyy et al.

the use of high-speed devices. Compensation of the transverse capacitive parameters of extra high voltage lines is carried out using shunt reactors installed at the ends of the line. The shunt reactors, which are equipped with the extra high voltage power transmission, generally perform three most important functions, namely, the compensation of the charging power in normal modes, the limitation of internal overvoltages when the line is put into operation and in emergency modes, as well as the reduction of the make-up arc currents for the implementation of a successful single phase auto reclose during elimination of the most probable single-phase arc short circuits. Keywords Conception smart grid · Open phase mode · Power transfer capability · STATCOM · Controlled shunt reactor · FACTS · Charging power · Secondary arc current · Single phase auto reclose · Open phase mode

1 Introduction Today, in Ukraine the requirements for reliability and efficiency of electricity transmission between energy systems and electricity markets are growing. The importance of increasing the reliability and efficiency of electricity transmission requires providing the operator with a system of transmission of ancillary services [1], solving network constraints [2], reducing of electricity losses. This task is especially relevant given the implementation of a new electricity market in Ukraine [3], increasing the part of renewable energy sources [4] and further action to integrate the electricity market of Ukraine with European markets [5, 6]. According to IEC 63097 [7] the System Capabilities for FACTS (Flexible AC Transmission Systems) include fast voltage control, increased transmission capacity over long lines, power flow control in meshed systems and power oscillation damping. FACTS devices can be divided into two main groups. The first it is shunt devices such as controlled shunt reactors and STATCOM, second is series capacitors. The main benefits of FACTS include the following: • dynamic voltage control, to limit over voltages over lightly loaded lines and cable systems. This advantage reduces the need for ancillary services to regulate the voltage provided by power plants, in particular hydropower plants; • increased power transmission capability and stability of power corridors, without any need to build new lines. This allows increasing the capacity of electric networks, in particular for the implementation of concluded contracts for the supply of electricity between parts of the power system where there are network constraints; • facilitating connection of renewable generation by maintaining grid stability while fulfilling grid codes. This possibility allows the transmission of electricity produced from RES, which often have an unsatisfactory forecast of electricity supply [4];

Efficiency Increase of Open Phase Modes …

33

• support of fast restoration by stabilizing the network after fault conditions. This feature allows you to use FACTS to provide ancillary services to the transmission system operator. And also increase the reliability of electrical networks during direct start-up of an asynchronous machine in the complex load’s composition [8, 9]. An increase in electricity consumption, the unit capacity of power plants, installed capacity of thermal, nuclear and hydraulic power plants and an increase in the transmission distance of energy characteristic of modern energy industry have led to the fact that the majority of units are connected to the power system using extra high voltage transmission lines. The desire of researchers and developers to find solutions to the problems of increasing the power transfer capability and controllability of power transmissions is due to the practical needs of developing power systems, an increase in the power of generating sources, the distance of power transmission and the formation of complex interconnected power systems. An indispensable condition for this is the search for further ways and the solution of the problems of reducing capital and operating costs, reducing losses, ensuring the specified operating parameters, reliability indicators and power transfer capability. The considered controlled power transmission lines satisfy most of these requirements, and, in this regard, they can be considered as promising for use in power systems. Today, there is the problem of increasing the flexibility of extra high voltage power lines. Improving the efficiency of operation of lines of this voltage class will solve the problem of regulating the modes of the combined power systems by using controlled reactive power compensation devices. The use of such devices will enable FACTS to be introduced in bulk electric networks as an element of the Smart Grid conception. One of the main problems of operating in open phase modes is an increase in power transfer capability. To solve this problem, authors propose the use of controlled shunt reactors. Controlled shunt reactors and STATCOM have found application in bulk electric networks to solve many problems and operational problems. To date, there is no work on the use of controlled shunt reactors and STATCOM in open phase modes [10–21].

2 Method for Determining the Reduction of Power Transfer Capability at Open Phase Mode One of the typical examples of the use open phase modes in extra high voltage power lines is the single phase auto reclosure (SPAR) of the damaged phase. The chapter addressed the issue of using controlled shunt reactors and STATCOM in the SPAR cycle to increase efficiency of extra high voltage transmission line as backbone line of bulk electrical power networks. Existing articles present the results of the use of controlled compensation devices of reactive power in normal steady state modes, not

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in abnormal modes (open phase modes) that precede emergency ones [10–26]. The using of SPAR provides in most cases the elimination of the most common damage to the EHV transmission line- single-phase arc short circuit. The work aims is presenting the results of research on a new type of power transmission—controlled high voltage transmission lines, which can significantly increase the power transfer capability and create fundamentally new possibilities for controllability of AC power transmissions and bulk electrical power networks. The subject of novel type of transmission line is especially relevant at the present time in connection with the intensive development of research and development of the so-called flexible AC power transmission line—FACTS [10–16]. More than fifteen years have passed since the industrial manufacture and introduction of the first bias-controlled shunt reactor (CSR). At the moment, the experience of supplying CSR numbers more than 80 units for various voltage classes (35–500 kV). Due to the considerable length of power lines and the specifics of building electrical networks, controlled shunt reactors are widely used throughout its territory in many countries. The history of the creation of controlled reactors and their use begins in the fifties of the last century, when the development and construction of high voltage power transmission lines began [10–16]. However, despite of the significant experience in operating CSR, as well as theoretical studies, there are a number of questions regarding the use of CSR, which are relevant to this day. Controlled shunt reactors, has primary windings connected in a star with the grounded neutral, and secondary windings connected in a star which three-phase group of bidirectional thyristor valves is connected to free conclusions, and also the actions carried out by means of CSR in the compensation mode feeding of the arc of single phase short circuit [11–13]. Figure 1 shows a diagram of connecting CSR to an extra high voltage power line. The power transfer capability is inversely proportional to its equivalent inductive reactance. Therefore, the relative decrease in the transmission power transfer capability in the open phase mode will be numerically equal to the relative increase in the equivalent transmission resistance, which is determined according to the complex equivalent circuit shown in Fig. 2. Fig. 1 Principle scheme of extra high voltage with installed controlled shunt reactor

EG

T1

L

CSR1

T2

CSR 2

Efficiency Increase of Open Phase Modes … Z G1

ZT 1(1)

Z L (1) / 2

EG

35 Z L (1) / 2

U

Z CSR1(1)

ZG 2

ZT 1(2)

Z L (2) / 2

Z CSR 2(1)

Z L (2) / 2

Z CSR1(2)

ZT 1(0)

Z L (0) / 2

Z CSR1(0)

ZT 2(1)

ZT 2(2)

Z CSR 2(2)

Z L (0) / 2

ZT 2(0)

Z CSR 2(0)

Fig. 2 Complex equivalent circuit of open phase mode

For the mode under consideration, the additional resistance introduced into the direct sequence circuit to obtain the direct sequence current value is Z 1 =

Z eq0 Z eq2 · 100 Z eq0 + Z eq2

(1)

where Z eq0 —equivalent zero sequence resistance, Z eq2 —equivalent negative sequence resistance. The reducing of transmission capacity of an extra high voltage transmission in an open-phase mode is determined by the expression: δP =

Z eq1 · 100 Z eq1 + Z 1

(2)

where Z eq1 —direct sequence equivalent resistance. The resistance of the reverse sequence equivalent circuit of the power lines and transformers will match the parameters of the direct sequence equivalent circuit. For autotransformers, the high and medium voltage windings of which are connected to a grounded star, zero-sequence parameters can conditionally be adopted direct sequence parameters.

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The resistances of the equivalent circuit for a controlled shunt reactor for the forward and reverse sequence are determined by the rated power and voltage. The resistance of the zero sequence is determined by the design of the controlled shunt reactor. The calculation results for the reduction in throughput in the pause of SPAR according to the formula (2) are shown in Table 1. As can be seen from the calculation results, the reduction in the transmitted power reaches 70–80%. Such a decrease in power even for a long pause time of the SPAR (about 0.6–1 s) is unacceptable and significantly reduces the efficiency of using EHV lines as the main one for transmitting large values of power in the bulk electrical network. Such a sharp decrease in the transmission line capacity in the open phase mode of operation must be eliminated in order to increase the reliability of the operation of the EHV transmission lines. One of these technical means is devices for controlled reactive power compensation as STATCOM and CSR. Development of visual analytical expressions that allow you to show how CSR can be taken into account when determining the magnitude of the arc feeding current in single phase automatic reclosing cycles of the line, as well as the magnitude of the recovering voltage on the disconnected phase of the line after the arc is extinguished. The practice of operating CSR on the line indicates that the use of universal control Table 1 The list of EHV lines of integrated electrical power system δP (%)

№

The name of the line

1

Khmelnitsky NPP—Rzeszow (Poland)

396

78.19

2

South Ukrainian NPP—Isaccea (Romania)

403

77.58

3

Kievska Substation—Rivne NPP

353

79.58

4

Zaporizhzhya NPP—Kakhovka Substation

190

80.15

5

Substation Western Ukraine—Albertirsa (Hungary)

479

81.12

6

Zaporizhzhya NPP—Dniprovska Substatio

137

78.59

7

Zaporizhzhya NPP—Dniprovska Substation

211

78.96

8

Substation West-Ukrainian—Rivne NPP

282

79.48

9

Substation Pivdennodonbaska—Substation Donbass

220

79.42

10

Zaporizhzhya NPP—Substation Pivdennodonbaska

150

77.41

11

Substation Dniprovska—South Ukrainian NPP

333

82.58

12

Substation Zaporizhzhya—KuTES substation

323

81.59

13

Substation Western Ukraine—PS Vinnytsia

361

82.05

14

CHAES—Khmelnitsky NPP

136

83.25

15

Substation West Ukrainian—Substation Vinnitsa

177

81.27

16

Substation North Ukrainian—Kursk NPP

228

80.45

17

Vinnitsa—South Ukrainian NPP

304

79.59

18

Substation West Ukrainian—Khmelnitsky NPP

190

82.69

19

Substation Vinnitsa—South Ukrainian NPP

126

83.59

Length (km)

Efficiency Increase of Open Phase Modes …

37

algorithms, which are traditionally incorporated into the reactor automation, can lead to an unsuccessful SPAR in the line. This is due to the fact that the required level of preliminary magnetization for switching on the CSR into the network is achieved in a much longer time than the time of the no-current pause of the SPAR. An increase in the time of the SPAR line can lead to a violation of the dynamic stability of the electrical system. Thus, the development of a control algorithm for the CSR, which makes it possible to reduce the time for ensuring the readiness of the reactor for switching on, or the development of measures that make it possible to abandon the manufacturer’s ban on starting the reactor without preliminary magnetization, are important tasks that can be solved by using STATCOM.

3 Devices for Controlled Compensation of Reactive Power in Main Electric Networks One of the main technical problems in the development of modern power systems is the problem of effective forced control of energy flows through bulk networks. Currently, based on modern circuitry and elements of power electronics, a number of effective FACTS devices have been developed, which are designed to implement such control [16–21]. One of the FASTS devices is controlled shunt reactors, which perform a wide range of tasks in power systems [16–21]. Unlike the traditional shunt reactor (SR), which is a passive element of the network and is designed only to compensate for excess charging power in extra high voltage power lines; CSR is an active element that also allows you to control the power system. However, it should be noted that CSR have a much more complex design than SR and therefore require higher costs for their installation and operation. The graphs of the dependences of the resistance versus the change in the inductance of the controlled shunt reactors are shown on Fig. 3. Taking into account all these considerations, almost all countries have abandoned the switching of shunt reactors, which determines the need to analyze the mode of transmission of electricity through lines in the presence of controlled shunt reactors [17–21, 27–38]. Therefore, the feasibility of using CSR for transmission lines is a reasonable and promising measure to increase the efficiency of the bulk electrical networks. The charging power of the ultrahigh voltage transmission line is equal to: 2 l Q Ch = jωCUnom,

(3)

where ω—is the angular velocity; C—capacity of transmission line, which is calculated by the formula, Unom —rated line voltage, l—line length. The degree of charge power compensation is determined by the following expression:

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Fig. 3 Ranges of regulation of reactive power of controlled shunt reactors

   K = − n/ ω2 Cl L C S R

(4)

where L C S R —the inductance of the controlled shunt reactor; n—number of groups of control shunt reactors. Figure 4 shows the degree of compensation of charging power depending on the length of the line in the case of using different numbers of groups of CSR. Thus, through the use of CSR, the degree of compensation of the charging power is changed, thereby equating the transmitted power to the natural one at which purely active power is transmitted. In Fig. 4 the graph of the change in the decrease in the transmitted power depending on the change in the power of the CSR groups is shown. As can be seen from the figure, when CSR is applied in the SPAR pause, the changes are insignificant, which indicates the ineffectiveness of CSR application in the SPAR cycle. A change

Fig. 4 Changes in the degree of compensation of charging compensation when regulating the CSR inductance

Efficiency Increase of Open Phase Modes …

39

in the CSR inductance, even in such wide ranges of change in inductive reactive power, does not increase the efficiency of SPAR application, in contrast to normal operating conditions. Figure 5 illustrate the ineffectiveness of using CSR as a means of increasing the efficiency of the use of SPAR and searching for new means. The power range at which an increase in the transmitted power on the line in the open-phase mode is achieved does not correspond to the power for which modern CSR can be manufactured. In the case of production of STATCOM at high power, it is impractical due to its high cost. Thus, the direct use of charging power controls is impossible due to the power adjustment range shown in the figures (Figs. 5 and 6) to increase the throughput in the SPAR pause.

Fig. 5 Change in the value of the transmitted power along the line in open phase mode at CSR ranges

Fig. 6 Changes in power reduction in open phase line operation with non-factory CSR range

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4 An Application of STATCOM to Reduce the Pause of Single Phase Auto-Reclose The main task arising in the application of SPAR is to provide the required for the success of its implementation the duration of the no-current pause in each specific designed or operated overhead line of extra high voltage [39–46]. The requirements for the duration of the no-current pause of the SPAR are due to the fact that after the emergency phase is disconnected from both sides, the arc continues to burn together with the short circuit, receiving recharge from the non-disconnected phases of the line located at a relatively short distance from the emergency phase. When using SPAR, one of the main tasks is the correct choice of a no-current pause [47–57]. The main problem of ensuring a reliable SPAR is to reduce the time of the feeding arc burning by reducing the feeding current and the recovery voltage. As shown by the studies presented in [39–60], there are cases when the traditional measure compensation reactor CR cannot suppress the arc in the pause of SPAR. The arc has two components which we have to suppress: C L Iar c = Iar c + Iar c

(5)

C L where Iar c —capacitance component and Iar c —inductance component. To suppress the secondary arc of the current is set CR with a certain value of resistance, according to the method described in [39–44]. Capacitive current for EHV transmission lines in the case of an ideal wiring transposition scheme [43–47] is defined by the following expression:

⎤⎡ ⎤ ⎤ ⎡ VA 0 CM CM I Ac ⎣ I Ab ⎦ = jωl ⎣ C M 0 C M ⎦⎣ VB ⎦ I Ac CM CM 0 VC ⎡

(6)

where I Ac , I Bc , ICc —current of phases, C M —mutual capacitance, V A , VB , VC — phase voltage. The chapter considers the case when a short circuit occurred in phase A and, accordingly, the switches of the damaged phase are in the open state. Using expression (6) we can calculate the capacitive current of phase A, which receives power from two connected phases: I Ac = jω|C M |(VB + VC )

(7)

From the expression it is seen (7) that the current value depends on the phase-tophase capacitance. It should be noted that the EHV transmission lines are of great importance for the interphase capacity and, accordingly, the cycle of operation of SPAR is not always successful under such conditions.

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The main purpose of the CR is to create such a resonant circuit in which the interphase inductive current generated by shunt reactors with the installed CR—X n should have the same value as the current caused by the interphase capacitors. In this case, the equivalent reactor current: ⎡

⎤ ⎡ ⎤⎡ −1 ⎤⎡ ⎤−1 ⎡ ⎤ I A1 0 1 1 1 X I0 0 1 1 1 VA ⎣ I B1 ⎦ = − j ⎣ 1 a 2 a ⎦⎣ 0 X −1 0 ⎦⎣ 1 a 2 a ⎦ ⎣ VB ⎦ II IC1 VC 1 a a2 0 0 X −1 1 a a2 I2

(8)

−1 where X −1 I I = X I 2 the conductivity of the shunt reactor of the direct sequence. −1 X I 0 = (X I I + 3X n )−1 the conductivity of the shunt reactor of the zero sequence. Solving (8), we obtain phase inductive currents:

⎤ ⎡ ⎤⎡ ⎤ αs βm βm VA I AL ⎣ I B L ⎦ = − j ⎣ βm αs βm ⎦⎣ VB ⎦ IC L βm βm αs VC ⎡

(9)

−1 −1 −1 + 2X 11 )/3 diagonal elements of the matrix CSR; βm = (X 10 − where αs = (X 10 −1 X 11 )/3 not diagonal elements of the matrix CSR. In case of short circuit inductive current CSR is determined:

I AL = − jβ M (VB + VC )

(10)

The total arc current is defined as: Iar c = j (VB + VC )(ω|C M | − β M )

(11)

In order for the sum of the vectors of capacitive and inductive currents to be zero for 



the EHV transmission line I AC + I AL = 0 it is necessary that the inductive conduc−1 −1 − X 11 )/3 = ωC M . tivity is equal to the capacitive interfacial conductivity (X 10 The result of solving this expression is the resistance of the necessary inductance resistance: X n = (ωCm +

1 −1 ) − X 1L X 1L

(12)

The problem of using CSR in the pause of SPAR is that the level of preliminary biasing for switching on (for reset or gaining power) is achieved in a much longer time than the time of the no-current pause of SPAR. In this case, in order to increase the throughput, it is necessary to use STATCOM with a high speed and allows faster control of reactive power flows in the line. Power line balancing is especially important for long single-circuit lines and in case of damage of one of the sections of transmission line improves balancing mode.

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Balancing improves power mode in case of damage of one of the line sections, in which the damaged section operates in two phase mode. An additional balancing effect is created by connecting the STATCOM transformer winding in the triangle. Switching power elements of STATCOM inverter and matching transformers should be designed to overload caused by overvoltage. The use of STATCOM makes it possible to ensure the conditions for extinguishing the follow current arc during the automatic reclosure. For the development of AC power transmissions of 750 kV and higher class, the problem of ensuring a successful automatic reclosure is crucial. The voltage at the point of short-circuits during the no-current pause of the automatic reclosure increases in proportion to the nominal line voltage and its length. Therefore, on 750 kV lines compensated by conventional shunt reactors, restoration of the electric strength of the arc channel within a limited time of the no-current pause may not occur. An increase in the no-current pause of automatic reclosure for heavily loaded power transmission lines can lead to a violation of the stability of parallel operation. Therefore, a three-phase automatic reclosure must be fast-acting (FPAR) with a pause of up to 0.4 s. However, when lengths of line segments 750 kV 400–500 km due to the relatively small losses in line and shunt reactors in the pause reclosure will occur weakly damped oscillatory processes in circuits “line capacitance—shunt reactors”. Due to oscillatory processes in the automatic reclosure pause, repeated arcing is possible at the point of the short circuit. As a result, the FPAR will be unsuccessful. Connecting the STATCOM to the damaged phase of the line enhances the damping of oscillations due to the action of the STATCOM transformer windings connected in the triangle. The windings form a short-circuit path for common mode voltage waves. Special control of the moments of switching on the power elements during the SPAR pause will also contribute to the damping of oscillations and, therefore, reduce the pause time of the SPAR. An even more important role of STATCOM is performing ensuring the success flowing SPAR mode. The overvoltage level at the moment of single phase auto reclosing is lower than at three-phase auto reclosing. Considering that of all types of short circuits on ultra-high-voltage lines, singlephase short circuits account for 80–85%, it can be concluded that ensuring the successful flow of SPAR is of exceptional importance for the reliability of these lines. However, with an increase of the class of line voltages, the problem of SPAR becomes even more complicated than the problem of FPAR. The reason for this is the arc feeding current during the SPAR pause, which at line lengths of 300–500 km can reach 150–200 A. Rapid extinction of the arc is possible provided that its current does not exceed 10–20 A. Compensation of the arc feeding current at SPAR is provided by a thyristorreactor group (TRG) or IGBTs which connected to the star-connected transformer windings. Specifying the desired mode of switching power elements control can be fully compensated capacitive feeding current. If the line at the moment of SPAR transmits energy through the undamaged phases, an additional component of the arc feeding current appears due to the mutual inductances of the damaged and undamaged phases. The analysis carried out in relation to the parameters of the 750 kV line with a length of up to 500 km shows

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that the specified STATCOM power semiconductor elements, with proper control, can compensate for this component of the arc current. Elimination of resonant overvoltages and the possibility of using SPAR on long EHV lines in such modes can be achieved by limiting the influence of the arc feeding current. The most effective measure is the installation of a special STATCOM in the neutral grounding circuit of a three-phase group of shunt reactors. Such a group of devices, formed by the so-called four-beam reactor with STATCOM, leads to a decrease in the make-up current due to compensation of the phase-to-phase capacitances of the overhead line, which make the main contribution to the creation of the make-up current [9, 39]. The main task arising from the use of SPAR is to establish the required for the success of its implementation, the duration of the no-current pause in each specific designed or operated overhead transmission line. An approximation of the dependence of the arc extinguishing time with a probability of 0.9 on the amplitude of the feed current is proposed. tspar = 0.2 + 2.86 · 10−3 · Iar c

(13)

The technique is based on the analysis of voltages (currents) in the calculated power transmission scheme [27–35], which includes the overhead power transmission line EHV and included at the terminal substations SR, designed to compensate for the reactive power generated by the line. The main electrical parameters of 750 kV overhead lines [43] were slightly refined taking into account the parameters of specific types of supports of the designed overhead line and using the methodology for calculating the longitudinal and transverse parameters of overhead lines [44]. The Fig. 7 shows the changes in the arc current value from the STATCOM inductance value, which in this case compensates for the excess charging capacitive power.

Fig. 7 Reduction of the arc current by changing the inductance of STATCOM

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Fig. 8 Reducing the duration of the SPAR by changing the inductance STATCOM

Figure 7 clearly shows how STATCOM, operating in inductance mode, reduces the arc current. Also by using, formula (11) can be investigated as the duration of the pause of the SPAR Fig. 8 is reduced by changing the inductance STATCOM. At values from 250 O and above, a significant reduction in the duration of the SPAR can be achieved.

5 Conclusions In open-phase modes, electrostatic (capacitive) coupling and electromagnetic (inductive) coupling of the disconnected phase of the overhead line with its undamaged phases remaining under the operating voltage and current create a make-up current in the arc channel of the disconnected phase, which prevents the rapid deionization. The recharge arc burning time increases with the increase in the recharge current amplitude and the voltage recovery rate. To eliminate the influence of the arc feeding current in such modes, various measures can be taken, one of which is the STATCOM installation, which operates in the charging power compensation mode, included in the common neutral of a group of three single-phase shunt reactors. The proposed methods: selection of the STATCOM inductance for overhead transmission lines, calculations of the arc feeding current on the disconnected phase of the overhead line after arc extinction, as well as determining the time of the no-current pause of the SPAR are based on analytical expressions for the charging current and on lines with symmetrical phase parameters, as well as on the recommended time dependence extinguishing the make-up arc and the no-current pause of the SPAR from the amplitude of the steady-state arc current. The chapter proposes a method for determining the reduction in line capacity in open-phase mode (single-phase automatic reclosing). On the basis of the proposed

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method, the reduction of the throughput for the actually operated EHV lines is analyzed. Through the use of modern reactive power compensation devices, the ineffectiveness of the use of controlled shunt reactors and STATCOM for the purpose of increasing the line capacity during the SPAR pause was determined. The ineffectiveness of the use of the above means is associated with the impossibility of direct application due to the adjustment ranges at which an increase in throughput is achieved. In the case under consideration, a controlled shunt reactor or STATCOM operating in inductive mode compensates for the charging capacitive power, which is created by capacitors between the phases and the ground of the remaining phases of the overhead line. The ineffectiveness of compensation for the charging power of the line is due to the fact that the arc current receives a feed, including from the phase-to-phase capacities of the non-disconnected phases of the line. In this regard, compensation of phase-to-phase capacities is necessary. The analysis showed that the STACOM can provide this condition, i.e. reduce the arc feeding current to the required level The solution to an important scientific and technical problem of increasing the transmission capacity of an ultra-high power transmission line in full-phase mode was implemented on the basis of the use of STATCOM. The principle due to which it was possible to increase the throughput of the line is based on reducing the duration of the pause of the OAPV by compensating the capacitive component of the feed arc current by the inductive operating mode of STATCOM.

References 1. Blinov, I., Parus, Y.P: Approach of reactive power pricing for ancillary service of voltage control in Ukraine. In: 2014 IEEE International Conference on Intelligent Energy and Power Systems, Conference Proceedings, pp. 145–148. https://doi.org/10.1109/ieps.2014.6874167 2. Kyrylenko, O.V., Blinov, I.V., Parus, Y.V., Ivanov, H.A.: Simulation model of day ahead market with implicit consideration of power systems network constraints. Tekhnichna Elektrodynamika 5, 60–67 (2019). https://doi.org/10.15407/techned2019.05.060 3. Ivanov, H., Blinov, I., Parus, Y.: Simulation model of new electricity market in Ukraine. In: 2019 IEEE 6th International Conference on Energy Smart Systems. https://doi.org/10.1109/ ess.2019.8764184 4. Ivanov, H.A., Blinov, I.V., Parus, E.V., Miroshnyk, V.O.: Components of model for analysis of influence of renewables on the electricity market price in Ukraine. Tekhnichna Elektrodynamika 4, 70–73 (2020). https://doi.org/10.15407/techned2020.04.072 5. Blinov, I., Parus, Y.: Features of use of the net export function properties for the congestion management on the “day-ahead” market. Tekhnichna Elektrodynamika 6, 63–68 (2015) 6. Blinov, I., Parus, Y.: Congestion management and minimization of price difference between coupled electricity markets. Tekhnichna Elektrodynamika 4, 81–88 (2015) 7. IEC 63097/TR/Ed1: Smart Grid Roadmap. International Electrotechnical Commission, p. 308 (2016) 8. Butkevych, O.F., Chyzhenko, O.I., Popovych, O.M., Trach, I.V.: An influence of the facts upon an electrical network’s mode during direct start-up of an asynchronous machine in the complex load’s composition. Tekhnichna Elektrodynamika 6, 63–68 (2018). https://doi.org/10.15407/ techned2018.06.062

46

V. V. Kuchanskyy et al.

9. Butkevych, O.F., Chyzhenko, O.I., Popovych, O.M., Trach, I.V.: A study of transitional modes of the electric network with the powerful electromechanical load and FACTS. In: 2019 IEEE 6th International Conference on Energy Smart Systems (2019). https://doi.org/10.1109/ess. 2019.8764223 10. Davidson, C.C.: Power transmission with power electronics. In: 14th European Conference on Power Electronics and Applications (2011) 11. Gole, A.M.: Dynamic reactive compensation: reactive power fundamentals. In: 2011 IEEE/PES Power Systems Conference and Exposition, 20–23 March 20 (2011) 12. Han, Y., Xu, L.: A survey of the Smart Grid Technologies: background, motivation and practical applications. Przeglad Elektrotechniczny 87(6), 47–57 (2011) 13. Karimulla, S., Niranjan Kumar, P.S., Amer, G.: Enhancement of power transmission system performance. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology, Nagercoi, pp. 50–56 (2011). https://doi.org/10.1109/icetect.2011.576 0090 14. Karthikeyan, M., Ajay-D-Vimalraj, P.: Optimal location of shunt FACTS devices for power flow control. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology, Nagercoil, pp. 154–159 (2011) 15. Siddiqui, A.S., Deb, T., Iqbal, F.: Optimal location of FACTS device using meta heuristic search algorithm. In: 2015 Annual IEEE India Conference (INDICON), New Delhi, pp. 1–4 (2015). https://doi.org/10.1109/indicon.2015.7443412 16. Liao, H., Milanovi´c, J.V.: On capability of different FACTS devices to mitigate a range of power quality phenomen. IET Gener. Transm. Distrib. 11(5), 1202–1211 (2016). https://doi. org/10.1049/iet-gtd.2016.1017 17. Wei-jie, Z., Xiao-xin, Z., Ya-lou, L., Xing, Z., De-chao, X.: Inverse-hyperbolic dynamic model for extra and ultra voltage magnetically controlled shunt reactor. Int. Conf. Electr. Control Eng. Wuhan 2010, 2820–2823 (2010). https://doi.org/10.1109/iCECE.2010.689 18. Belyaev, A.N., Serguei, S.V.: Steady-state and transient stability of 500 kV long—distance AC transmission lines with magnetically controlled shunt reactors. In: 2005 IEEE Russia Power Tech, St. Petersburg (2015) 19. Evdokunin, G.A., Ragozin, A.A., Seleznev, Y.G.: New technical solution to the problems of long-distance AC power transmission lines. In: 9th International Power System Conference St. Petersburg (1994) 20. Gama, C.: (1999) Brazilian North-South interconnection control-application and operating experience with a TCSC. In: IEEE Power Engineering Society Summer Meeting 18–22 July 1999, vol. 2, pp. 1103–1108 21. Bernard, S., Trudel, G., Scott, G.: A 735 kV shunt reactors automatic switching system for hydro-quebec network. IEEE Trans. Power Syst. 11(4), 2024–2030 (1996) 22. Kuznetsov, V., Tugay, Y., Kuchanskyy, V.: Influence of corona discharge on the internal ovevoltages in highway electrical networks. Tech. Electrodyn. 2017(4), 55–60 (2017) 23. Milanovic, V., Zhang, Y.: Global minimization of financial losses due to voltage sags with facts based devices. IEEE Trans. Power Deliv. 25(1), 298–306 (2010) 24. Joshi, B.S., Mahela, O.P., Ola, S.R.: Reactive power flow control using static VAR compensator to improve voltage stability in transmission system. In: IEEE International Conference on Recnt Advances and Innovations in Engineering, December 2016 (2016) 25. Chandrasekhar, R., Chatterjee, D., Bhattarcharya, T.: A hybrid FACTS topology for reactive power support in high voltage transmission systems. In: IECON 2018—44th Annual Conference of the IEEE Industrial Electronics Society October 21–23, 2018 at the historic Omni Shoreham Hotel, Washington DC, USA, pp. 65–70 (2018) 26. Fujii, T., Temma, K., Morishima, N., Akedani, T., Shimonosono, T., Harada, H.: 450MVA GCT—STATCOM for stability improvement and over-voltage suppression. In: 2010 International Power Electronics Conference—ECCE ASIA, Sapporo, pp. 1766–1772 (2010) 27. Kuchanskyy, V.V.: The prevention measure of resonance overvoltages in extra high voltage transmission lines. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 436–441 (2017). https://doi.org/10.1109/ukrcon.2017.8100529

Efficiency Increase of Open Phase Modes …

47

28. Kuchanskyy, V.V.: Application of controlled shunt reactors for suppression abnormal resonance overvoltages in assymetric modes. In: 2019 IEEE 6th International Conference on Energy Smart Systems, pp. 122–125 (2019) 29. Gu, S., Dang, J., Tian, M., Zhang, B.: Compensation degree of controllable shunt reactor in EHV/UHV transmission line with series capacitor compensation considered. In: Proceedings of International Conference on Mechatronics, Control and Electronic Engineering (MCE 2014), Shenyang, China August 29–31, 2014, pp 65–68 (2014) 30. Kuchanskyy, V., Malakhatka, D., Ihor, B.: Application of reactive power compensation devices for increasing efficiency of bulk electrical power systems. In: 2020 IEEE 7th International Conference on Energy Smart Systems, Kyiv, Ukraine, pp. 83–86 (2020). https://doi.org/10. 1109/ess50319.2020.9160072 31. Kuchanskyy, V.: The application of controlled switching device for prevention resonance overvoltages in nonsinusoidal modes. In: 2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO), Kiev, pp. 394–399 (2017). https://doi.org/10.1109/elnano. 2017.7939785 32. Kuchanskyy, V.: The prevention measure of resonance overvoltges in extra high voltage transmission lines. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kiev, pp. 436–441 (2017). https://doi.org/10.1109/ukrcon.2017.8100529 33. Kuchanskyy, V., Zaitsev, I.O.: Corona discharge power losses measurement systems in extra high voltage transmissions lines. In: 2020 IEEE 7th International Conference on Energy Smart Systems (ESS), Kyiv, Ukraine, pp. 48–53 (2020). https://doi.org/10.1109/ess50319.2020.916 0088 34. Kuchanskyy, V., Rubanenko, O.: Influence assesment of autotransformer remanent flux on resonance overvoltage. UPB Sci. Bull. Ser C: Electr. Eng. 82(3), 233–250 (2020) 35. Measures and technical means for increasing efficiency and reliability of extra high voltage transmission lines. https://www.researchgate.net/publication/344101083_Measures_and_tec hnical_means_for_increasing_efficiency_and_reliability_of_extra_high_voltage_transmiss ion_lines. Accessed 24 September 2020 36. Kuznetsov, V., Tugay, Y., Kuchanskyy, V.: Investigation of transposition EHV transmission lines on abnormal overvoltages. Tekhnichna Elektrodynamika 2013(6), 51–56 (2020) 37. Blinov, I.V., Zaitsev, I.O., Kuchanskyy, V.V.: Problems, methods and means of monitoring power losses in overhead transmission lines. In: Babak, V.P., Isaienko, V., Zaporozhets, A.O. (eds.). Systems, Decision and Control in Energy I, pp. 123–136. Springer (2020) 38. Kuznetsov, V., Tugay, Y., Kuchanskyy, V.: Abnormal overvoltages in the modes of transmission lines EHV. Tekhnichna Elektrodynamika 2012(2), 40–41 (2012) 39. Tugay, Y.: The resonance overvoltages in EHV network. In: IEEE Sponsored Conference EPQU’09—International Conference on Electrical Power Quality and Utilisation, pp. 14–18 (2009) 40. Dias, O., Tavares, M.C., Magrin, F.: Hardware implementation and performance evaluation of the fast adaptive single-phase auto reclosing algorithm. Electr. Power Syst. Res. 168, 169–183 (2019) 41. Ortega, J.S., Tavares, M.C.: Transient analysis and mitigation of resonant faults on halfwavelength transmission lines. IEEE Trans. Power Deliv. 35(2):1028–1037 (2020). https:// doi.org/10.1109/tpwrd.2019.2935870 42. Mestas, P., Tavares, M.C.: Effects of arc extinction time reduction during three-phase reclosing. In: IEEE PES Transmission & Distribution Conference and Exhibition—Latin America (T&DLA), Lima, pp. 1–5 (2018). https://doi.org/10.1109/tdc-la.2018.8511721 43. Acosta, J.S., Tavares, M.C.: Enhancement of overhead transmission line capacity through evolutionary computing. In: IEEE PES Transmission & Distribution Conference and Exhibition—Latin America (T&D-LA), Lima, pp. 1–5 (2018). https://doi.org/10.1109/tdc-la.2018. 8511761 44. de Mattos, L.M.N., Tavares, M.C., Mendes, A.M.P.: A new fault detection method for singlephase autoreclosing. IEEE Trans. Power Deliv. 33(6), 2874–2883 (2018). https://doi.org/10. 1109/TPWRD.2018.2855105

48

V. V. Kuchanskyy et al.

45. Dias, O., Magrin, F., Tavares, M.C.: Comparison of secondary arcs for reclosing applications. IEEE Trans. Dielectr. Electr. Insul. 24(3), 1592–1599 (2017). https://doi.org/10.1109/TDEI. 2017.006188 46. Sarmiento, J.S.A., Tavares, M.C.: Enhancement the overhead transmission lines’ capacity by modifying the bundle geometry using heuristics algorithms. In: 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Xi’an, pp. 646–650 (2016). https:// doi.org/10.1109/appeec.2016.7779583 47. Tavares, M.C., Talaisys, J., Camara, A.: Voltage harmonic content of long artificially generated electrical arc in out-door experiment at 500 kV towers. IEEE Trans. Dielectr. Electr. Insul. 21(3), 1005–1014 (2014). https://doi.org/10.1109/TDEI.2014.6832243 48. Garcia, A., Tavares, M.C.: Long arcs in free air: stationary parameters for secondary arc current range. Power Syst. Comput. Conf. Wroclaw 2014, 1–6 (2014). https://doi.org/10.1109/PSCC. 2014.7038501 49. Djuric, M., Terzija, C.: A new approach to the arcing faults detection for fast autoreclosure in transmission systems. IEEE Trans. Power Deliv. 10(4), 1793–1798 (1995) 50. Bo, Z., Johns, A., Aggarwal, R., Goody, J., Gwyn, B.: Digital simulation of an EHV transmission system for design and real time testing of new protection. In: First International Conference on Digital Power System Simulators. IEEE, p. 57 (1995) 51. Boisseau, A.C., Wyman, B.W., Skeats, W.F.: Insulator flashover deionization times as a factor in applying high-speed reclosing circuit breakers. Trans. Am. Inst. Electr. Eng. 68(2), 1058–1067 (1949) 52. Giesbrecht, J., Ouellette, D., Henville, C.: Secondary arc extinction and detection real and simulated. In: Developments in Power System Protection, IET 9th International Conference, pp. 138–143 (2008) 53. Luxenburger, R., Schegner, P.: Determination of secondary arc extinction time and characterization of fault conditions of single-phase autoreclosures. In: 2005 International Conference on Future Power Systems, pp. 5–10 (2005) 54. Milne, K.: Single-pole reclosing tests on long 275-Kv transmission lines. IEEE Trans. Power Appar. Syst. 82(68), 658–661 (1963) 55. Kizilcay, M., Pniok, T.: Digital simulation of fault arcs in power systems. Eur. Trans. Electr. Power 1(1), 55–60 (1991) 56. Tavares, M., Talaisys, J., Portela, C., Camara, A.: Harmonic content and estimation of length variation of artificially generated electrical arc in out-door experiments. In: IEEE Electrical Power and Energy Conference. IEEE, Oct. 2011, pp. 346–351 (2011) 57. Camara, A.S., Portela, C.M., Tavares, M.C.: Single-phase autoreclosure studies considering a robust and reliable secondary arc model based on a gray-box model. In: International Conference on High Voltage Engineering and Application. IEEE, Nov. 2008, pp. 486–489 (2008) 58. Camara, A.S.B., Portela, C.M.J.C., Tavares, M.C.: Single-phase auto-reclosure studies: some basic aspects on main elements representation. Int. Conf. High Volt. Eng. Appl. 2008, 482–485 (2008) 59. Santos, G., Tozzi, C., Tavares, M.: Visual evaluation of the length of artificially generated electrical discharges by 3D-snakes. IEEE Trans. Dielectr. Electr. Insul. 18(1), 200–210 (2011) 60. Zhou, L., Yi, Q., Qin, M., Zhou, L., Zhou, X., Ye, Y.: Using novel unified power flow controller to implement two phases operating in extra-high-voltage transmission system. In: International Conference on Power System Technology 13–17 Oct. 2002, pp. 1913–1917 (2002)

Nonlinear Mathematical Model of Optimal Solar Photovoltaic Station Design Maryna Bilenko , Ihor Buratynskyi , Iryna Leshchenko , Tetiana Nechaieva , and Sergii Shulzhenko

Abstract As legal amendments regarding the stimulation of renewable generation development in Ukraine came into force shifting the main measure from the use of fixed feed-in “green” tariff to the implementation of renewable auctions the requirements for the design of the renewable generation equipment structure changed too. This based on the change of actual criteria used for a renewable generation project. The main indicator became a minimum production cost of electricity which allows for the project to compete with other ones participated in the renewable generation auction. To assess the minimum production cost of electricity generated by solar photovoltaic station a simple non-linear mathematical model of optimal solar photovoltaic station design is proposed. The examples of calculations made with the model demonstrate that essential changes in the production cost of electricity could be achieved depending only on the equipment structure of the solar photovoltaic station which depends on its geographical location. Keywords Renewable energy · Photovoltaic power station · Production cost of electricity · Mathematical programming model · DC/AC ratio · Optimization

1 Introduction The domination of fixed feed-in “green” tariff as the main measure to speed up the development of renewable electricity generation in Ukraine is gradually diminishing. The legal amendments approved by Verkhovna Rada of Ukraine in 2019 [1, 2] and the appropriate Order of National Energy and Utilities Regulatory Commission of Ukraine [3] anticipate as the main measure the auctions for renewable generation according to the yearly quotas approved by the Cabinet of Ministers of Ukraine. These M. Bilenko NPC “UKRENERGO”, Kyiv, Ukraine e-mail: [email protected] I. Buratynskyi (B) · I. Leshchenko · T. Nechaieva · S. Shulzhenko Institute of General Energy, NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_3

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legal instruments could lead to the change in criteria used by independent investors to approve the decision regarding the type of renewable generation (wind or solar), its output power, and even design which capable to ensure maximum efficiency or wise versa minimum production price to be competitive compared to other projects participating in the auction. The one main gain that could be obtained from these new legal amendments for the national power sector is to implement a more stable and less unpredictable renewable generation as the maximization of production leads to a decrease in production cost. Another gain is a potential possibility to involve renewable generation in supplying power reserves for the whole Power System, at least some volumes of rump-down down power reserves which could cause more economically favorable utilization of conventional station’s power units. The main challenge for investors in a renewable generation according to the new rules of law is to provide maximum economic efficiency of generation allowing compete with other projects listed in the auction which from the point of view investment spending is an optimal design of future power plant which will be suited to participate at different segments of the electricity market. The article is dedicated to the formulation of a simple nonlinear mathematical model of optimal solar photovoltaic station (PV-station) design which provides a minimum production price of electricity generation. The model is programmed using the standard MS Excel’ Solver Add-on. The examples of calculations made with the model demonstrate that essential changes in the production cost of electricity could be achieved depending only on the equipment structure of the PV-station which depends on the geographical location of the PV-station.

2 Literature Review and Problem Statement The papers dedicated to the optimization of PV-station equipment structure are published in recent times use financial efficiency as a criterium [4, 5]. The common approach is to increase the overall power output from PV-station allowing minimize the busbar production cost of electricity. To study technical measures allowing an increase of power output a PV-station (Fig. 1) is treated as a combination of equip-

Fig. 1 The simplified PV-station structure (LV is low voltage, HV is high voltage)

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ment generating Direct Current (DC) and Alternating Current (AC). The generation level of DC depends on the amount of photovoltaic modules (PV-pannels) used in the PV-station, and the generation level of AC depends on the capacity of inverters utilizing DC from PV-pannels within a single PV-station [4]. The advantages and disadvantages of PV-array oversizing are discussed in the article [6] indicates that the main advantage of PV-array oversizing is the reduction of financial costs because of a longer operating time of the inverter at maximum power, but in that case, the inverter should be properly cooled otherwise its lifetime will be essentially reduced. Another study [7] conducted for Denmark and the USA dedicated to the assessment of the impact of PV array oversizing on the operating life-time and reliability of inverters showed possible expenses increase to maintain inverters in good operating conditions. Due to the increase of the oversizing factor, the lifetime of inverter decreases slightly and this directly depends on the geographical location of a PV-station and thus irradiance. The approaches and methods dedicated to the increase of PV-station economic efficiency for the USA market were studied in [8]. The authors studied the sensitivity of economic indicators that depend on the operation of PV-station in different electricity markets in the USA. There are several methods proposed for improving the accuracy of assessing economic efficiency increases to avoid investors making infeasible decisions regarding the implementation of PV-station at the concrete market conditions. Another perspective method to increase the economic efficiency of PVstation is to increase electric generation using tracking systems. Article [9] indicates as an efficient way to use single-axis tracking systems. The study for PV-station economic efficiency increase in terms of determination of best DC/AC ratio presented in [10]. This study examines different PV-station located across Western Europe based on the analysis of the influence of different irradiance conditions, type of inverters, and PV-station design on the annual amount of electricity produced. The main finding is that for high-efficiency inverters, the process of optimizing the equipment structure of a PV-station is more flexible. Also the influence of the intensity of solar radiation and ambient temperature on the operation of the inverter during its overload has been studied for Germany and Brazil [11]. The iterative method for determining the optimal installed capacity of inverters for PV-stations for five sites in Malaysia located in areas with different solar radiation and ambient temperature is proposed in [12]. The authors showed that PV-station economic efficiency could be increased by applying the proposed method. The amount of “lost” electrical energy production when limiting the generation due to lack of inverter’s installed capacity compared to PV array was estimated in [13]. The proposed simulation method allows determining the key parameters of the storage system, designed to save electricity lost and thus to improve the PV-station efficiency. The common approach of assessing PV-station economic efficiency based on the use of Levelized cost of electricity production (LCOE) was used in [14–16]. The optimization of PV-station design by minimum LCOE criteria and based on the variation of the number of connected PV-pannels and their topology, the distance

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between PV-pannels and the angle of PV-pannels were studied in [14] for Denmark. The influence of various economic parameters (such as interest rate, inflation rate, depreciation time) and technical factors (utilization factor of installed capacity, the use of different types of photovoltaic modules) on LCOE level was studied in [15]. The methods for LCOE calculation were studied in [16]. The authors stress that due to practical difficulty to accurately estimate discount and inflation rates for the whole PV-station lifetime the LCOE indicator could be only approximate assessment. The steady decrease of PV-pannels specific cost which is more rapid compared to the decrease of inverters cost is a motivation for investors to enlarge the square of a PV-pannels field. But the question becomes in what proportion should be the equipment of DC and AC of the PV-station. As shown at already published studies [5–14] the excess of produced DC by PV-pannels of PV-station even if part of it is irrecoverably lost during periods of maximum (peaking) generation could lead to the overall economic efficiency (lead to a minimization of electricity production cost) of PV-station if the installed capacity of AC equipment is suited well to the capacity of DC equipment. A common method for estimation of economic efficiency of a power plant of any type in comparison with another one, or to estimate the efficiency of some technical options regarding plant design is to calculate levelized cost of electricity for the whole lifecycle of the project [17, 19], but taking into account essential variability of solar radiation during not only a year but even during a single day some different method is proposed in the article that allows conducting a more accurate and detailed analysis of how production cost of electricity (PCOE) depends from both solar radiation available at the PV-station location and its hardware design (structure of equipment). The proposed method is based on an already known approach of including a probabilistic variable into calculations [18, 19]. But instead it is proposed to use a shorter period of time one day not the whole life cycle. It allows constructing different series of insolation profiles for different calendar days and for geographical places and performing a detailed and clear analysis of all factors affecting the production cost of electricity for that “virtual” day and PV-station geographical location.

3 Model Description The goal of the proposed model is to find minimum PCOE (1) for a single day for a PV-station located at some geographical place as a result of optimization according to the set of exogenously defined parameters: P DC (c DC + o DC + x AC/DC (c AC + o AC ))

PC O E = t

T  t=1

P DC

· i f (It ≥

x AC/DC )then(x AC/DC )else(I

(1) t)

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53

where PDC is exogenously defined installed peaking capacity of DC equipment of the field of photovoltaic modules (MWDC ); x AC/DC is variable corresponding to the AC/DC ratio, a fraction of exogenously defined installed peaking capacity of DC equipment, hence installed capacity of the AC equipment (PAC ) is defined by this variable and installed capacity of DC equipment (PDC ) and equal to x AC/DC PDC ; cDC is specific capital expenditures (CAPEX) for DC equipment of the field of photovoltaic modules (USD/MWDC ); oDC is specific operating expenditures (OPEX) for DC equipment of the field of photovoltaic modules (USD/MWDC ); cAC is CAPEX for AC equipment (USD/MWAC ); oAC is OPEX for AC equipment (USD/MWAC ); T is number of equal time periods during a single day, number (e.g. 24 if a day divided for 24 time periods (each period corresponding to 1 h), or 48 if each time period corresponding to 0.5 h or 30 min); t Δ is fraction of hour to calculate electricity generation during some period of time (e.g. 1 if a day divided for 24 time periods, 0.25 (corresponding to 15 min) if a day divided for 96 time periods); I t is exogenously defined flat level of insolation for time period during a day at some geographical location, a fraction between 0 and 1 (obtained as a result of the processing of the local instrumental measurements). To formulate the model let assume that the peaking capacity of DC equipment of the field of photovoltaic modules is exogenously defined. Hence, the exact goal of the model to find the best or optimal AC equipment capacity compared to the already known capacity of DC equipment, or to determine the value of AC/DC ratio (x AC/DC ) between capacity of DC equipment and AC equipment. The AC/DC ratio is served as the only variable of the model. The objective function is to minimize PCOE which is presented as a result of the division of daily expenses for the generation (the numerator of the formula (1) by the amount of electricity generated (the denominator of the formula (1) during the single day: PC O E → min

(2)

It is important to note that the objective function consists of two interdependent parts. From one side, as formulated in the denominator, the volume of generated electricity during one day depends on the capacity of the inverter or more definitely correspondence between the inverters capacity and the PV-pannels capacity. And on the other side (numerator), the daily expenses depend on CAPEX and OPEX which directly depend on PV-station design (equipment structure) which is again treated as the correspondence between the inverters capacity and the PV-pannels capacity. The only one constrain for the model is to limit x AC/DC : 0 < x AC/DC ≤ 1

(3)

The variable x AC/DC should be definitely greater than 0 (zero) because if it will be equal to zero then PCOE will be infinite that is mathematical nonsense, also from the practical reasons commercial PV-station connected to the grid with AC installed capacity equal to zero is nonsense too as it will not generate any electric power for sale. From the other side, the variable x AC/DC should not be greater than 1 (one),

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because of practical reasons the excess of inverter capacity (portion of AC equipment capacity greater than DC installed capacity) will never be used and investments to that excess of capacity is a “wasted investments”. The method for calculation of the objective function parameters, namely daily CAPEX and OPEX of DC and AC parts of the PV-station is based on the use of the standard LCOE calculation method using capital recovery factor (CRF) during lifecycle: C RF =

r 1 − (1 + r )−n

(4)

where r is discount rate (%), n is PV-station operating lifetime (years). The specific daily CAPEX for DC equipment cDC is defined: c DC = (c I _DC + c K _DC ) C R F/365

(5)

where cI_DC is specific capital costs on DC equipment (USD/MWDC ); cK_DC is specific discounted loan payments for attracted credit funds for investment in DC equipment (USD/MWDC ); 365 is average number of days in a year. Specific capital costs on DC equipment cI_DC is defined as the specific costs of DC equipment, taking into account the additional costs of engineering, procurement and construction (EPC): c I _DC = c E_DC (1 + k E PC )

(6)

where cE_DC is specific costs on DC equipment (USD/MWDC ); k EPC is the share of EPC costs of production equipment costs (%). Specific discounted interest payments on loans attracted for investment DC equipment cK_DC is defined: c K _DC =

m 

ctK _DC (1 + r )t−l−1 t=l+1

(7)

where cK_DC is specific annual interest payments on loans attracted for investment t DC equipment in years t (USD/MWDC ); m is loan repayment period (years); l is construction period (years). The specific annual interest payments on loans attracted for investment DC equipment in years is defined:  ctK _DC = (1 + r l )t−1

 (−r l )c I _DC γ r l c I _DC γ l 1−t ((1 + r ) − 1 − 1 − (1 + r l )m 1 + rl

(8)

Nonlinear Mathematical Model of Optimal Solar Photovoltaic …

55

where r l is interest rate (%); γ is share of investments costs covered by external loan (%). The specific daily operating expenditures for DC equipment oDC is defined: o

DC

n+l  r G s G + r S s S + c O&M_DC = C R F/365 (1 + r )t−l−1 t=l+1

(9)

where r G is the average specific land square needed to place DC equipment (ha/MWDC ); sG is the annual rental price (USD/ha); r S is the staff ratio (employee/MWDC ); S S is the annual employee salary, taking into account taxes and social payments (USD/employee); cO&M_DC is the specific annual operating and maintenance (O&M) costs of DC equipment (photovoltaic modules, mounting system, DC cables and other DC materials) (USD/MWDC ). The specific daily CAPEX for AC equipment are similarly defined as the specific daily CAPEX for DC equipment cAC is defined: c AC = (c I _ AC + c K _ AC ) C R F/365

(10)

where cI_AC is specific capital costs on AC equipment (USD/MWAC ); cK_AC is specific discounted loan payments attracted credit funds for investment for AC equipment (USD/MWAC ). Specific capital costs on AC equipment are similarly defined as the specific cost of AC equipment, taking into account the additional costs of EPC is defined: c I _ AC = c E_AC (1 + k E PC )

(11)

where cE_AC is specific costs on AC equipment (USD/MWAC ). Specific discounted interest payments on loans attracted for investment for AC equipment cK_AC are similarly defined as the specific discounted interest payments on loans attracted for investment for AC equipment: c K _ AC =

m 

ctK _ AC (1 + r )t−l−1 t=l+1

(12)

where cK_AC is specific annual interest payments on loans attracted for investment t for AC equipment (USD/MWAC ). The specific annual interest payments on loans attracted credit funds for investment for AC equipment in years is defined:  ctK _ AC = (1 + r l )t−1

 (−r l )c I _AC γ r l c I _ AC γ l 1−t ((1 + r ) − 1 − l m 1 − (1 + r ) 1 + rl

(13)

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The specific daily operating expenditures for AC equipment oAC consists of the total discounted costs O&M reduced to the daily value is defined: o

AC

=

n+l 

c O&M_ AC C R F/365 (1 + r )t−l−1 t=l+1

(14)

where cO&M_AC is the specific annual O&M costs AC equipment of inverters, transformer substations, AC cables low and high voltage and other AC materials (USD/MWAC ).

4 Results The calculations were done using the following input data for the parameters of Eqs. (4)–(14) which on average are reflecting the market prices for PV-station project in Ukraine in 2020 (Table 1). The data about irradiation for two real geographical regions of Ukraine and two real reference days were used as the input. The first geographical region is Kirovohrad region which is located in the center steppe region of Ukraine, and the second one is Ivano-Frankivsk region which is located in a mountain region of western Ukraine. The Table 1 Input data for equations Indicator name

Unit

Value

Installed DC power

MWDC

10

Discount rate

%

11

Operating lifetime

years

25

Construction periods

years

1

Specific costs on DC equipment

USD/MWDC

500,000

Specific costs on AC equipment

USD/MWAC

215,000

Share of EPC costs of equipment costs

%

17.2

Share of investments costs covered by external loan

%

70

Interest rate

%

8

Loan repayment period

years

6

Average specific land square

ha/MWDC

2

Annual rental price

$/ha

300

Staff ratio

employee/MWDC

0.5

Annual employee salary

USD/employee

6,000

Specific annual O&M costs DC equipment

USD/MWDC

6,400

Specific annual O&M costs AC equipment

USD/MWAC

1,600

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57

Fig. 2 Input and modeling data for PV-station located in Kirovohrad region for 22 June, 2019

reference days are selected on the principle to assess different operation conditions of PV-station. One day is close to the theoretical “bell irradiation profile” (the absence of clouds). And the second one is for a semi-cloudy day is the irradiance profile is intermittent (unstable). The first reference day is 22 June of 2019 (the longest day of a year) is the near “bell” irradiation for Kirovohrad region (Fig. 2) and intermittent irradiation for Ivano-Frankivsk region (Fig. 4). The second reference day is 22 September of 2019 usually the day with good weather conditions for electricity generation. Air is not too hot and long enough of day is the near “bell” irradiation for Ivano-Frankivsk region (Fig. 5) and intermittent irradiation for Kirovohrad region (Fig. 3). Each graph (Figs. 2, 3, 4 and 5) contains two vertical axes. The left one corresponds to electricity generation by the reference case PV-station with an equal installed capacity of DC and AC equipment (presented as “real data” curve) and the curve of electricity generation of PV-station with equipment structure obtained as a result of the modeling (presented as “model data” curve). The right axis corresponds to the irradiation (insolation) profile obtained as a result of local instrumental measurements (presented as an “irradiance” curve). As seen in Figs. 2, 3, 4 and 5 the “model data” curve has a flat fragment starting at about 11:30 until some 15:00. This result of the modeling for all reference days and geographical location of PV-station proves that minimum PCOE is reached in situations when installed AC equipment is less than DC ones. Another interesting result is as more intermittent (unstable) irradiance during a day the less value of AC installed power needed compared to DC ones. As seen in Fig. 3 the maximum power output of PV-station with equal DC and AC installed power (10 MW) during a semi-cloudy day is 4.05 MW and for that conditions, the

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Fig. 3 Input and modeling data for PV-station located in Kirovohrad region for 22 September, 2019

Fig. 4 Input and modeling data for PV-station located in Ivano-Frankivsk region for 22 June, 2019

optimal level of AC power output, provided minimum PCOE, is about 2.78 MW or 68% compared to DC installed capacity. For Fig. 4 where irradiance is higher compared to ones from Fig. 3 the optimal AC installed capacity compared to DC ones is 77%. And for Fig. 2 this value is 98.8% and for Fig. 5 this value more than 80%.

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59

Fig. 5 Input and modeling data for PV-station located in Ivano-Frankivsk region for 22 September, 2019

Table 2 The results show the economic efficiency of the model Region

Date

Real PCOE (USD/MWh)

Optimal AC/DC-ratio

Model PCOE (USD/MWh)

Reduction PCOE (%)

Kirovohrad

22.06.19

13.90

0.74

12.90

7.19

Kirovohrad

22.09.19

62.30

0.28

50.25

19.34

Ivano-Frankivsk

22.06.19

19.87

0.72

18.56

6.59

Ivano-Frankivsk

22.09.19

17.74

0.68

16.22

8.57

The detailed results of the modeling (Table 2) prove that depending on a level of irradiance and its daily profile (close to “bell” or intermittent) the reduction of PCOE is in the range 6–8% for clearly (cloudless) and 8–20% for cloudy days. From Table 2 it is seen that with a decrease in solar radiation intensity a lower optimized level of AC equipment installed capacity is required. For reference cases on 22 June 2019 (Figs. 2 and 4), the AC/DC ratio after optimization is about 74%, for the case with the lowest solar radiation (22 September 2019, Figs. 3), the AC/DC ratio is only 28%. Optimization of the equipment structure always allows reducing the cost of electricity generation of PV-station (PCOE). Depending on the irradiance intensity the reduction of PCOE varies from several percent (for the days with high and stable irradiance) to about 20% for the days with intermittent and low irradiance (Fig. 3, line 2 in Table 2).

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5 Discussion and Conclusions The proposed mathematical nonlinear optimization model allows optimizing the structure of the PV-station in terms of DC/AC ratio. Optimization of the equipment structure allows reducing the cost of electricity production for each geographical region of the PV-station location. The model allows estimating the cost of electricity production at the initial stage of the project life cycle, before construction taking into account all important but somewhat aggregated economic, technical, and outer environment factors (as listed in Figs. 2, 3, 4 and 5). The proposed model provides results using as input the typical (reference case) irradiation profile for a single day. It could be improved, e.g. taking into account a more detailed presentation of PV-station equipment structure and using an input the irradiance profiles for a whole year and not only for a single day. Also, further research in this area could be the improvement of the model to take into account the possibility to save lost electricity due to the oversizing of a PV-station equipment structure by installing e.g. an energy storage system.

References 1. Law of Ukraine “Pro vnesennia zmin do deiakykh zakoniv Ukrainy shchodo zabezpechennia konkurentnykh umov vyrobnytstva elektrychnoi enerhii z alternatyvnykh dzherel enerhii”. https://zakon.rada.gov.ua/laws/show/2712-19#Text (in Ukrainian) 2. Law of Ukraine “Pro alternatyvni dzherela enerhii”. https://zakon.rada.gov.ua/laws/show/55515#Text (in Ukrainian) 3. Order of National Energy and Utilities Regulatory Commission of Ukraine “On approving the regulatory legal acts which are regulating the activity of guaranteed buyer and purchase of the electric energy under the “green” tariff”. https://zakon.rada.gov.ua/laws/show/v0641874-19# Text (in Ukrainian) 4. Buratynskyi, I., Nechaieva, T., Shulzhenko, S.: Optimization of the equipment structure of a photovoltaic solar power station. Probl. Gen. Energy 2(61), 17–22 (2020). https://doi.org/10. 15407/pge2020.02.017 (in Ukrainian) 5. Zipp, K.: Why array oversizing makes financial sense. Solar Power World (2018) 6. Mounetou, R., Bejar Alcantara, I., Incalza, A., Justiniano, J.P., Loiseau, P., Piguet, G., Sabene, A.: Oversizing array-to-inverter (DC-AC) Ratio: what are the criteria and how to define the optimum? In: 29th European Photovoltaic Solar Energy Conference and Exhibition, pp. 2813– 2821 (2014). https://doi.org/10.4229/EUPVSEC20142014-5BV.2.5 7. Sangwongwanich, A., Yang, Y., Sera, D., Blaabjerg, F.: Impacts of PV array sizing on pv inverter lifetime and reliability. In: Proceedings of 2017 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 3830–3837. IEEE Press (2017). https://doi.org/10.1109/ecce.2017. 8096675 8. Drury, E., Denholm, P., Margolis, R.: The impact of different economic performance metrics on the perceived value of solar photovoltaics. Technical Report, NREL (2011) 9. Silva, M., Castro, R., Batalha, M.: Technical and economic optimal solutions for utility-scale solar photovoltaic parks. Electronics 9(3), 400 (2020). https://doi.org/10.3390/electronics9 030400

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10. Mondol, J.D., Yohanis, Y.D., Norton, B.: Optimal sizing of array and inverter for grid-connected photovoltaic systems. Sol. Energy 80, 1517–1539 (2006). https://doi.org/10.1016/j.solener. 2006.01.006 11. Burger, B., Ruther, R.: Inverter sizing of grid-connected photovoltaic systems in the light of local solar resource distribution characteristics and temperature. Sol. Energy 80, 32–36 (2006). https://doi.org/10.1016/j.solener.2005.08.012 12. Khatib, T., Mohamed, A., Sopian, K., Mahmoud, M.: An iterative method for calculating the optimum size of inverter in PV systems for malaysia. Przegl˛ad Elektrotechniczny (Electr. Rev.) 88, 281–284 (2012) 13. Muller, N., Kouro, S., Zanchetta, P., Wheeler, P., Bittner, G., Girardi, F.: Energy storage sizing strategy for grid-tied PV plants under power clipping limitations. Energies 12, 1812 (2019). https://doi.org/10.3390/en12091812 14. Kerekes, T., Koutroulis, E., Sera, D., Teodorescu, R., Katsanevakis, M.: An optimization method for designing large PV plants. IEEE J. Photovolt. 3(2), 814–822 (2013). https://doi.org/10.1109/ JPHOTOV.2012.2230684 15. Bano, T., Rao, K.V.S.: Levelized electricity cost of five solar photovoltaic plants of different capacities. Procedia Technol. 24, 505–512 (2016). https://doi.org/10.1016/j.protcy.2016.05.086 16. Aldersey-Williams, J., Rubert, T.: Levelised cost of energy—a theoretical justification and critical assessment. Energy Policy 124, 169–179 (2019). https://doi.org/10.1016/j.enpol.2018. 10.004 17. Swisher, J., Jannuzzi, G., Redlinger, R.: Integrated Resource Planning. UNEP & RISO National Laboratory, Denmark, 259 (1997) 18. Feretic, D., Tomsic, Z.: Probabilistic analysis of electrical energy costs comparing: production costs for gas, coal and nuclear power plants. Energy Policy 33, 5–13 (2005). https://doi.org/ 10.1016/S0301-4215(03)00184-8 19. Shulzhenko, S.: Efficiency indicators of power plants operation and development in the liberalized electricity market conditions. Probl. Gen. Energy 2(20), 7–13 (2009) (in Ukrainian)

Simulation of Efficiency Enhancement of Electric Power Generation by Wind Turbines in Wind Cadaster Various Zones Volodymyr Podgurenko , Yulii Kutsan , Oleg Getmanets , and Volodymyr Terekhov Abstract Based on the multi-year measurements of wind velocity and its distribution according to wind velocity levels at the Adzhigol pilot wind power station, the quantitative indicators of 43 wind turbines of megawatt-class by the world’s major manufacturers were analyzed in various wind cadaster zones. The obtained parameters (energy production and operation time in various zones of wind cadaster) were taken as a basis for the developed mathematical models of optimal power generation by a wind turbine. A new mathematical model was pioneered for calculating a capacity factor with regard for the operation time period in the zone of strong winds and also a new model of multiple linear regression for defining the dependence of the operation duration in the zone of strong winds upon the hub height and rated wind speed of power characteristic of the wind turbine. The said models enable the calculation of annual production of electric power based only on the three passport parameters (rating, hub height and rated wind speed of the wind turbine) without reference to power curve and wind cadaster of expected construction site of the wind power station. Software-based methods of computer-assisted realization of these models are suggested in the article. An example is given regarding enhancement of operating efficiency for industrial wind power station owing to increase of operation time in the strong winds zone at the wind conditions of the Northern Black Sea region of Ukraine. Keywords Wind turbine · Wind power station · Wind cadaster zones · Mathematical model · Efficiency

V. Podgurenko Admiral Makarov National University of Shipbuilding, Mykolaiv, Ukraine Y. Kutsan · V. Terekhov (B) G.E. Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] O. Getmanets V.N. Karazin Kharkiv National University, Kharkiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_4

63

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1 Introduction It is common knowledge that distribution of the wind velocity throughout the year adheres to the laws of probability. In such a case, wind velocity probability maximum distribution is, basically, asymmetrical towards low winds, and a mean wind velocity is always observed to its right [1] (Fig. 1). From the entire wind cadaster it is possible to single out three typical zones [2, 3]: A—low winds, when a wind turbine (WT) cannot work at all (V < V min ), as a rule V min = 3–4 m/s, which is below the starting one for the majority of WTs; B— medium winds (V min < V < V rate ), usually 4–13 m/s, when WT operates not in a rated mode, at a reduced power; C—high winds (V rate < V < V max ), usually 13–25 m/s, when WT operates in a rated mode, at full power. It is an obvious point that the best option, a so-called optimum, of utilizing the capacity will be in zone C, whereas a less effective variant will be in zone B. In numerous research papers by foreign authors dedicated to wind technologies, to the authors’ knowledge, the matter relating to the WT operating efficiency in various zones has not yet been studied. Cavallo [4] alone implicitly points to the relationship between low values of capacity factor (CF) with short duration of operation in the zone of rated power (in our case, in zone C). Gipe [5] in his fundamental monograph “Wind energy comes of age” not only states that

Fig. 1 Typical zones of wind cadaster

Simulation of Efficiency Enhancement …

65

WTs, as compared to traditional electric power installations, rarely operate at rated power for a long time, but he also elucidates the point. The WT capacity in terms of generating capacity should be evaluated not by generator power, but by the size of rotor diameter. Evaluations of WT in kilowatts give but a rough idea of how much power can be generated, whereas the rotor diameter is a far surer indicator. WT evaluations based on generator capacity were borrowed from the networking engineers who described them using the term well known to them: generator size. WT manufacturers persist in using a rated power for the purpose of identifying their products, though a rated power conveys little as to the quantity of power to be generated by WT. Regretfully, a rated power is on an upward trend all over the world. With this in mind, when choosing WT and the sites for wind power stations (WPS) construction, it is vitally important to assess the operation duration of a particular WT in zone C at a chosen site and, if required, to ensure the possibility of increasing the same.

2 Methods The construction of industrial WPS in the Northern Black Sea region is being carried out on the adjacent to the Adzhigol pilot wind power station (AWPS) territories, for which the distribution according to velocity levels has been adequately researched [6, 7]. Figure 2 presents the distribution of wind velocities probability at wind vane height of 31.5 m based on the results of the wind velocity measurements at the AWPS site taken with the aid of the measuring complex NRG LOGGER Symphonie

Fig. 2 Distribution of wind velocities probability at the wind vane height of 31.5 m

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9200 (USA) for a period of 26 months (from May 1997 till March 1999 and from August till October 2003 [6]). Figure 2 shows that WT (rated wind speed is 12 m/s) operates in a rated mode during only 1.3% of calendar time, i.e. there is available an unused reserve for enhancement of operating efficiency of WT (WPS) due to increase of operation time in zone C. The paper [8] presents a detailed analysis of the WT parameters effect (generator rated power P, rotor diameter D and hub height H) upon annual energy production Q by means of simulated deployment of 43 WTs of megawatt class by major world manufacturers (Table 1) in the wind conditions of AWPS. For the above-mentioned set of WTs, the operation time in zone C was determined for further study of its influence on a full annual energy production Q. For the above-mentioned set of WTs, the operation time in zone C was determined for further study of its influence on a full annual energy production Q. Over the years of observation, a mean wind speed, by the results of the measurements taken by a representative meteorological station (MS), corrected to the measurements conditions at the AWPS site (the wind vane height of 31.5 m), amounted to 6.0 m/s [6, 7]. This wind speed was taken as a basic minimum value for calculations to be made by various methods, thus, in our opinion, providing most reliable prediction for a long period of WPS operation. To evaluate the volume of electric power generation by various WTs, it is necessary to recalculate the wind speed levels with regard for the hub height of the WTs under examination instead of the wind vane location heights. For recalculation of wind velocities at various heights, the approximate power-law relation suggested by Hellman [9, 10] was applied: Vhub = Vwane



h hub h wane

m .

(1)

where: V wane —wind speed at wind vane height = 31.5 m; V hub —sought-for value of wind speed at hub height of the WT under investigation; m—wind shear exponent whose value is chosen depending upon the type of underlying surface at the WT site (a so-called vertical profile coefficient of wind velocity). According to special studies [6, 7], for the AWPS wind conditions, a mean value of m = 0.227. Having adapted the formula (1) for the Eq. (2) below, it will be possible to determine the wind velocities on the rotor arrangement axes (speed at the hub heights) of the WTs under consideration in the AWPS conditions.  Vhub = Vwane

h hub h wane

m

 = Vwane

h hub 31.5

0.227 .

(2)

Table 2 presents calculated quantitative data of WT operation from Table 1 in various zones of wind cadaster for typical wind conditions of AWPS. It should be noted that the operation time in zone C for WT with H > 100 m is comparatively short and only comes to 17.2% of the calendar time (see Nos. 36

Simulation of Efficiency Enhancement …

67

Table 1 Operation indices of WTs by some global major companies No

Manufacturer, WT

P, MW

D, m

H, m

Q, MW·h

CF, %

Vrate , m/s

LEITWIND 1

LTW70

1.7

70

65

4167

28.0

12

2

LTW77

1.5

77

65

4471

34.0

12

1.5

77

80

4792

36.5

12

1.5

80.3

60

4541

34.6

10.5

1.5

80.3

80

5155

39.2

10.5

99

5295

33.6

12.5

3 —||— 4

LTW80

5 —||— ENERCON 6

E66/20.70

1.8

70

7

E70–2.3

2.3

71

57

4564

22.7

14.5

2.3

71

100

6221

30.9

14.5

2.3

71

113

6491

32.2

14.5

8 —||— 9 —||— 10

E82–2.0

2.0

82

78

6284

35.9

12.4

11

E82–2.0

2.0

82

100

6899

39.4

12.4

2.0

82

138

7819

44.5

12.4

2.4

92

70

6746

32.1

12.5

2.0

80

78

5440

31.1

15

2.0

80

80

5599

32.0

15

2.0

80

100

6095

34.8

12

1.8

90

80

6126

38.9

12

1.8

90

95

6599

41.9

12

1.8

90

105

6806

43.2

12

2.0

90

95

6882

39.3

12

2.0

90

105

7171

40.9

12

2.0

90

125

7454

42.6

15

3.0

90

80

7855

29.9

15

12 —||— MITSUBISHI 13

MWT92–2.4

VESTAS 14

V80–2.0

15 —||— 16 —||— 17

V90–1.8

18 —||— 19 —||— 20

V90–2.0

21 —||— 22 —||— 23

V90–3.0

(continued)

68

V. Podgurenko et al.

Table 1 (continued) No

Manufacturer, WT

24

P, MW

D, m

H, m

Q, MW·h

CF, %

Vrate , m/s

3.0

90

105

8497

32.3

13

3.0

112

84

9895

37.7

13

3.0

112

94

10,622

40.4

13

3.0

112

119

11,269

42.9

12.5

2.5

80

65

5707

26.0

14.5

2.5

80

85

6114

27.9

14.5

2.5

90

85

7297

33.3

13

2.5

90

100

7801

35.6

13

2.5

90

117

8276

37.8

13

2.5

90

141

8852

40.4

13

2.5

90

160

9259

42.3

13

2.5

100

85

8533

39.0

11.5

2.5

100

100

9049

41.3

11.5

2.5

100

117

9573

43.7

11.5

2.5

100

141

10,149

46.3

11.5

2.5

100

160

10,548

48.2

11.5

3.2

120.6

90

11,665

41.6

13

3.2

120.6

100

12,102

43.2

13

3.2

120.6

120

12,759

45.5

13

3.2

120.6

140

13,380

47.7

13

—||— 25

V112–3.0

26 —||— 27 —||— FUHRLANDER 28

FL2500–80

29 —||— 30

FL2.5–90

31 —||— 32 —||— 33 —||— 34 —||— 35

FL2.5–100

36 —||— 37 —||— 38 —||— 39 —||— 40

WTU–3.2

41 —||— 42 —||— 43 —||— —||— = the same

B

C

2710 65

3197 71.5

3519 73.4

2982 65.7

2896 56.2

3706 70

3674 80.5

4988 80.2

4512 69.5

4148 66

2

3

4

5

6

7

8

9

10

2136 34

1979 30.5

1233 11.8

890 19.5

1589 30

2259 43.8

1559 34.3

1274 26.6

1274 22.5

1457 35

Energy production, MW h/% of total

A=0

Zones

1

WT No. as per Table 1 B

C

1463 16.7

1463 16.7

1463 16.7

1463 16.7

1463 16.7

1463 16.7

1463 16.7

6255 71.4

6439 73.5

6910 73.5

6439 73.5

5791 66.1

6255 71.4

6439 73.5

1042 11.9

858 9.8

38.7 4.4

858 9.8

1506 17.2

1042 11.9

858 9.8

Operation time, hours/% of annual

A=0

Table 2 Operation indices of WTs by some global major companies

32

31

30

29

28

27

26

25

24

23

WT No. as per Table 1

Zones B

C

5671 68.5

5196 66.6

4698 64.4

4739 78.2

4372 76.6

6751 59.9

6104 57.5

6769 68.4

6895 81.1

6253 79.6

2605 31.5

2605 33.4

2599 35.6

1335 21.8

1335 23.4

4518 40.1

4518 42.5

3126 31.6

1602 18.8

1602 20.4

Energy production, MW h/% of total

A=0 B

C

1463 16.7

1463 16.7

1463 16.7

1463 16.7

1463 16.7

1042 11.9

534 6.1

1506 17.2

1042 11.9

534 6.1

(continued)

6255 71.4

6763 77.2

5791 66.1

6255 71.4

6763 77.2

Operation time, hours/% of annual

A=0

Simulation of Efficiency Enhancement … 69

B

C

4763 69

4732 60.5

4687 69.5

4373 80.4

4531 80.9

4383 71.9

4250 69.4

3888 58.9

12

13

14

15

16

17

18

2711 41.1

1876 30.6

1712 28.1

1068 19.1

1067 19.6

2059 30.5

3087 39.5

2136 31

Energy production, MW h/% of total

A=0

Zones

11

WT No. as per Table 1

Table 2 (continued) B

C

1463 16.7

1463 16.7

1463 16.7

1463 16.7

2768 31.6

0

5791 66.1

6255 71.4

6439 73.5

6763 77.2

5134 58.6

7718 88.1

1506 17.2

1042 11.9

858 9.8

534 6.1

858 9.8

1042 11.9

Operation time, hours/% of annual

A=0

40

39

38

37

36

35

34

33

WT No. as per Table 1

Zones B

C

9957 85.3

5644 53.5

6384 62.9

5808 60.7

5285 58.4

5928 69.5

5494 59.3

6247 70.6

1709 14.7

4904 46.5

3765 37.1

3765 39.3

3764 41.6

2605 30.5

3765 40.7

2605 29.4

Energy production, MW h/% of total

A=0 B

C

1463 16.7

1463 16.7

1463 16.7

1463 16.7

1463 16.7

534 6.1

1962 22.4

1506 17.2

1042 11.9

1506 17.2

(continued)

6763 77.2

5335 60.9

5791 66.1

6255 71.4

5791 66.1

Operation time, hours/% of annual

A=0

70 V. Podgurenko et al.

B

C

4096 60.2

4798 69.7

5087 70.9

4442 59.6

20

21

22

3012 40.4

2084 29.1

2084 30.3

2710 39.8

Energy production, MW h/% of total

A=0

Zones

19

WT No. as per Table 1

Table 2 (continued) B

C

1463 16.7

1463 16.7

5791 66.1

6225 71.4

1506 17.2

1042 11.9

Operation time, hours/% of annual

A=0

43

42

41

WT No. as per Table 1

Zones B

C

7102 53.1

7939 62.2

8768 72.4

6278 46.9

4819 37.8

3334 27.6

Energy production, MW h/% of total

A=0 B

C

1463 16.7

1463 16.7

1463 16.7

5335 60.9

5791 66.1

6255 71.4

1962 22.4

1506 17.2

1042 11.9

Operation time, hours/% of annual

A=0

Simulation of Efficiency Enhancement … 71

72

V. Podgurenko et al.

through 38 of Table 2). Against the other positions: 4.4% (No. 8), 6.1% (No. 24), 9.8% (Nos. 9, 16), 11.9% (Nos. 11, 21, 31) and so on. The most important distinctive feature of the indices obtained was the use of actual multiple-year real measurements of wind velocities and distribution of their probabilities according to velocity levels at the AWPS site. That is what accounts for their main difference from those hypothetical values employed by many research fellows earlier. In view of the same, the present quantities data were taken as a basis for computer simulation.

3 Results and Discussion 3.1 Constructing the Model of Electric Power Generation with WT Operating in Various Zones of the Wind Cadaster Let us study the effect of the operation duration in zone C of the wind cadaster upon WT annual energy production (AEP) with subsequent development of mathematical model (MM) for this dependence. The aim of the model construction is to determine in the form of objective numerical indices the efficiency of operational modes of real WTs in each of the wind cadaster zones, thus facilitating the ascertainment of the essence of the current processes and effective utilization of capacities, and eventually—the optimization of operating parameters of industrial WPSs in specific wind conditions of Ukraine. To identify the degree of correlational (statistical or stochastic) influence of all the parameters given in Tables 1 and 2 upon the operation time in zone C with further determination of their cross-impact [11, 12], draw up Table 3, leaving out manufacturers’ names, but maintaining the line numbering of previous tables. The following symbolic notations are used in Table 3: factors being explained: Q (MW·hour)—turbine AEP or CF (%); explanatory factors: TC (hour)—operating time in zone C of wind cadaster, P (MW)—turbine rated power, H (m)—hub height of WT, D (m)—rotor diameter, V rate (m/s)—wind rated speed for the given WT. To determine the degree of statistical correlation between all the factors of Table 3 with the aid of “Correlation” tool from the “Data Analysis” pack of MS Excel [13, 14] program we shall compute the correlation matrix (Table 4). The correlation matrix is an evidence of a strong correlation between TC and CF (0.836) and also of a moderate correlation [15] between TC and Q (0.536). A sufficiently close correlation between CF and TC shows that TC can be used as an integral index of WT operational efficiency, similar to CF. The TC criterion integrity proves its dependence both upon WT technical characteristics in the form of parameter V rate (−0.631—moderate negative correlation), and upon wind load at the hub height H (0.620—moderate correlation). The revealed correlations allow suggesting linear regression model of the type:

Simulation of Efficiency Enhancement …

73

Table 3 Source table No Q

CF

TC

P

H

D

Vrate No Q

CF

TC

P

H

Vrate

4167 28.0

858 1.7

65 70

12

23

7855 29.9

534 3.0

80

15

2

4471 34.0

858 1.5

65 77

12

24

8497 32.3

534 3.0 105 105

15

3

4792 36.5

858 1.5

80 77

12

25

9895 37.7 1042 3.0

84

84

13

4

4541 34.6 1042 1.5

60 80.3 10.5 26 10,622 40.4 1506 3.0

94

94

13

5

5155 39.2 1506 1.5

80 80.3 10.5 27 11,269 42.9 1506 3.0 119 119

13

6

5295 33.6

858 1.8

99 70

12.5 28

5707 26.0

534 2.5

65

80

14.5

7

4564 22.7

387 2.3

57 71

14.5 29

6114 27.9

534 2.5

85

80

14.5

8

6221 30.9

387 2.3 100 71

14.5 30

7297 33.3 1042 2.5

85

90

13

9

6491 32.2

858 2.3 113 71

14.5 31

7801 35.6 1042 2.5 100

90

13

10 6284 35.9 1042 2.0

80

D

1

78 82

12.4 32

8276 37.8 1042 2.5 117

90

13

11 6899 39.4 1042 2.0 100 82

12.4 33

8852 40.4 1042 2.5 141

90

13

12 7819 44.5 1042 2.0 138 82

12.4 34

9259 42.3 1506 2.5 160

90

13

13 6746 32.1

858 2.4

70 92

12.5 35

8533 39.0 1042 2.5

85 100

11.5

14 5440 31.1

534 2.0

78 80

15

36

9049 41.3 1506 2.5 100 100

11.5

15 5599 32.0

534 2.0

80 80

15

37

9573 43.7 1506 2.5 117 100

11.5

16 6095 34.8

858 2.0 100 80

15

38 10,149 46.3 1506 2.5 141 100

11.5 11.5

17 6126 38.9 1042 1.8

80 90

12

39 10,548 48.2 1962 2.5 160 100

18 6599 41.9 1506 1.8

95 90

12

40 11,665 41.6

19 6806 43.2 1506 1.8 105 90

12

41 12,102 43.2 1042 3.2 100 120.6 13

20 6882 39.3 1042 2.0

95 90

12

42 12,759 45.5 1506 3.2 120 120.6 13

21 7171 40.9 1042 2.0 105 90

12

43 13,380 47.7 1962 3.2 140 120.6 13

22 7454 42.6 1506 2.0 125 90

12

534 3.2

90 120.6 13

Table 4 Correlation matrix Q

CF

TC

P

H

D

Q

1

CF

0.706

1

TC

0.536

0.836

1

P

0.845

0.229

0.106

1

H

0.629

0.732

0.620

0.337

1

D

0.928

0.676

0.517

0.765

0.373

1

Vrate

−0.050

−0.563

−0.631

0.363

−0.115

−0.171

C F = ao + a1 T C.

Vrate

1

(3)

After using the “Regression” tool from the “Data Analysis” pack of MS Excel [14] program, we shall obtain the summary Table 5.

74

V. Podgurenko et al.

Table 5 Summary table of regression analysis Regression statistics Multiple R

0.836

R square

0.699

Adjusted R square

0.692

Standard error

3.363

Observations

43

ANOVA (Analysis of variance) df

SS

MS

F

Significance F

Regression

1

1078.508

1078.508

95.378

2.925E−12

Residual

41

463.619

11.308

Total

42

1542.127

Coefficients

Standard error

t-stat

P-value

Lower 95%

Upper 95%

a0

24.261

1.447

16.770

5.828E−20

21.339

27.183

a1

0.0124

0.001

9.766

2.925E−12

0.010

0.015

And MM of equation: C F = 24.261 + 0.0124 · T C.

(4)

A very-low value of R2 [16] is indicative of the fact that the present model describes only around 69% of observations data, which is its disadvantage. A low value of determination coefficient R2 is largely due to a poor accuracy of determination of annual energy production Q, and respectively, CF value, as well as of operation time in typical zones of wind cadaster. The analysis of Table 2 indicates the presence of only six values of operational time in zone C. Let us form WT into groups with regard for time TC, with simultaneous averaging of their parameters CF, H and V rate (Table 6). Let us again use the “Correlation” tool from the “Data Analysis” pack of MS Excel program. It is evident from Table 7 that grouping and averaging have essentially improved the correlation relationship indices between the factors under investigation. Table 6 Averaged parameters in terms of operation time in zone C

TC

CF

387

26.8

H 78.5

14.5

V rate

534

31.5

83.3

14.6

858

33.0

84.6

12.9

1042

38.6

97.7

12.4

1506

42.7

114.2

12.1

1962

48.0

150.0

12.2

Simulation of Efficiency Enhancement …

75

Table 7 Correlation matrix TC

CF

H

TC C¯F

1 0.706

1

H¯ V¯rate

0.536

0.836

1

0.845

0.229

0.106

V rate

1

Using the data given in Table 6, we shall conduct the regression analysis (Table 8). As the result, we obtain high-quality [17, 18] MM of regression: C F = 23.2504 + 0.0129 · T C.

(5)

And now let’s construct the model of multiple linear regression to determine the dependence of operation time in zone C (TC) on the V rate and H parameters. Let us again employ (use) the “ Regression” tool from the “Data Analysis” pack of MS Excel program (Table 9). As the result, we obtain MM of regression: T C = 1892.234 + 15.669 · H − 185.459 · Vrate .

(6)

From MM (6) it follows that increase in the hub height by 1 m causes the increase in the operation time in zone C, basically by 16 h, whereas decrease in rated speed V rate by 1 m/s increases the time TC by 185 h. With the aid of a widely known [19] relationship Table 8 Summary table of regression analysis Regression statistics Multiple R

0.984

R Square

0.969

Adjusted R square

0.961

Standard error

1.551

Observations

6

ANOVA (Analysis of variance) df

SS

MS

F

Significance F

Regression

1

296.387

296.387

123.158

0.000375

Residual

4

9.626

2.407

Total

5

306.013

Coefficients

Standard error

t-stat

P-value

Lower 95%

Upper 95%

a0

23.2504

1.3728

16.9370

0.0001

19.4390

27.062

a1

0.0129

0.0012

11.0977

0.0004

0.0097

0.0161

76

V. Podgurenko et al.

Table 9 Summary table of regression analysis Regression statistics Multiple R

0.995

R Square

0.991

Adjusted R square

0.984

Standard error

74.847

Observations

6

ANOVA (Analysis of variance) df

SS

MS

F

Significance F

Regression

2

1,765,607

882803.3

157.5858

0.0009156

Residual

3

16,806

5602.048

Total

5

1,782,413

Coefficients

Standard error

t-stat

P-value

Lower 95%

Upper 95% 4058.76

a0

1892.234

680.776

2.780

0.069

−274.298

aH

15.669

1.748

8.965

0.003

10.107

21.232

aV

−185.459

41.403

−4.479

0.021

−317.221

−53.697

Q = C F · P · T,

(7)

where: T —annual operation time of WT, it is possible with the aid of MM (5) and (6) to quickly determine the annual energy production Q at the Northern Black Sea region conditions on the basis of three passport parameters (P, V rate , H) alone, without reference to WT power curve and to the wind cadaster at the area of expected construction of WPS. Let’s determine common relative errors of MM (5) and (6) through an example of annual energy production Q predictions (Table 10): Maximum relative error of predictions of MM (5) and (6) reaches 16%, yet, on average, deviations amount to about 7%, which is considered to be a quite acceptable result.

3.2 Comparison of MM Predictions with Actual Results of Operation Validity and admissibility of assumed idealizations in developing any dynamic model of real system can be checked and evaluated only by comparison of theoretical research results with empirical data. Commercial operation of the first WPSs in the wind conditions of Ukraine provided a rich actual material. We shall use the results of their operation in the comparative study with the purpose of increasing the extent of conclusive force and validity of inferences.

7819

6746

5440

5599

6095

6126

6599

6806

6882

7171

7454

14

15

16

17

18

19

20

21

22

6221

8

13

4564

7

12

5295

6

6899

5155

5

11

4541

4

6491

4792

3

6284

4471

2

10

4167

1

9

Measured Q

No. WT

7746

7038

6684

6334

6016

5537

5604

4895

4824

6707

8039

6693

5914

7214

6685

4934

5954

5086

4555

4615

4216

4778

Predicted Q

8 7 11

−370

−464

−723

2 4

133

3

7

9

10

8

13

11

−292

198

472

583

589

491

704

616

1

3

−220 39

3

206

6

12

−659

370

1

0

69

4

177 −14

6

15

−611 255

Relative error, |%|

Residuals

Table 10 Relative errors of models (5) and (6) predictions

43

42

41

40

39

38

37

36

35

34

33

32

31

30

29

28

27

26

25

24

23

No. WT

13,380

12,759

12,102

11,665

10,548

10,149

9573

9049

8533

9259

8852

8276

7801

7297

6114

5707

11,269

10,622

9895

8497

7855

Measured Q

12,573

11,440

10,307

9740

11,494

10,653

9591

8838

8174

10,708

9867

8805

8052

7388

6602

5717

10,672

9344

8813

8671

7343

Predicted Q

807

1319

1795

6

10

15

16

9

−946 1925

0 5

−18 −504

2

4 211

359

16

6 11

−1015 −1449

3 −529

1

8

0

5

12

−251

−91

−488

−10

597

1278

11

2

1082

7

512

Relative error, |%|

−174

Residuals

Simulation of Efficiency Enhancement … 77

78

V. Podgurenko et al.

The analysis of the source Table 2 shows that of the annual theoretical time resource (8760 h), the share of the wind annual available resource, at which the wind turbines operate, is T op = 7297 wind hours (83%). This is the situation for 40 WTs out of the 43 WTs being under examination. Two turbines No. 11 and No. 12 have T op = 100%, whereas No. 13 has T op = 68%. Of the total time resource, the share of zone A is 1463 h (16.7%), the vast majority of hours is in zone B, wherein WTs run not in a rated mode, but in a partial one, producing essentially smaller volume of traded electrical power than they could produce. MM (5) indicates the feasibility of enhancing the WT production efficiency due to increase of the operating hours in zone C, whereas MM (6) indicates the ways of increasing the operating hours not only by increasing the height H, but also by decreasing rated velocity V rate . This can be done by enlarging the rotor diameter without changing rated power P. Let’s evaluate the energy production of WT FL 2.5 − 100 (No. 36) at the present time of operation in zone C: TC = 1892.234 + 15.669 · 100 − 185.459 · 11.5 = 1326 h; [by (5)] CF = 23.250 + 0.0129 · TC = 40.36%; [by (6)] Q = 0.4036 · 2.5 · 8760 = 8838 MW hour, [by (7)] i.e. relative error in calculation is 2%. In the Northern Black Sea region conditions, it is quite realistic to increase the time TC, for instance, by enlarging the rotor diameter of WT FL 2500-100 or by replacing the same with WT Gamesa G126 of similar power (2.5 MW) and hub height of 102 m, but with rotor diameter D = 126 m and rated speed V rate = 10.0 m/s. Now we shall determine its energy production: TC = 1892.234 + 15.669 · 102 − 185.459 · 10.0 = 1636 h (by 23% more than that of FL 2.5 − 100); CF = 23.50 + 0.0129 · 1636 = 44.35% (by 10% more than that of FL 2.5 − 100); Q = 0.4435 · 2.5 · 8760 = 9713 MW hour (by 10% more than that of FL 2.5 − 100). Thus, the increase of operation time in zone C is an essential reserve for enhancing the operational efficiency of commercial WPS. The traditional practice in WPS construction does not consider such reserve. Neglect of the importance of the wind cadaster various zones in WT energy production is encountered in the wind power generation literature. Notably, the fact is disregarded that the operation time in zone C, as well as CF, is an integral index of WT operational efficiency in specific wind conditions. An invaluable and major feature of practical importance of the developed models consists in absence, when using the models, of any additional direct measurements of various parameters, in particular the models do not require the processing of meteorological data for representative MS (meteorological stations), which contributes to considerable reduction of errors. And it is owing to the fact that the development is based on actual many-year measurement, performed with the help of Logger computer-aided system, of wind velocities at the pilot AWPS; apart from that, use

Simulation of Efficiency Enhancement …

79

is made of actual exponent of wind velocity change versus height, which fact has essentially improved the validity of calculations.

4 Conclusions The mathematical models of WT operation in the wind cadaster various zones, first developed by using the computer-based simulation methods allow to promptly and truly determine its optimal parameters depending upon the wind load of the area where the construction of a commercial WPS is expected. The suggested mathematical models make possible to design and manufacture the required wind turbine in case of unavailability of the WTs with necessary design parameters in the world market. Such approach determines a practical value of the present paper, as at the stage of engineering design it helps to make a choice of a WT with optimal technical-and-economic features.

References 1. Gurmu, A.T.: Assessment of wind power potential at Zeway, Central Rift Valley. IOSR J. Environ. Sci. Toxicol. Food Technol. 2, 11–18 (2013). https://doi.org/10.9790/2402-0241118 2. Colmenar-Santos, A., Campíñez-Romero, S., Enriquez-Garcia, L., Pérez-Molina, C.: Simplified analysis of the electric power losses for on-shore wind farms considering Weibull distribution parameters. Energies 7, 6856–6885 (2014). https://doi.org/10.3390/en7116856 3. Wang, D., Li, H., Wu, F., Wang, L.: Modelling analysis of methods for wind turbine annual energy production (2016). https://doi.org/10.2991/icseee-15.2016.101 4. Cavallo, A.J.: Baseload wind energy systems. Proc. EWEC 1106–1111 (1994) 5. Gipe, P.: Wind energy comes of age, 536 pp, pp 155–161. Wiley, New York (1995) 6. Podgurenko, V., Bordyugov, V.: Technical-and-analytical offer on construction of industrial WPS in the Ochakov Region Coastal Area 114, 29–36 (2009) 7. Podgurenko, V., Petrov, Y.: Pre-investment calculations of the efficiency of industrial wind power station construction on the sites adjacent to the Adzhigol WPS in Nikolaev region. Choosing effective wind turbines for the set-completing of industrial WPS, 69 pp. (2010) 8. Podgurenko, V., Getmanets, O., Terekhov, V.: Efficiency enhancement of electric power generation by wind turbine on the basis of mathematical modeling. Èlektron. Model. 42(2), 121–127 (2020). https://doi.org/10.15407/emodel.42.02.12 9. Tong, W.Q.: Fundamentals of wind energy. WIT Trans. State-of-the-Art Sci. Eng. 44 (2010). https://doi.org/10.2495/978-1-84564-205-1/01 10. Wass, R.: Design of wind turbine tower height and blade length: an optimization approach. Mechanical Engineering Undergraduate Honors Theses 70, 64 pp., p. 14 (2018). http://schola rworks.uark.edu/meeguht/70 11. Mphahlele, M., Olugbara, O., Ojo, S.: Cross-impact analysis experimentation using two techniques to revise marginal probabilities of interdependent events. ORiON 27 (2012). https://doi. org/10.5784/27-1-95 12. Chao, K.: A new look at the cross–impact matrix and its applications in futures studies. J. Futures Stud. 12(4), 45–52 (2008) 13. https://www.excel-easy.com/examples/correlation.html

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V. Podgurenko et al.

14. Carlberg, C:. Statistical analysis: Microsoft Excel 2016. Que Publishing, 576 pp. (2017). ISBN10 0789759055; ISBN-13 978-0789759054 15. Mindrila, D.: Scatterplots and correlation. Khan Academy (2014). https://www.westga.edu/aca demics/research/vrc/assets/docs/satterplots_and_correlation_notes.pdf 16. Figueiredo, D., Júnior, S., Rocha, E.: What is R2 all about? Leviathan-Cadernos de Pesquisa Polútica 3, 60–68 (2011). https://doi.org/10.11606/issn.2237-4485.lev.2011.132282 17. Dahiru, T.: P-value, a true test of statistical significance? A cautionary note. Ann Ib Postgrad Med. 6(1), 21–26 (2008). https://doi.org/10.4314/aipm.v6i1.64038 18. Excel easy. Regression. https://www.excel-easy.com/examples/regression.html 19. Pfaffel, S., Faulstich, S., Rohrig, K.: Performance and reliability of wind turbines: a review. Energies 10, 1904 (2017)

Comparative Analysis and Recommendations for the Use of Frequency Regulation Technologies in Integrated Power Systems with a Large Share of Wind Power Plants Oleksandr Zgurovets

and Mykhailo Kulyk

Abstract The analysis of publications and the state of the problem showed that the rapid and significant growth of the share of wind power plants (WPPs) in the structure of the generating sources of the integrated power systems (IPSs) is an objectively determined process, the intensity of which will continue to grow in the future. The main driving factors of this process are the rapid technological progress of both wind power plants and new technologies for energy storage sources production, such as high-power battery energy storage systems (BESSs). At the same time, the analysis showed that there is a mismatch between the level of technological development and theoretical research in this area. This concerns, first of all, the models of frequency stabilization processes in IPS with a large share of WPPs in their structure. The models available in the literature that can calculate the processes of frequency regulation in these systems are linear. They cannot take into account nonlinear limitations, which are present in real sources, used as regulating power (restrictions on the maximum regulator power, the maximum speed of its change, the width of the dead band, and the module of frequency deviance). The linear models also do not take into account the nonlinearity of dependence of the WPP output power on wind speed and peculiarities of its frequency spectrum. Ignoring nonlinear limits in the mentioned models leads to grossly inaccurate and even incorrect results. With the use of the author’s model, which takes into account these nonlinear constraints, a series of calculations was performed to identify the possibilities of using each of the known classes of power plants and electrical units for automatic frequency control in the IPS with powerful WPPs (coal, gas-oil power plants, gas piston units, battery energy storage systems). Variants for each of the technologies where the frequency in the IPS met the requirements of the European power system ENTSO-E (|f| ≤ 0.02 Hz) and the IPS of Ukraine (|f| ≤ 0.2 Hz) were considered. It was found that for IPS, which are comparable to IPS of Ukraine in terms of the combination of operating conditions, only BESS is available for use as a frequency regulator in terms of technological and economic factors. It was also investigated and established that O. Zgurovets (B) · M. Kulyk Institute of General Energy of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_5

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to ensure the European requirements for frequency stability in the IPS, the power of the regulator must be not less than the total power swing of the WPP. Keywords Integrated power system · Wind power plant · Frequency · Power · Regulator

1 Introduction Intensive implementation of wind power plants in the structure of integrated power systems leads to the need for comprehensive study of generation processes and their impact on the operation of the entire power system. Unstable nature of power supply by these power plants to the grid influences first the processes of frequency regulation in the power system. Exactly from the aspect of influence on frequency, the task of modeling the processes of power generation on the WPP is considered in this publication. Generation processes at WPP, which are part of the power system, actually add additional disturbances to the power system and, as a result, significantly complicate the process of automatic load and frequency control (ALFC). Nevertheless, there are almost no practical studies in the literature on real data on the impact of generation processes and features of WPPs on the transition processes of frequency and load control in integrated power systems. Besides, there are practically no researches from the point of view of ALFC on peculiarities of WPPs, in particular, influence of time constant of wind turbine that reflects its constructive specificity caused, among other things, by inertia of moving parts. Nowadays, in the world practice, attempts of mass use of WPPs as part of IPSs face the need to ensure adequate use of capacity of high-speed (peak) generators, which should work simultaneously with WPPs to maintain the frequency. An example of such a “tandem” is the use by Denmark of peak electric power of large Norwegian hydroelectric power plants to ensure stable operation of its power system with a large share of WPPs energy. However, this solution cannot be recognized as universal, acceptable for an ordinary country by a number of factors, the main of which are: geographical restrictions, energy security, economic feasibility, import opportunities. These factors have different weight for almost all countries. Therefore, the solution to the problem of providing additional high-speed generating capacities to compensate for instability of WPPs generation within the IPS will surely have regional (national) peculiarities. Geographical limitations are a purely natural factor. Norway, in particular, cannot export energy to Ukraine due to its long distance and lack of necessary infrastructure. The key factor in energy security is the reliability of imported energy supply. Reliability, in turn, depends on the stability of political relations between the supplier and the consumer, the stability of the economic and political systems of the supplier, and others. Indicative in this regard is the difficult situation with Ukraine’s import of Russian gas, when the terms of its supply were constantly changed by Russia

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in a unilateral format, depending on the fulfillment of its political requirements to Ukraine. The factor of economic expediency of ensuring stability of frequency and capacity in IPS with a large share of WPPs in its structure by importing the required volumes of peak electricity is very important. Peak (high-speed) electricity is too expensive. For example, if the IPS of Ukraine will import electricity necessary for its normal functioning at the current installed capacity of wind and solar (SPP) power plants (more than 7 GW), according to the tariff of Denmark for Norway, the total income of the IPS of Ukraine will not be enough to pay the potential importer (authors’ assessment). Such “business” threatens the bankruptcy of the energy market, or uncontrolled growth of tariffs for electricity, that is, the prospect of the country’s default. The factor of possibility to import electricity to ensure stabilization of WPP frequency and power is also important practically for most countries. The fact is that such electricity is very scarce, because it must provide the possibility of very rapid changes in its output. Among traditional technologies, such properties have only powerful mountain hydroelectric power plants (Scandinavia, Alps, Siberian rivers of Russia). In particular, low-power plain Ukrainian hydro power plants (HPPs) are unable to provide electricity with the necessary technological indicators and quality [1]. Countries with geographical data similar to Ukraine’s are unable not only to generate but also to import electricity with such properties. Thus, only a limited number of countries can provide the necessary regulation capacity by importing electricity to balance the WPP operation. However, in the current state, the vast majority of countries in the structure of their energy systems have a certain amount of wind and solar capacity. At the same time, there were often no additional fast regulating sources introduced. Certain specialists claim that the combination of wind power plants when working together “stabilizes itself”. If it were so, we would have another “miracle of nature”. In fact, everything looks simpler. As a part of each power system there are fast backup power generators (the above-mentioned ALFC system), the purpose of which is to ensure stable operation of the whole power system in emergency modes. Under normal conditions, this reserve is in passive standby mode and automatically turns on when the frequency in the power system goes down/up beyond the normal range. When the WPPs is put into operation as part of the IPS without additional stabilizers, the frequency will go out (decrease/increase) beyond the permissible values, the ALFC system will take it as a force majeure situation and put its reserves into operation. Everything would be fine, but in case of an accident in the system, at least remotely similar to the Chernobyl one (for example, emergency shutdown of the 1000 MW nuclear unit), there would be nothing to eliminate it, because all the reserves will be already taken away by the WPPs. Under such conditions 5–7 years ago for ordinary power systems like the IPS of Ukraine, objective predictions could foresee only very limited use of wind and solar power plants in their structure. However, the situation changed radically after the appearance on the market of electrical storage technologies of lithium-ion units of super large power and capacity. Now in the integrated power systems of industrially

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developed countries such accumulator batteries with the unit capacity of 100 MW and 400 MWh are used exactly for neutralization of disturbances of frequency and power caused by the presence of WPP and SPP in their structure. These technologies are rapidly progressing, increasing their unit power output and storage capacity and reducing specific investments. In addition, these sources have a speed that is more than two orders of magnitude higher than that of hydroelectric power plants, the fastest of traditional power generation technologies. Thus, with the advent of new technologies for the accumulation of electricity, ordinary IPSs were able to bring in their structures significant capacities of WPPs and SPPs. However, it should be noted that the use of these opportunities requires a strict sequence. Unacceptable is the activity of the management of the integrated power systems of some countries, which, being under the influence of certain myths (self-regulation of the aggregate WPPs), or on the implementation of unjustified laws (in particular, on the “green” tariff), unreasonably and thoughtlessly increase the capacity of renewable sources (WPPs and SPPs). The result of such “activities” is well known: blackout for a few days in the power system of South Australia, shutdown of large groups of consumers in Germany, bringing the power market of Ukraine to bankruptcy. Therefore, introduction of renewable sources into the structure of the IPS requires such a composition and sequence of pre-project works and studies: – study of features (first of all, spectral composition) of renewable sources capacities operating in the corresponding region; – creation of adequate, reliable models of IPS, which use non-traditional sources and which (models) study processes of frequency and power stabilization; – development of problem-oriented software based on these models; – performing research using the developed software to determine the volumes and conditions of WPPs and SPPs use within the IPS; – determination of regime and technical-economical characteristics of IPS using WPPs and SPPs; – development of recommendations on the use of regulators to ensure stability of the IPS operation with the use of WPPs and SPPs; – development of recommendations on volumes of WPPs and SPPs use in conditions of a particular IPS. In the current situation, the practice of introducing into the structure of the majority of IPS capacities of WPPs and SPPs is carried out mainly by the trial and error method, mainly due to the absence of reliable corresponding models in the energy systems, or experience in their use. At the same time, errors, as mentioned above, often have to be paid quite a high price. In this section, in conjunction with the publication [2], the authors present the results of their research according to the above algorithm of pre-project work for the integration of wind power plants into the structure of integrated power systems.

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2 Literature Review and Problem Statement This publication is a continuation and development of the research given in [2]. The publication [2] contains a detailed list and analysis of literary sources that are not only directly [3–11] but also indirectly [12–23] related to solving the problem of frequency and power stabilization in integrated power systems with a large share of WPPs power in their structure. Therefore, in order to prevent duplication of this material, in this review we will focus on the sources that are absent in the review [2] and contain complete mathematical models capable of studying the processes of frequency and power stabilization at least in separate fragments of the corresponding IPS. Such a study is useful and necessary for comparing the functionality of known mathematical models of this purpose with the source model [2] and their adequacy and accuracy. Since the processes occurring during frequency regulation are transients, the corresponding models are developed both as a system of differential equations and as Laplace transfer functions. The analysis of existing publications concerning both the modeling of the system elements [24–29] and the power system as a whole [28–31] has shown that the most widespread approach in modeling is the use of transfer functions. With all the convenience of recording a system in the form of transfer functions, its main disadvantage is that this apparatus can be used only for linear (linearized) systems of differential equations. Thus, the method of transfer functions in relation to the study of transients in the energy system in the process of frequency stabilization can be used only in cases when the changes in the system are minor and linearized well. The considered processes of frequency stabilization in systems with significant amounts of renewable sources with their variable capacity imply the work with disturbances that can cause nonlinear behavior of elements included in the energy system. This is primarily due to technological limitations, which are typical for regulators-generators. That is why modeling such systems using transfer functions is simply impossible and requires the use of differential equation systems with nonlinear limitations and numerical methods to solve them. For this reason, we decided to use our own model, which would include both a complete description of the power system and its elements, taking into account the existing nonlinear limitations of regulators in the form of a system of differential equations and its solution using numerical methods. This ensures the necessary accuracy and adequacy of the model and practical calculations. As noted earlier, the integration of large volumes of WPP and SPP into the power systems of many countries may lead to problems associated with the lack of balancing capacity. That is why it is necessary to conduct research to determine which classes of power plants can be used as regulators and what volumes are required to ensure reliable compensation for variable power generated by renewable energy sources. In addition to the above mentioned HPPs and the novel BESSs, the possibility of using

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thermal power plants (TPPs) and gas-fired piston power plants (GPPPs) as regulators should be also investigated. Despite their low rate of output power change and possible negative environmental impact [32], they can be considered as an alternative for power balancing. The amount of the required capacity of the frequency regulators depends both on the value of the total installed capacity of the WPP and the regulators performance. At the same time, we can consider that the capacity of the regulator is sufficient if its regulation capabilities are enough to keep the frequency deviation in the range of ±0.02 Hz (requirements ENTSO-E) or ±0.2 Hz (actual for the IPS of Ukraine permissible frequency deviation in normal operation). Therefore, it is necessary to study the processes of frequency stabilization in the power system with large volumes of WPP and regulators based on HPP, TPP, GPPP, and BESS, as well as to determine the necessary amount of regulation capacity to ensure frequency stability within the normative limits.

3 Mathematical Model The publication [2] presents and studies a mathematical model of the processes of frequency and power regulation in the IPS with a large share in its structure of WPPs capacities. An important part of this metamodel is the model reflecting the WPP operation. However, within the framework of one publication it is difficult to display all the necessary features of WPP functioning, especially since they have a number of negative phenomena in the process of generation: zero guaranteed power, its dependence on the geography of WPP location, a very wide range of frequencies in the WPP power profile. Traditional power plants do not have these negative features. That is why the authors consider it reasonable to provide additional, more detailed analysis of the means of modeling WPP functioning in the structure of IPS. The main requirements to mathematical models of WPP functioning in the IPS is as follows: – the models should reflect the key technological features of the WPP, affecting the power supply to the grid and, consequently, the dynamics of frequency changes in the power system; – the models should contain parameters that allow to vary the installed WPPs capacity during the calculations when operating in the IPS; – it should be possible to integrate the final mathematical record of WPPs models with the model of automatic frequency and load control system. One of the most common approaches to WPP modeling consists in forming the corresponding transfer function, where the input is the wind model, which in its full form is a meteorological model. However, such models have substantial surplus of necessary parameters for the tasks of investigation of automatic frequency and load control processes. It is very important that such model is linear and cannot take into account nonlinear technological constraints inherent to energy system objects.

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Currently, in many countries (including Ukraine) the most common are threeblade winged wind turbines with a capacity of about 3 MW or more. When developing the mathematical model of WPP, it was taken into account that the wind speed is the main determining factor in modeling the process of power supply to the network. Besides, the important factors, which were considered at development of mathematical model, are: – the influence of power system current frequency on wind turbine operation, because they work in parallel; – the inertia moment of the wind wheel and the generator of the WPP influence the dynamics of the wind turbine speed change; – the current frequency of the power system is taken into account in the generalized model, where it is an independent variable; – the wind turbine inertia moment is calculated from the passport data of a particular wind turbine and is taken into account as a time constant in the corresponding differential equation; – wind direction generally reduces the maximum efficiency of a wind turbine due to the inertia of the wind wheel orientation mechanisms. Moreover, in powerful bladed wind generators, the control system changes the vane direction if the wind in the new direction operates more than 10 min. Thus, for the development of the mathematical model of WPP, suitable for its use in the generalized model of IPS, it was necessary, first of all, to develop a mathematical model of wind speed. The real graphs of wind speed (Fig. 1) and WPP power output to the grid represent complex curves with numerous extrema and fractures, therefore, for their approximation it is most appropriate to use discrete Fourier transform (DFT) [33]. As the initial data for building such a model, it is natural and expedient to use the results of field measurements of wind speed taken from operating wind units 7

wind speed, m/s

6

5

4

3

0

10

20

30

40

50

60 100 300 500 700 900 1100 1300 1500 time, s

Fig. 1 Fragments of natural data on wind speed

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[34]. They represent a table of wind speed and power taken at an interval of about 5 h with a measurement step of 2 s (8955 points). According to this transformation, the function f (t), which approximates the wind speed, is set at a time interval [0, T ] on 2N + 1 points (in T /2N steps), best approximated by the dependency  1 v(t) = A0 + (Ak cos kω0 t + Bk sin kω0 t), 2 k=1 N

(1)

where ω0 = 2π/T, k is the harmonic number, and the coefficients A0 , Ak , Bk are calculated by the formulas  2N  1  2π k v(tn ) cos tn , k = 0, 1, . . . , N , N n=0 T

(2)

 2N  1  2π k v(tn ) sin tn , k = 0, 1, . . . , N , Bk = N n=1 T

(3)

Ak =

where v(t n )—tabular wind speeds at points t n . Calculation of Fourier series coefficients and construction of corresponding charts were performed in MathCAD software environment. Figure 2 shows the spectrum Ck , m/s 4.4 A0/2

0.6

0.4

0.2

0

4

18

Fig. 2 Wind speed spectrum

180 (3 m)

1800 (30 m)

1,8·104 ∞ (5 h)

Тk , s

Comparative Analysis and Recommendations for the Use …

wind speed, m/s

5.5

89

actual data approximated data

5

4.5

4

3.5 0

50

100

time, s

Fig. 3 Comparison of the results of the mathematical wind model with actual data

 of amplitudes Ck = A2k + Bk2 , k = 1, …, N of the whole given 5-h wind speed diagram. For better perception, the values of harmonic periods T k are deferred along the abscissa axis, and the values of their amplitudes C k are deferred along the ordinate axis, and each k-th harmonic with the same purpose is represented by a rectangle with the width of the corresponding difference of periods T k and T k + 1. The component of the spectrum A0 /2 is shown in point T k = ∞. Periods T k are presented in logarithmic scale. In the area of small periods, these rectangles degenerate almost in line, and the spectrum becomes almost solid. The developed model was verified by comparing actual and calculated values. Figure 3 shows graphs of wind speed at the interval (duration—120 s) of 5-h time interval as an example. It can be seen that actual and calculated (approximated) wind speeds practically coincide. This indicates a fairly high accuracy of the performed approximation and the possibility of its use in the generalized model of the IPS. Discrete Fourier transform differs from periodic Fourier transform with the fact that in it (DFT) number of harmonics is limited by number N in case of odd number of points or integer part of number M/2, where M—the total number of points otherwise. From Fig. 2 we can see that the period of the highest harmonics is 4 s, which is caused by the step of measuring the wind speed of 2 s. Therefore, the dependence of DFT accuracy on the step of wind speed measurement follows, namely, with the growth of natural data in a certain period of time the accuracy of wind speed approximation will increase and vice versa. Although this trend is general, specific recommendations for the optimal wind velocity measurement step will vary significantly depending on the geographical location of a particular wind turbine and should be determined during pre-project studies.

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An important characteristic of the WPP is how its power depends on the wind speed. This characteristic is individual for each class of the WPP and set by the manufacturer in tabular form. Therefore, the table defining the dependence of wind turbine power on wind speed Pi (vi (t i )) should be transformed into an analytical function for its further use in the WPP model. Our research showed that the initial static capacity of one of the common wind turbines Vestas V112 with a capacity of 3 MW is approximated with high accuracy by a polynomial of 9-th order Pi (vi ) = −1.277 · 10−3 vi9 + 8.995 · 10−2 vi8 − 2.733vi7 + 4.694 · 101 vi6 − 5.021 · 102 vi5 + 3.467 · 103 vi4 − 1.544 · 104 vi3 + 4.275 · 104 vi2 − 6.676 · 104 vi + 4.473 · 104

(4)

with wind speed limits vmin = 3 m/s, vmax = 13 m/s, vlim = 25 m/s,

Pi (vmax ) = Pi (vlim ).

(5)

Figure 4 presents the graph of the approximating function (4) with the actual values for this wind turbine. The power values were calculated in steps v(t) = 0.5 m/s. Table 1 shows the absolute and relative approximation errors together with the calculated and actual data. Their values with excess meet both research and practical requirements. The use of this approach to WPP modeling as a part of the model given in [2] allows us to take into account both wind features and features of used wind turbines in addition to nonlinear constraints typical for power system elements. In combination with the developed software package, these models allow us to investigate 3.5

output power, MW

3 2.5 2 1.5 1 0.5

0 3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 wind speed, m/s actual approximated

Fig. 4 Polynomial approximation of static power of the Vestas V112-3.0 MW wind turbine

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Table 1 Calculated, actual static power values of the Vestas V112-3.0 MW wind turbine and approximation errors Wind speed (m/s)

Approximated power (kW)

Actual power (kW)

Absolute error (kW)

Relative error (%)

3

23.72

23

0.72

3.13

4

133.35

130

3.35

2.57

5

298.42

301

−2.58

−0.86

6

557.53

557

0.53

0.1

7

913.85

912

1.85

0.2

8

1373.31

1377

−3.69

−0.27

9

1957.77

1954

3.77

0.19

10

2570.66

2572

−1.34

−0.05

11

2975.65

2988

−12.35

−0.41

12

3068.17

3065

3.17

0.1

13

3075.00

3075

0

0

25

3075.00

3075

0

0

the processes of frequency stabilization in power systems with different classes of regulators and wind power plants in their composition.

4 Modelling Example With the use of the developed mathematical models, several series of studies with different types and capacities of regulators were carried out on the calculated power system with the parameters given in Table 2. The calculated power system was constructed using equivalent generators, consumer, and system losses. It includes both traditional generators, such as HPP, TPP and nuclear power plant (NPP), and Table 2 Parameters of the power system model System element Steepness of frequency characteristic Time constant (s) Initial power (MW) (MW/Hz) HPP

0.37

5

2000

TPP

0.73

5

4600

NPP

1.81

5

7400

Load

25.5

0.5

Regulator

Variable

Variable

15,474 1644

WPP

0.4

5

1330

Losses

–

–

1500

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O. Zgurovets and M. Kulyk

11

wind speed, m/s

10 9 8 7

6 5 4

0

10

20

30

40

50

60 70 time, s

80

90

100

110

120

Fig. 5 Wind speed graph

renewable—WPP. The power system was regulated by the capacity, which varied depending on a series of studies. Taking into account that the source of disturbances in the power system is a powerful WPP, all studies were related to the swing (change) of its power during considered time interval. Maximum power of the WPP and its swing in the performed studies were 2974 MW and 2046 MW, respectively. As the wind that was used in the simulation process, was selected 120 s interval, shown in Fig. 5. The first series of studies was devoted to determining the necessary capacity of regulators depending on their type to compensate for the instability of WPP power. Coal-fired thermal power plant (CTPP), gas-fired thermal power plant (GTPP), GPPP, HPP, and BESS with the power change rate of 0.1%, 0.133%, 0.6%, 2.5% and 714% of the installed power per second respectively were considered as regulators. The study determined the probable deviation of frequency in the system when using as regulator each of the listed classes of power plants with an installed capacity equal to the power swing of the WPP. In the case when the obtained deviation of frequency in the system exceeded the value of 0.2 Hz, which is the maximum permissible deviation in the power system of Ukraine, there was additionally found such an installed capacity of the regulator that would provide it. Since the fastest traditional regulator of the considered is HPP, they were also studied to keep the frequency deviation within 0.02 Hz, which meets the requirements of the European network of transmission system operators ENTSO-E. Due to its high performance, the BESS as a regulator easily kept the frequency with a deviation of 0.02 Hz even at a power equal to the power swing of WPP, which is 2045 MW (Table 3). Figure 6 shows the graphs of frequency deviation in the power system in case of using the proposed classes of regulators, when their installed capacity is equal to the power swing of WPP generation. The figure shows that coal-fired thermal power plant

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Table 3 Frequency deviation in the power system depending on the installed power of the regulator and its type Regulator type

Installed regulator capacity (MW)

CTPP

2045 250,000

GTPP

2045

Δ f, Hz

1

0.1

3.935947 0.199736

1

2045

0.133

3.647845 0.199709

1

0.6

2.0484

2.5

1.840708

20

2045

BESS

|Δf|max (Hz)

92

40,700 HPP

Regulator power change rate, % of the installed capacity per second

122

188,000 GPPP

Ratio of regulator installed power to WPP power swing

0.199862

1

9766

4.78

15,000

7.33

2045

2045

0.199938 0.019112 714

0.003366

4 3.5 3 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 0

10

20

CTPP

30

40 GTPP

50

60 time, s

70

GPPP

80

90 HPP

100

110

120

BESS

Fig. 6 Frequency deviation in the power system with different regulator classes at Preg = PWPP

managed the worst with frequency stabilization, followed by gas-fired thermal power plant and gas piston power plant. All of them during the study time interval could not even return the frequency to its nominal value, instead, the frequency deviation only increased with time. At the same time, the use of HPP as a regulator showed better results. Although the maximum frequency deviation exceeded 0.2 Hz, the regulator was able to return the frequency deviation to 0 Hz more often. The use of BESS as a regulator demonstrates its high efficiency: the frequency deviation during the study is reliably maintained at a minimum level that meets all requirements.

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During the studied time interval, the WPP power varies a number of times with the maximum deviation of 2045 MW, and at the end of the interval equals 743 MW. The worst performance rate at a coal-fired power plant, so that within 120 s it was able to change its capacity only by 212 MW, which leads to a frequency deviation above 3.9 Hz. Gas-fired thermal power plant behaves in a similar way: although its speed is higher by a third, the change in power during the whole period of the study is only 276 MW, and the frequency deviation exceeds 3.6 Hz. The regulator based on gas piston units has several times higher power change rate than the thermal power plant ones, it is clearly seen in Fig. 7. The curve of the graph obviously resembles a broken line, which is explained by the regulator’s attempts to follow the changes in the WPP power both upwards and downwards. The largest power deviation of the regulator is observed at the end of the interval and is equal to the value of 727 MW, which almost corresponds to the final deviation of the WPP power compared to the initial moment at t = 0 s. The frequency deviation at the end of the interval reaches 1.97 Hz with a maximum of over 2 Hz at t = 117.6 s. HPP performance is 4 times faster than GPPP performance, which has a positive impact on the quality of regulation. The power graph is a more expressive broken line, which is more consistent with the change in power HPP. The regulator based on HPP maintains a higher symmetry of frequency deviations and, despite for delays, returns deviations up to 0 Hz. The frequency deviation at the end of the interval is 0.23 Hz with a maximum value of 1.84 Hz at time t = 88.45 s. The performance of the regulator based on BESS exceeds by an order of magnitude both the performance of hydroelectric power plants and the speed of change in the power of WPP. For this reason, the graph of BESS power output mirrors the graph of WPP power, which allows to keep the frequency deviation within the range of 0.0034 Hz on the entire investigated interval. 3000

2500 power, MW

2000 1500

1000 500 0 0

10 WPP

20

30 CTPP

40

50

60 70 time, s

GTPP

80

GPPP

Fig. 7 Power output graph of WPP and regulators at Preg = PWPP

90

100

HPP

110

120

BESS

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Table 4 Frequency deviation in the power system depending on the installed capacity of BESS PBESS /PWPP

1.05

1

0.974

0.874

0.8

PBESS (MW)

2147

2045

1991

1787

1636

|f |max (Hz)

0.00337

0.00337

0.01987

0.19872

0.43301

To meet the requirements of frequency stability in studies with use of TPP, GPPP and HPP in the role of regulators to increase the resulting rate of output power change, their installed power was increased above values corresponding to the power swing of WPP. As a result, there was obtained significant volumes of equivalent regulators (Table 3), which will be quite difficult and economically inexpedient to implement in practice. In the second series of studies was conducted more detailed study of how the process of frequency regulation in the power system depends on the ratio of the regulator capacity to power swing of WPP. To reduce the impact of the regulator’s power change rate, in role of the compensating generator was selected BESS. In the performed series of researches, the power system operation modes were studied both with excess regulator power (by 5% more than WPP power swing) and with regulator power shortage (up to 20% less than WPP power swing). The obtained results are summarized in Table 4. It can be concluded from the data obtained that the excessive power of the regulator does not affect on frequency stabilization. The maximum frequency deviation under such conditions coincided with the maximum frequency deviation under equal values of PBESS and PWPP and reached 0.00337 Hz. This can be explained by the fact that the regulator’s speed is sufficient to compensate for the change in WPP generation at equal power, so the additional capacity of the regulator is not required. On the other hand, the lack of power in the regulator has a rather significant effect on the frequency deviation. Thus, the lack of 2.6% power is barely enough to maintain the frequency at 0.02 Hz. This deviation occurs due to the appearance of a power imbalance of about 51 MW at a time t = 36.4 s (Fig. 8a, b). Figure 8c, d shows the case when the lack of more than 12.6% of the regulator’s capacity in relation to the WPP power swing leads to a power imbalance, which reaches 243 MW at a time t = 36.25 s and causes a frequency deviation of almost 0.2 Hz. Another important observation is that the dependence of the maximum frequency deviation |f |max on the ratio PBESS /PWPP is exponential: for example, with a power shortage of 20%, the frequency deviation increases sharply above 0.43 Hz. This dependence may be caused by the effect of load self-regulation. Load self-regulation provides an additional regulation effect, which is more noticeable against the small shortage of regulation power.

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c) 0.05

0.015 0.01

0 -0.05

0

Δf, Hz

Δf, Hz

0.005 -0.005 -0.01

-0.15

-0.015

-0.2

-0.02 -0.025

-0.1

0

10

20

30 40 time, s

50

-0.25

60

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3000

power, MW

d) 4000

power, MW

b) 3500

2000 1500 1000 500 0

0

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30 40 time, s

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30 40 time, s

50

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2500

2000 1500 1000 500

0

10

20

BESS

30 40 time, s WPP

50

60

WPP+BESS

0

BESS

WPP

WPP+BESS

Fig. 8 Fragments of graphs of frequency deviation (a, c) and power of WPP, BESS and WPP + BESS (b, d) under conditions PBESS /PWPP = 0.974 (a, b) and 0.874 (c, d)

5 Conclusions Based on the results of the research, it is possible to conclude that only BESS meets the IPS requirements, which are comparable to IPS of Ukraine in terms of the aggregate operating conditions, for use as a frequency regulator by technological and economic factors. The use of regulators based on other classes of power plants, such as coal-fired and gas-fired TPP, gas-fired piston plants, and HPP, due to their low speed, requires the installation of significant capacity. Such capacities exceed possible changes in the power of installed WPP by tens, and in some cases, hundreds of times. Meeting such requirements is costly, which becomes economically unattainable, considering the low utilization rate of the mentioned regulators’ capacities. Moreover, for countries with insufficient hydropower resources, the use of HPP as a regulator becomes almost impossible.

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It was found that in order to meet the European requirements of frequency stability in IPS, in case of using fast BESS-based regulators, their power must be no less than the total power swing of WPPs. Using such regulator with a capacity greater than the WPP total power change does not affect the regulation process, leaving the excess power unused. Insufficient regulator power causes the frequency control quality to be reduced, which drops rapidly as the regulator capacity decreases compared to the WPP power change. In the case of a small power shortage, it is partially compensated by the self-regulation effect.

References 1. Kulyk, M.M., Kyrylenko, O.V.: The state and prospects of hydroenergy of Ukraine. Tech. Electrodyn. 4, 56–64 (2019). https://doi.org/10.15407/techned2019.04.056 2. Kulyk, M., Zgurovets, O.: Modeling of power systems with wind, solar power plants and energy storage. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298 (2020). Springer, Cham. https:// doi.org/10.1007/978-3-030-48583-2_15 3. Asghar, F., Talha, M., Kim, S.H.: Fuzzy logic-based intelligent frequency and voltage stability control system for standalone microgrid. Int. Trans. Electr. Energy Syst. 28(4) (2018). https:// doi.org/10.1002/etep.2510 4. Bahloul, M., Khadem, S.K.: Design and control of energy storage system for enhanced frequency response grid service. Paper presented at the Proceedings of the IEEE International Conference on Industrial Technology, 2018-February, pp. 1189–1194 (2018). https://doi.org/ 10.1109/icit.2018.8352347 5. Bisht, M.S., Sathans: Fuzzy based intelligent frequency control strategy in standalone hybrid AC microgrid. Paper presented at the 2014 IEEE Conference on Control Applications, CCA 2014, pp. 873–878 (2014). https://doi.org/10.1109/cca.2014.6981446 6. Hao, X., Zhou, T., Wang, J., & Yang, X.: A hybrid adaptive fuzzy control strategy for DFIGbased wind turbines with super-capacitor energy storage to realize short-term grid frequency support. Paper presented at the 2015 IEEE Energy Conversion Congress and Exposition, ECCE 2015, pp. 1914–1918 (2015). https://doi.org/10.1109/ecce.2015.7309930 7. Hassan, S.Z., Li, H., Kamal, T., Awais, M.: Stand-alone/grid-tied wind power system with battery/supercapacitor hybrid energy storage. Paper presented at the Proceedings of 2015 International Conference on Emerging Technologies, ICET 2015 (2016). https://doi.org/10.1109/ icet.2015.7389179 8. Indu, P.S., Jayan, M.V.: Frequency regulation of an isolated hybrid power system with superconducting magnetic energy storage. Paper presented at the Proceedings of 2015 IEEE International Conference on Power, Instrumentation, Control and Computing, PICC 2015 (2016). https://doi.org/10.1109/picc.2015.7455752 9. Jamroen, C., Dechanupapritta, S.: Coordinated control of battery energy storage system and plug-in electric vehicles for frequency regulation in smart grid. Paper presented at the 2019 IEEE PES GTD Grand International Conference and Exposition Asia, GTD Asia 2019, pp. 286–291 (2019). https://doi.org/10.1109/gtdasia.2019.8715962 10. Melo, S.P., Brand, U., Vogt, T., Telle, J.S., Schuldt, F., Maydell, K.V.: Primary frequency control provided by hybrid battery storage and power-to-heat system. Appl. Energy 233–234, 220–231 (2019). https://doi.org/10.1016/j.apenergy.2018.09.177 11. Yan, S., Wang, M., Yang, T., Hui, S.Y.R.: Instantaneous frequency regulation of microgrids via power shedding of smart load and power limiting of renewable generation. Paper presented at the ECCE 2016—IEEE Energy Conversion Congress and Exposition, Proceedings (2016). https://doi.org/10.1109/ecce.2016.7855207

98

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12. Electricity and Energy Storage (Updated January 2020). World Nuclear Association. http://www.world-nuclear.org/information-library/current-and-future-generation/electr icity-and-energy-storage.aspx 13. Arani, M.F.M., El-Saadany, E.F.: Implementing virtual inertia in DFIG-based wind power generation. IEEE Trans. Power Syst. 28(2), 1373–1384 (2013). https://doi.org/10.1109/ TPWRS.2012.2207972 14. Arul, P.G., Ramachandaramurthy, V.K.: Mitigating techniques for the operational challenges of a standalone hybrid system integrating renewable energy sources. Sustain. Energy Technol. Assess. 22, 18–24 (2017). https://doi.org/10.1016/j.seta.2017.05.004 15. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Technical provision of diagnostic systems. In: Diagnostic Systems for Energy Equipments. Studies in Systems, Decision and Control, vol. 281, pp. 91–133. Springer, Cham (2020). https://doi.org/ 10.1007/978-3-030-44443-3_4 16. Cho, S., Jang, B., Yoon, Y., Jeon, W., Kim, C.: Operation of battery energy, storage system for governor free and its effect. Trans. Korean Inst. Electr. Eng. 64(1), 16–22 (2015). https://doi. org/10.5370/KIEE.2015.64.1.016 17. Habib, M., Ladjici, A.A., Harrag, A.: Microgrid management using hybrid inverter fuzzy-based control. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04420-5 18. Guney, M.S., Tepe, Y.: Classification and assessment of energy storage systems. Renew. Sustain. Energy Rev. 75, 1187–1197 (2017). https://doi.org/10.1016/j.rser.2016.11.102 19. Naranjo Palacio, S., Kircher, K.J., Zhang, K.M.: On the feasibility of providing power system spinning reserves from thermal storage. Energy Build. 104, 131–138 (2015). https://doi.org/ 10.1016/j.enbuild.2015.06.065 20. Orihara, D., Saitoh, H.: Evaluation of battery energy storage capacity required for batteryassisted load frequency control contributing frequency regulation in power system with wind power penetration. IEEJ Trans. Power Energy 138(7), 571–581 (2018). https://doi.org/10.1541/ ieejpes.138.571 21. Wen, Y., Dai, Y., Zhou, X., Qian, F.: Multiple roles coordinated control of battery storage units in a large-scale island microgrid application. IEEJ Trans. Electr. Electron. Eng. 12(4), 527–535 (2017). https://doi.org/10.1002/tee.22408 22. Lytvynchuk, V.A., Kaplin, M.I., Bolotnyi, N.P.: The method of design an optimal underfrequency load shedding scheme. In: IEEE 6th International Conference on Energy Smart Systems, pp. 14–17 (2019) https://doi.org/10.1109/ess.2019.8764241 23. Xie, P., Li, Y., Zhu, L., Shi, D., Duan, X.: Supplementary automatic generation control using controllable energy storage in electric vehicle battery swapping stations. IET Gener. Transm. Distrib. 10(4), 1107–1116 (2016). https://doi.org/10.1049/iet-gtd.2015.0167 24. Mullane, A., O’Malley, M.: The inertial response of induction-machine-based wind turbines. IEEE Trans. Power Syst. 20(3), 1496–1503 (2005). https://doi.org/10.1109/tpwrs.2005.852081 25. Biroon, R.A., Pisu, P., Schoenwald, D.: Large-scale battery energy storage system dynamic model for power system stability analysis. In: 2020 IEEE Texas Power and Energy Conference (TPEC) (2020). https://doi.org/10.1109/tpec48276.2020.9042536 26. Ekanayake, J.B., Holdsworth, L., Wu, X.G., Jenkins, N.: Dynamic modeling of doubly fed induction generator wind turbines. IEEE Trans. Power Syst. 18(2), 803–809 (2003). https:// doi.org/10.1109/tpwrs.2003.811178 27. WECC Type 4 Wind Turbine Generator Model—Phase II (2013). https://www.wecc.biz/Rel iability/WECC-Type-4-Wind-Turbine-Generator-Model-Phase-II-012313.pdf 28. Akram, U., Nadarajah, M., Shah, R., Milano, F.: A review on rapid responsive energy storage technologies for frequency regulation in modern power systems. Renew. Sustain. Energy Rev. 120, 109626 (2020). https://doi.org/10.1016/j.rser.2019.109626 29. Lalor, G., Mullane, A., O’Malley, M.: Frequency control and wind turbine technologies. IEEE Trans. Power Syst. 20(4), 1905–1913 (2005). https://doi.org/10.1109/tpwrs.2005.857393 30. Chang, C.S., Fu, W.: Area load frequency control using fuzzy gain scheduling of PI controllers. Electr. Power Syst. Res. 42(2), 145–152 (1997). https://doi.org/10.1016/s0378-7796(96)011 99-6

Comparative Analysis and Recommendations for the Use …

99

31. Anderson, P.M.: Power systems control and stability. In: Anderson, P.M., Fouad, A.A. (eds.) United States of America: IEEE Press Power Engineering Series, 678 pp. (2003) 32. Iatsyshyn, A., Artemchuk, V., Zaporozhets, A., Popov, O., Kovach, V.: Mathematical approaches for determining the level of impact of ash-slag dumps of energy facilities on the environment. Stud. Syst. Decis. Control 298, 1–13 (2020). https://doi.org/10.1007/978-3-03048583-2_1 33. Kulyk, M.M., Dryomin, I.V.: Discrete Fourier transform application for wind speed simulation. Prob. Gen. Energy 3(34), 5–10 (2013) 34. Kolesnikov, A.: What is the wind? http://al-kolesnikov.livejournal.com/17152.html

Increasing Vertical Resolution in Electrometry of Oil and Gas Wells Mykyta Myrontsov , Oleksiy Karpenko , Oleksandr Trofymchuk , Viacheslav Okhariev , and Yevheniia Anpilova

Abstract The paper considers new software implementation of effective methods for wells electrometry modelling. For electrical logging, it is based on the original approach of using the integrated current method. That made it possible to quickly and accurately solve the problem of determining the influence of the location of the return electrode in the electrical logging probes at a finite distance on the vertical resolution of the method. For induction logging, the proposed method allows not only to quickly and accurately solve the direct problem. These methods allow not to change the type and number of equations in the system of linear algebraic equations when modeling of cuts changes, but to change only values of system coefficients. Proposed methods were implemented in software, tested and successfully used for modeling in geophysical research for complicated well cuts. These approaches have made it possible to improve the accuracy of solving the iterative inverse electrometry problem. Its use the result of solving the direct problem at each iteration step. The method of significantly increasing the vertical resolution of the induction logging probes with any length was proposed. There are many reasons for the problem of defining the boundaries of productive intervals in vertical and inclined vertical oil and gas wells. For many of the conditions inherent in the Dnipro-Donetsk basin, one of the main reasons is the insufficient vertical resolution of geophysical well research complexes used in practice. The use of electrometry logging equipment asymmetric probes is one of the factors in the context of the problem. For illustration, we can cite the well-known problem of water-oil contact position determining. The solution of it is the subject of many studies. Due to inaccurate determination of perforation, the error in determining the limits of productive intervals leads to an error in determining the daily flow rate of useful fluid. Accordingly, it is a factor of the error of the initial conditions of further technical operation of the well as man-caused impact to the environment.

M. Myrontsov (B) · O. Karpenko · O. Trofymchuk · V. Okhariev · Y. Anpilova Institute of Telecommunications and Global Information Space, National Academy of Sciences of Ukraine of the Kyiv, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_6

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Keywords Electrometry · The direct problem · The inverse problem · Oil and gas well · Electrical logging · Induction logging

1 Introduction The relevance of the research lies within the fact that wells drilling and operation is the only possible way to extract oil and gas. Therefore, well-logging methods development is of relevant nature. Considering that coring is much more complicated, expensive and longtime, the importance of the electrical method of near-well conductivity research is evident, because electrometry can give an answer to the two main questions interesting to the WL customers as part of well logging (WL) [1]: – what is the amount of hydrocarbons in a well? – where are they exactly located? Electrometry equipment widely used in Ukraine and former Soviet Union countries [2]. It is not always capable of answering these questions for actual conditions of layering compound logs opened by a well, such as thin-layered and anisotropic layers, false collectors, collectors with abnormally low resistance, collectors with residual oil saturation, etc. [3]. This equipment called “lateral logging probe + lateral logging + induction logging” was developed in 1950s–1960 [4, 5]. Now it is outdated and incapable to solve the problem of geoelectrical parameters determining for complicated well cuts. Such cuts opened by a well in the conditions of Dnieper-Donets depression. It should also be noted that electrical logging (EL) can answer these questions in cases when highly conductive wells filled with a conductive drill fluid [6, 7], when the imputed value of specific resistivity of drill fluid is 0.01–2.00 m. Induction logging (IL) can answer these questions in cases when wells filled with a conductive drill fluid [8–10], when the imputed value of specific resistivity of drill fluid is 0.5–300.0 m. The ability to set the value of signal registering particular EL equipment in a well that opened any geological structure can be solved by direct problem solution. There are many methods of direct problems’ numerical solution. Among them, the two most common are a finite difference and finite elements. We used the original method of integral currents. This numerical method is the most convenient because the solving system of linear algebraic equations (SLAE) does not change just as near-well model changes. At the research execution stage, this allowed selecting the parameters of developing devices to provide the most accurate opportunity to determine the geoelectrical parameters of the current models of wells. Also we can solve such problems using modeling, such as focusing effect to lateral logging, misalignment, current drain, errors, temperature influence, well influence, “screen” effect, Groningen-effect, etc. An original method was also used to solve direct IL problems and the problem of increasing IL vertical resolution.

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This allowed increasing the vertical resolution of the IL to a value comparable to the recording step along the well. The research is part of a long-term study of the possibilities to improve the efficiency of solving well electrometry inverse problems [11–17]. The standard complex of electrometry used in Ukraine and CIS countries was considered in the research. Namely, there is BKZ + focused probe of lateral logging (BK-3) + compensated probe of induction logging (IK) 6F1 or 7I1.6 [18–21].

2 Modeling in Electrical Logging What do we mean by “EL direct problem”? Firstly, we mean some way to obtain a value which is equal to the particular probe measurement value, a specific physical principle of measurement in a certain well [22–27]. To describe the implementation of this method, we need to formalize some concepts. As “measurement” we mean physical laws, their mathematical formulation and possibility of their modeling in one or another convenient way. From the viewpoint of mathematics, we need to solve the equation of divergence in an area with no sources of current with variable coefficients for direct EL problem solution:   div j = div σ E = 0 (1) From the viewpoint of modeling, it means to execute the numerical solution in any convenient way, but with an error does not exceed predetermined, preferably small enough value. For example, we can use a finite difference or finite element method. Now we will use the method of integral currents. For this, let’s replace the environment with a discrete model described by a heterogeneous SLAE. This SLAE describes electrical integrator. Let’s write the Second Kirchhoff law in each node that is the integral similarity of current density vector continuity Eq. (1): Ui

j+1

− Ui

j+1,i

R j,i

j

j

−

Ui − Ui j,i

j

j−1

R j−1,i

+

Ui+1 − Ui j,i+1

R j,i

j

j

−

j

Ui − Ui−1 j,i

R j,i−1

= 0,

i = 1, n, j = 1, m j

(2)

where Ui —potential in the node j, i; n—quantity of nodes in vertical direction; j+1,i j,i+1 m—quantity of nodes in radial direction; R j,i , R j,i —resistivity between nodes ( j + 1, i), ( j, i) i ( j, i + 1), ( j, i) appropriately (for nodes in vertical direction we need to change denominator to appropriate step):

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 j+1,i

R j,i  j,i+1

R j,i

= Z

=

ρ r

dr 2π z(r0 + ( j − 0.5)r )

dz  ρ  π (r0 + (i + 0.5)r )2 − (r0 + (i − 0.5)r )2

(3)

(4)

In (3), (4) approximation step by coordinates r, z appropriately, r0 —probe radius. That system (2) will not change in the type and number of equations for any number and location of coaxial boundaries with coordinates or boundaries normal to the well axis with coordinates in a model with plane-parallel ordering. Only (3) and (4) will change, since the change in conducting environment model will only result to re-calculation of concerned integrals which are system coefficients. Case when specific resistivity (SR) ρ = ρ(z, r ) changes with its derivative continuously. It is not require changes in the number or system equations type. Thus, the solution of the EL direct problem is as follows: – rectangular grid problem (discrete model of environment); – solution of system (2) for a homogeneous environment with a SR equal to one for calculation probe coefficients; – systems (3), (4) coefficients calculation and its solution for a predetermined model of the environment. Boundary conditions for derivative functions (Neumann condition) at infinity and at insulating surface of the device perform automatically: because of the network’s limitedness, current cannot propagate in the direction of the absent grid. Non-zero boundary conditions of the electrodes surfaces are achieved by adding to the right side of Eq. (2) the current magnitude flowing from the electrode. Thus, we have an inhomogeneous SLAE with a unique solution which can be expressed through system determinant using Cramer formula. For the specific geometry of the probe (specific boundary conditions), solution of direct problem for any 2D-positional relation of specific resistivity does not require a SLAE change, but only requires a change in its coefficients. That system without generality limitation allows the use of grid with an irregular pitch. Let’s establish a proposed method for solving EL direct problem. Transition from the differential Eq. (1) to its integral similarity (2) by the choice of (3), (4) is reasonable. The transition from an integral form to a differential form transfers system (2) to a finite difference system with a rectangular grid. It allows using all the theorems about convergence and sustainability of the method in a homogeneous environment. Accordance (2) with (1) follows from the equation of continuity which has such form in the cylindrical coordinate system: ∂ jr ∂ jz jr + + =0 r ∂r ∂z

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Integral components of Ir , Iz current related to its density: Ir = jn · r · 2π · z   2   2 r r · π · r = jz · r · r · 2π I z = jz · r + 2 − r− 2 Let’s use the transformations: ∂ jr Ir ∂ Ir 1 Ir jr + = + · − r ∂r r 2 · 2π · z ∂r r · 2π · z r 2 · 2π · z 1 ∂ Ir · = ∂r r · 2π · z ∂ jz ∂ Iz 1 = · ∂z ∂z r · r · 2π And will get: ∂ jr ∂ jz 1 jr + + = · r ∂r ∂z r



∂ Ir ∂ Iz + ∂r ∂z

=0

or: ∂ Iz ∂ Ir + =0 ∂r ∂z which is equivalent of Eq. (2) describing our discrete model. For example, we can compare calculated values of the probes coefficients with calculated using analysis to verify the numerical method of EL direct problem solving. Also using this method, the influence of the real distance of the inverted electrode in the BKZ and BK probes was researched. This effect significantly impairs the vertical resolution of each probe as shown in Fig. 1a, b. The measured apparent resistivity (AR) for the BKZ probes is significantly distorted in the regions near the boundaries of the beds.

3 Modeling in Induction Logging There are a lot of implemented many numerical methods to solve the direct problem of IL numerically. The number of they include the author’s method. It was proposed, implemented and actually based on the representation of the environment as a union of elementary rings with two additions:

106 Fig. 1 a Diagrams of BKZ and BK-3 for a single formation without a penetration zone opened by a well (ρW = 1 m). b Diagrams of BKZ and BK-3 for a pack of three layers opened by a well, with a zone of penetration  (ρW = 1 m, D d = 6)

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– we consider the contours of the coils as elementary rings taking into account their geometric (diameter, cross section, etc.) and physical (conductivity) characteristics; – we consider the interaction of currents as a mutual induction of all elementary rings of the system. k iωt k iωt e + i IIm e : In General, the change in current in the k generator coil I = IRe k k (where IRe , IIm – active and reactive components) is provided by the applied electric driving force (EDF): k iωt k iωt e + iεIm e ε = εRe

where k = Ak = const εRe

(5)

S εIm = B k = const

(6)

We use the principle of mutual induction: when the current I j in the elementary ring j changes in the elementary ring there is an EDF εi j : εi j = −Mi j

dI j dt

where Mi j —mutual induction coefficient. The total EDF εi that occurs in the i elementary ring will be the sum of the EDF induced by individual currents in the other rings: εi =

j

εi j = −

j

Mi j

dI j dt

In order to obtain the final system of equations, we use: 1.

Ohm’s law in integral form:

ε i = I i Ri where Ri is the resistance of the elementary ring. Due to the axial symmetry: Ri =  ρ ddlSii (ρ—average SR on the cross section d Si of the elementary ring);

Ci

2.

The formula for the lean after the hour of the change of the constant frequency:

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dIi i iωt i iωt = iωIRe e − iωIIm e dt 3.

The fact that:

Mi j = M ji In the end we get: i IRe Ri − ω



j

Mi j IIm = 0

j i IIm Ri + ω



j

Mi j IRe = 0

j

Adding the values or, respectively, the right parts of Eqs. (5), (6) to the right-hand sides of these equations, which correspond to the change in the active Ak and reactive B k components of the EDF of the generator coils, we obtain an inhomogeneous and nondegenerate SLAE in which the number of unknowns is equal to the number of equations. This method turned out to be convenient for studying those features of IR problems that cannot be investigated in the approximate fate theory.

4 Increasing the Vertical Resolution of Induction Logging It turns out that IL probes with different depth of study also have different spatial resolution along the axis of the well (vertical for vertical wells). Therefore, the exact determination of geoelectrical parameters of thin and remote from the well wall areas is complex. If in the study of formations whose thickness significantly exceeds the length of the probes, this problem does not arise. Then in the study of packs of thin formations, it is one of the most important. Namely, this task is the most relevant today. Different approaches are used for solving. The list of approaches consists the introduction of the correction “for accommodating”, “mathematical focusing”, “deconvolution method”, synthetic probes using and others. However, these methods are generally not regularized while the correct approach would be factorization based on regularization. Factorization of the inverse IL problem is a non-trivial problem, at least because the inverse problems themselves. Generally, they are incorrect and therefore require the use of regularization methods. For the IL problem, factorization requires the solution of the Fredholm’s equation, a problem incorrectly posed by Hadamard and well known in both geophysics and mathematics. Let’s consider it.

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The equation connecting SP and AP in the linear Doll’s theory in the case of a homogeneous infinite formation can be written in the form of: 

g z  σ z − z  dz

σ˜ (z) =

(7)

where is z the coordinate in the cylindrical coordinate system, g is the vertical geometric factor of the probe, σ˜ —apparent conductivity (AC), σ —specific conductivity (SC). By selecting a bounded interval L and decomposing on it the functions included in (5) in the Fourier series: σ˜ (z) =

∞

σ˜ n · e−inz

n=−∞ ∞

σ (z − x) =

σn · e−inz · einx

n=−∞

g(x) =

∞

gn · e−inx

n=−∞

we can write the equations that connect the coefficients of their schedules: σn =

σ˜ n gn∗

(8)

Therefore, we use the solution of the IL inverse problem, based on the representation of the required function by a Fourier series with coefficients calculated by (6). In this case, we denote the function represented by the Fourier series of a limited number n of members as σ˜ n . Let’s consider the question of choosing the number of members of such a series. The obviousness of such a choice follows at least from a comparison of the form of the result obtained by this method, depending on the number of members of the schedule. Obviously, practical use of the proposed approach requires a strict mathematical criterion. It should be the minimum distance between σ˜ and σn in some normalized space. For example, in L 1 or L 2 the following criterion will mean the need to find the minimum value of the functional:        

   FL1 (n) = σ˜ (z) − g z − z σ˜ n z dz  · dz   L

or

L

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Fig. 2 Probe I0.5. Conductivity: 1—apparent; 3—restored; 3—true

    FL2 (n) =  L

 2      σ˜ (z) − g(z  − z)σ˜  (z  )dz   · dz n     L

The number of members of the series in this case can be considered a regularization parameter. This parameter can also be selected from the condition of the minimum residual relative to not resistance but conductivity:  ⎞−1  ⎛      



 FL1 (n) = σ˜ −1 (z) − ⎝ g z  − z σ˜ n z  dz  ⎠ dz   L

L

and       FL2 (n) =  L

 ⎞−1 2 ⎛      σ˜ −1 (z) − ⎝ g(z  − z)σ˜  (z  )dz  ⎠  dz n     L

Figures 2, 3, 4, 5 present the results of conductivity recovery for probes of different lengths. It is obvious that for such a section the vertical resolution of all probes of the complex becomes almost the same and corresponds to the resolution of the smaller probe of the complex.

5 Solve the Inverse Problem There are several possible approaches to solve the inverse problem. All of them are based on minimizing functionality. Here are the main:

Increasing Vertical Resolution in Electrometry … Fig. 3 Probe I0.85. Conductivity: 1—apparent; 3—restored; 3—true

Fig. 4 Probe I1.26. Conductivity: 1—apparent; 3—restored; 3—true

Fig. 5 Probe I2.05. Conductivity: 1—apparent; 3—restored; 3—true

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  n  T  ρi − ρiP 2

T 1 T F ρ1 , . . . , ρn =  n i=1 ρiT where n is the number of equipment probes; ρiT —calculated values of AR for the model under consideration; ρiP —actually obtained values of AR. There are also some variations of the functional record, which will be minimized in the process of solving the inverse problem. For example, in the form [28]:   n  T  ρi − ρiP 2

T 1 T F ρ1 , . . . , ρn =  n i=1 δi ρiT where δi —the relative error of the i probe. Or [29]:   n  T  ρi − ρiP 2

T 1 T F ρ1 , . . . , ρn =  n i=1 δi ρiT + χi where χi —the absolute error of the i probe. As a criterion for the proximity of the found solution with the desired true value, we can consider the minimization of the functional:   n   ρiT − ρiP 2

T T Ki (9) F ρ1 , . . . , ρn =  ρiT i=1 where K i —weight coefficients of each probe of the complex, which can be changed by the interpreter. In the presented example n is equal to 8: probes BK-3, IK, and 6 probes BKZ (A0.4M0.1N, A1.0M0.1N, A2.0M0.5N, A4.0M0.5N, N6.0M0.5A, A8. 0M1.0N, N0.5M2.0A (inverted A2.0M0.5N) [30–34]. The effectiveness of this approach is clearly visible in the model material which is shown in Fig. 6.

6 Increasing of the Electrometry Complex Vertical Resolution To select the values of the weights in (7), the vertical resolution of each BKZ probe was investigated. As shown in Fig. 1a the boundary effects of each of the probes make a significant error in determining the boundaries of even a single formation.

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Fig. 6 The result of solving the inverse problem BKZ + BK for a formation with a thickness of 12 m (R zone—found SR of zone, R bed—SR of bed, R bed—SR of bed in the absence of the zone, Rbed—the required value SR of bed): a—vci K = 1; b—K A4.0M0.5N = 0, K A8.0M1.0N = 0; c—K A4.0M0.5N = 0, K A8.0M1.0N = 0 (and separately K 7F1.6 = 1)

For a pack of several (Fig. 1b shows an example of a pack of three layers with penetration) the result is even worse. However, it is due for reducing the weights of the probes (with a significant marginal effect compared to probes with a small one) that the result is significantly improved. Figure 7 shows the results of applying this approach to a well filled with a conductive solution of resistivity ρW = 0.068 m. In other logging methods, the problem of determining the boundaries of the layers arises to a lesser extent, and therefore it is desirable to use electrometry methods in combination with others [35–37]. The problem of accurately defining formation boundaries is of great concern in avoiding environmental consequences in the event of improper operation of production wells [38–48]. In general, the extraction of hydrocarbons from wells are on the agenda of many purely environmental and anthropogenic issues. The paper used a number of original approaches in the implementation of software, which are widely used in other fields of science [48–51].

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Fig. 7 Comparison of the results of solving the inverse problem: a—the proposed method, b— traditional method, c—gamma logging data in conventional units, d—spontaneous polarization potential in conventional units (collectors are highlighted in gray)

7 Conclusions The research was based on a simultaneous combination of three approaches: – use of functional (5) to solve the inverse problem of the electrometry complex; – study of the influence of different probes on the distortion of the results in the vicinity of the beds boundaries with the subsequent adequate choice of their weights in (5);

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– the use of the method of increasing the IK probes vertical resolution by Fredholm equation solving for the first kind of convolution type; – effective methods for solving the direct problem of the EL and IL. The main result of the work was a significant increase in the definition of the boundaries of productive formations in the studied reservoirs. It has high importance for the environmental assessment of the possible consequences of the operation of such formations.

References 1. Anderson, B.I.: Modeling and Inversion Methods for the Interpretation of Resistivity Logging Tool Response, p. 377. DUP Science, Delft (2001) 2. Yehurnova, M.G., Zaykovsky, M.Y., Zavorotko, Y.M., Tsyokha, O.G., Knishman, O.S., Mulyar, P.M., Demyanenko, I.I.: Oil and Gas Prospects of Ukraine. Oil and Gas Potential and Features of lithogeophysical structure of Lower Carboniferous and Devonian deposits of the DnieperDonets depression, p. 196. Scientific Opinion, Kyiv (2005) (in Ukrainian) 3. Kurgansky, V.M., Tishaev, I.V.: Electric and Electromagnetic Methods of Well Research: Textbook, p. 175. Kyiv University Publishing and Printing Center, Kyiv (2011) (in Ukrainian) 4. Alpin, L.M.: Palettes of Lateral Logging Sounding (BKZ). Moskow, Gostoptekhizdat (1958). (in Russian) 5. Alpin, L.M., Komarov, S.G.: BKZ Palettes. Working Set. Gostoptechizdat, Moskow (1953). (in Russian) 6. Latyshova, M.G.: A Practical Guide to Interpreting Diagrams of Geophysical Methods of Well Research, p. 182. Nedra, Moskow (1981) (in Russian) 7. Ilyinsky, V.M.: Lateral Logging, p. 144. Nedra, Moskow (1971) 8. Plyusnin, M.I.: Induction Logging, p. 140. Nedra, Moskow (1968) 9. Doll, G.G.: Theory of the Induction Method of Research of Well Sections and Its Application in the Wells Drilled with a Clay Solution on Oil. Questions of Field Geophysics, pp. 252–274. Gostoptechizdat, Moskow (1957) (in Russian) 10. Kaufman, A.A.: Theory of Induction Logging, p. 236. M. Nauka (1965) 11. Myrontsov, M.L.: Electrometry effective inverse problem solving method. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2020). https://doi.org/10.3997/2214-4609.2020geo090 12. Myrontsov, M.L.: Lateral logging sounding and lateral logging complex effective inverse problem solving method. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2020). https://doi.org/10.3997/2214-4609.2020geo092 13. Myrontsov, M.L.: The method to research equivalent solutions zones for inverse problem of well logging electrometry. In: XIII International Scientific Conference “Monitoring of Geological Processes and Ecological Condition of the Environment”, Extended Abstracts (2019). https:// doi.org/10.3997/2214-4609.201903217 14. Myrontsov, M.L.: The method to solve the inverse problem of lateral logging sounding and lateral logging. In: XIII International Scientific Conference “Monitoring of Geological Processes and Ecological Condition of the Environment”, Extended Abstracts (2019). https:// doi.org/10.3997/2214-4609.201903244 15. Myrontsov, M.L.: The problem of equivalence in inverse electrometry problems of oil and gas wells. In: 18th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2019). https://doi.org/10.3997/2214-4609.201902070

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16. Myrontsov, M.L.: Solution of the inreverse problem of electrometry oil and gas wells (on the example of the BKZ + BK + IK complex). In: 18th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2019). https://doi.org/10.3997/22144609.201902069 17. Myrontsov, M.L.: A new method and program for multiprobe electric logging quantitative interpretation. In: Geoinformatics 2012: 11th International Conference on Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2012) 18. Dakhnov, V.N.: Interpretation of the Results of Geophysical Studies of Well Sections, p. 492. Gostoptechizdat, Moskow (1955) (in Russian) 19. Dakhnov, V.N.: Interpretation of Geophysical Well Research Results, p. 368. Nadra, Moskow (1972) (in Russian) 20. Dakhnov, V.N.: Logging of Wells. Interpretations of Logging Diagrams, p. 36. ONTI, Moskow (1941) (in Russian) 21. Dakhnov, V.N.: Electrical and Magnetic Methods of Well Research, p. 368. Nadra, Moskow (1967) (in Russian) 22. Kneller, L.E., Potapov, A.P.: Solution of direct and inverse problems of lateral logging for the purposes of automatic interpretation in a thin-layer section. Appl. Geophys. 128, 137–144 (1992). (In Russian) 23. Kneller, L.E., Potapov, A.P.: Solution of direct and inverse problems of electrologging in radially inhomogeneous media. Geol. Geophys. 1, 83–96 (1989). (in Russian) 24. Kneller, L.E., Potapov, A.P.: Solution of direct and inverse problems of induction logging taking into account the vertical and radial inhomogeneity of the geoelectric section. Izv. High Schools. Ser. Geol. Explor. 9, 95–102 (1990) (in Russian) 25. Kneller, L.E., Potapov, A.P.: Solution of direct and inverse problems of electrologging for environments with a radial gradient of electric resistance. In: VNII Economics of Mineral. Raw Materials and geol.-Development Works, vol. 8, pp. 3–11. VIEMOS, Moskov (1989) (in Russian) 26. Kolosov, A.L.: Direct, Mixed and Inverse Problems of Well Electrometry, p. 196. Scientific Opinion, Kiev (1985) (in Russian) 27. Kolosov, A.L.: Solving Computer Electrometry Problems on a Computer, p. 148. Scientific Opinion, Kiev (1977) (in Russian) 28. Glinskikh, V.N., Epov, M.I.: Two-dimensional reconstruction of the geoelectric image according to high-frequency electromagnetic logging. Karotagnik 6(147), 59–68 (2006). (in Russian) 29. Kneller, L.E., Potapov, A.P.: Determination of specific electrical resistance of formations at radial and vertical inhomogeneity of well section. Geol. Geophys. 1, 52–64 (2010). (in Russian) 30. Zundelevich, S.M., Sokhranov, N.N., Shtemler, A.M.: Quality control of BKZ data and other types of resistance logging. Appl. Geophys. 61, 212–220 (1970). (in Russian) 31. Komarov, S.G.: Lateral Logging Sounding, vol. 39. Azerbaijan Oil Industry (1938) (in Russian) 32. Komarov, S.G.: Geophysical Methods of Well Research, p. 368. Gostoptekhizdat, Moskow (1962) (in Russian) 33. Krasnozhon, M., Kosachenko, V., Ruchko, V.: Technology, “Geosearch” for the study of oil and gas fields. Geol. Ukraine 1–2, 32–40 (2009). (in Russian) 34. Kulinkovich, A.E.: Lateral Logging Sounding Palettes and Methods of Their Application, p. 44. Gostoptekhizdat, Moskow (1958) (in Russian) 35. Karpenko O.: Neural networks technologies in oil and gas well logging. In: 17th International Conference on Geoinformatics: Theoretical and Applied Aspects. Extended Abstracts (2018). https://doi.org/10.3997/2214-4609.201801798 36. Zelenko, Y., Karpenko, O.: Spatial prediction of gas-generating sequences based on evaluation of vitrinite reflectivity in Dnieper-Donets Depression. In: 14th International Conference on Geoinformatics: Theoretical and Applied Aspects. Extended Abstracts (2015) 37. Solodkiy, Y.V., Karpenko, O.M.: Estimation of Gas Saturation in Nearfield Reservoir Bed by Geophysical Data, vol. 5, pp. 10–15. Scientific Bulletin of the National Mining University, Dnipropetrovsk (2014) (in Ukrainian)

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38. Popov, O., Yatsyshyn, A.: Mathematical tools to assess soil contamination by deposition of technogenic emissions. In: Dent, D., Dmytruk, Y. (eds.) Soil Science Working for a Living: Applications of Soil Science to Present-Day Problems, pp. 127–137. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-45417-7_11 39. Iatsyshyn, A., Iatsyshyn, A., Artemchuk, V., Kameneva, I., Kovach, V., Popov, O.: Software tools for tasks of sustainable development of environmental problems: peculiarities of programming and implementation in the specialists’ preparation. E3S Web Conf. 166, 01001 (2020). https://doi.org/10.1051/e3sconf/202016601001 40. Popov, O.O., Iatsyshyn, A.V., Kovach, V.O., Artemchuk, V.O., Kameneva, I.P., Taraduda, D.V., Sobyna, V.O., Sokolov, D.L., Dement, M.O., Yatsyshyn, T.M.: Risk assessment for the population of Kyiv, Ukraine as a result of atmospheric air pollution. J. Health Pollut. 10(25), 200303 (2020). https://doi.org/10.5696/2156-9614-10.25.200303 41. Anpilova, Y., Yakovliev, Y., Drozdovych, I.: Landscape and geological factors of water and ecological conditions technogenesis of Donbas at the post-mining stage. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2020) 42. Lukianova, V., Trofymchuk, O., Anpilova, Y.: Environmental safety of motor transport enterprises within the urban areas. J. Ecol. Eng. 21(4), 231–236 (2020) 43. Trofymchuk, O., Yakovliev, Y., Klymenko, V., Anpilova, Y.: Geomodeling and monitoring of pollution of waters and soils by the Earth remote sensing, vol. 19, (1.4), pp. 197–204. In: 19th International Multidisciplinary Scientific GeoConference SGEM 2019, Extended Abstracts (2019) 44. Trofymchuk, O., Anpilova, Y., Yakovliev, Y., Zinkiv, I.: Ground deformation mapping of Solotvyno mine area using radar data and GIS. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2020) 45. Okhariev, V.: Improvement of statistical method for environmental monitoring datasets interpretation. In: Conference Proceedings, Monitoring 2019 (2020) 46. Trofymchuk, O., Okhariev, V., Trysnyuk, V.: Environmental security management of geosystems. In: Conference Proceedings, 18th International Conference of Geoinformatics: Theoretical and Applied Aspects (2019) 47. Okhariev, V., Trysnyuk, V.: Environmental aspects of Ukrainian energy sector in connection with transition to low-carbon development. In: Conference Proceedings, Monitoring (2019) 48. Trysnyuk, V., Okhariev, V., Trysnyuk, T., Zorina, O., Kyrylo, A., Radlowska, C.: Improving the algorithm of satellite images landscape interpretation. Conference Proceedings, 18th International Conference of Geoinformatics: Theoretical and Applied Aspects (2019) 49. Trofimchuk, A.N.: Unsteady oscillations of a liquid-saturated poroelastic soil layer. Int. J. Fluid Mech. Res. 29(1), 124–133 (2020) 50. Trofymchuk, O., Kaliukh, Y., Dunin, V., Berchun, Y.: On the possibility of multi-wavelength identification of defects in piles. Cybern. Syst. Anal. 54, 600–609 (2018) 51. Trofymchuk, O., Kaliukh, I., Klymenkov, O.: TXT-tool 2.380-1.1: Monitoring and early warning system of the building constructions of the Livadia palace, Ukraine (Book Chapter). In: Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools: Volume 1: Fundamentals, Mapping and Monitoring (2017)

Heat Power Engineering

Optimization Models of Industrial Furnaces and Methods for Obtaining Their Numerical Solution Artur Zaporozhets , Vladyslav Khaidurov , and Tamara Tsiupii

Abstract The chapter describes two mathematical optimization models for research and improving the efficiency of modern industrial and muffle furnaces on an electrical basis. The first mathematical model involves finding the temperatures of internal spot heaters, the location of which is known in advance. It is necessary to find such temperatures of these heaters so that during their operation the temperature of the object, which is in the furnace itself, is close to the specified one. A description of the linear and nonlinear cases is given. The second mathematical model assumes finding the locations of the furnace spot heaters, the temperatures of which are already known. It is necessary to find the optimal arrangement of these heaters, provided that the deviation from the temperature at the furnace object as a result of the operation of these heaters should be as close as possible to the set temperature at this object. The chapter presents a general nonlinear case of this mathematical model. The numerical solution of the optimization models is obtained using high-speed optimization methods that are derived from the classical Newton’s method. Also, a comparative analysis of the work of the methods by the number of calls to the procedure for solving the direct problem of heat conduction is given. The largest number of calls to this procedure was taken by 100%. Keywords Optimization mathematical model · Newton’s method · Inverse problem of thermal conductivity · Muffle furnace · Simulation · MATLAB

A. Zaporozhets (B) · V. Khaidurov Institute of Engineering Thermophysics of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] V. Khaidurov e-mail: [email protected] T. Tsiupii Kyiv International University, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_7

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1 Models of Optimal Function of Industrial Furnaces One of the most relevant in today’s conditions is the study of the thermophysical characteristics of the internal heaters of industrial and muffle furnaces [1], examples of which are shown in Figs. 1 and 2. This chapter describes a rather urgent optimization problem—it is necessary to find the optimal temperature of the furnace, the heating system of which is implemented on the basis of point heaters operating on an electrical basis. The first part of the research is to calculate the temperature field inside the furnace when the temperatures of the heaters are known. This problem is reduced to the numerical solution of the direct problem of thermal conductivity (DPTC) [2, 3]: the temperature of the heaters are known (additional internal conditions), all data about the medium are known (initial condition, boundary conditions, thermal diffusivity, etc.) and it is necessary to find the distribution of the temperature field in furnace. The second part of the research focuses on calculating the temperatures of the heaters and their location to maintain the temperature in the furnace. Obviously, each of the tasks of this part of the study is a problem of conditional industrial optimization, and the constraint imposed on the problem is the classical heat conduction equation. The first optimization problem can be described as follows. It’s given a furnace that cannot heat an object that is in it to a necessary temperature. In order to improve

Fig. 1 Examples of industrial furnaces on an electrical basis

Fig. 2 Examples of muffle furnaces on an electrical basis

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Fig. 3 Furnace and the position of the object inside it

the efficiency of the furnace, heaters are installed in it and location of which is set by two spatial coordinates on the plane. The task is reduced to the fact that it is necessary to find such temperature values of these heaters so that the temperature distribution on the object is close to the required one (preset by the user of the furnace). The second optimization problem is described as follows. The furnace has builtin heaters whose temperatures are known. It is necessary to find the location of these heaters (coordinates on the plane) in the furnace area so that the temperature distribution on the object in the furnace is close to the predetermined one. The above two optimization problems are related to inverse problem of thermal conductivity (IPTC) [4–6]. The calculation of temperature fields of such technical problems is carried out by the methods of finite differences [7] and finite elements [8, 9]. To simplify the task, it will be restricted the geometry of a simple shape: the furnace object is a rectangle, which is placed in the area of the given furnace, which also has the shape of a rectangle (Fig. 3). Next, it must be moved to the mathematical formulation of the described industrial optimization models [10, 11]. Models (linear and nonlinear) that are implemented in the MATLAB application software are described below. Computational grids obtained with COMSOL multiphysics software package. All models are reduced to finding a numerical IPTC solution for identifying internal heat sources (temperatures and their geometric location).

2 Mathematical Description of Models Obviously, for the first industrial optimization problem described above, it is necessary to find the minimum of the functional [12, 13]:

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¨ J (α) =

(Tactual − T (α))2 ds,

(1)

Dobject

for which the constraint is set in the form of the heat conduction equation: ∇(k∇T ) = 0 Ha D ∈ (x, y), D ⊂ R 2 ,

(2)

where Tactual —preset temperature for the furnace  object; Dobject —the area in which the object is located; D—the area of furnace Dobject ⊂ D ; α = (α1 , α2 , ..., αn )— the sought-for temperatures of the heaters built into the furnace; n—the number of spot heaters built into the furnace. The boundary conditions for (1)–(2) generally have the following form: 

 ∂ T  pT + q − = r, ∂→ n 

(3)

→ where —boundary of computing area D ∈ (x, y), D ⊂ R 2 ; − n —normal vector to the boundary of the computing area D; p, q, r —real numbers defined by the condition of the task. It is obvious that the described model (1)–(3) is generally nonlinear, since nothing said about its coefficients characterizing the object and the computing area of the furnace as a whole. The minimization of the quadratic functional is carried out by the methods described in [12, 13]. The second model can be described as follows. Let’s find the minimum of the functional: ¨    2  Tactual − T α, β ds (4) J α, β = Dobject

with restriction ∇(k∇T ) = 0, D ∈ (x, y), D ⊂ R 2 ,

(5)

where Tactual —predetermined temperature that an object in a furnace must correspond; α = (x1 , x2 , ..., xn )—required coordinates of the abscissa of the location of the heaters; β = (y1 , y2 , ..., yn )—required coordinates of the ordinate of the location of the heaters. The boundary conditions for Eqs. (4)–(5) are also given by Eq. (3).

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Finding the value of the function, which is minimized, requires a numerical solution of the differential Eq. (5). Software implementations of models (1)–(2) and (4)–(5) are shown below.

3 Features of Finding Numerical Solutions of Mathematical Models The solution of the described models from the previous paragraph is carried out using the same optimization methods. It is obvious that functionals (1) and (3) are quadratic and, moreover, convex over the area Dobject of the object, which means that they have a single minimum, which is global. In Newton’s method, for a given function, it needs to find the gradient and the matrix of second derivatives in all its arguments (Hesse matrix). First, find the gradient of the functional for (1) [14, 15]: ∂J = −2 ∂α1

¨

(Tactual − T (α1 , α2 , . . . , αn ))Tα1 (α1 , α2 , . . . , αn )d xd y,

Dobject

∂J = −2 ∂α2

¨

(Tactual − T (α1 , α2 , . . . , αn ))Tα2 (α1 , α2 , . . . , αn )d xd y, (6)

Dobject

... ... ... ... ... ... ... ... , ¨ ∂J = −2 (Tactual − T (α1 , α2 , . . . , αn ))Tαn (α1 , α2 , . . . , αn )d xd y. ∂αn Dobject

So, based on Eq. (6), the gradient vector for functional (1) can be written as follows:   ∂J ∂J ∂J −−−−−−−−−−−−−−−→ . (7) gradJ (α1 , α2 , . . . , αn ) = , ,..., ∂α1 ∂α2 ∂αn The next step is to determine the Hessian matrix for functional (1): ⎛ ⎜ H (α1 , α2 , . . . , αn ) = ⎜ ⎝

where

∂2 J ∂α12

.. .

∂2 J ∂αn ∂α1

··· .. . ···

∂2 J ∂α1 ∂αn

.. .

∂2 J ∂αn2

⎞ ⎟ ⎟, ⎠

(8)

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∂2 J = −2 ∂αi ∂α j

¨



−Tα j (α1 , α2 , . . . , αn )Tαi (α1 , α2 , . . . , αn )+

d xd y.

+(Tactual − T (α1 , α2 , . . . , αn ))Tαi α j (α1 , α2 , . . . , αn )

Dobject

(9) Finally, based on (7) and (9), it can be written an expression for the second derivatives in the Hessian, which is written in the form (8): ∂2 J =2 ∂αi ∂α j

¨ Dobject

Tα j (α1 , α2 , . . . , αn )Tαi (α1 , α2 , . . . , αn )− −(Tactual − T (α1 , α2 , . . . , αn ))Tαi α j (α1 , α2 , . . . , αn )

 d xd y. (10)

It is obvious that the sought function (temperature field) cannot be specified analytically, therefore it can be calculated numerically. This means that all derivatives, including (6) and (10), must also be searched numerically, since at each stage a discrete (numerical) solution is obtained. For numerical notation, it can be used central difference schemes during finding derivatives of the first and second orders, that is, like this: ⎤ ⎡ (Tactual − T (α1 , α2 , . . . , αn ))× ⎞⎥ ⎛ ¨ ⎢ T (α1 , . . . , αi + αi , . . . , αn ) ⎥ ⎢ ∂J ⎢ −⎟⎥ = −2 lim ⎥d xd y. (11) ⎢ ⎜ 2α i ⎟⎥ αi →0 ⎢ ×⎜ ∂αi ⎠ ⎝ ⎦ ⎣ , . . . , α − α , . . . , α T (α ) Dobject 1 i i n − 2αi Assuming in (11) that αi and α j are sufficiently small, it can be rewritten the formulas for the derivatives in the following form: T (α1 , . . . , αi + αi , . . . , αn ) − T (α1 , . . . , αi − αi , . . . , αn ) 2αi  ⎡  ⎤ T α1 , . . . , αi − αi , . . . , α j − α j , . . . , αn +   ⎢ ⎥ ⎢ +T α1 , . . . , αi + αi , . . . , α j + α j , . . . , αn −⎥ 1 ⎥. = ×⎢   ⎥ 4α j αi ⎢ ⎣ −T α1 , . . . , αi − αi , . . . , α j + α j , . . . , αn −⎦   −T α1 , . . . , αi + αi , . . . , α j − α j , . . . , αn

Tα i (α1 , α2 , . . . , αn ) =

Tαi α j

(12) Let finally write down the elements of the gradient vector of the functional:

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∂J = −2 ∂α1 ∂J = −2 ∂α2

¨ Dobject

¨ Dobject

127

⎛

⎞ (Tactual − T (α1 , α2 , . . . , αn ))× ⎝ T (α1 + α1 , . . . , αn ) − T (α1 − α1 , . . . , αn ) ⎠d xd y, × 2α1 ⎛ ⎞ (Tactual − T (α1 , α2 , . . . , αn ))× ⎝ T (α1 , α2 + α2 , . . . , αn ) − T (α1 , α2 − α2 , . . . , αn ) ⎠d xd y, × 2α2

... ... ... ... ... ... ... ... , ⎛ ⎞ ¨ (Tactual − T (α1 , α2 , . . . , αn ))× ∂J ⎝ T (α1 , . . . , αn + αn ) − T (α1 , . . . , αn − αn ) ⎠d xd y. = −2 ∂αn × 2αn Dobject

(13) The definition of the Hessian elements for (10) using (12) is written as follows: ∂2 J = ∂αi ∂α j ¨ =2 Dobject

¨ −2 Dobject

    ⎤ T α1 , . . . , α j + α j , . . . , αn − T α1 , . . . , α j − α j , . . . , αn ×⎥ ⎢ 2α j ⎢ ⎥ ⎢ ⎥d xd y− ⎣ T (α1 , . . . , αi + αi , . . . , αn ) − T (α1 , . . . , αi − αi , . . . , αn ) ⎦ 2αi ⎤ ⎡ (Tactual − T (α1 , α2 , . . . , αn ))×  ⎞⎥ ⎛  ⎢ ⎢ T α1 , . . . , αi − αi , . . . , α j − α j , . . . , αn + ⎥ ⎢   ⎟⎥ ⎜ ⎥ ⎢ ⎜ +T α1 , . . . , αi + αi , . . . , α j + α j , . . . , αn −⎟⎥d xd y. ⎢ 1 ⎜ ⎟⎥ ⎢ ×   ⎜ ⎟ ⎥ ⎢ 4α j αi ⎝ −T α1 , . . . , αi − αi , . . . , α j + α j , . . . , αn −⎠⎦ ⎣   −T α1 , . . . , αi + αi , . . . , α j − α j , . . . , αn ⎡

(14) Formulas (13) and (14) underlie the implementation of nonlinear models described by Eqs. (1)–(2) and (4)–(5) respectively. Below are the main results of modeling based on Eqs. (1)–(2). The material, from which the object of furnace is made, is aluminum. The computing area of the problem is a square with side 2, the center of which is at the origin. The boundary conditions of the model (1)–(2) are as follows: Tx (−1; y) = Tx (1; y) = 0, T (x; −1) = 100 ◦ C, T (x; 1) = 50 ◦ C. The object in the furnace is a rectangle, Dobject (x, y) : [−0, 5; 0, 5] × [−0, 2; 0, 2]. The temperature that is set on the object in the furnace is 200 °C. The thermal conductivity coefficient for (1)–(2) is taken from the material library of the COMSOL Multiphysics application software package. The furnace has 6 built-in point heaters, the location of which is set by the spatial coordinates: (−0.75; 0.75), (0;

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300

T(x,y)

200

200

150

100

0 1

100

0

y

-1

-1

0

-0.5

0.5

1 50

x

Fig. 4 Implementation of model (1)–(2). Temperature distribution in the furnace as a result of the action of internal heaters

0.75), (0.75; 0.75), (0.75; −0, 75), (0; 0.75) and (0.75; 0.75), respectively. Figures 4 and 5 show the results of calculations for model (1)–(2). Finding numerical solutions of nonlinear mathematical models for optimizing industrial furnace temperatures were found by 5 methods: the classical Newton’s method, Newton’s method with a variable step based on the polyline method, Newton’s method with a variable step based on the trapezoid method, Newton’s

Fig. 5 Comparison of the temperature distribution with the Tactual value in the area of the object in the furnace

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method with a variable step based on the method of means, Newton’s method with a variable step based on the predictor–corrector method and the method of means obtained in [12, 13]. The internal heater temperatures were 221.234 °C, 250.763 °C, 221.652 °C, 213.842 °C, 217.732 °C and 213.832 °C, respectively. As can be seen from Figs. 4 and 5, the temperature at the object in the furnace is close to 200 °C. The value of functional (1) is 0.21. Comparison of the obtained results was carried out at the Institute of Engineering Thermophysics of the National Academy of Sciences of Ukraine on a muffle furnace SNOL-1,6.2,5.1/11-I2M. The deviation of the calculated temperature field according to the model (1)–(3) relative to the real temperature field obtained as a result of measurement did not exceed 5.3%.

4 Linear Case of a Mathematical Model for the Optimization of an Industrial Furnace If limitation (2) is linear, then the coefficient k does not depend on temperature (in the event that its value changes slowly with temperature). Then the sought-for solution to model (1)–(2) is the temperature field that is formed inside the furnace under the action of internal heaters. It can be viewed as a linear combination of the temperature fields of each of the furnace heaters and the temperature field of the furnace without the influence of internal heaters [16, 17]. A description of the linear case of searching for temperatures of internal heaters of an industrial furnace is given below.

4.1 Description of the Linear Model as a Special Case As mentioned above, the solution of the model (1)–(2) can be written using the equation: T (x, y) = T0 (x, y) +

N 

αk Tk (x, y).

(15)

k=1

where Tk (x, y)—temperature distribution of the k-th heater separately; T0 (x, y)— system temperature distribution without taking into account the influence of internal heaters; αk —coefficients of increasing the influence of internal heaters, which are determined by solving a mathematical model.

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4.2 Numerical Solution of the Linear Case for Model (1)–(2) To carry out a numerical analysis of this model, it is necessary to fulfill 5 main stages before developing the software package: 1.

finding the temperature field without the action of internal heaters: ∂ 2 T0 ∂ 2 T0 + = 0. 2 ∂x ∂ y2

(16)

The boundary conditions are as follows: T0 (x, −1) = 100, T0 (x, 1) = 50, T0x (−1, y) = 0, T0x (1, y) = 0; 2.

(17)

finding the temperature field of each heater separately: ∂ 2 Tk ∂ 2 Tk + = 0, ∂x2 ∂ y2

(18)

where k = 1, n, n—the number of heaters in the furnace. The boundary conditions are as follows: Tk (x, −1) = Tk (x, 1) = 0, Tkx (−1, y) = T0x (1, y) = 0.

(19)

The internal conditions are as follows: Tk (x, y)|G k = T pr esk ,

3.

(20)

where G k —the area of the k-th heater, and G k ⊂ D, G k ∩ Dobject = ∅, k = 1, n,G i ∩ G j = ∅, i, j = 1, n, i = j; T pr esk —the calculated temperature of the k-th heater—positive number (for calculations it is taken equal to 1); solution of a linear system of equations with respect to the unknown vector α = (α1 , . . . , αn ), obtained from the condition of minimizing the functional: Aα = b, where left and right parts of the system are as follows:

(21)

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⎛ ˜ ⎜ Dobject ⎜ ⎜ A=⎜ ⎜ ˜ ⎝

⎜ Dobject ⎜ b=⎜ ⎝ ˜

˜

T1 (x, y)T1 (x, y)d xd y · · · .. .

..

. T1 (x, y)Tn (x, y)d xd y ..

Dobject

⎛ ˜

131

Tn (x, y)T1 (x, y)d xd y

Dobject

.

˜

.. . Tn (x, y)Tn (x, y)d xd y

Dobject

(Tactual − T0 (x, y))T1 (x, y)d xd y (Tactual

⎞

⎞ ⎟ ⎟ ⎟ ⎟, ⎟ ⎠ (22)

⎟ ⎟ ··· ⎟; ⎠ − T0 (x, y))Tn (x, y)d xd y

Dobject

4.

calculation of a linear combination of the obtained temperature fields with the found coefficients from the previous paragraph, namely: T (x, y) = T0 (x, y) +

n 

αk Tk (x, y);

(23)

k=1

5.

visualization of the obtained result.

Formulas (16)–(23) specify the main procedure for finding a solution to the linear model for identifying the parameters of internal heat sources of the furnace. Let’s test the model using specific examples and input data. The model is tested using the same furnace information. The thermal conductivity coefficient for the model (1)–(2) is taken as an average value of 82.51·10–6 m2 /s. The simulation results are shown in Figs. 6, 7, 8, 9, 10, 11 and 12. Figure 10 graphically shows the temperature field, which is the solution of the problem. The results of calculating the linear case for model (1)–(2) are given below using the finite element method. To determine the effectiveness of the obtained numerical solution of the mathematical model, the value of the functional is determined, which is contained in this model. It is the criterion for the adequacy of the sought parameters of the model, as well as the obtained temperature field, which is formed due to their inclusion. The obtained calculations of the model (1)–(2) are confirmed by the corresponding act of implementation of the main research results at the Institute of Engineering Thermophysics of the National Academy of Sciences of Ukraine. Figure 13 shows the efficiency of the methods obtained in this work in terms of the number of calls to the DPTC solution procedure for model (1)–(2). Now let’s consider model (4)–(5). For this optimization model, it can be introduced the following notation: γ = (α1 , . . . , αn , β1 , . . . , βn ). Then all partial derivatives of functional (4) have the following form:

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Fig. 6 Temperature distribution in the furnace without the influence of internal heaters

Fig. 7 Determination of the temperature field of the 1st and 2nd internal heaters (functions T1 (x, y) and T2 (x, y))

∂J = −2 ∂γ1

¨ Dobject

⎡⎛

⎞ ⎤ (Tactual − T (γ1 , γ2 , . . . , γ2n ))× ⎣⎝ T (γ1 + γ1 , . . . , γ2n ) − T (γ1 − γ1 , . . . , γ2n ) ⎠d xd y ⎦. × 2γ1

... ... ... ... ... ... ... ... ⎡⎛ ⎞ ⎤ ¨ (Tactual − T (γ1 , γ2 , . . . , γ2n ))× ∂J ⎣⎝ T (γ1 , . . . , γ2n + γ2n ) − T (γ1 , . . . , γn − γ2n ) ⎠d xd y ⎦. = −2 ∂γ2n × 2γ2n Dobject

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Fig. 8 Determination of the temperature field of the 3th and 4th internal heaters (functions T3 (x, y) and T4 (x, y))

Fig. 9 Determination of the temperature field of the 5th and 6th internal heaters (functions T5 (x, y) and T6 (x, y))

Derivatives of the second order are calculated similarly. So, for model (4)–(5) it has a system of equations with 2n unknowns. This model cannot be linear, since it is impossible to obtain directly the temperature fields of each spot heater separately, the location of which is unknown. Model (4)–(5) is solved in a complex manner, that is, the minimization of the quadratic functional (4) is carried out by the methods described in [12, 13]. Figure 14 shows the main results of the operation of the methods on the model (4)–(5).

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Fig. 10 Resulting temperature field in the furnace, taking into account the action of internal heaters

Fig. 11 Comparison of the temperature distribution with the Tactual value in the area of the object in the furnace

Models (1)–(2) and (4)–(5) can be actively used in industry to modernize industrial and muffle furnaces through their reconstruction in order to improve their efficiency [18, 19].

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Fig. 12 Contour plot of the temperature field of the problem

Fig. 13 Comparison of the Newton’s classical method with the obtained modifications in [12, 13]

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Fig. 14 Number of DPTC calls that are required to obtain a numerical solution to model (4)–(5) by the Newton’s classical method and its modifications

5 Analysis of Simulation Results The input data of the considered models (1)–(2) and (4)–(5) are the initial data for the models, which are reduced to IPST. Therefore, in the first place, comparisons are made with the corresponding IPST models. Figure 15 shows a comparison of the preset temperatures of the model heaters for solving the DPST and the result of calculating the temperatures of point heaters using model (1)–(2). According to the data for the mathematical model (1)–(2), which are shown in Fig. 15, the absolute deviation of the heater temperatures calculated in this model from the real values does not exceed 2%. A similar analysis was carried out for model

Fig. 15 Comparison of the simulation results for (1)–(2) with the known values that were specified during solving the DPST, which corresponds to this model

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Table 1 Comparison of the found geometric positions of the heaters, obtained as a result of the numerical solution of the mathematical model (4)–(5), with real values Heaters

Defined coordinate x Defined coordinate y Resulting in DPST in DPST coordinate x v IPST

Resulting coordinate y v IPST

Heater №1

−0.75

0.75

−0.797140511

Heater №2

0

0.75

0.001016594

0.795654887

Heater №3

0.75

0.75

0.761298087

0.799821165

Heater №4

−0.75

−0.75

−0.773324819

Heater №5

0

−0.75

0.041665929

−0.73184487

Heater №6

0.75

−0.75

0.792592287

−0.768088104

0.777920666

−0.79088635

(4)–(5). Comparison of the found locations of point heaters with real ones (which were specified during solving the DPST) are given in Table 1. The error in model (4)–(5) directly depends on the choice of the grid. In most cases, it can happen that, at a certain iteration of the optimization method, there are no heaters in the found coordinates of the grid nodes. Then there are two ways out of this situation: (1) place each heater in the grid node closest to the calculated position; (2) carry out the correction of the computational grid with the addition of new nodes in which the location of each heater is obtained (for example, according to Rupert’s algorithm [20]). The total number of iterations in the process of minimizing functional (1) for model (1)–(2) by modifying Newton’s method using the predictor– corrector method was 15 iterations, and the total number of iterations to minimize functional (4) for model (4)–(5) by the same method—19 iterations.

6 Conclusions The chapter describes two mathematical optimization models for research and improving the efficiency of modern industrial and muffle furnaces on an electrical basis. The first mathematical model involves finding the temperatures of internal spot heaters, the location of which is known in advance. It is necessary to find such temperatures of these heaters so that during their operation the temperature of the object that is in the furnace itself is close to the specified one. A description of the linear and nonlinear cases of the first mathematical model is given. The second model is that the temperatures of the heat sources are known, but their locations are not. The same mathematical model does not have a linear case, since is considered in general terms. The numerical solution of the nonlinear mathematical optimization models, considered in the chapter, was obtained by a number of modifications of the classical Newton method. It should be noted that such methods provide the numerical solution of such resource-intensive problems. In order to reduce the amount of operations that

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is necessary to obtain a numerical solution of these models, various modifications of the classical Newton’s method were create. Also, these methods assume the use of the optimal stage at each iteration. This excludes the possibility of divergence of the methods in comparison with the classical Newton method. During solving problems for which the quadratic functional is considered for piecewise differentiable functions or the functional itself has local extrema, it is effective to use the obtained deterministic methods with stochastic or population methods of global optimization. The latter methods do not require data from the stage of differentiation of the objective function or functional in general, but such methods can require a lot of computations if they are used without deterministic methods.

References 1. Danaila, I., Joly, P., Kaber, S.M., Postel, M.: An Introduction to Scientific Computing: Twelve Computational Projects Solved with MATLAB. Computational Science and Engineering. Springer, New York (2007). https://doi.org/10.1007/978-0-387-49159-2 2. Konôpková, Z., McWilliams, R.S., Gómez-Pérez, N., Goncharov, A.F.: Direct measurement of thermal conductivity in solid iron at planetary core conditions. Nature 534(7605), 99–101 (2016). https://doi.org/10.1038/nature18009 3. Pan, W., Yi, F., Zhu, Y., Meng, S.: Identification of temperature-dependent thermal conductivity and experimental verification. Meas. Sci. Technol. 27(7), 075005 (2016). https://doi.org/10. 1088/0957-0233/27/7/075005 4. Cui, M., Gao, X., Zhang, J.: A new approach for the estimation of temperature-dependent thermal properties by solving transient inverse heat conduction problems. Int. J. Therm. Sci. 58, 113–119 (2012). https://doi.org/10.1016/j.ijthermalsci.2012.02.024 5. Huntul, M.J., Lesnic, D.: An inverse problem of finding the time-dependent thermal conductivity from boundary data. Int. Commun. Heat Mass Transf. 85, 147–154 (2017). https://doi. org/10.1016/j.icheatmasstransfer.2017.05.009 6. Mohebbi, F., Sellier, M.: Estimation of thermal conductivity, heat transfer coefficient, and heat flux using a three dimensional inverse analysis. Int. J. Therm. Sci. 99, 258–270 (2016). https:// doi.org/10.1016/j.ijthermalsci.2015.09.002 7. Vadivambal, R., Jayas, D.S.: Non-uniform temperature distribution during microwave heating of food materials—a review. Food Bioprocess Technol. 3(2), 161–171 (2010). https://doi.org/ 10.1007/s11947-008-0136-0 8. Bakshi, S.R., Patel, R.R., Agarwal, A.: Thermal conductivity of carbon nanotube reinforced aluminum composites: a multi-scale study using object oriented finite element method. Comput. Mater. Sci. 50(2), 419–428 (2010). https://doi.org/10.1016/j.commatsci.2010.08.034 9. Fomichev, P., Zarutskiy, A., Lyovin, A.: Researches of the stressed-deformed state of the power structures of the plane. In: Systems, Decision and Control in Energy I, pp. 37–49. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48583-2_3 10. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Principles of construction of systems for diagnosing the energy equipment. In: Diagnostic Systems for Energy Equipments, pp. 1–22. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-444 43-3_1 11. Zaporozhets, A.O.: Methods and means for the control of the fuel combustion process. In: Control of Fuel Combustion in Boilers, pp. 1–33. Springer, Cham (2020). https://doi.org/10. 1007/978-3-030-46299-4_1

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12. Golovnya, B., Khaidurov, V.: Some high-speed methods for solving nonlinear inverse heat conduction problems. Cherkasy Univ. Bull. Appl. Math. Inform. 1–2, 71–90 (2017). https:// nbuv.gov.ua/UJRN/VchuM_2017_1-2_9 13. Golovnya, B.P., Khaidurov, V.V.: Effective method for solving nonlinear inverse heat conduction problem. Cherkasy Univ. Bull. Appl. Math. Inform. 18(311), 87–98 (2014) 14. Roosta-Khorasani, F., Mahoney, M.W.: Sub-sampled Newton methods. Math. Program. 174(1– 2), 293–326 (2019). https://doi.org/10.1007/s10107-018-1346-5 15. Martin, J., Wilcox, L.C., Burstedde, C., Ghattas, O.: A stochastic Newton MCMC method for large-scale statistical inverse problems with application to seismic inversion. SIAM J. Sci. Comput. 34(3), A1460–A1487 (2012). https://doi.org/10.1137/110845598 16. Steinboeck, A., Wild, D., Kiefer, T., Kugi, A.: A mathematical model of a slab reheating furnace with radiative heat transfer and non-participating gaseous media. Int. J. Heat Mass Transf. 53(25–26), 5933–5946 (2010). https://doi.org/10.1016/j.ijheatmasstransfer.2010.07.029 17. Zanoli, S.M., Pepe, C.: Two-layer linear MPC approach aimed at walking beam billets reheating furnace optimization. J. Control Sci. Eng. (2017). https://doi.org/10.1155/2017/5401616 18. Zaporozhets, A.O.: Experimental research of a computer system for the control of the fuel combustion process. In: Control of Fuel Combustion in Boilers, pp. 89–123. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46299-4_4 19. Zaporozhets, A.: Analysis of control system of fuel combustion in boilers with oxygen sensor. Period. Polytech. Mech. Eng. 63(4), 241–248 (2019). https://doi.org/10.3311/PPme.12572 20. Shewchuk, J.R.: Delaunay refinement algorithms for triangular mesh generation. Comput. Geom. 22(1–3), 21–74 (2002). https://doi.org/10.1016/S0925-7721(01)00047-5

Development of a System for Diagnosing Heat Power Equipment Based on IEEE 802.11s Artur Zaporozhets , Vitaliy Babak , Anastasia Sverdlova , Volodymyr Isaienko , and Kateryna Babikova

Abstract The possibility of using a mesh network for diagnosing heat power equipment is considered. A comparison is made between traditional Ethernet and IEEE 802.11s based networks. A working prototype of a measuring module of a system for diagnosing energy equipment, which is an element of a mesh network, has been developed. A functional diagram of the relationship of the measuring modules of the equipment diagnostic system has been constructed. Aspects of the installation and management of communications, energy saving in IEEE 802.11s are considered. Keywords Mesh network · IEEE 802.11s · Heat power equipment · Diagnostic system · Sensors

1 Introduction Due to low prices for equipment, as well as ease of installation, wireless network technologies are widespread. Today, Wi-Fi technology, which is based on the IEEE 802.11 standard [1], is the most popular and developing technology of wireless data networks. The IEEE 802 LAN/MAN Committee on the Standardization of Local and Urban Networks has developed the IEEE 802.11s [2] addendum to the basic standard [1], which describes the mesh network technology, which is one of the most promising and attracts the attention of a large group of researchers. A mesh network is a multi-step network whose devices (mesh stations, MP, Mesh Points) have the functions of a router and are able to use various ways to forward a packet [3]. Mesh technology becomes especially necessary in the absence of a wired infrastructure to connect stations. In this case, the packets are forwarded from one

A. Zaporozhets (B) · V. Babak · A. Sverdlova Institute of Engineering Thermophysics of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] V. Isaienko · K. Babikova National Aviation University, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_8

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mesh station to another before reaching the gateway with the wired network. For greater reliability, a mesh station may have more than one neighboring mesh station. The idea of mesh networks originates from mobile ad hoc networks (MANET), in which the routing function was implemented at the IP level. The MANET protocol had limited performance since the IP level does not have information on wireless transmission conditions and neighboring nodes [4]. In particular, the IP level does not provide information on the probability of packet distortion by noise and collimation, as well as on the modulation and coding scheme used in each of the connections. In contrast, in IEEE 802.11s mesh networks, routing mechanisms operate at the MAC layer, where this information is available, which makes routing much more efficient [5]. At the expense of the routing protocol working at the MAC level, the mesh-technology allows several times to increase the network coverage area. This is important because as the transmission rate increases, the network coverage falls, and the mesh technology compensates for this effect. Depending on the scenario, there are different requirements for mesh devices. So, in home mesh networks, the main requirements are simplicity and low cost of mesh devices, low power consumption, support for quality of service and mobility. In office mesh networks, requirements for energy saving, maintaining quality of service and mobility are significantly reduced. In the mesh networks of university campuses and municipal public mesh networks, support of mobility is central. In commercial public mesh networks, both mobility and differentiated quality of service should be provided. Mesh transport networks are built on the basis of fixed devices, so mobility support is not required, the cost of devices and energy consumption are not so important, and the main requirement is high network efficiency—high throughput with ensuring differentiated (or even guaranteed) quality of information flows through multi-step connections [6, 7]. The difference in requirements leads to the practical impossibility of developing clear, detailed mesh-technology specifications that would be optimal for all scenarios. But the above capabilities of the mesh network reveal wide possibilities for constructing hierarchical diagnostic systems, in particular, in thermal power engineering [8, 9].

2 Features of the Mesh Network In existing 802.11 networks, terminal (subscriber, final) stations (STAs) are associated with access points (APs) and can only interact with them. APs have access to other networks (for example, Ethernet), but cannot exchange information with each other (Fig. 1 up). In the mesh network, in addition to terminal stations and access points, there are special devices—mesh network nodes (Mesh Point—MP), which are able to interact with each other and support mesh services (Fig. 1 down). One device can combine several functions. So, the nodes of a mesh network in combination with access points are called mesh access points (Miss Access Point, MAP). Mesh Point Portals (MPP), being MPs, connect a mesh network with external networks.

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Fig. 1 802.11 network architecture: up—standard; down—mesh network

Thus, in terms of other devices and higher layer protocols, the mesh network is functionally equivalent to a broadband Ethernet network, all nodes of which are directly connected at the data link layer. It should be noted that the changes in the IEEE 802.11s standard practically do not affect the physical level. All innovations are related to the MAC-sublevel link level. In addition, the 802.11s standard addresses the routing of packets within a mesh network (in fact, the network and transport layer of the OSI model), which goes beyond the original IEEE 802.11 framework. The structure of MAC layer packets in a mesh network (Fig. 2) is similar to the standard format of 802.11 network packets [5]. The format of the MAC packet header in a mesh network fully complies with the MAC header of the data packet defined in the IEEE 802.11 standard (with the exception of the HT Control field (HIOP Throughput Control) designed to support IEEE 802.11n standard equipment. The first three header fields and the control field FCS sums are present in all MAC layer packets.

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Fig. 2 The format of the MAC frame with mesh-headers

The difference between the 802.11s MAC packets is the presence of a meshheader in the beginning of the data fields. This header is present in data packets if and only if they are transmitted from the mesh node to the mesh node via the connection established between them, it also joins one of the Multihop Action types of the control packets. The mesh header contains four fields. The mesh flag byte controls the handling of the mesh header. So far, only the first two bits are used, which simply determine the size of the expanded mesh address. The Mesh Time to Live (MTL) field contains the maximum number of steps between nodes that a packet can make in a mesh network. Thus, the lifetime of a packet during multi-step forwarding is limited, which helps to combat the formation of cyclic routes. The packet number in the sequence (Mesh Sequence Number) eliminates the appearance of duplicate packets during broadband and multicast forwarding. The Mesh Address Extension field may include additional addresses (Address 4, Address 5 and Address 6, each 6 bytes), which allows mesh packets to contain up to 6 addresses. Address 4 is used in control packets of the Multihop Action type (for handover in a mesh network), since the address 4 field is missing in the format of control packets of the MAC layer. Addresses 5 and 6 can be used to transmit the address of the final sender and recipient, if both or one of them is not MP. This is possible if the nodes outside the mesh network communicate through the mesh network. It is also possible when two MP devices interact through the root node of the mesh network, that is, two separate mesh paths are used (from the sender to the root node and from the root node to the receiver). In general, the advantages of a mesh network compared to a traditional WLAN network are given in Table 1 [10].

2.1 Install and Manage Communications in IEEE 802.11s Compatibility of devices from different manufacturers on the same network provides the concept of profiles. A profile contains the profile ID itself, the routing protocol identifier, and the routing protocol metric identifier. The device supports multiple

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Table 1 Comparison of traditional WLAN and Mesh networks WLAN network

Mesh network

Goal

Designed for use inside buildings and covering building areas or small open spaces

Designed to cover large open areas, mainly where there is no LAN

Structure

Cable transmission lines, built on topology “star” or hub

Wireless transmission lines based on point-to-point connection between access points

Advantages

Do not require separate power sources; simplicity and low cost of implementation; a new way to learn LAN structure

Easy installation; allow you to deploy networks outside buildings; do not require LAN infrastructure

job profiles, but only one of them can be active. Mandatory for all 802.11c devices, the profile uses Hybrid Wireless Mesh Routing Protocol (HWMP, Hybrid Wireless Mesh Protocol) and the channel transmission time metric (Airtime Link Metric). Figure 3 shows a functional diagram of the interrelationships of the measuring modules in the system for diagnosing the state of heat and power equipment based on a mesh network. Figure 4 shows a functional diagram of the measuring module of the system as an element of the mesh network. The mechanism for establishing connections is based on the periodic sending of a standard “open connection” message. In response, it may receive a message “confirm connection” or “close connection”. A connection between two neighboring MPs is considered established if and only if both MPs sent each other “open connection”

Fig. 3 Functional diagram of the relationship of the measuring modules of the system of diagnosing possession (S—slave-module; M—master-module (all modules in radius G); G (GSM receiver)— main module with GSM transmitter, R—receiver)

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Fig. 4 Functional diagram of the measuring module of the system as a mesh element (DB— database)

commands and answered with a connection confirmation (in any sequence). For each established connection, a lifetime is provided during which it must be used or confirmed.

2.2 Power Saving in IEEE 802.11s The power saving mode in mesh networks is optional. So, MAP nodes are always active, because at any time devices that do not support 802.11s and the corresponding power saving mode can access them. However, for devices with autonomous power supply (various sensors, laptops, telephones, etc.), energy saving is an urgent task [11]. Network nodes are required to report their ability to maintain a sleep (power saving) mode. To do this, use the information field (capability information field) in the beacon and in the responses to the trial packages. In the same field it is reported that the node is in the energy saving mode or has a connection with the node that is in this mode. If a device that wants to work in power saving mode sees that its neighbor does not support this feature, then it may either not establish connections with such a device, or set it up, but refuse to switch to power saving mode. The node cannot switch from active mode to power saving mode (and back) until it informs all devices with which it is connected, about its desire to switch. For informing “neighbors” about changing the power saving mode, empty data packets are used (null-data frame).

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A node in sleep mode wakes up periodically to receive a beacon from its neighbors or to send its own. The node wakes up at least once during the so-called DTIM interval (Delivery Traffic Indication Message) and remains active during the ATIM window (Announcement Traffic Indication Message). All nodes of the mesh network support power saving mode, postpone sending packets destined for devices in sleep mode (including broadcast and multicast), and send them only at the designated time interval. The presence of these packets is indicated by the source node in a Mesh TIM message in a beacon or in a transmitted ATIM frame following the DTIM beacon. Devices in power saving mode “listen” to such messages about the presence of data for them, and if they are detected, they remain active after the ATIM window. If a node has received a broadcast or multicast packet, then it remains active until it receives a packet in which the More Data field indicates that there are no more data addressed to it, or the Mesh TIM element with the same information. Sleeping nodes can wake up at any time if they have a transmission packet in their queue. In this case, such a node will remain asleep, as a last resort, until the next point in time TBTT. The power saving mode is different for synchronous and asynchronous MPs. So, asynchronous MPs use their own ATIM and DTIM values, and all the nodes with which they are connected, anf save these parameters for further work. Synchronous MPs, when joining the network, use common ATIM and DTIM values that they receive from the beacon of their neighbors. In this case, all sleeping devices in the network will wake up simultaneously. The program code element of the diagnostic system based on the Mesh network is given below. void processDataMessage() // Data=0312AAAABBBBBBBBcrlf where 03 is rxSender, 12 is node (hex), AAAA is integer data, BBBBBBBB is the time stamp { String s; unsigned long node; unsigned long rxNodeValueA0; unsigned long rxNodeValueA1; unsigned long rxNodeTimestamp; unsigned long rxSender; unsigned long age; unsigned long previousage; //printlcdstring(“.”); // print a dot as data comes in s = “000000” + stringMid(radioString,6,2); // node is 2 bytes rxSender = hexToUlong(s); // get where this data came rxSender s = “000000” + stringMid(radioString,8,2); // node number node = hexToUlong(s); s = “0000” + stringMid(radioString,10,4); // get the 2 bytes A0 value rxNodeValueA0 = hexToUlong(s); s = “0000” + stringMid(radioString,14,4); // get the 2 bytes A1 value rxNodeValueA1 = hexToUlong(s); s = stringMid(radioString,18,8);

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rxNodeTimestamp = hexToUlong(s); age = (unsigned long) (timeCentral - rxNodeTimestamp); previousage = (unsigned long) (timeCentral - nodeTimestamps[node]); if (age < previousage) // more recent data so update node { nodeTimestamps[node] = rxNodeTimestamp; // update the time stamp nodeValuesA0[node] = (int) rxNodeValueA0; // update the values nodeValuesA1[node] = (int) rxNodeValueA1; // A1 as well

3 Measuring Module of the Diagnostic System Diagnosing the technical condition of heat and power equipment of complex technical systems is a task [12, 13]. To solve it, the team of the Department for Monitoring and Optimization of Heat Power Processes of the Institute of Technical Thermophysics of the National Academy of Sciences of Ukraine carried out research works, in the framework of which a number of methods, models, structures and software tools were developed and implemented at the enterprise of “Mashynostroenie LLC” . Figure 5 shows a working prototype of the measuring module as an element of the system for diagnosing heat power equipment. Figure 6 shows the wiring board of the measuring module, developed in the course of research work. The system can include sensors of the physical quantities used to diagnose a specific system [14, 15]. Depending on the object of diagnosis, the system may include: • thermocouples or thermistors—for determine the temperature;

Fig. 5 Working prototype of the measuring module of diagnostic system: a 3-d model; b real appearance

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Fig. 6 Measuring module’s board: a 3-d model; b CAD system topology

• accelerometers—to measure vibration parameters; • measuring microphones—to determine the level of acoustic noise; • sensors of electrical quantities—to measure the parameters of the functioning of transformers; • pressure sensors—to monitor the vacuum in the furnace; • gas sensors—to determine the concentration of harmful substances in the smoke path; • heat energy meters—to determine the current mode of operation of heat engineering equipment, etc. The developed measuring modules are already being tested on actually functioning objects. In the course of experimental studies of the diagnostic system layout, the temperature of the smoke path of the boiler was measured. In Fig. 7 shows a graph of the temperature of the pipe with the flue gases, depending on the excess air ratio (EAR, α) (discreteness—5 min), obtained using the temperature sensor of the diagnostic system of thermal power equipment. The development of new passive diagnostic systems for determining the technical condition of the elements of heat power equipment, increasing their sensitivity and reliability requires solving the following main tasks: • the construction of adequate mathematical models of noise and rhythmic signals accompanying the work of the elements of heat and power equipment and reflect the physics of their occurrence; • determination of the most informative characteristics and parameters that allow you to monitor and diagnose the technical condition of the elements of thermal power equipment; • development of statistical methods and software for the experimental determination of new informative characteristics and parameters.

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Fig. 7 The graph of the temperature change of the pipe with outgoing gases depending on the EAR (discreteness—5 min)

4 Conclusions The developed methods and technical means allow to actualize the application of the “mesh network” concept in the hierarchical structure of the heat power system. The application of the developed measuring modules provides an opportunity to comprehensively assess the technical state of the heat power equipment by simultaneously measuring various function parameters of its individual elements.

References 1. IEEE Std 802.11-2007, Revision of IEEE Std 802.11-1999. IEEE Std 802.11-2007, IEEE Standard for Information Technology—Telecommunications and information exchange between systems—Local and metropolitan area network—Specific requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications. IEEE Computer Society, June 2007 2. Ko, N.-S., Kim, M.-H., Park, H.-S. FD-AQM: Fairness-aware delay-controlled active queue management in 802.11s-based multi-radio multi-channel wireless Mesh networks. IEEE Commun. Lett. 19(5), 839–842 (2015). https://doi.org/10.1109/lcomm.2015.2412534 3. Chakraborty, S., Nandi, S.: IEEE 802.11s Mesh backbone for vehicular communication: fairness and throughput. IEEE Trans. Veh. Technol. 62(5), 2193–2203. https://doi.org/10.1109/ tvt.2013.2239672 4. Chi, K.-H., Shih, Y.-C., Liu, H.-H., Wang, J.-T., Tsao, S.-L., Tseng, C.-C.: Fast handoff in secure IEEE 802.11s mesh networks. IEEE Trans. Veh. Technol. 60(1), 219–232 (2011). https://doi. org/10.1109/tvt.2010.2090050 5. Carrano, R.C., Magalhaes, L.C., Muchaluat Saade, D.C., Albuquerque, C.V.N.: IEEE 802.11s Multihop MAC: A Tutorial. IEEE Commun. Surv. Tutor. 13(1), 52–67 (2010). https://doi.org/ 10.1109/surv.2011.040210.00037

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6. Islam, Md.S., Alam, M.M., Hong, C.S., Lee, S.: eMCCA: an enhanced mesh coordinated channel access mechanism for IEEE 802.11s wireless mesh networks. J. Commun. Netw. 13(6):639–654 (2011). https://doi.org/10.1109/jcn.2011.6157481 7. Lei, L., Zhou, J., Chen, X., Qi, L., Cai, S.: Modelling and analysing medium access delay for differentiated services in IEEE 802.11s wireless mesh networks. IET Netw. 1(2), 91–99 (2012). https://doi.org/10.1049/iet-net.2012.0010 8. Eremenko, V., Zaporozhets, A., Babak, V., Isaienko, V., Babikova, K.: Using Hilbert transform in diagnostic of composite materials by impedance method. Periodica Polytech. Electr. Eng. Comput. Sci. 64(4). https://doi.org/10.3311/PPee.15066 9. Zaporozhets, A.: Experimental research of a computer system for the control of the fuel combustion process. In: Control of Fuel Combustion in Boilers. Studies in Systems, Decision and Control, vol. 287, pp. 89–123 (2020). https://doi.org/10.1007/978-3-030-46299-4_4 10. Chakraborty, S., Nandi, S.: Distributed service level flow control and fairness in wireless mesh networks. IEEE Trans. Mob. Comput. 14(11), 2229–2243 (2015). https://doi.org/10. 1109/TMC.2015.2400453 11. Wang, M., Vandermaar, A.J., Srivastava, K.D.: Review of condition assessment of power transformers in service. IEEE Electr. Insul. Mag. 18(6), 12–25 (2002). https://doi.org/10.1109/MEI. 2002.1161455 12. Jadin, M.S., Taib, S.: Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography. Infrared Phys. Technol. 55(4), 236–245 (2012). https://doi.org/ 10.1016/j.infrared.2012.03.002 13. Arriola-Medellin, A., Manzanares-Papayanopoulos, E., Romo-Millares, C.: Diagnosis and redesign of power plants using combined pinch and exergy analysis. Energy 72, 643–651 (2014). https://doi.org/10.1016/j.energy.2014.05.090 14. Zaporozhets, A.: Analysis of control system of fuel combustion in boilers with oxygen sensor. Periodica Polytech. Mech. Eng. 64(4), 241–248 (2019). https://doi.org/10.3311/PPme.12572 15. Zaporozhets, A.: Development of software for fuel combustion control system based on frequency regulator. In: CEUR Workshop Proceedings, vol. 2387, pp. 223–230. http://ceurws.org/Vol-2387/20190223.pdf

Modeling the Coal Industry Technological Development Considering Environmental Restrictions Vitaliy Makarov , Mykola Kaplin , Tetiana Bilan , and Mykola Perov

Abstract The subject of study is the directions and volumes of technological reequipment of the mining enterprises in order to ensure the competitiveness of the coal industry and reduce the negative impact on the environment. The purpose of the article is to develop mathematical models and tools for optimizing the technological development of coal mining based on the use of environmental and economic indicators of the efficiency of coal production advanced technologies, taking into ac-count the capture and utilization of coalbed methane. Research methods: mixedinteger linear programming for developing a model for optimization of technological development of the coal industry, taking into account state-of-the-art environmental considerations; multicriteria optimization and benchmarking in order to form many prospective options for technological conversion of mines; expert assessments for developing an information base. A model of production type has been constructed, based on production activities of coal mining and taking into account the formation of coalbed methane. The software implementation of the model made it possible to determine the effective technologies and the optimum complete set of treatment complexes for re-equipment of state mines for achieving maximum production and ensuring environmental safety. The volume of coalbed methane emissions reduction for the period up to 2040, as well as the volume of solid waste generated in the process of coal production have been determined. Keywords Coal industry · Mathematical model · Ecology · Coalbed methane · Solid waste

1 Introduction In Ukraine, coal is the only energy resource with enough reserves for hundreds of years, which determines its leading role in ensuring energy security requirements. Various aspects of the efficient functioning of power systems while ensuring energy V. Makarov · M. Kaplin · T. Bilan (B) · M. Perov Institute of General Energy of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_9

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security of the country are discussed in many papers [1–6]. However, the current technical and technological state of coal production is extremely unsatisfactory and needs a thorough upgrade. The correct choice of coal mining techniques and technologies, the timely replacement of obsolete and physically obsolete equipment depends on the efficiency of the coal mining enterprises and the industry as a whole. Coal production, among other industries, has the most negative and profound impact on the environment. Emissions of pollutants and greenhouse gases to the environment as a result of the activities of coal enterprises are a source of significant environmental problems. In addition, mines must pay for environmental pollution, which leads to an increase in economic losses from their economic activities. Substantial environmental damage is caused by methane released into the atmosphere by the mine’s ventilation and degassing systems, which contributes to the greenhouse effect. In this regard, creating mathematical models and software tools to optimize the coal industry technological development, taking into account the current environmental considerations is urgent [7, 8]. Ukrainian and foreign scientists addressed different methodological issues in the field of development of the coal industry: Kiyashko Yu. I. (assessment of the efficiency of mines in different options for the use of treatment equipment) [9], Kulik M. M., Alaverdyan L. M. (optimization of coal industry development) [10, 11], Pavlenko I. I. (forecasting the development of the coal industry with limited investment) [12], Henderson J. (model of supply and demand in the markets of coal products) [13], Suwala W. (model of restructuring of the coal industry) [14] and others. The aim of the study is to develop mathematical models and tools for optimizing the technological development of coal mining based on the use of energy and economic indicators of the efficiency of coal production advanced technologies, taking into account the capture and utilization of coalbed methane. This aim envisages solving the following tasks: improvement of the mathematical model for forecasting technological development of the coal industry, taking into account state-of-the-art environmental considerations; improvement of software and information tools for model implementation; determination of methane emission reductions and volumes of solid waste generated in the coal mining process. The following research methods are used in the work: mixed-integer linear programming in order to develop a model for optimization of technological development of the coal industry, taking into account modern environmental considerations; multicriteria optimization and benchmarking in order to form many promising options for technological conversion of mines; expert assessments in order to form an information base.

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2 Model of Development of the Coal Industry with Environmental Constraints A model for optimizing the coal mining technological development [15], taking into account the capture and utilization of coalbed methane, can be interpreted as the following problem of mixed-integer linear programming. It is necessary to determine the number of binary intensities ξv j of the use of the technological conversion of mine j, which give the maximum coal production forecasted as a result of the modernization Vj NM  

xvfjor ec · ξv j

→

max

(1)

j=1 v j =1

under conditions: increase of the forecasted production capacity of the mine Vj 

xvfjor ec · ξv j − X cur j

v j =1

   ≥ 0 

(2) j=1, N M

limitation of the projected production capacity of the mine to its minimum and maximum values, which are determined by the initial set of conversion options Vj  v j =1

min

xvfjor ec · ξv j − X j

   ≥ 0 

  Vj   max xvfjor ec · ξv j − X j ≤ 0  v j =1

(3) j=1, N M

(4) j=1, N M

the formation of coalbed methane in volume yν j at the estimated production volume f or ec

xv j j

, which will take place in the variant v j of technological conversion of the mine   μν j · xvfjor ec · ξv j − yν j = 0 j=1, N M ν j=1, V j

(5)

distribution of the total volume of mine methane yν j by components that determine its captive and freely allocated part according to the variant v j of technological re-equipment of mine j   − yνr es = 0 j=1, N M yν j − yνcap j j ν j=1, V j

(6)

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the relationship between the total volume of mine methane yν j released during the operation of the mine j in the technological conversion, and the achievable volume of its capture in the selected type of degassing installations   cap · y − y = 0 Ξνcap  j=1, N M ν j ν j j

(7)

ν j=1, V j

use in the version of technological conversion of lava v j , degassing installation with cap maximum installed capacity G ν j   cap cap cap yνcap − G · ζ + δ = 0  j=1, N M ν ν ν j j j j ν j=1, V j

(8)

formation of total volume of captive methane Vj 

ym

yvym − Yj j

v j =1

   = 0 

(9) j=1, Nw

the formation of the total amount of free methane   Vj   r es r es yv j − Y j = 0  v j =1

(10)

j=1, N M

the formation of total volume of captive methane in the general mine degassing equipment, including the reservoir and separator out p

gj

cap

· Yj

cap, out p

− Yj

  = 0

(11)

j=1, N M

the use in the version of technological conversion of the mine j common mine degassing equipment, including the collector and separator with a maximum capacity cap G¯ j cap

Yj

  cap cap cap − G¯ j · Z j +  j = 0

j=1, N M

(12)

the competitiveness of the coal industry in the global coal market, which is expressed by the lower total price of coal of own production relative to the price of imported coal of the relevant brands and quality indicators max K =

N M   j=1

f or ec

1 + Rj



∗ f or ec,min

· Sj

− C¯ p, j



·

Vj  v j =1

xvfjor ec · ξv j ≤ 0

(13)

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limited investment in mine conversion j ⎛ ⎝

⎞

Vj 

K vTj · ξv j + K vUU · ζv j ⎠ + K jSU · Z j j

cap

≤

K Tj, Σ

(14)

v j =1

limited investment in the conversion of all mines in the industry NM  j=1

⎡⎛ ⎣⎝

Vj 

⎞

⎤

K vTj · ξv j + K vUU · ζv j ⎠ + K jSU · Z j ⎦ ≤ j

K ΣT

(15)

v j =1

not exceeding the volume of captive methane lava v j in the mine j the installed capacity of the degassing installation   ≥ 0 δνcap  j=1, N M j ν j=1, V j

(16)

not exceeding the volume of captive methane of the mine j of the installed capacity of the general mine degassing equipment (collector and separator) cap

j

  = 0

j=1, N M

(17)

In the model (1)–(17): f or ec xv j —estimated production capacity of the lava during the reconstruction or technical re-equipment according to the variant v j , tonne/year; X¯ min , X¯ max — j j minimum and maximum forecasted coal production in the set of feasible conversion options determined from additional optimization problems according to the method described in (Makarov 2018), respectively; X cur j —current production capacity of mine j, tonne/year; ξv j —required binary variable for the choice of mine reconstruction or refit j; μν j —specific amount of methane release during coal mining, m3 /tonne; yν j —the total amount of methane release during the operation of the lava by the cap option of conversion of ν j in the mine j, m3 /year; yν j —the volume of captured methane in the operation of the lava with the installation of degassing installation —the amount of residual free and converted in option ν j in the mine j, m3 /year; yνr es j methane in the operation of the lava with the installation of degassing installation cap and converted in option ν j in the mine j, m3 /year; Ξν j —mine methane capture coefficient when installing a degassing plant in a lava converted after option X in cap mine j, dimensionless units; G ν j —the maximum throughput of the degassing unit cap in the lava converted to option ν j in the mine j, m3 /year; ζν j —a binary variable of the state of use of the degassing unit in a bench converted in option ν j in the cap mine j; δν j —the excess of the capacity of the degassing unit in the lava converted under option ν j in mine j, the actual volume of coal mine methane capturing by cap this equipment, the model equalization variable, m3 /year; Y j —total volume of

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captive methane during operation of mine j with installation of degassing installations, m3 /year; Y jr es —volume of residual free methane during operation of mine j with out p installation of degassing installations, m3 /year; g j —technological coefficient of external emission of mine methane by the general mine degassing equipment of mine cap, out p —volume of external emission of mine methane by j, dimensionless units; Y j cap general mine degassing equipment of mine j, m3 /year; G¯ j —maximum capacity of cap the general mine degassing equipment of the mine j, m3 /year; Z j —a binary variable cap of the state of use of the general mine degassing equipment of mine j;  j —volume of excess of the boundary capacity of the degassing equipment of mine j of the actual volume of capturing mine methane by this equipment, model equalization variable, —the most competitive price difference between coal produced and m3 /year; max K imported with comparable consumer characteristics, UAH; K vTj —investments in the —investoption of reconstruction or technical re-equipment of the mine j, UAH; K vUU j ments in degassing equipment in the variant of reconstruction X with the technical re-equipment of the mine j, UAH; K jSU —investments in general mine degassing equipment for technical re-equipment of the mine j, UAH; K Tj, Σ —marginal amount of investment in the reconstruction or technical re-equipment of the mine j, UAH; K ΣT —the total amount of investments in the reconstruction or technical re-equipment of the considered group of mines during the investment period T, UAH;v j —index f or ec of the variant of reconstruction of the mine j; R j —forecast profitability of the f or ec

mine j; S j —estimated cost of coal production of mine j, taking into account operating costs for operation of degassing installations and general mine equipment f or ec —estimated production capacity of for mine methane utilization, UAH/tonne; X j mine j, tonne/year; C¯ p, j —the estimated value of the price for imported, the type and quality characteristics of which are comparable with the corresponding coal mines j, UAH/tonne; N M —the number of mines of the surveyed group of mining enterprises; V j —number of options for reconstruction of mine j. The objective function of the model is the aggregate forecasted coal production of all mines (1), and the optimization variables are the binary variables of intensity of use of options of modernization of separate sections, as well as binary variables of intensity of use of degassing equipment intended for capture and utilization of mine methane. Equations (5)–(7) describe the “production” of coalbed methane as a by-product of a coal enterprise, the distribution of its volume into the utilized and residual components, and the determination of the utilization volume, respectively. The relation (8) characterizes the work of the degassing unit of the lava with the given marginal production capacity. In the equation of balance of the volume of necessary investments in the mine (14), the binary variable of use of the installation corresponds to the cost of its purchase and installation. Equation (12) provides the condition of sufficient capacity of the methane reservoir and separator for processing its total volume produced by degassing installations of the lavas. The cost of this mine equipment is also taken into account by the corresponding factor in the equation of balance of capital expenditures (14).

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In this way, the proposed model of coal industry technological development establishes the relationship between the volumes of coal production and the volumes of coal mine methane extraction, the required production capacity of equipment for its utilization, and the capital costs for such equipment. These costs when forecasting the coal industry development can significantly affect the order of mine refurbishment within modernization programs, as well as the choice of options for the installation of mining complexes. The software implementation of the model above mentioned uses standard and special software. The standard is MS Excel spreadsheet with worksheets of source information. Special software – LP Solve application (Solver) (v. 5.5.2.0).

3 Areas of Modernization of the Coal Industry The forecast of coal production for the period up to 2040 [16] at the stage of development of the industry in the period until 2025 provides for active modernization of the mine fund and bringing the industry to the level of profitability. In particular, the outdated mine equipment will be modernized, funds will be invested in new technologies, and labor productivity will be increased. As defined in [15], the largest increase in production capacity is possible at stateowned mines. Therefore, to calculate technological and economic criteria for the implementation of efficient coal mining technologies in Ukraine, using the methods [17], a list of state mines that need modernization using efficient coal mining technologies has been determined. This list includes state mines that are currently in the territory controlled by the Ukrainian authorities. The current rates of technical re-equipment should exceed the rates of the recent times by 2–2.5 times, when on average 20–25 benches with new treatment complexes on the basis of modern shield mounts, cleaning combines and conveyors should be introduced [18]. On the territory controlled by the Ukrainian authorities, 40% of the mine fund needs the modernization of the planned replacement of mining equipment is needed. In all lava with methane exceeding the standards, it is mandatory to install degassing equipment. The variants of the optimal complete set of mining complexes, determined by the method [15] are presented in Table 1. With the introduction of high-performance domestic technologies of coal production in the benches with a gentle deposit of layers, the options of the complete set of treatment complexes 1–7 (1G–7G) are used. These options include the use of the most progressive pillar system for the development of strata with lavas 200–350 m long. On steep formations, it is effective to use mining units according to options 8 and 9 (8G, 9G). The index “G” in the number of the complete set of the treatment complex means that the degassing equipment is included in the complex.

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Table 1 The variants of the optimal complete set of mining complexes Version

Coal layer power (m)

Lava length (m)

1, 1G

0.85–1.4

2, 2G

0.85–1.3

3G

3,

Purification complexes Shield mounts

Cleaning combines

Conveyors

200

1KD-90

UKD-200-250

SP-250

200

DM

UKD-200-250

SP-250

0.85–1.5

220

DM

RKU-10

SP-251

4, 4G

1.0–1.6

220

1KDD

RKU-10

SP-251

5, 5G

1.1–1.5

220

2KD-90

RKU-10

SP-251

6G

1.35–2.0

350

3KD-90

KDK-500

KSD-27

7, 7G

1.35–2.4

350

2KDD

KDK-500

KSD-27

8, 8G

0.8–1.3

60

1 ANSch

9, 9G

1.1–2.2

60

2 ANSch

6,

The modernization of the state mine fund can be carried out by 2025. This requires raising UAH 2.6 billion of investment funds for the purchase of mining equipment, including 240 UAH million on degassing equipment.

4 Forecast of Mine Methane Emissions The forecast of methane emissions from Ukrainian mines is provided for two variants—without and with the use of degassing equipment. The results of forecasting methane emissions from mines located in Ukrainian-controlled territories for the period up to 2040 are presented in Table 2. Methane output from coal production reaches 950–1000 million m3 in the period 2025–2035, and 675 million m3 in 2040. With the implementation of degassing equipment in the modernizated mines, it is possible to achieve methane emissions reductions of up to 440 million m3 in 2025, 410 million m3 in 2030, 390 million m3 in 2035 and up to 280 million m3 in 2040.

5 The Amount of Solid Waste Generated During Coal Mining The amount of solid waste W j and specific waste W S, j per 1 ton of coal mined in the mine is calculated by formulas: f or ec

Wj =

A E, j X j

100

, tonne

(18)

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Table 2 Methane emissions from mines up to 2040, million m3 Mine

Before modernization/after modernization 2025

2030

2035

2040

im. M.S. Surgaya

54.2/21.7

67.7/27.1

72.2/28.9

72.2/28.9

Pivdenno-Donbaska

1.9/1.9

2.1/2.1

2.1/2.1

2.1/2.1

Centralna

35.8/14.3

42.9/17.2

47.7/19.1

47.7/19.1

Krasnolimanskaya

29.8/11.9

31.7/12.7

33.5/13.4

Kurakhivska

0.9/0.9

1.1/1.1

1.3/1.3

1.3/1.3

N 1/3 Novogrodivska

11.4/4.6

11.4/4.6

11.4/4.6

11.4/4.6

Kotlyarevskaya

15.2/6.0

16.1/6.4

16.1/6.4

16.1/6.4

Ukraine

3.1/3.1

3.3/3.3

3.3/3.3

3.3/3.3

Dobropilska

21.2/8.5

23.6/9.4

25.9/10.4

25.9/10.4

Almazna

33.1/13.2

39.7/15.9

49.4/19.8

49.4/19.8

Pioneer

14.6/7.3

17.1/6.8

20.5/8.2

20.5/8.2

Novodonetska

19.2/9.6

21.8/8.7

25.7/10.3

25.7/10.3

Belitskaya

9.2/4.6

14.8/5.9

18.5/7.4

18.5/7.4

Centralna

6.2/2.5

6.2/2.5

6.2/2.5

6.2/2.5

Severnaya

12.3/4.9

12.3/4.9

12.3/4.9

12.3/4.9 5.7/2.3

Toretska

5.7/2.3

5.7/2.3

5.7/2.3

Pokrovskoye

185.0/92.5

185.0/74.0

146.5/58.6

Bilozirskaya

49.9/20.0

59.9/24.0

66.5/26.6

Krasnolimanske

15.9/6.4

15.9/6.4

15.9/6.4

Toshkivska

2.6/2.6

3.5/3.5

3.5/3.5

3.5/3.5

Gorskaya

21.6/8.7

29.0/11.6

29.0/11.6

29.0/11.6

Zolotoye

25.8/10.4

31.0/12.4

31.0/12.4

31.0/12.4

Carbonite

13.6/5.4

18.2/7.3

18.2/7.3

Privolianska

2.3/2.3

2.7/2.7

3.2/3.2

3.2/3.2

Novodruzheska

23.9/9.6

30.5/12.3

30.7/12.3

30.7/12.3

im.D.F. Melnikova

12.4/5.0

14.1/5.6

14.1/5.6

14.1/5.6

Stepova

45.0/18.0

22.5/9.0

Pavlohradska

31.4/15.7

12.5/5.0

Yuvileyna

14.2/5.7

15.2/6.1

15.2/6.1

15.2/6.1

Blagodatna

20.0/10.0

20.0/8.0

20.0/8.0

20.0/8.0

Ternivska

18.5/9.3

66.5/26.6

Samarska

22.1/11.1

22.1/8.0

Dniprovska

14.0/7.0

14.0/5.6

14.0/5.6

14.0/5.6

im. Heroyiv kosmosu

53.3/26.7

53.3/21.3

53.3/21.3

53.3/21.3

Zakhidno-Donbaska

50.4/20.2

46.2/18.5

46.2/18.5

46.2/18.5 (continued)

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Table 2 (continued) Mine

Before modernization/after modernization 2025

im M.I. Stashkova

2030

2035

2040

30.4/12.2

8.3/6.5

Lisova

11.4/4.6

11.4/4.6

Chervonohradska

22.8/9.1

26.6/10.6

30.4/12.2

Stepova

38.0/15.2

49.4/19.8

57.0/22.8

Buzhanskaya

0.5/0.5

0.5/0.5

0.5/05

0.5/0.5

Total

977/424

1001/408

947/387

676/279

W S, j = f or ec

where X j formula:

Wj f or ec

Xj

, tonne/tonne

(19)

is the estimated volume of production at the mine, determined by

f or ec Xj

=

nj 

xvfjor ec , tonne;

(20)

v j =1

A E, j —the operational ash content of the coal mined in the mine, which is determined by formula: n j A E, j =



v j =1

n

f or ec

Adev j · xv j

v j =1



f or ec

xv j

(21)

where n j —the number of lavas in the mine j; Adev j —the operational ash content of coal produced in the lava v j , %. The calculations of the operational ash content of coal Adev j were carried out according to [19] and industry reporting according to the formula [20]: Adev j =

d d d Adcb,v j · Mcb,v j · dcb,v + Adcbr,v j · Mcbr,v j · dcbr,v + Ads,v j · Ms,v j · ds,v j j j d d d Mcb,v j · dcb,v + Mcbr,v j · dcbr,v + Ms,v j · ds,v j j j

(22)

where Adcb, v j , Adcbr, v j , Ads, v j —ash content of the coalbed, coal bed roof and soil of lava v j , respectively, %; Mcb, v j , Mcbr, v j , Ms, v j —coalbed thickness, coalbed roof cut d d d and soil cut of lava v j , respectively, m; dcb, v j , dcbr, v j , ds, v j —density of the coalbed, 3 coal bed roof and soil of lava v j , respectively, tonne/m . The results of calculations of volumes of solid waste generated in the process of coal production are shown in Table 3.

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Table 3 Specific content of solid waste in 1 tonne of coal produced, tonne/tonne Mine

Before modernization

After modernization

Difference (±)

im. M. S. Surgaya

0.12

0.05

−0.07

Pivdenno-Donbaska

0.31

0.24

−0.07

Centralna

0.29

0.17

−0.12

Capitalna

0.44

0.44

0

Krasnolimanskaya

0.38

0.16

−0.22

Kurakhivska

0.52

0.36

−0.16

N 1/3 Novogrodivska

0.41

0.22

−0.19

Kotlyarevskaya

0.28

0.28

0

Ukraine

0.51

0.49

−0.02

Dobropilska

0.39

0.39

0

Almazna

0.47

0.46

−0.01

Pioneer

0.40

0.40

0

Novodonetska

0.43

0.43

0

Belitskaya

0.52

0.50

−0.02

Centralna

0.36

0.29

−0.07

Severnaya

0.50

0.38

−0.12

Toretska

0.30

0.16

−0.14

Pokrovskoye

0.37

0.36

−0.01

Bilozirskaya

0.38

0.36

−0.02

Krasnolimanske

0.38

0.16

−0.22

Toshkivska

0.30

0.25

−0.05

Gorskaya

0.22

0.21

−0.01

Zolotoye

0.38

0.37

−0.01

Carbonite

0.28

0.28

0

Privolianska

0.47

0.40

−0.07

Novodruzheska

0.35

0.30

−0.05

im. D. F. Melnikova

0.37

0.35

−0.02

Stepova

0.35

0.35

0

Pavlohradska

0.40

0.40

0

Yuvileyna

0.34

0.34

0

Blagodatna

0.43

0.43

0

Ternivska

0.43

0.43

0

Samarska

0.42

0.42

0

Dniprovska

0.42

0.42

0

im. Heroyiv kosmosu

0.40

0.40

0

Zakhidno-Donbaska

0.36

0.36

0 (continued)

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Table 3 (continued) Mine

Before modernization

After modernization

im M. I. Stashkova

0.40

0.40

Difference (±) 0

Lisova

0.27

0.45

+0.18

Chervonohradska

0.45

0.44

−0.01

Stepova

0.18

0.21

+0.03

Buzhanskaya

0.51

0.23

−0.28

6 Conclusions The improved model of coal mining technological development differs from its base formulation by taking into account the environmental impacts of the mine work by linking coal production volumes with the volumes of coal mine methane extraction, the required production capacity of the equipment for its utilization, and the capital costs of such equipment. Taking these costs into account in the face of increased environmental requirements for the operation of the coal industry is a necessary element in forecasting its development, in particular the financial support for modernization programs. The software implementation of the model made it possible to determine the effective technologies and the optimum complete set of treatment complexes for reequipment of state mines, provided they achieve maximum production volumes and ensure environmental safety. With the introduction of degassing equipment, methane emissions will be reduced by 275 million m3 in 2020, 550 million m3 in 2025, 590 million m3 in 2030, 560 million m3 in 2035 and 400 million m3 in 2040. The introduction of advanced coal mining technologies will reduce the average volumes of solid waste from coal production by an average of 5–6%.

References 1. Bilan, T.R., Kaplin, M.I.: Security aspects of the balance of carbonaceous fuels under conditions of establishing the new fuel supply schemes in Ukraine. Prob. Gen. Energy 4(47), 23–29 (2016). https://doi.org/10.15407/pge2016.04.023 2. Bilan, T., Rezvik, I., Sakhno, O., But, O., Bogdanov, S.: Main approaches to cable aging management at nuclear power plants in Ukraine. Nucl. Radiat. Saf. 4(84), 54–62 (2019). https:// doi.org/10.32918/nrs.2019.4(84).07 3. Kulyk, M.M., Kyrylenko, O.V.: The state and prospects of hydroenergy of Ukraine. Tech. Electrodyn. 4, 56–64 (2019). https://doi.org/10.15407/techned2019.04.056 4. Lytvynchuk, V.A., Kaplin, M.I., Bolotnyi, N.P.: The method of design an optimal underfrequency load shedding scheme, pp. 14–17. In: IEEE 6th International Conference on Energy Smart Systems (2019). https://doi.org/10.1109/ess.2019.8764241

Modeling the Coal Industry Technological Development …

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5. Popov, O.O., Iatsyshyn, A.V., Kovach, V.O., Artemchuk, V.O., Kameneva, I.P., Taraduda, D.V., Sobyna, V.O., Sokolov, D.L., Dement, M.O., Iatsyshyn, T.M.: Risk assessment for the population of Kyiv, Ukraine as a result of atmospheric air pollution. J. Health Pollut. 10(25), 200303 (2020). https://doi.org/10.5696/2156-9614-10.25.200303 6. Zaporozhets, A., Eremenko, V., Serhiienko, R., Ivanov, S.: Methods and hardware for diagnosing thermal power equipment based on smart grid technology. In: Shakhovska, N., Medykovskyy, M. (eds.) Advances in Intelligent Systems and Computing III. CSIT 2018. Advances in Intelligent Systems and Computing, vol 871, pp. 476–489. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01069-0_34 7. Kaplin, M.I., Makarov, V.V., Bilan, T.R.: The balance-optimization model of the Ukrainian power sector and fuel industries mutually coordinated operation in the view of European environmental legislation. Prob. Gen. Energy 1(52), 5–11 (2018). https://doi.org/10.15407/pge 2018.01.005 8. Makarov, V.V., Kaplin, M.I., Perov, M.O.: Consideration of environmental constraints in modeling the development of coal industry. Prob. Gen. Energy 4(59), 36–44 (2019). https:// doi.org/10.15407/pge2019.04.036 9. Kiyashko, YuI: Estimation of the efficiency of the mines in various applications of treatment equipment. Coal Ukraine 5, 24–26 (2001) 10. Kulyk, M.M.: The role of coal in the formation of fuel and energy balances and optimization of the development of the coal industry in Ukraine. Prob. Gen. Energy 6, 7–16 (2002) 11. Alaverdian, L.M.: Economic-mathematical model of optimal development of the coal industry of Ukraine. Visnyk MNTU, seriia Ekonomika 1, 121–123. (2010) 12. Pavlenko, I.I.: Mechanism of development of the coal industry with limited investments. Ekonomika promyslovosti 1, 93–97 (2007) 13. Henderson, J.M.: A short-run model for the coal industry. Rev. Econ. Stat. 37(4), 336–346 (1955) 14. Suwala, W.: Models of coal industry in Poland. Gospodarka surowcami mineralnymi 26, 41–52 (2010) 15. Makarov, V.: Optimization of Technological Development of Coal Mining in Ukraine. Economic System Development Trends: The Experience of Countries of Eastern Europe and Prospects of Ukraine: Monograph, pp. 345–363. Baltija Publishing, Riga, Latvia (2018). https:// doi.org/10.30525/978-9934-571-28-2_18 16. Kulik, M.M., Gorbulin, V.P., Kirilenko, O.V.: Conceptual approaches to energy development in Ukraine (analytical materials), p. 78. In: Institute of General Energy of the National Academy of Sciences of Ukraine, Kyiv (2017) 17. Makarov, V.M., Perov, M.O., Makortets’kyi, M.M., Novytskyi, IYu.: Method of identifikation of promising mines of Ukraine. Naukovyi visnyk NHU 6(116), 123–127 (2010) 18. Kosarev, V.V.: New mining equipment of Dongiprouglemash—the basis for the intensification of coal mining. Coal Ukraine 9, 5–9 (2003) 19. Bolshakov, P.Y., Turchanin, G.I. (eds.): The Cadastre of Coal Mines, Intended for Mining by Mines and Sections of the State Coal Industry of Ukraine with the Characteristics of Mining and Geological, Mining Conditions and Coal Quality Indicators, p. 125. DonUgi, Donetsk (2001) 20. SOU 10.1.00185755.001-2004. Coal is brown, rocky and anthracite. Methodology for calculating quality indicators. Minpalyvenerho Ukrainy, Kyiv, 39 p. (2004)

Comparative Efficiency Assessment of Using Biofuels in Heat Supply Systems by Levelized Cost of Heat into Account Environmental Taxes Olga Bogoslavska , Valentyna Stanytsina , Volodymyr Artemchuk , Oleksandr Garmata , and Viktoriia Lavrinenko Abstract In December 2019, the European Commission officially presented The European Green Deal, a new EU economic development program aimed at achieving climate neutrality on the European continent by 2050. Many previous global, European, and national programs also aim to reduce emissions of pollutants into the atmosphere. In this context, one of the directions is the development of alternative energy sources (in particular the wider use of biofuel boilers) on the one hand and increasing environmental tax rates on the other. When choosing the optimal heating boilers, the practice of using the LCOH indicator is common. The authors show that this indicator can change significantly over time due to changes in fuel prices, environmental tax rates, etc., so the corresponding risks when investing in various heating projects must be taken into account. This work is aimed at revealing the essence of these risks, as well as to determine the change in LCOH on the example of boilers with a capacity of 100, 500, and 1000 kW, which burn the following fuels: natural gas, anthracite, wood chips, wood pellets, sunflower husk pellets, straw briquettes, wood, peat briquettes. 13 variants of LCOH calculations were made for boilers with a capacity of 100 kW, 10—for boilers of 500 kW and 9—for boilers of 1000 kW (a total of 32 variants) for the prices of 2016 and 2020 in Ukraine. In addition, it should be noted that the authors summarized the specific emissions of pollutants (kg/t of fuel) and emissions of pollutants for fuel (g/GJ) for the above fuels. Among the pollutants, the following NOx , SOx , CO2 , and PM10 were taken into account. Studies have shown the most important factors that create significant risks for suboptimal investment in such heating systems. O. Bogoslavska · V. Stanytsina (B) Institute of General Energy of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] V. Artemchuk G.E. Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, Kyiv, Ukraine State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”, Kyiv, Ukraine O. Garmata · V. Lavrinenko National Pedagogical Dragomanov University, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_10

167

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Keywords Heat supply · Heating · Solid fuel boiler · Alternative fuels · Biofuel · LCOH · Environmental tax rates · Emissions · NOx · SOx · CO2 · PM10

1 Introduction There have been recent significant changes in the energy sector of Ukraine, including the use of renewable energy sources, as well as individual and district heating. The issues of energy sovereignty of the country, reducing dependence on imported energy resources, primarily natural gas, are becoming more and more important. Some documents have been adopted to strengthen the legislative framework for the introduction of renewable energy sources (RES), in particular the use of biomass, and the stimulation of substitution of natural gas in heat supply by other energy sources. The actuality of energy projects use of biomass and substitution of fossil fuels has increased significantly. The most dynamic development is the use of woodwork waste, chips and pellets, sunflower husk burning. Interest in the energy use of grain straw, waste and remains of corn and energy crops is intensively growing. Some Ukrainian companies have already launched production of biomass boilers for both domestic and industrial consumers. Looking for new ways to provide the country with its own energy resources and energy conservation are imperative necessities of nowadays. Diversification of energy resources, optimization of energy consumption and implementation of energy efficient technologies based on renewable energy are the main components that could solve the problem of energy sovereignty of country. In the legislation of Ukraine for the first time the term “unconventional and renewable energy sources” was interpreted in the Law “On energy conservation” dated July 1, 1994. The essence of this term was defined as sources that permanently exist or periodically appear in the environment in the form of solar energy, wind energy, thermal heat, energy of sea waves, rivers and biomass [1]. “Alternative energy sources” according to the national legislation is a slightly more general concept than “renewable energy sources”, as it refers not only to environmentally friendly renewable energy sources, but also to secondary energy resources, as stated in the Law of Ukraine “On Alternative Energy Sources”, which in addition to the types of green energy sources listed in the previous legislative document noted other, such as biomass energy, gas from organic waste, gas obtained from sewage treatment plants. The legislative framework of Ukraine divides alternative energy sources and secondary energy resources. The last include blast-furnace and coke gases, as well as coal deposit methane degassing, transformation of the waste energy potential of technological processes [2]. The policy of the European Union is characterized by the consistent implementation of the principles of energy efficiency and conservation, as well as the largescale implementation of clean technologies based on renewable energy. Government strategies, programs and plans are an effective way for EU countries to stimulate

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the development of renewable energy sources. Relevant national legislation has been adopted, some of which are listed below. In the National Security Strategy of Ukraine until 2020, the priorities of the policy of state significance are to create conditions to ensure national sovereignty, including energy independence. Among the main directions of reforms declared by the Association Agreement between Ukraine and the European Union, as well as by the Sustainable Development Strategy “Ukraine—2020”, is to ensure energy independence through the development of renewable energy, taking into account the environmental component [3, 4]. The basic principles of the global significance of climate change and the offered package of major solutions related to all sectors of the economy that are required for implementation are defined in the Concept of implementation of state policy in the field of climate change. Biomass is a versatile energy source that can substitute fossil energy in the energy sectors electricity, heat and transport fuels as well as non-energy use of fossil resources, for example replacing cokes in steel industries or replacing petroleum based polymers with biobased polymers. As a result of increasing efforts to mitigate greenhouse gas (GHG) emissions and improve energy supply security by diversification and reducing dependencies on fossil energy carriers, the use of biomass for energy purposes (bioenergy) has grown exponentially in the last decade [5]. In the European Union (EU), member states have agreed on binding targets to increase the share of renewable energy to 20% of gross final energy consumption by 2020, as was set out in the Renewable Energy Directive (RED) 2009/EC/28 (EC, 2009). To meet this target, EU member states expect that bioenergy will increase with 44% by 2020 compared to actual production in 2010 and with the largest growth anticipated in electricity and liquid transport fuels [6]. Beyond 2020, the EU is committed to reduce GHG emissions with 40% compared to 1990 levels by 2030 and at least 27% renewable energy without country specific targets and 27% energy saving compared to 2007. In its communications on the Energy Union Strategy and the EU climate and energy framework for 2030, the European Commission announced that a new Renewable energy package for the post-2020 period will be presented in 2016–2017, including an improved bioenergy policy. The latter should maximize the resource efficient use of biomass in order to deliver robust and verifiable GHG savings and to allow for fair competition between the various uses of biomass resources in the construction sector, paper and pulp industries and biochemical and energy production. This should also encompass the sustainable use of land, the sustainable management of forests in line with the EU’s forest strategy and address indirect land use effects as with biofuels [7]. While comparing the distribution of biofuels by type in EU countries, we can remark a significant share of solid biofuels, which is about 70% of the total amount of biomass used [8]. The share of biofuels in the total energy supply in 2016 was 3.1%, that significantly lower than the corresponding level in the EU countries, which was 18% in Ukraine [9].

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The Government [10] in 2014 set a common goal for the development of renewable energy in the country—in 2020 the contribution of renewable energy in the gross final energy consumption had to reach 11%, the main contribution of biomass has been foreseen in the heating/cooling sector in the amount of 5000 thousand toe, which is 85% of the contribution of all renewable energy sources.

2 Literature Review and Problem Statement Ukraine has a large amount of plant waste due to its highly developed agricultural sector. The main ones are straw of cereal crops, nutritious remains of corn and sunflower [11]. Structure and volume as well as energy potential of crop waste are given in Tables 1 and 2. The use of waste as fuel has several special features. Plant wastes as fuel have some negative properties and require special attention to their usage. Thus, straw may contain chlorine and alkali metals, due to which in the process of its combustion such chemical compounds as calcium chloride and sodium chloride are formed [12]. These compounds cause corrosion of steel elements of power equipment, especially at high temperatures. Another peculiarity of straw as fuel is Table 1 Crop waste structure and volume Crop waste

Total volume, million tons

Share available for energy production,%

Economic capacity, million tons

Cereal straw

33.50

30

10.05

Rapeseed straw

3.90

40

1.56

Corn waste

37.00

40

14.80

Waste of sunflower including husks

19.10

40

7.64

Total

93.50

37

34.05

Table 2 Energy capacity of crop waste Crop waste

Humidity, %

Lower heating value, MJ/kg

Energy potential, million tons

Cereal straw

20

14.50

10.05

4.92

Corn production waste

50

8.00

14.80

4.04

Sunflower production waste

60

6.00

7.64

1.57

32.49

10.53

Total

Total energy potential, million tons of fuel equivalent

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relatively low melting point of ash—800–950 °C (for comparison—wood ash has about 1200 °C), which can lead to slagging of power equipment elements. Nevertheless, constructive and technological solutions have been developed that reduce such negative impact and allow to eliminate limitations of plant waste combustion [13]. Ecological aspects of biomass combustion in boilers and related problems are considered in papers [14–28 etc.]. Combustion of biomass is characterized by the complexity of processes and their dependence on many factors that are difficult to process control. As a result, emission indicators can vary greatly depending on the type of biomass, its state before combustion, combustion method, thermal power of the power plant, aerodynamics and mixture formation in the furnace, the nature of heat exchange in the furnace, implementation of primary and secondary measures to reduce emissions. As a rule, the combustion technologies used to depend on the thermal power of the power plant, and in low-power plants, simple technologies are often implemented with a minimum level of automation of the combustion process [29]. The authors of [30] carried out a study of the values of pollutant emissions obtained from the results of industrial analytical control at boiler plants of housing and communal services in the Vitebsk region for the period of their operation in 2011–2017. They mean that combustion of wood fuel leads to an increase in—pollutants into the atmospheric air and negatively affects the state of the environment in the places of operation of fuel-burning plants operating on biofuel. The organizational and technical measures proposed by them are not able to completely solve the problem of reducing pollutant emissions into the atmospheric air. Indeed, in addition to purely technical problems in operating organizations of the district level, there is a lack of highly qualified personnel; low-efficiency traditional, minimally modernized equipment; lack of own, investment, credit financial means. All processes related to the combustion of organic fuels in boilers of different capacities are sources of pollutants. If in large energy many efficient methods of combustion of low-grade fuels in industrial boilers have been developed (lowtemperature and high-temperature fluidized bed, low-temperature vortex technology, gas generation with subsequent combustion of artificial gas), then during combustion of such fuels in low-power boilers problems. During the combustion of low-grade fuels, a mineral residue is formed—ash. Although the ash content of biofuels is quite low, the fusible characteristics of the ash directly affect the operation of the boiler. Melting of ash can lead to slag of a fire chamber and the emergence of dense deposits on convective heating surfaces. Volatile ash contaminates the air [31]. The combustion of wood fuel in the domestic sector leads to emissions of a complex mixture of pollutants into the atmosphere. This is due to a series of chemical reactions, mainly associated with the oxidation of the carbon and hydrogen present in the wood and their conversion to CO2 and water. Burning wood produces CO2 , but the UNFCCC considers its emissions to be neutral in terms of the overall balance (emissions/removals) since the amount of CO2 emitted from burning wood fuel is assumed to be equal to the amount of CO2 fixed by trees and plants throughout their life [32].

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The maximum permissible standards for the emission of pollutants by heating boilers with a capacity of up to 300 kW that burn coal, firewood, pellets are established by the EN 303-5 standard [33], which regulates the maximum emission of CO, organic carbon, and dust depending on the class of the boiler. With regard to the emission of pollutants during the combustion of biomass in industrial boilers of small and medium power (up to 50 MW), which most often burn wood in the form of chips and wood waste, waste from the processing industry, and straw, the requirements for the maximum permissible emission of pollutants are set in each country independently and differ in size, units of measurement, reference conditions for their determination. Further, these requirements are cited as they are given in the literature. Among the EU countries, Denmark is one of the leaders in the energy use of biomass, and the current requirements for the emission rates of boilers for its combustion are given in Table 3. It should be noted that Danish environmental legislation prohibits the creation of new boiler houses with a capacity of up to 5 MW for burning coal, brown coal, and other similar fossil fuels. From a comparison of the environmental standards established in Denmark, it follows that for boilers with a capacity of more than 1 MW that burn biofuels, more stringent requirements have been set compared to coal-fired boilers. There are no clear requirements for boilers with a capacity of up to 120 kW burning wood fuel and burning straw with a capacity of up to 1 MW. In accordance with Danish environmental legislation, the best available technology applies to fuels and boiler output levels for which no requirements have been established. When operating boilers with straw combustion, Table 3 Specific pollutant emissions from fuel combustion Fuel

Specific pollutant emissions, kg/t fuel

Fuel pollutant emission index, g/GJ

NOx

SOx

CO2

PM10

NOx

SOx

CO2

PM10

Natural gas

2.127 [*]

0 [*]

1943.4 [*]

0.00 [*]

64.31 [34]

0 [34]

58,748 [34]

0 [*]

Coal

2.065 [*]

51.30 [*]

1918.9 [*]

47.20 [*]

100.9 [34]

2506 [34]

93,740 [34]

2305.9 [*]

Wood chips

0.928 [*]

0.112 [*]

1020 [*]

0.903 [*]

91 [35]

11 [35]

100,000 [36]

88.5 [35]

Wood pellet

1.36 [*]

0.187 [*]

1700 [*]

0.51 [*]

80 [35]

11 [35]

100,000 [36]

30 [35]

Sunflower husk pellets

1.36 [37]

3.2 [37]

1816.1 [37]

0.091 [*]

75.56 [*]

207.4 [37]

100,893 [37]

5.911 [*]

Straw briquettes

1.38 [37]

2 [37]

1544.2 [37]

0.171 [*]

89.03 [*]

127.4 [37]

99,624 [37]

10.892 [*]

Firewood

1.228 [*]

0.149 [*]

1512 [*]

1.195 [*]

91 [35]

11 [35]

112,000 [*]

88.5 [35]

Peat briquettes

2.76 [*]

2 [*]

1860.3 [*]

0.51 [*]

178.1 [*]

127.4 [*]

106,000 [*]

30 [*]

*Calculated by the authors on the basis of data [34–39]

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significant emissions occur through the furnace door when loading fuel and removing focal residues. Therefore, the requirement to place straw incinerators no closer than 200 m from residential buildings is justified [29]. Thus, the main problems of combustion of low-grade fuels in low-power boilers: – Emissions of solid particles (ash). – Resin deposition in boiler flues. – Melting of ash, sludge of grate. Information on the environmental performance of low-power boilers in low, but the certification of such equipment in Ukraine, suggests that harmful emissions during the operation of such equipment meet the requirements of regulations of Ukraine [40]. Emissions of pollutants from fuel combustion are usually indicated in mg/nm3 as documents regulating emissions (EU Directives, standards of Ukraine [41] and other countries [42, 43], standards of EU countries [33], Orders of the Ministry of Environment [44]), and articles [45–48]. In a few sources, the values are given in g/GJ or g/t. In general, was surprised by the lack of data on emissions from the combustion of biofuels in boilers in IRENA publications. Taxation of pollutant emissions in Ukraine is carried out in accordance with Section VIII of the Tax Code, which specifies the tax rates for emissions of various pollutants per t (2451.84 hrn—NOx , 2451.84 hrn—SO2 , 10 hrn—CO2 , 92.37 hrn— PM10 ) [49]. In EU countries, biofuels are considered CO2 -neutral fuels. According to the Guidelines for National Greenhouse Gas Inventories, which are IPCC recommendations, carbon dioxide emissions from biomass combustion are declared as zero in the energy sector. In EU member states, taxes on carbon dioxide emissions from biomass energy production do not apply. In Ukraine, the carbon tax applies to biomass [50, 51]. For several years, the tax rate was less than 1.5 Eurocents per ton, from January 1, 2019, the rate increased sharply 24 times to 30 Eurocents [49].

3 Research Methods For economic comparison of energy projects of different biomass types, the method of levelized cost of energy was used [52, 53]. The levelized cost of heat (LCOH) is widely used for determining optimal projects in heating [15, 54–61]. In particular, work [15] proposed a new dynamic price model based on the LCOH and the predicted hourly heat demand. Sometimes the term LCOH is used when it comes to burning fuel and producing energy as heat. This method is universal and convenient when comparing different types of heat production technologies and is used by many organizations, including the International Energy Agency (IEA). The general calculation formula of this

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method is as follows: N

It +Mt +Ft (1+r)t Ht t=1 (1+r)t

t=1

LCOH = N

(1)

where I t is investment expenditures in year t M t is operations and maintenance expenditures in year t F t is fuel expenditures in year t H t is energy (heat) generation in year t r is discount rate N is lifetime of the technology. The use of LCOH makes it possible to specify prospective heat generation technologies for implementation, which is necessary for forecasting the structure of fuel and energy consumption for heating systems [61]. It is necessary to take into account the environmental factor in the LCOH as both the environmental requirements for incinerators and the environmental tax rate are constantly growing. Therefore, when determining the LCOH for small combustion plants, it is necessary to take into account the environmental tax. It is proposed to calculate the average cost of thermal energy for the life cycle, taking into account the environmental tax, by the expression: N LCOHTeco =

It +Mt +Ft +Tteco (1+r)t N Ht t=1 (1+r)t

t=1

(2)

where Tteco is environmental tax. Thus, the change in LCOH due to the environmental tax is determined by the expression: LCOHeco tax = LCOHTeco − LCOH

(3)

Taxation of pollutant emissions in Ukraine is carried out in accordance with Section VIII of the Tax Code of Ukraine [49]. The expenditures included in the formula for determining the average cost of heat energy during the life cycle are divided into permanent (administrative) and variable (operational) costs. For the estimation of capital expenditures, in addition to the cost of basic equipment, it is necessary to take into consideration the cost of construction, installation and commissioning of additional equipment for cleaning of combustion gases (if necessary, and in the case of construction of a separate boiler house—the cost of building a boiler house pumping equipment of the system of chemical preparation of water tanks for the stock of chemically purified water meters of water heat and electricity construction of a chimney of additional work associated with the connection of the chimney to the boiler house) [59]. Value of capital expenditures may differ

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significantly even in similar projects, which is related to construction conditions, technological solutions, level of automation, etc. When assessing future operational costs, it is important to bear in mind that the electricity consumption per unit of solid fuel boilers is higher than that of gas boilers, as the operation of such equipment includes the operation of electric drives in the fuel warehouse in the fuel supply system from the warehouse and from the operational bunker, in some cases—also the automatic ash removal system and fuel shredding system. When used in centralized heating systems, specific electricity consumption may be around 35–45 kWh/Gcal for pellet systems and around 30–55 kWh/Gcal for chips (compared to 23–30 kWh/Gcal for gas boilers). The possible increase in the number of service and maintenance personnel should also be taken into consideration, especially when using manual boilers. Usually solid fuel boilers require additional maintenance (periodic cleaning of heat exchange surfaces from soot) and, unlike gas boilers, are more likely to malfunction due to the mechanical equipment of the fuel supply system. Additional costs for biomass boilers may include ash disposal costs. The analysis of annual operating costs carried out in [62], shows that the share of fuel costs is 70–80% in large boiler houses and 50–60% in smaller ones. Electricity and water costs range between 15–20%, and large values are associated with small boilers. Salary costs for maintenance staff in large boiler-houses are 5–8%, and in small boiler-houses, especially those with steam boilers, 15–20%. Allocations from initial capital expenditures for depreciation and repair are in the range of 6–12%.

4 Purpose and Objectives of the Study The aim of the article is to study the impact of changes in prices for fuel, equipment, etc. and environmental tax rates, which occur over time, on the cost of thermal energy from small boilers on different fuels. Object of study. We chose solid fuel boilers of domestic production ARS, Gefest and Kotlant of various capacities (100, 500 and 1000 kW) for the model calculations characteristics of which are given in Table 5. The main types of fuel for such heating equipment are firewood, anthracite, peat, straw briquettes, sunflower husk pellets, wood chips and other woodworking waste. Calculations were made for two periods— for 2016 and 2020. Fuel prices are market-based for the corresponding period. When allocating capital expenditures, 30% of the cost of installation works, 10% of the cost of commissioning works and 5% of the cost of the boiler were charged for the project development. In our calculations, we will assume that all boilers are located in existing buildings, so construction and installation works are not taken into account. LCOH calculations for natural gas boilers are also presented. 13 LCOH calculations were performed for boilers with a capacity of 100 kW, 10—for boilers of 500 kW and 9—for boilers of 1000 kW (a total of 32 options) for the prices of 2016 and 2020.

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The efficiency of boilers of the same model, but running on different fuels, is different. Calculations were performed for the case of boiler loading by 80%. Due to the lack of regime maps of boilers, the efficiency of the specified load is assumed to be the same as the passport. According to the regime maps of other boilers and literature sources [63], the efficiency of boilers at 100% load is lower than at lower load.

5 Research Results and Discussion Over the past 4 years in Ukraine, the cost of almost all types of fuel has increased by 33–75%, mostly—firewood; gas and wood pellets became cheaper (Table 4). Prices for fuel and boilers are taken from online ads in 2016 and 2020. The exchange rate of the national currency—the hryvnia (hrn, UAH)—is constantly unstable and has fluctuated in the last 4 years in the range of 25.24– 35.35 hrn/EUR. As of November 1, 2016, at the exchange rate of the National Bank of Ukraine, 1 euro cost 27.93 hrn, as of September 15, 2020-33.25 hrn. As equipment and fuel are produced in Ukraine, wages and taxes are charged in UAH, to properly account for financial changes over the period, all calculations are given in hryvnia below, prices for calculations are accepted on the specified dates. Boilers also rose in price by an average of 15% (Table 5). As it was expected that the more powerful the boiler, the cheaper it is in terms of 1 kW of installed capacity. For 100 kW of boilers 1 kW of installed capacity on average in 2020 costs 766 UAH, for 500 kW of boilers—433 UAH, for 1000 kW—358 UAH. The effect of the discount rate on LCOH. As shown by the results of calculations, the value of the discount rate has little effect on LCOH—an increase in the discount rate from 0 to 10% leads to an increase LCOH by 7.89–18.45 hrn/Gcal or 1–5.2% at Table 4 The cost of fuel in 2016 and 2020 and its calorific value

Fuel

Calorific value, MJ/kg [38]

Cost of fuel hrn/t, gas in hrn/1000 m3 2016*

2020**

Natural gas

33.08

5541

4277

Anthracite

27

2400

3500

Wood chips

10.2

800

1200

Wood pellets

17

2500

2100

Sunflower husk pellets

18

900

1200

Straw briquettes

15.5

1100

1500

Firewood

13.5

667

1167

Peat briquettes

17.55

1100

1600

*At the time of settlements 1 euro = 27.93 hrn **At the time of settlements 1 euro = 33.25 hrn

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Table 5 The cost of boilers in 2016 and 2020 and their installed capacity The boiler

Power of the boiler, kW

The cost of the boiler, hrn 2016

ARS 100 Comfort ARS 100 BM

99

59,400

2020 68,310

The cost of 1 kW of installed power, hrn 2016

2020

600

690

99

69,400

79,820

701

806

Gefest Profi-P 100

100

64,500

79,200

645

792

Gefest Profi-U 100

100

62,800

75,400

628

754

Kotlant KB-100

100

57,000

78,800

570

788

Kotlant KB-500

500

209,000

212,400

418

425

ARS 500

520

184,000

227,700

354

438

ARS 500 BM

500

197,600

227,230

395

454

Gefest Profi-P 500

500

205,400

207,500

411

415 345

ARS 1000 ARS 1000 BM Gefest Profi-P 1000

1000

300,000

345,000

300

980

289,600

333,050

296

340

1000

318,000

388,000

318

388

2016 prices. In 2020, a corresponding increase in the discount rate leads to an increase LCOH by 8.94–21.22 hrn/Gcal or 1.3–4.3%. In both cases, the same trend—the lower the power of the boiler, the greater the impact of the discount rate on LCOH as a percentage (1.3–2.7% for boilers of 1000 kW and 2.1–4.3% for boilers of 100 kW in 2020) and in cash (8.94–10.21 hrn/Gcal for boilers of 1000 kW and 18.16–21.22 hrn/Gcal for boilers of 100 kW). LCOH in different years. The results of LCOH calculations at the prices of 2016 and 2020, including the fuel component, and the contribution of the environmental tax to the cost of thermal energy, are presented in Table 6. In 2016, by the value of LCOH boilers can be divided into 4 groups. The lowest LCOH was for boilers on firewood and sunflower husk pellets, then—for boilers on peat and straw briquettes. The third group includes wood chip and anthracite boilers. The most expensive was thermal energy from wood pellet boilers and natural gas (Fig. 1). In general, the more expensive the fuel, the more expensive the thermal energy, despite the fact that the more expensive fuel has a higher calorific value, an exception for wood chips and firewood. A feature in 2016 was the significant difference between the minimum and maximum LCOH—641 hrn or more than 3 times. In 2020, as in 2016, the cheapest was the production of thermal energy in woodfired boilers and on sunflower husk pellets. The rest of the boilers can not be divided into groups (Fig. 2). Due to the change in fuel prices, LCOH from anthracite and natural gas boilers is almost the same, in 4 years the thermal energy from gas boilers has become cheaper and anthracite has become more expensive. The difference between the maximum and minimum LCOH decreased to 495 hrn or 2.3 times.

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Table 6 LCOH and its components in 2016 and 2020, hrn/Gcal Boiler and type of fuel

LCOH* 2016

2020

LCOH fuel component

LCOH eco tax

2016

2020

2020

1. ARS 100 Comfort, anthracite

588.15 864.81 453.77 661.75 33.59

2. ARS 100 Comfort, wood chips

534.83 803.75 400.46 600.69

1.32

3. ARS 100 Comfort, natural gas

876.12 845.41 741.75 642.35

0.78

4. ARS 100 BM, wood pellets

812.98 776.01 669.24 562.17

1.03

5. ARS 100 BM, sunflower husk pellets

371.28 517.23 227.54 303.39

3.16

6. ARS 100 BM, straw briquettes

437.95 654.25 322.96 440.41

2.42

7. Gefest Profi-P 100, straw briquettes

456.31 647.12 322.96 440.41

6.95

8. Gefest Profi-P 100, wood pellets

802.59 768.88 669.24 562.17

1.03

9. Gefest Profi-P 100, sunflower husk pellets

360.89 510.11 227.54 303.39

3.16

10. Gefest Profi-U 100, firewood

349.41 584.06 217.64 380.87

1.14

11. Gefest Profi-P 100, peat briquettes

423.35 627.31 291.58 424.11

3.50

12. Kotlant KB-100, wood pellets

920.17 873.82 789.37 663.07

1.21

13. Kotlant KB-100, peat briquettes

467.24 700.12 336.44 489.36

4.03

14. Kotlant KB-500, wood pellets

810.43 716.26 741.81 623.12

6.18

15. Kotlant KB-500, peat briquettes

384.78 553.02 316.17 459.88

9.14

16. ARS 500, anthracite

517.42 757.39 453.77 661.75 38.61

17. ARS 500, wood chips

471.67 707.67 410.47 615.71

18. ARS 500, natural gas

802.94 750.37 741.75 658.40

3.79

19. ARS 500 BM, wood pellets

735.41 657.72 669.24 562.17

5.58

6.59

20. ARS 500 BM, firewood

290.90 488.84 224.74 393.29

6.27

21. Gefest Profi-P 500, straw briquettes

394.46 537.48 326.51 445.25

7.03

22. Gefest Profi-P 500, wood pellets

737.19 654.40 669.24 562.17

5.58

23. Gefest Profi-P 500, sunflower husk pellets

295.49 395.62 227.54 303.39

7.75

24. ARS 1000, anthracite

496.35 720.45 453.77 661.75 38.61

25. ARS 1000, firewood

311.26 529.64 261.72 458.00

7.30

26. ARS 1000, wood chips

460.02 687.35 410.47 615.71

6.59

27. ARS 1000, natural gas

791.29 730.04 741.75 658.40

3.79

28. ARS 1000 BM, wood pellets

718.82 634.14 669.24 562.17

5.58

29. ARS 1000 BM, firewood

289.99 492.70 240.41 420.72

6.71

30. Gefest Profi-P 1000, straw briquettes

377.89 521.04 326.51 445.25

7.03

31. Gefest Profi-P 1000, wood pellets

720.62 637.96 669.24 562.17

5.58

32. Gefest Profi-P 1000, sunflower husk pellets

278.92 379.18 227.54 303.39

7.75

*No include environmental tax

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Fig. 1 LCOH and fuel component of LCOH at 2016. The number on the x-axis corresponds to the version in Table 6, boilers are grouped by type of fuel

Fig. 2 LCOH and fuel component of LCOH at 2020. The number on the x-axis corresponds to the version in Table 6, boilers are grouped by type of fuel

Despite the fact that wood pellets fell in price by 16%, the cost of thermal energy from 100 kW of boilers on this type of fuel remained the highest. In general, for 2020 the trend remained the same as in 2016—the more expensive the fuel, the more LCOH, an exception only for wood chips. For all types of fuel in 2016 and 2020, the trend continues—the more powerful the boiler, the lower LCOH.

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Fig. 3 LCOH in 2016 and 2020, hrn/Gcal

Over 4 years, LCOH from wood pellet boilers decreased by 4–11.5%, from natural gas boilers—3.5–7.7%. Heat production from other fuels went up from 34% (sunflower husk pellets) to 70% (firewood) (Fig. 3). Fuel component of LCOH. The fuel component ranged 61–94% in 2016 (Fig. 1) and 59–92% in 2020 (Fig. 2). The impact of the value of the environmental tax on LCOH. The environmental tax is calculated for 2020 for the following pollutants—CO2 , NOx , SO2 , solids. In Ukraine, there is an environmental tax for CO2 emissions from the combustion of all biomass. According to paragraph 240.7 of the Tax Code of Ukraine, the environmental tax on CO2 emissions is not paid if the annual total CO2 emissions do not exceed 500 tons per year, so for boilers of 100 kW CO2 tax is not charged. For other boilers, the tax on CO2 emissions is calculated for the entire amount. The environmental taxes component of LCOH (LCOH eco tax) is the lowest for boilers of 100 kW due to the lack of charges for CO2 emissions—1.03–3.50 hrn/Gcal (Fig. 4). Next in line with the growth of this component are boilers on: natural gas (3.79 hrn/Gcal), wood pellets (5.58–6.18 hrn/Gcal), firewood and wood chips (6.27– 6.71 hrn/Gcal), straw briquettes (6.95–7.05 hrn/Gcal), sunflower husk pellets (7.75 hrn/Gcal), peat briquettes (9.14 hrn/Gcal), anthracite (33.59 hrn/Gcal for a boiler of 100 kW and 38.61 hrn/Gcal for others). The largest contribution to this component for anthracite boilers is made by the SO2 emissions tax. In percentage terms, the environmental taxes component of LCOH is 0.1–5.1% of LCOH, the lowest values refer to boilers of 100 kW and natural gas, the largest—to boilers on anthracite. For anthracite boilers, the higher the boiler capacity, the higher the percentage of the environmental taxes component of LCOH—3.8% for 100 kW of boiler and 5.1% for 1000 kW of boiler.

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Fig. 4 LCOH eco tax and LCOH in 2020, hrn/Gcal

6 Conclusions Combustion of biofuels and substitution of fossil fuels, primarily natural gas, is important for Ukraine. Biomass is a renewable local fuel, which could be an inexhaustible source of energy if the f a boiler unit is built taking into account local peculiarities. In residential or production areas where there are difficulties with gas or electric supply, a solid fuel boiler becomes a really advantageous alternative solution. The most common biomass for use in residential and municipal facilities is wood-based fuel such as firewood, pellets, briquettes or chips. To determine the feasibility of introducing small biofuel boilers, LCOH calculations were performed for 3 groups of boilers: low-power boilers (100 kW)—for heating one building; 500 kW and 1 MW—for heating several buildings or areas for 8 types of fuel—wood chips, wood pellets, sunflower husk pellets, straw briquettes, firewood, peat briquettes, natural gas and anthracite. To determine the impact of price changes over time, calculations were performed for 2016 and 2020 prices. Analysis of boiler prices in 2016 and 2020 showed that they rose by an average of 15%. It was found that the more powerful the boiler, the lower the cost of 1 kW of installed capacity. During the study period, wood pellets and gas fell slightly, other fuels rose by 33–75%. It is determined that the value of the discount rate has no significant effect on LCOH—its increase by 10% leads to an increase in LCOH by 1–5%. The fuel component in LCOH is 60–93%. In Ukraine, a tax for CO2 emissions is charged for all boilers, including biomass. The ecological tax component is 0.1–5.1% of LCOH, the lowest values refer to boilers of 100 kW and on natural gas, the largest—to boilers on anthracite. The

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environmental taxes component of LCOH is 0.1–5.1% of LCOH, the lowest values refer to boilers of 100 kW and natural gas, the largest—to boilers on anthracite. The cheapest is thermal energy from boilers on sunflower husk pellets and firewood, the most expensive—on wood pellets, gas and anthracite. The change in prices in 4 years has not changed the leaders, the production of thermal energy in boilers on wood pellets and natural gas has become somewhat cheaper. Thus, it is most expedient in Ukraine to use pellets from sunflower husk, straw briquettes and firewood, and the use of wood chips and wood pellets is not economically justified. Taking into account that the forest cover of Ukraine is low, it is advisable to replace the use of firewood with fuel from energy crops. The use of anthracite is economically justified, even though the largest for these boilers is the environmental tax component of LCOH. When deciding to buy a boiler, keep in mind that fuel prices change quite quickly, as well as environmental legislation, which significantly affects the cost of thermal energy. In addition, an important part of the cost of thermal energy is the logistics component, which can have a significant impact on the final calculations of projects. The most profitable projects are those that use waste of their own production as fuel. If there is no agricultural waste in the area in sufficient quantity for use as fuel, it would probably be reasonable to grow energy crops there, thus reducing impact of the significant logistical component.

References 1. Law of Ukraine on energy saving. Online: https://zakon.rada.gov.ua/laws/show/74/94-%D0% B2%D1%80#Text 2. Law of Ukraine on Alternative Energy Sources. Online: https://zakon.rada.gov.ua/laws/show/ 555-15 3. National Security Strategy of Ukraine. Online: https://zakon.rada.gov.ua/laws/show/287/2015 4. Strategy of sustainable development “Ukraine—2020”. Online: https://zakon.rada.gov.ua/laws/ show/5/2015 5. Lamers, P., Marchal, D., Heinimö, J., Steierer, F.: Global woody biomass trade for energy. Int. Bioenergy Trade 17, 41–63 (2014) 6. van Stralen, J.N.P., Uslu, A., Dalla, Longa F., Panoutsou, C.: The role of biomass in heat, electricity, and transport markets in the EU27 under different scenarios. Biofuels Bioprod. Biorefin. 7, 147–163 (2013) 7. Sustainable and optimal use of biomass for energy in the EU beyond 2020. Online: https://ec. europa.eu/energy/sites/ener/files/documents/biosustain_annexes_final.pdf 8. Bioenergy Europe. European Bioenergy Outlook 2018. Online: http://achbiom.cl/wp-content/ uploads/2019/02/STATISTICAL-REPORT-2018.pdf 9. Pravdyuk, N.L.: Formation of the bioenergy market in Ukraine and its information-analytical support. Ekonomika APK 5, 51–62 (2018). Online: http://nbuv.gov.ua/UJRN/E_apk_2018_5_8 10. Order of the Cabinet of Ministers of Ukraine “On the National Action Plan for Renewable Energy for the period up to 2020”. Online: https://zakon.rada.gov.ua/laws/show/902-2014-p 11. Morozov, R.V., Fedorchuk, Ye.M.: Assessment of the bioenergy potential of crop residues and energy crops in agriculture of Kherson oblast. Sci. Bull. Kherson State Univ. 10, 111–117 (2015)

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12. Geletukha, G.G., Zhelezna, T.A., Bashtovyi, A.I.: Energy and ecological analysis of technologies for energy production from solid biomass. Part 1. Ind. Heat Eng. 39, 58–64 (2017) 13. Zabarny, G.M.: Energy potential of non-traditional energy sources of Ukraine (2002) 14. Mattson, E.D., Neupane, G.: LCOH estimated from existing geothermal district heating systems in the U.S. Trans. Geotherm. Resour. Counc. 41, 171–179 (2017) 15. Li, H., Song, J., Sun, Q., Wallin, F., Zhang, Q.: A dynamic price model based on levelized cost for district heating. Energy Ecol. Environ. 4(1), 15–25 (2019). https://doi.org/10.1007/s40974019-00109-6 16. Bermúdez, C.A., Porteiro, J., Varela, L.G., Chapela, S., Patiño, D.: Three-dimensional CFD simulation of a large-scale grate-fired biomass furnace. Fuel Process. Technol. 198 (2020). https://doi.org/10.1016/j.fuproc.2019.106219 17. Karim, M.R., Bhuiyan, A.A., Sarhan, A.A.R., Naser, J.: CFD simulation of biomass thermal conversion under air/oxy-fuel conditions in a reciprocating grate boiler. Renew. Energy 146, 1416–1428 (2020). https://doi.org/10.1016/j.renene.2019.07.068 18. Molchanov, O., Krpec, K., Horák, J.: Electrostatic precipitation as a method to control the emissions of particulate matter from small-scale combustion units. J. Clean. Prod. 246 (2020). https://doi.org/10.1016/j.jclepro.2019.119022 19. Pawlak-Kruczek, H., Arora, A., Mo´scicki, K., Krochmalny, K., Sharma, S., Niedzwiecki, L.: A transition of a domestic boiler from coal to biomass—emissions from combustion of raw and torrefied palm kernel shells (PKS). Fuel 263 (2020). https://doi.org/10.1016/j.fuel.2019. 116718 20. Shi, Y., Liu, Q., Shao, Y., Zhong, W.: Energy and exergy analysis of oxy-fuel combustion based on circulating fluidized bed power plant firing coal, lignite and biomass. Fuel 269 (2020). https:// doi.org/10.1016/j.fuel.2020.117424 21. Syrodoy, S.V., Kostoreva, J.A., Kostoreva, A.A., Asadullina, L.I.: Ignition of wood and coal particle mixtures in conditions of steam and water boiler furnaces. J. Energy Inst. 93(2), 443– 449 (2020). https://doi.org/10.1016/j.joei.2019.07.007 22. Zhou, A., Xu, H., Xu, M., Yu, W., Li, Z., Yang, W.: Numerical investigation of biomass cocombustion with methane for NOx reduction. Energy 194 (2020). https://doi.org/10.1016/j.ene rgy.2019.116868 23. Zaporozhets, A.: Analysis of control system of fuel combustion in boilers with oxygen sensor. Period. Polytech. Mech. Eng. 64(4), 241–248 (2019). https://doi.org/10.3311/PPme.12572 24. Zaporozhets, A.O.: Methods and means for the control of the fuel combustion process. Control of fuel combustion in boilers. Stud. Syst. Decis. Control 287, 1–33 (2020). https://doi.org/10. 1007/978-3-030-46299-4_1 25. Zaporozhets, A.O.: Research of the process of fuel combustion in boilers. Control of fuel combustion in boilers. Stud. Syst. Decis. Control 287, 35–60 (2020). https://doi.org/10.1007/ 978-3-030-46299-4_2 26. Popov, O.O., Iatsyshyn, A.V., Kovach, V.O., Artemchuk, V.O., Kameneva, I.P., Taraduda, D.V., Sobyna, V.O., Sokolov, D.L., Dement, M.O., Yatsyshyn, T.M.: Risk assessment for the population of Kyiv, Ukraine as a result of atmospheric air pollution. J. Health Pollut. 10(25), 200303 (2020). https://doi.org/10.5696/2156-9614-10.25.200303 27. Iatsyshyn, A., Iatsyshyn, A., Artemchuk, V., Kameneva, I., Kovach, V., Popov, O.: Software tools for tasks of sustainable development of environmental problems: peculiarities of programming and implementation in the specialists’ preparation. E3S Web Conf. 166, 01001 (2020). https://doi.org/10.1051/e3sconf/202016601001 28. Maliarenko, O., Horskyi, V., Stanytsina, V.,·Bogoslavska, O.,·Kuts, H.: An improved approach to evaluation of the efficiency of energy saving measures based on the indicator of products total energy intensity. Stud. Syst. Decis. Control 298, 201–216 (2020). https://doi.org/10.1007/ 978-3-030-48583-2_13 29. Zhovmir, N.M.: Analysis of the regulatory requirements for the emission of pollutants during the combustion of biomass. Online: http://dspace.nbuv.gov.ua/bitstream/handle/123456789/ 59058/10-Zhovmir.pdf

184

O. Bogoslavska et al.

30. Nizhnikov, A.V.: Optimization of wood fuel combustion to reduce emissions of pollutants into the air. Online: https://lib.vsu.by/jspui/bitstream/123456789/18085/1/57-61.pdf 31. Bodnar, L.A.: Problems of bedrooming low-grade palishes in little sheet boilers. Online: https:// ir.lib.vntu.edu.ua/bitstream/handle/123456789/4724/336.pdf 32. Code of Good Practice for Solid Fuel Combustion and Small Combustion Plants. 8 March 2019. Effect of woodfuel combustion on emissions, air quality. Online: https://www.unece.org/filead min/DAM/env/documents/2019/AIR/WGSR/ECE_EB.AIR_WG.5_2019_4-1902864R.pdf 33. EN 303-5 Heating boilers Part 5: Heating boilers for solid fuels, hand and automatically stocked, nominal heat output of up to 300 kW—technology, requirements, testing and marking. Online: https://standards.globalspec.com/std/1539323/EN%20303-5 34. Calculation of emissions into the atmosphere during natural gas combustion. Online: http:// www.te.ukrstat.gov.ua/files/respondent/2tp.pdf 35. A.4 Small combustion. Online: https://www.eea.europa.eu/ru/publications/rukovodstvoemep-eaos-po-inventarizaciivybrosov-2016/chast-b-glavy-posvyaschennye-tehnicheskim-asp ektam/1-energetika/1-a-szhiganie/1-a-4-maloe-szhiganie/view 36. Tables for guidelines. Online: https://www.altstu.ru/media/f/Prilozhenie-2-tablicy.pdf 37. Levitska, O.G.: Comparative analysis of emissions of harmful substances in the use of alternative natural gas to biofuels. Visnyk Lviv State Univ. Life Saf. 20, 90–95 (2019). Online: http:// nbuv.gov.ua/UJRN/Vldubzh_2019_20_15 38. Alfa-Invest. Online: https://a-invest.com.ua/aktualno/tablitsa-teplotvornosti.html 39. GKD 34.02.305-2002 “Emissions of pollutants substances into the atmosphere from power plants”. Online: http://lib.sumdu.edu.ua/library/docs/rio/2005/m976.doc 40. Stepanov, D.V.: Technical and environmental indicators of water heating boilers with a capacity up to 100 kW. Online: http://stepanov.vk.vntu.edu.ua/file/5728d5f46070c9cceac53 0b94ff057aa.pdf 41. DSTU 2326–93. Heating water boilers with heat output up to 100 kW. General technical conditions. Online: http://online.budstandart.com/ua/catalog/doc-page?id_doc=56199 42. Heating hot-water boilers with capacity from 0.1 to 4.0 MW. General specifications. Online: http://docs.cntd.ru/document/1200030183 43. STB 1626.2-2006 Boiler installations. Plants functioning with biomass. Pollutant emission standards. Online: http://gost-snip.su/download/stb_16262_2006_ustanovki_kotelnie_ustano vki_rabotayuschie_na_biomasse_ 44. Technological standards of permissible emissions of pollutants into the atmosphere from boilers operating on sunflower husk. Online: https://zakon.rada.gov.ua/laws/show/z1023-09#Text 45. Zajac, G., Szyszlak-Barglowicz, J., Slowik, T., Wasilewski, J., Kuranc, A.: Emission characteristics of biomass combustion in a domestic heating boiler fed with wood and Virginia mallow pellets. Fresenius Environ. Bull. 26(7), 4663–4670 (2017) 46. Biomass for Heat and Power Technology Brief EA-ETSAP and IRENA 2015. Online: https:// www.irena.org/-/media/Files/IRENA/Agency/Publication/2015/IRENA-ETSAP_Tech_B rief_E05_Biomass-for-Heat-and-Power.pdf 47. Verma, V.K., Bram, S., Delattin, F., Laha, P., Vandendael, I., Hubin, A., De Ruyck, J.: Agropellets for domestic heating boilers: standard laboratory and real life performance. Appl. Energy 90(1), 17–23 (2012). https://doi.org/10.1016/j.apenergy.2010.12.079 48. Bala-Litwiniak, A.: Environmental and economic aspects of combustion of biomass pellets containing a waste glycerol. Combust. Sci. Technol. 1–11 (2020). https://doi.org/10.1080/001 02202.2020.1746774 49. Tax Code of Ukraine. Online: http://sfs.gov.ua/nk/rozdil-viii–ekologichniy-poda/ 50. Savchuk, S.: Together with the Bioenergy Association of Ukraine we are working out ways to exempt energy producers from biomass from paying CO2 emissions tax. Online: https:// www.kmu.gov.ua/news/sergij-savchuk-iz-bioenergetichnoyu-asociaciyeyu-ukrayini-opraco vuyemo-shlyahi-zvilnennya-virobnikiv-energiyi-z-biomasi-vid-splati-podatku-na-vikidi-co2 51. Open letter of the Bioenergy Association №467 dated “11” November 2019. Online: https:// uabio.org/wp-content/uploads/2020/02/uabio-letter-467-on-exemption-biomass-from-co2tax.pdf

Comparative Efficiency Assessment of Using Biofuels …

185

52. Proiefed Costs of Generating Electricity. International Energy Agency (2010) 53. Dubovskoy, S.V.: An improved method for assessing technologies for combined production of electricity and heat at the average cost of the life cycle. Probl. Gen. Energy 1(36), 46–54 (2014) 54. Gabbrielli, R., Castrataro, P., Del Medico, F., Di Palo, M., Lenzo, B.: Levelized cost of heat for linear fresnel concentrated solar systems. Energy Procedia 49, 1340–1349 (2014). https:// doi.org/10.1016/j.egypro.2014.03.143 55. Hassanzadeh, A., Jiang, L., Winston, R.: Coupled optical-thermal modeling, design and experimental testing of a novel medium-temperature solar thermal collector with pentagon absorber. Sol. Energy 173, 1248–1261 (2018). https://doi.org/10.1016/j.solener.2018.08.022 56. Hennessy, J., Li, H., Wallin, F., Thorin, E., Räftegård, O.: Economic feasibility of commercial heat-to-power technologies suitable for use in district heating networks. Energy Procedia 142, 1721–1727 (2017). https://doi.org/10.1016/j.egypro.2017.12.555 57. Huang, J., Fan, J., Furbo, S., Chen, D., Dai, Y., Kong, W.: Economic analysis and optimization of combined solar district heating technologies and systems. Energy 186 (2019). https://doi. org/10.1016/j.energy.2019.115886 58. Huang, J., Fan, J., Furbo, S., Chen, D., Dai, Y., Kong, W.: Economic analysis and optimization of household solar heating technologies and systems. Sustain. Energy Technol. Assess. 36 (2019). https://doi.org/10.1016/j.seta.2019.100532 59. Geletukha, G.: Preparation and implementation of projects to replace natural gas with biomass in the production of thermal energy in Ukraine. Practical guide (2015) 60. Bratukhina, E.A.: Influence of logistics costs on the cost of production of enterprises for the production of industrial equipment (2014). Online: http://e-koncept.ru/2014/54704.htm 61. Kuts, G.O.: Estimation of a condition and forecast of structure of fuel and energy for systems of heat supply of Ukraine taking into account regional features. Probl. Gen. Energy 4(51), 23–32 (2017). https://doi.org/10.15407/pge2017.04.023 62. Somov, V.V.: Analysis of the characteristics of heating and production boilers with the use of economic and mathematical methods. Scientific and practical issues of improving low-power thermal power plants. Issue 3. Improvement of boiler installations of MO objects (1996) 63. Sigal, I.Ya.: Modernization of gas boilers of thermal power plants, combined heat and power plants and boiler houses in accordance with the requirements of EU environmental directives. Energy Technol. Resour. Sav. 4, 61–71 (2017). Online: http://nbuv.gov.ua/UJRN/ETRS_2017_ 4_12

Cybersecurity and Computer Science

Requirements for a Taxonomy of Cyber Threats of Critical Infrastructure Facilities and an Analysis of Existing Approaches Maksym Komarov , Andrii Davydiuk , Alla Onyskova , Volodymyr Tkachenko , and Serhii Honchar Abstract Nowadays, information protection and cybersecurity are one of the urgent tasks defined by the normative documents of our country in the field of national security and defence. In a state of tense foreign policy conditions, information protection is an urgent problem of our society. This article presents a unified approach to the systematization of cyber threats. The essence of this approach is to eliminate uncertainties regarding the description of cyber threats. Based on the results of the analysis of existing works on this topic, the advantages and disadvantages of existing approaches are identified. It is important to note that the classification of vulnerabilities in information systems is currently developing rapidly, but the problem of classification of threats remains relevant. The vulnerability remains only a tool for realizing the existing threat. Given the possible risks of information security and cybersecurity of information systems and process control systems at critical infrastructure facilities, a study of known cyberattacks has been conducted. Based on the experience of the world community in the field of cybersecurity and information technology, the classification of methods of intrusion and types of attacks on information systems. Using a formalized approach with the help of drawings and diagrams, the vectors of attacks, the structure of the attack and the development of the information security incident are presented. The description of possible approaches to the development of cyber threat taxonomy is investigated and presented. The definition of key concepts is given. The results of this study can be useful for assessing information security risks, in particular in determining the criticality of the organization’s assets, in creating information security management systems and developing appropriate technical regulations to ensure the continuity of business processes of the organization.

M. Komarov (B) · A. Davydiuk · A. Onyskova · V. Tkachenko · S. Honchar Pukhov Institute for Modelling in Energy Engineering, National Academy of Sciences of Ukraine, 15, General Naumov Str., Kyiv 03164, Ukraine e-mail: [email protected] A. Davydiuk e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_11

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Keywords Cybersecurity · Taxonomy · Vulnerability · Cyber threat · Classification · Attack

1 Introduction The constant increase in the number of cyberattacks necessitates the creation of organized or synergistic structures that provide up-to-date information on identified information security vulnerabilities, their prompt elimination, the creation of detection and intrusion prevention systems (active and passive audit systems—Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Security Information and Event Management (SIEM), Next Generation Firewalls (NGFW), etc. There are very large arrays of information (Big Data) on current cyberattacks and vulnerabilities in information and telecommunications systems. However, often this information (especially regarding attacks) is very heterogeneous, unstructured and of little use for further analysis. As a result, there is a need to develop models and tools that are intended to organize and systematize the accumulated knowledge, i.e. the need to create the taxonomy that will provide a more accurate assessment of existing cyber risks. In addition to a meaningful and systematic description of cyberattacks, in practice the taxonomy of attacks is required for their further analysis in order to accumulate knowledge in risk assessment and create models of threats and violators at the design stages of critical systems, including automated process control systems (hereinafter—APCS) critical infrastructure facilities (hereinafter—CIF), as well as for the development of security policy, the creation of active audit tools and building an information security management system [1–3]. The word “taxonomy” comes from the Greek taxis—order and nomos—law. According to the definition given in [4], “Taxonomy is an abstract structure of categorized instances, which includes a comprehensive study of the subject area and the creation of a theoretical model of the complete set of studied objects, which allows to determine the features that can be used as a basis classification”. The modern strict definition of taxonomy can also be found in the dictionary of standards in electrical engineering and electronics [5]: “Taxonomy is a classification scheme that divides the body of knowledge and determines the relationship of parts.” The definition of the term “attack” is also formulated in [4]: “Attack—a sequence of actions taken by anyone to achieve an unauthorized result, i.e. actions aimed at violating the rules of operation of the system, which are set by its owner.” A subject that performs such actions will be called an attacker, and a system that is attacked will be called an object of attack. In order for a taxonomy to be able to solve basic problems, it must meet certain natural and reasonable requirements. Lists of such requirements are set out in many attacks classification works. These requirements are not absolutely clear and it is difficult to fully satisfy them all. In practice, the taxonomy is mostly a compromise between them. Here are the basic requirements for taxonomy.

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• Mutual exclusion. The taxonomy should be arranged so that the choice of one category excludes all others. That is, the categories of taxonomy, as attributes (identifiers) of sets consisting of attacks belonging to them, do not intersect. This requirement is necessary to give meaning to the concept of “attack class”. • Completeness. The taxonomy covers all possible attacks and allows them to be classified. It is a natural requirement that a taxonomy cover the entire field of computer attacks, not just some part of it. Based on these two requirements, it can be concluded that the classification categories should form a breakdown of the set of attacks. • Determinacy. The procedure itself (or classification scheme) by which attacks can be classified must be clearly defined. • Clarity of terms. All terms used in the taxonomy should be clearly defined and explained so that there are no misunderstandings or discrepancies in the understanding of a term. • Objectivity. In the taxonomy should be considered only those information about the attack, which can be obtained due to the properties of the object as a result of unbiased observation. • Usefulness. The taxonomy should be a system that can be used to obtain information about the field of study. For example, given the attack class, you can get constructive information about the attack itself. • Comprehensibility. The taxonomy should be accessible for understanding not only by professionals, but also by individuals who are not experts in the field of information security. • Unambigious. Each category must be defined very clearly to avoid ambiguity in defining the categories to which the attack should be attributed. • Conforming. The terminology used in the taxonomy should be consistent with generally accepted terminology in the field of information security. • Repeatability of results (repeatable). When classifying the same object by two different persons, the same results must be obtained. As you can see from the list, some requirements intersect in content, some are a consequence of others. Therefore, it is advisable to combine or remove some requirements. Finally, content requirements can be divided into two groups: 1. 2.

basic—requirements directly to the meaning and structure of categories; secondary, which relate mostly to the form of presentation of the taxonomy. After making such changes, the list of requirements will look like this: a. b.

basic requirements: mutual exclusion, completeness, usefulness, determinacy, objectivity, extensibility; additional requirements: clarity of terms, accessibility (comprehensibility), conforming.

Another, new requirement has been added to the basic requirements—extensibility (or the possibility of expansion). Its essence is that the structure of the taxonomy must, firstly, allow the possibility of adding new categories, and secondly, that they are organically incorporated into it, namely that their introduction required minimal

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changes in the basic framework of the taxonomy. This is an important requirement because the information and telecommunication sphere is developing very dynamically, new technologies are constantly appearing and, as a result, new methods and technical means of carrying out attacks. Therefore, it is impossible to develop a taxonomy (quite detailed), which would not require improvements and changes over time.

2 Literature Analysis and Problem Statement There are several approaches to the problem of classifying cyberattacks. Traditionally, attacks are divided into categories depending on the effect they create: violation of information confidentiality, violation of information integrity and denial of service (violation of information availability). The danger of threats and attacks in APCS is determined by assessing the possible consequences of their implementation from the standpoint of impact on the operation of APCS, and the severity of such consequences—the risk factor of this attack, which is determined by expert method. In this case, one of the options is to classify the severity of possible consequences according to the following indicators [6]: • physical impact—impact on the population, the number of injured, dead, injured, as well as the number of evacuees); • economic impact—the impact on GDP, the amount of economic losses, both direct and indirect; • environmental impact—impact on the population and the environment; • political influence—influence on the confidence and efficiency of government; • interconnection with other elements of critical infrastructure and duration of impact. Given the above categories of impacts, violations of information security of critical infrastructure facilities, it is possible to list the consequences of these impacts: • • • • • • • • • •

violation of national security; facilitating the commission of an act of terrorism; loss or reduction of production; injuries or deaths; damage to equipment; release (leakage, evaporation) or theft of dangerous materials; environmental damage; criminal or civil obligations; loss of private or confidential information; loss of brand image or customer trust.

It should be noted that the elements of this list are not independent. Obviously, one effect can lead to another.

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Each threat is characterized by the probability of its implementation and the damage caused by it. Thus, the indicator of the urgency of the threat of CIF will be proportional to the probability of realization of this threat and the coefficient of its danger. In the classification of threats it is possible to distinguish two of their most important types: – the intention to cause harm, which manifests itself in the form of the announced motive of the subject; – the possibility of harm—the existence of sufficient conditions and factors. The peculiarity of the first type of threats is the uncertainty of the possible consequences, the ambiguity of the question of whether the threatening subject has the forces and means sufficient to carry out the intention. The possibility of harm lies in the existence of sufficient conditions and factors. The peculiarity of threats of this type is that the assessment of the potential of a set of factors that can serve to transform these opportunities and conditions for harm, can be carried out only by the subjects of threats.

3 Purpose and Objectives of the Study The purpose of determining the relevance of information security threats is to determine whether there is the possibility of violating the confidentiality, integrity or availability of information contained in the CIF, and whether the violation of at least one of these information security features will lead to unacceptable damage. In the process of identifying threats to information security at all stages of the life cycle of information systems, it is necessary to regularly identify sources of threats, assess their capabilities and identify threats to information security on this basis. Data on violators and their ability to implement information security threats, obtained by identifying sources of threats, are included in the model of information security threats. The analysis shows that to identify information security threats in the CIF it is necessary to determine: – sources of threats: opportunities (type, potential) of violators; – vulnerabilities that can be used in the implementation of information security threats; – favorable conditions for the implementation of information security threats; – assets: objects of influence of CIF on which the threat to information security is directed; – threat risk factor: the result and consequences of the implementation of information security threats.

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The threat to the security of information circulating on the CIF will be considered relevant if for the specified CIF with the specified structural and functional characteristics and features of functioning there is a probability of realization of the considered threat by the violators with the corresponding potential and its realization will lead to unacceptable losses from violation of confidentiality, integrity or availability of information. This is due to the fact that in automated CIF systems there is a fairly close relationship between automated systems and physical processes and actuators. Therefore, information security breaches in these systems can have consequences in the industrial sector. Given the above, the risk of threat in automated CIF systems from many threats will be determined by assessing the possible consequences of its implementation from the standpoint of impact on the operation of automated CIF systems, and the severity of such consequences—the risk factor of this threat. The probability of threat realization can be determined on the basis of the analysis of statistical data on the frequency of realization of information security threats (occurrence of security incidents) in automated CIF systems and/or similar systems. In the absence of such statistics, the relevance of the threat is determined by assessing the possibility of realizing the threat to information security, which, in turn, is determined by assessing the level of security of the automated CIF system and the potential of the violator required to implement this threat. The risk factor for the threat can be determined based on an assessment of the extent of the consequences of the breach of confidentiality, integrity or availability of information in automated CIF systems.

4 Research Methods The relevance of information security threats is determined in relation to threats for which the following is determined by the expert method: – the opportunities (potential) of the violator are sufficient to realize the threat to information security; – in the automated system of CIF there are potential vulnerabilities which can be used at realization of a certain threat to information security; – structural and functional characteristics and features of the automated system of the CIF do not exclude the possibility of using the methods necessary to implement a particular threat, i.e. there is a scenario of threat implementation; – the implementation of the threat to information security will lead to a violation of confidentiality, integrity or availability of information, which may result in unacceptable negative consequences, causing significant harm.

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5 Research Results The sources of information on the initial data on information security threats and their characteristics can be basic and standard models of information security threats, defined by regulations for different classes and types of automated systems. The main disadvantage of this division is its poor informativeness (and, consequently, applicability), because information about the class of attack is almost impossible to say about its features. However, it is clear that the effect of the attack is an important property and this parameter in one form or another is used in many taxonomies. Another approach to the classification of cyberattacks is the classification of vulnerabilities in hardware and software of information and telecommunications systems. One of the first works in this direction is the work [7]. Partial distribution by type of vulnerability was used in [4]. Later, this approach was continued. In [8] a rather detailed classification of vulnerabilities is given. However, this approach is too narrow and often does not fully reflect the nature of the attack, so it is used mainly only for special classes of tasks (for example, when testing software). One possible approach is a division based on the initial access possessed by the attacker. The most famous example of such an approach is the Anderson matrix [9], who proposed to base the classification on the possibility or impossibility of an attacker’s access to a computer or its component. Therefore, the category to which the attack belongs depends on the initial privileges of the attacker. Therefore, you can make the following matrix 2 × 2 (Table 1). This matrix indicates that all attacks are divided into three categories, because this is not possible when the attacker does not have access to the computer (such access is not allowed), but he is allowed to use data stored on the computer, and run programs. Category B is divided into three subcategories, depending on the attacker. Thus, the full list of categories looks like this: 1. 2.

External invasion. Internal intrusion: a. b. c.

Wrong user (masquerader). Legal user (legitimateuser). Hidden user (clandestineuser).

Table 1 Anderson’s matrix The attacker has no right to launch (use) the program (information) The attacker does not have access to the computer

Category A External invasion

The attacker has access to computer

Category B Internal invasion

The attacker has the right to launch (use) the program (information)

Category C Abuse of power

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Abuse of power.

The difference between a wrong user, legal and hidden, is that the wrong user is disguised as a legal user and, for example, from the point of view of the system, is no different from him. The hidden user acts to remain unnoticed by detection mechanisms or to avoid them in some way. For example, if an attacker was able to find out the password of a legal user and used it to gain access, he acted as a wrong user. If he replaced part of the system files to gain access, he acted as a hidden user. Following the further development of approaches, it is possible to notice that authors of some works tried not to reject any properties and parameters of attacks, and to make the general list of types of attacks. The most famous works in this direction are works [10–12]. In general, the same ideas were used later [12]. The undoubted advantage of the approach based on the selection of a list of typical attacks is the fulfillment of the usefulness requirement, because in most cases the type of attack provides much more information than knowledge of any of its properties. At the same time, the scope of such an approach is very limited, because when using it, it is extremely difficult to meet the first two significant requirements—completeness and mutual exclusion. As a result, such lists usually contain categories of attacks that intersect significantly, and the question of their completeness remains open. There are nine main categories of invasion techniques used to develop attack types (Table 2). Categories of intrusion techniques: 1. 2. 3. 4. 5. 6. 7. 8. 9.

External. Hardware. Disguise. Malware. Bypassing security mechanisms. Active abuse. Passive abuse. Inert abuse. Indirect abuse.

The first way to classify attacks is to analyze all parameters separately and consider them independent. This approach is implemented in [13], which uses the so-called concept of “measurements”. Its main idea is that the properties of the attack are stratified into several independent dimensions, each of which has its own list (or tree) of categories. In [13], the taxonomy of network and computer attacks was proposed, which is based on a method of dividing attack parameters into several dimensions and four main dimensions and several auxiliary ones. Consider these dimensions: • The first dimension is a list of types of attacks (for example, denial of service) (Table 3). • The second dimension is the target of the attack. If the attack has several objects of attack, then in this dimension there should be several records (Table 3).

Requirements for a Taxonomy of Cyber Threats of Critical … Table 2 Types of attacks

Attack

197 Attack content

External Visual observation

Monitoring the keyboard or monitor

Deception

Deception of operators and users

Garbage collection

Extract information from virtual baskets

Hardware Logical recovery

Extract information from discarded or stolen media

Listening

Data interception

Intervention Physical attack

Destruction or damage to equipment, power supplies

Physical removal

Removal of equipment and data warehouses

Disguise Imitation

Use of false identifiers

Usurpation of communication lines or hosts Attacks with substitution of parameters Network confusion

Masking the physical location or route

Malware

Creating the possibility of further malicious actions

Trojan horses

Introduction of malicious code

Logicbombs

A variety of Trojan horses

Worms

Mastering distributed resources

Viruses

Attachment to programs and reproduction

Bypass

Bypassing security mechanisms

Exploitation of vulnerabilities Hacking passwords Active abuse Basic (continued)

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Table 2 (continued)

Attack

Attack content

Incremental attacks

Gradual escalation of privileges, slow progress towards the goal

Denial of service

Making massive attacks

Passive abuse Review

Random or custom search

Data collection and output

Database use and traffic analysis

Hidden channels

Use of hidden channels or other ways of information leakage

Inert abuse Indirect abuse

• The third dimension is the vulnerabilities used in the attack. This dimension usually contains CVE-descriptions of vulnerabilities (Common Vulnerabilities of Exposures), if several vulnerabilities are used, then there are several records in this dimension. • The fourth dimension is, in fact, the result or purpose of the attack. We will describe all these measurements in more detail (Table 4). The third dimension, as mentioned above, contains standard descriptions of vulnerabilities used in the attack. Therefore, it eliminates the need to describe any common scheme, as for the first two. The fourth dimension classifies attacks that are carried out not only to achieve the main goal. For example, the worm, in addition to its direct purpose—infecting the computer, can be used for remote control or destruction of some files. The fourth dimension has five categories: 1. 2. 3. 4. 5.

direct (nominal) purpose of the attack; violation of the integrity of information; violation of information confidentiality; capture of resources; gaining control over part of the system for further use.

As Hansman [13] notes, other dimensions can be added to improve and develop taxonomy. As options for further detailing, it offers the following categories: – damage—describes the damage caused by the attack; – cost of recovery—describes the total cost of system recovery after the attack to the initial state; – spread—describes the speed and method of attack spread (for spreading attacks, such as viruses and worms); – methods of protection.

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Table 3 The first dimension of cyberattacks Types of threats

Contents of threats

Viruses

Infectious files

Means of realization of threats

Infectious system (boot) sectors Macroviruses Worms

Use mass mailing Recognize the state of the network

Trojans

Logic bombs

Buffer overflow

Stack overflow

Denial of service

Local (hostbased)

Exhaustion of resources

Networkbased

TCP flood

Storage overflow Failure UDP flood ICMP flood Distributed Network attacks

Substitution of packages Interception session Wireless attacks

Hacking of cryptographic algorithms of wireless networks

Web application attacks

Using malicious web scripts (CrossSiteScripting) Selection of parameters Use of incorrect cookies Database attacks Using hidden fields

Physical attacks

Simple Energy weapons:

HERF LERF EMP

Van Eck Password attacks

Guessing

Brute force and dictionary attacks

Exploiting implementation vulnerabilities Attacks—gathering information

Listening Network structure detection Scanning

Listening to packages

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Table 4 The second dimension of cyberattacks A target object

Types of equipment

Hardware

Hardware

Computer

Hard drives

Network equipment

Hub

Software/protocols

Versions

Subversion

Cable Peripheral equipment

Monitor Keyboard Mouse Printer

Software

Operating System

Windows family

Windows 10 Windows Server 2016

Linux family

Linux

2.2

FreeBSD

4.8

MacOS family

MacOS X

10.1

Server applications

Databases

2.4 5.1

10.2 Applications

Mail server Web server

IIS

4.0

Text Editor

MS Word

2000

5.0 Custom application

XP Email client Network

Protocol

Transport level

IP

Network level TCP

The second method, based on the same idea, is more flexible because it uses a treelike structure of top-level categorical classes. The implementation of this approach is described in [13], which followed a structural approach to classification. The taxonomy developed by the authors of [14] is a tree whose root is invasion (Fig. 1). The edges of this tree have labels and carry a semantic load: for example, from the

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Intrusion It was the result for Violation of Сonfidentiality Effects Probing Input

Originates from

Getting a Root Privilege

Denial of Service Remote Access

Aimed at

Location

Using the OS

System Component

Kernel Level

Module

Other Network Protocol

Application

Network

Locally Meaning

Root

ТСР/ІР User

IP Level

TCP Level

UDP Level

UDP Package Login Verification Error

Buffer Overflow

Addressing Error

TCP Package

ICMP Package

Exploit

Integrity Violation

Invalid Input

Use Delays

Serialization Error Exceptional Condition

Fig. 1 Scheme of invasion

root of the tree come two edges, which mean “done with” and “little result”. The dashed arrow means the relationship “is a subclass” between the vertices it connects. The use of such methods allows to obtain a more detailed description of the attack, but does not allow to display some of its structural features and scenario. This circumstance is a significant drawback given the constant improvement of modern defense systems and current trends towards increasingly sophisticated and sophisticated methods of attack. The third method is to combine application properties with structures. This approach was used in [4]. Its main idea is to introduce a hierarchy of concepts: the main concept is “incident”, it includes the concept of “attack”, and the concept of “attack” includes the concept of “action”. An incident can consist of several attacks, and each attack of several actions.

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OS—Operating System; Root—account of the main administrator (superuser); TCP/IP—stack of protocols, network model of data transmission, presented in digital form; IP (Internet Protocol)—routing protocol of the network layer of the TCP/IP protocol stack; TCP (Transmission Control Protocol)—the protocol of data transmission of the transport layer of the reference model OSI; UDP (User Datagram Protocol)—protocol of user data grams; ICMP (Internet Control Message Protocol)—protocol of Internet control messages.

J. Howard and T. Longstaff not only created the taxonomy, but also developed the “Common Language for Computer Security Incidents.” “This common language is not an attempt to create a comprehensive glossary of computer security terms. Instead, we have created a minimal set of high-level terms, along with a structure that reflects their relationship (taxonomy)” [4]. The taxonomy developed by these authors is presented in the form of a diagram in Fig. 2. The main concept in this taxonomy is incident, because the authors divide two concepts—incident and attack. The concept of incident includes the attacker, the attack and the purpose of the attack. An attack is an entity that is directly related to the attack process: the tool, the vulnerability, the action, the target object, and the unauthorized outcome. A tool is a tool used by an attacker in an attack. The set of action and target object is called an event.

6 Discussion of the Results The opposite of these approaches is the attempt to classify attacks by the associated direct and indirect risks. This approach is based on the classification of asset values and related threats. By identifying critical assets for an organization’s processes, you can determine the direct and indirect losses that will result from damage to those assets. Direct risks include loss of information, and indirect risks include reputational damage. Given that the magnitude of direct and indirect losses may differ, it is advisable to make an assessment, taking into account the resources of the organization, to address the existing risks. A risk-based approach, based on the analysis of connections in the system and the division of these connections into direct and indirect, will allow optimizing the system to address existing risks. The presence of the taxonomy presented in this article will make it possible to determine the optimal security solutions already at the design stage of the system, which will significantly save money and increase the level of protection. The proposed taxonomy differs from those described in [13, 14] in that it contains structural elements: incident, attack, event and the ability to combine these events. So, a sequence of attacks can

Copying

Script or Program

Autonomous Agent

Tool Environment

Distributed Tools

Data Sets

Corporate Hackers

Professional Attackers

Vandals

Observers

Fig. 2 Scheme of the incident

Reading

Configuration

Custom Team

Terrorists

Delete

Change

Theft

Substitute

Bypass

Authentication

Flood

Scanning

Realization

Information Exchange

Spies

Probing

Development / Design

Physical Attack

Hackers

Action

Vulnerability

Tool

Attackers

Attack

Incident

Event

Internet

Network

Computer

Component

Information

Process

Account

Target Object

Financial Losses

Information Conversion

Capture Resources

Destruction

Political Losses

Disclosure of Information

Denial of Service

Competition, Authority

Goal

Getting Access

Unauthorized Result

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be embedded in one incident. This property to some extent allows us to describe non-atomic (multi-pass) complex attacks and take into account their scenario.

7 Conclusions The requirements for the taxonomy of cyber threats, the analysis of existing work on this topic, as well as the possible approaches to the development of the taxonomy of cyber threats will allow to systematize knowledge in this area. These developments and existing approaches to the creation of taxonomies of cyber threats will form the basis of a universal domestic taxonomy of cyber threats of information processed at critical infrastructure facilities.

References 1. Komarov, M., Honchar, S.: Method of constructing information security management system for critical infrastructure. Model. Inf. Technol. 81, 12–19 (2017) 2. Mokhor, V., Honchar, S., Dybach, O.: Methods for the total risk assessment of cybersecurity of critical infrastructure facilities. Nucl. Rad. Saf. 2, 4–8 (2019) 3. Maevsky, O., Artemchuk, V., Brodsky, Y., Pilkevych, I., Topolnitsky, P.: Modeling of the process of optimization of decision-making at control of parameters of energy and technical systems on the example of remote earth’s sensing tool. Stud. Syst. Decis. Control 298, 111–122 (2020) 4. Howard, J.D., Longstaff, T.A.: A common language for computer security incidents. Sandia Report, Sandia National Laboratories (1998) 5. Radatz, J. (ed.): The IEEE Standard Dictionary of Electrical and Electronics Terms, 6th ed. Institute of Electrical and Electronics Engineers, New York (1996) 6. Council Directive 2008/114/EC: On the identification and designation of European critical infrastructures and the assessment of the need to improve their protection. http://eurlex.eur opa.eu 7. Attanasio, C.R., Markstein, P.W., Phillips, R.J.: Penetrating an operating system: a study of VM/370 integrity. IBM Syst. J. 15(1), 102–116 (1976) 8. Vijayaraghavan, G., Kaner, C.: Bug Taxonomies. Star East 2003, Orlando, FL (2003) 9. Anderson, J.P.: Computer security threat monitoring and surveillance. Technical Report Contract 79F296400, Washington (1980). https://csrc.nist.gov/csrc/media/publications/confer ence-paper/1998/10/08/proceedings-of-the-21st-nissc-1998/documents/early-cs-papers/and e80.pdf 10. Neumann, P.G.: Computer-Related Risks. ACM Press/Addison Wesley (1995) 11. Parker, D.B.: Computer Crime Criminal Justice Resource Manual. U.S. Department of Justice National Institute of Justice Office of Justice Programs (1989). https://www.ncjrs.gov/pdffil es1/Digitization/118214NCJRS.pdf 12. Parker, D.B.: Computer Security Reference Book. Chapter 34, Computer Crime, pp. 437–476. CRC Press, K.M. Jackson and J. Hruskh, U.S. Associate Editor Donn B. Parker, Boca Raton, Florida (1992) 13. Hansman, S.: A Taxonomy of Network and Computer Attacks Methodologies. University of Canterbury, New Zealand (2003). http://nzcsrsc08.canterbury.ac.nz/research/reports/Hon sReps/2003/hons_0306.pdf

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14. Undercoffer, J., Pinkston, J.: Modeling Computer Attacks: A Target-Centric Ontology for Intrusion Detection. University of Maryland, Baltimore County. https://www.csee.umbc.edu/ ~finin/papers/ijcai03OntologiesIDS.pdf

A New Task Scheduling Algorithm for GRID Systems with Non-alienable Resources Taras Uzdenov

Abstract This article is devoted to such an important topic today as GRID-systems. The general concept of such systems is formed, the basic characteristics and properties allowing to name the GRID system are resulted. Explains the basic principles of building a GRID. Difficulties and problems faced by developers in building such systems. It is explained which systems can be called GRID systems with nonalienable resources. The analysis of existing algorithms of planning of GRID systems which are constructed on the basis of not alienable resources is carried out. Static and dynamic planning methods are distinguished. The most used planning algorithms are listed. The statement of the dispatching problem is described. In addition, this publication describes a new approach to solving the problem of scheduling task flows on processes or GRID nodes of a system with non-alienable resources. The proposed approach is universal and allows to plan tasks for both static and dynamic systems. A new scheduling algorithm, developed on its basis, is also presented. In addition to the company, another algorithm is proposed, which gives very good results for large problems that can be parallelized. Keywords GRID · Task scheduling · Non-alienated recourses · FCFS · SJF · SJN · RR · Priority planning · Min-Min · MET · Max-Min · FBA · FSA

1 Introduction The idea of GRID is not new, it appeared in the last century, when it became possible to combine different PCs into groups under one program, or as such groups are called, clusters. But time does not stand still, the number of tasks grows, the number of resources also grows.

T. Uzdenov (B) State University Zhytomyr Polytechnic, Zhytomyr, Ukraine e-mail: [email protected] Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_12

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Fig. 1 Schematic representation of GRID-system

The power of tasks also increases and the cluster becomes insufficient to solve all tasks, then the idea to combine not only PCs but also clusters, not only those that are in one place, but also those that are geographically distant from each other one. However, the location may be different, even in different countries. As a result, in the mid-1990s the concept of GRID appeared [1]. So GRIDs are geographically distributed computing resources, or more simply, computers that are integrated into a single computing system. Such systems are created to solve problems that require large computing resources and cannot be solved, in a reasonable period of time, on one or a number of PCs clustered. GRID systems can include any computing resources. Be it a personal computer, laptop, server, or entire software and hardware complexes. They can be called a general concept—a node of the system. In this case, each node can work under the guidance of different operating systems and software. GRID unites various organizations, scientific institutions, universities around the world, which have a need for large computing resources and their own resources are not enough to solve their own problems. Schematically GRID-system shown in Fig. 1.

2 Characteristics of GRID Systems As mentioned above, GRID systems must meet certain requirements and features, in order for such a system could be called GRID. There is enough information on this

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topic, so this article highlights the most important, according to the author, the characteristics and principles of GRID. According to [1], a list of GRID characteristics is formed: • Diversity. The system can use computing modules (PCs) of different power, architecture, with different software and hardware (cross-platform). Ideally, the ability to connect any device that has a microprocessor, memory and communication channel. • Scalability. The ability to expand to unlimited sizes, i.e. the necessary subsystem that could control and manage a large number of geographically remote resources. It should be possible to identify and connect new resources to an existing system, roughly speaking, it should be “rubber”. • Dynamics. The ability to monitor the status of the task. GRID calculations often fail due to the fact that resources are geographically remote, communication channels are usually reliable, but still, for one reason or another, network crashes occur. Therefore, need a subsystem that can restore the node work or reallocate the task from the node in which the failure to other resources. • One interface. Although a GRID system is a collection of geographically remote PCs, the user sees it as a single personal computer or as a virtual machine. • Security. The system must implement a reliable mechanism for data transmission and storage, it is necessary to prevent the loss of calculation results. And in case of loss, the ability to recover from a database or archive, a possible version of the mirror or any other saving mechanism. The main thing is that the input data and calculation results are not lost. • Geographical Distance of Nodes. Resources must be geographically distant from each other, as described above, even in different countries or even continents. • Multiple Administrative Domains. Since the resources in GRID belong to different organizations, there should be several monitoring points for different owners. Different owners have their own sharing policies and access models. The mechanism should be able to cope with decentralized monitoring and synchronization of calculations from different organizations. • Resource Exchange. Each member of the GRID system must be able to work with the resources of other participants, according to a schedule, or priority, or any other scheme. In the list above, only the most important features of GRID systems are listed. Although it is much wider. A more detailed description can be found here [1–6].

3 Non-alienability of Resources for the GRID The list given in Sect. 2 characterizes the GRID system as a whole, but it does not have such an important characteristic as inalienability. What Implied? The fact is that the calculations, as mentioned above, would be good to perform on any device that has a processor and memory, and of course the communication channel. For

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example, connect to the GRID-system not only PC, supercomputer and cluster but also a mobile phone, tablet, monitor, printer, etc. But users will not be very happy when their device is loaded to work in the time when they need to work with it. Therefore, I would like to consider and add to the list of characteristics such a property of GRID as inalienability. Although this is one of the properties of GRID and it would be logical to describe it in the previous section, but since the author’s work is related to this characteristic, it was decided to allocate this issue in a separate section. And also in order to focus the reader’s attention on this property. Thus, inalienability: it is the ability to use resources connected to the GRIDsystem in parallel with the owner of these resources, and the number of resources that can be used for GRID must be set programmatically on the client side. That is, the user must decide how many of his resources he can allocate and when they can be used in GRID. It should be possible to change this characteristic. For example, there is a user who has a regular laptop or desktop PC that he can allow to work in GRID while he is not using it, say from 18-00 to 9-00 on weekdays and all day on weekends. Or he can allow you to use 10% of the power of his device for the GRID at any time of the day or night. But this should be decided only by the user, and therefore in a GRID-system with inalienable resources, a mechanism must be implemented that would implement the principle of inalienability. In addition, it would be very nice if user could specify what types of tasks the user allow solve on his device. Since tasks can be different in their characteristics, some may require a large bandwidth of the communication channel, and others only need a processor and memory, and do not require the transfer of large amounts of information to solve. For example, suppose you have the following nodes: • PC1 based on Intel Core i5 (3.7–4.6 GHz)/RAM 8 GB—communication channel with data transfer rate—256 Kbps. • PC2 based on Intel Celeron N4000 (1.1–2.6 GHz)/RAM 4 GB—communication channel with data transfer rate—100 Mbps. And there is such a task: to extract information from the WinRar archive, while the size of the archive is 5000 MB. Since PC1 is more powerful, it can be assumed that it will cope better with these types of tasks, but it has a rather slow communication channel and the time for data transfer and results will be much longer than the time of work. In addition, the already slow communication channel will be 100% loaded, which will cause inconvenience to the PC user. Perhaps it would be better to solve such a problem on PC2. The above task is purely theoretical, and as simple as possible for the reader to understand. Formed only in order to develop a correct understanding of this problem. Although on GRID systems it is possible to solve such tasks. These features should be taken into account, and divide the tasks into types, and give the user the opportunity to decide what types of tasks he is ready to accept and solve.

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Or implement a mechanism that could determine which types of tasks are best solved on a particular PC and divide the nodes by type. Thereby making life easier for the user who may simply not be competent in the matter.

4 Problems and Tasks in Building GRID Systems GRID is a rather complex system that is not so easy to implement and there are a number of problems and tasks faced by the developers of such systems [1, 7, 8]. The following is a list of the most important: • Not Standardization. There is no single standard for system architecture and software that will manage it. On the one hand, this “unleashes the hands” of developers, as they can build the system as they see fit, and on the other hand, in this case, there is a problem of synchronization with other systems and software exchange. • Software Availability. Currently, there is very little software with which to build a GRID. In addition, it is possible to use it only after obtaining a license. • Many Tasks. It is difficult to implement the use of the same resources for different types of tasks, especially in their geographical distribution. • Difficult Development Process. It is difficult to develop software, given the different architecture and different platforms on which the system resources run. In addition, it requires highly qualified developers. • Narrow Scope. As a rule, services are developed for specific tasks as it is problematic to implement the use of the same resources for different purposes. • It is Difficult to Administer and Manage. Given the geographical distribution and decentralization, it is difficult to manage the system. • Problem with Resource Exchange. When providing resources for a particular type of service, it is problematic to use them for others. • Trust and Security Issues. The issue is complex, and rather not technical, but a human factor, although the implementation of reliable mechanisms for encryption, information security, authentication, is not an easy task. • Ambiguous Business Model. There is no specific business model for GRID systems at all. • Task Planning. The task is complex and requires an individual approach to solving, depending on the type of tasks, type and diversity of resources. Actually, this issue will be further considered in this paper. • Fault Tolerance. As mentioned above, GRID it is a rather complex system, which involves geographically remote and heterogeneous resources and therefore to maintain it in working order is difficult, it is difficult to restore work. Effective mechanisms are needed that can cope with these tasks. In any GRID-system, there must be a scheduling mechanism that deals with the question of what tasks to send to which node, in order to minimize the execution time of the computational process. This is probably the main problem in building a

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GRID. The features of this process, its types, goals, methods, etc. will be discussed in the next section.

5 Tasks Scheduling Dispatching—(from the English. Dispatch—quickly perform)—the process of centralized operational control and remote control, using the operational transfer of information between the objects of dispatching and control point [9]. As mentioned above, in GRID systems, a scheduling mechanism or, as it is also called, planning must be implemented. It is necessary for the distribution of tasks for execution between the nodes of the system, in order to minimize the execution time and balance the load of the system. It is necessary to prevent a situation in which one node would be overloaded while the other is idle. Process scheduling in GRID-systems is quite a difficult task and currently there is no clear and unambiguous solution [10]. This feature inspires researchers from around the world to find new solutions for certain systems. Many improvements of existing algorithms have been implemented on this topic, as well as many new ones have been developed. You can get acquainted with the works on the topic of dispatching by following the links [11–25]. Like any mechanism, the manager works according to a predetermined algorithm. Different algorithms are used for different systems, depending on different features, both the algorithm itself and the system on which it should work. Currently, there are two main approaches to the development of task manager for GRID-systems with inalienable resources [26, 27]: • Static scheduling. • Dynamic scheduling.

5.1 Static Scheduling Static algorithms are used in systems where it is known in advance how much work and on which devices will need to be performed, i.e. the system does not change. In this case, the manager makes a plan or schedule, after which the system begins execution. This approach has a number of advantages and disadvantages. In this case, the system is quite predictable and does not require large planning costs. But at the same time the system is not flexible and the arrival of new tasks requires re-planning. For GRID-systems with inalienable resources, this problem adds to the fact that it is possible not only to increase the tasks, but also to change the resources involved. At any time, the connection with the currently used resource may be severed, or the arrival of new ones. Which requires an immediate change in the implementation

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plan and, as described above in the features of such systems, the redistribution of resources. In addition, such an approach requires the availability of all information about the tasks to be performed (time, deadlines, workload, etc.), but it is not always available.

5.2 Dynamic Scheduling Dynamic algorithms are used in systems where it is not known in advance how much work and on which devices will need to be performed, i.e. the system is constantly changing. In this case, it is possible to make a plan or work schedule only at the start-up stage of the system, after which, when the conditions change, the schedule must also change depending on the conditions. The advantages of this approach are the flexibility of the system and the adaptability of the dispatcher’s behavior. And the disadvantages include the high cost and unpredictability of behavior at one stage or another. In real systems often use mixed or, as they are called, hybrid algorithms of the dispatcher, which use both static and dynamic approaches, depending on the situation. One such approach is, for example, the scheduler in synchronous systems [28].

5.3 Static Scheduling Algorithms The following list recaptures the most popular algorithms used in static planning: FCFS (First Come First Served) The simplest planning algorithm, “first come—first served”. This algorithm is based on the principle of queuing (FIFO). Tasks are queued for execution and executed on the system without interruption until complete completion, after the task is completed, the next in order is taken from the queue, the process continues until all tasks are completed. In GRID systems, this algorithm is often used because it is simple, reliable and easy to implement. Although it has a number of disadvantages, namely: high average waiting time in the queue and high average and full execution time. SJF, SJN (Shortest Job First, Shortest Job Next) The shortest work first, or the shortest work next—a short-term planning algorithm. Can be with both displacing and not displacing. Priority Planning. Priority planning algorithms form a queue of tasks to be performed in accordance with the priorities of importance or urgency, the priorities are set by the user.

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The shortest completion time. First, the tasks with the shortest estimated completion time are performed. The obvious disadvantage of this approach is that it is difficult to estimate how long it will take to perform a task [29]. RR (Round Robin) Round Robin is an algorithm that refers to displacement planning. Processes, as in FCFS, are queued and executed one after the other, but only a certain, predetermined, quantum of time. If the task is not completed for this quantum, it goes to the end of the queue and waits again when it is allowed to perform. If the task is completed before the end of the quantum of time, it frees the system and the next task begins to run, and the countdown of the quantum begins again. This algorithm has a lower average latency than FCFS but there is a significant drawback, the total execution time depends significantly on the size of the time quantum, which may be different for different systems and tasks. If, for example, it is very large and all tasks are performed before the time quantum is completed, the algorithm will degenerate into FCFS, but if the time quantum is small, switching will be frequent, and in GRID systems can take a long time, which will significantly affect performance of the whole system.

5.4 Dynamic Scheduling Algorithms As mentioned above, dynamic scheduling differs from static scheduling in that the process of allocating tasks takes place constantly, depending on how the system changes. Therefore, these algorithms are often called load balancing algorithms, due to the specifics of their work. In the list below, only the most popular and well-known are presented, although there are many more. MET (Minimum Execution Time) Minimum execution time. MET assigns each resource task that can complete it the fastest, regardless of whether the resource is available at this time. This algorithm can cause a significant load imbalance in the system, but still it is one of the algorithms used in the SmartNet system [30, 31]. Min-Min A scheduling algorithm that works according to the following scheme: a task with a minimum predicted completion time is selected from a set of tasks, after which it is assigned to a resource with a minimum total execution time for all tasks. Then the next task with the minimum completion time is taken from the queue and assigned to the next free resource with the minimum total completion time for all tasks. The process continues until all tasks are assigned [32].

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Max-Min This algorithm is similar to the Min-Min algorithm, only the tasks are selected with the maximum, not the minimum, as in Min-Min, the predicted completion time. Resources are selected as in the algorithm Min-Min [32].

6 Statement of the Dispatching Task In the previous sections, the general model of the GRID system was described quite clearly and transparently, according to the author, and the main characteristics and problems faced by the developers of such systems were given. Much attention is paid to scheduling algorithms. All previous information has been presented for a better understanding by the reader of what will be discussed next. So, let’s say we have a lot of computing nodes that are different in terms of power, software, and hardware architecture, and we’ll just call them PCs. These PCs can include any devices, whether laptops, desktops, servers or clusters. PC data is interconnected by different communication channels, with different data rates. On the other hand, we have many tasks that we need to complete. The tasks may be different in their properties, and require different execution times and different computing power of the PC. Between them is a controller, or dispatcher, which decides which task to which node to set. The purpose of this manager is to distribute the tasks so that the total time to complete all tasks would be minimal. It must work according to a special algorithm. Actually, we need to define this algorithm. Figure 2 schematically shows the scheduling process. According to [33, 34] we can introduce the concept of data flow and process. where streams will be considered our tasks. And processes—existing nodes in our system. Therefore, we need to distribute the flows between the processes so that the total execution time would be the shortest.

Fig. 2 Schematic representation of the task manager for the GRID system

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7 A New Approach to Task Planning on GRID Systems As mentioned in the previous section, we need to define an algorithm that could distribute threads, or tasks between processes, nodes in our GRID. After a long search, an algorithm was found that could perform such a task. This algorithm is based on the Principle of Balance of Forces, or the Law of Equilibrium. The essence of this Law is as follows: for each force there is another force that balances it. Both forces are approximately the same, and they are constantly changing their potential. When the potential of the first force increases, the regulator automatically turns on and the process of equalization of potentials takes place. This Law is universal and understanding his would, allow according to the author, to use it not only for this task, but also in other areas of life. As for GRID systems, the following is the scheduling algorithm that works on this Principle. And not bad enough.

7.1 Flow Scheduling Algorithm (FSA) or Forces Balance Algorithm (FBA) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Force balance = 1. There are n threads and m processes. Select the 1st thread. Find a process in which the ratio of the 1st thread to the m process would be as close as possible to 1. Send the 1st thread to the found process. Select the 2nd thread. Find a process in which the ratio of the 2nd thread to the (m − 1) process would be as close as possible to 1. Send the 2nd thread to the found process. Choose the n thread. Find a process in which the ratio of the n thread to the (m − n) process would be as close as possible to 1. Send the n stream to the found process.

To perform our task, according to the algorithm, we need to determine how to find the relationship between flows and processes. Each thread can have different properties. For example, computational complexity, the amount of information that needs to be transmitted and returned, the speed of the communication channel, and others. In turn, each process can also have different properties. For example, computing power, memory capacity, etc. At this stage, we will not delve into the details, but simply summarize all the existing properties of both flows and processes. To do this, we introduce concepts such as thread power Pt and process power Pp.

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Now we can find the relationship between Pt and Pp. Given that the ratio should be close to 1, and the powers are measured by the same values, we can change the algorithm in such a way as to find not the ratio but the difference Pt and Pp, which will facilitate implementation. The difference should be zero. Now the algorithm can be reduced to the following form: 1. 2. 3. 4. 5. 6. 7.

For all flows and processes, we calculate Pt and Pp. Select the 1st thread. Find a process in which Pt − Pp will be minimal. Send the 1st thread to the found process. Choose the n thread. Find a process in which Pt − Pp will be as close as possible to zero, or equal to 0. Send the n thread to the found process. Below is the pseudo code of this program, for better understanding

for all tasks in meta-tasks calculate Pt endfor for all pcs in meta-pc calculate Pp endfor for all tasks in meta-tasks for all pcs in meta-pc find minimum delta Pt - pp endfor send task to fined pc del pc from meta-pc endfor

Based on the above, a system was built that simulates the operation of the GRID system. Namely, the scheduling process using various algorithms, including the force balance algorithm (FBA). To compare the efficiency of different algorithms, the following were selected: • FCFS as well known and widely used in various scheduling systems. • Min-Min, Max-Min as load balancing algorithms. • FBA-algorithm based on the proposed approach. As the results of the study showed, the FBA algorithm gives very good results compared to other algorithms. In addition, it would like to note that this algorithm is universal in its use, as it can be used for both static and dynamic planning. However, the implementations will be different, because the systems are different. It would like to emphasize, that its essence is to send the stream to the process, which is equal to it in capacity.

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7.2 Parallel Force Balance Algorithm In the same paper we present another implementation of this approach for parallel execution of one program on GRID. So, we have one big task that can be paralleled, on the one hand. And the GRID system, with disparate nodes, on the other. We need to parallelize this program to our system so that it runs as quickly as possible. Using the Principle of Balance of Forces, this can be done as follows: calculate sumP = 0 for all pcs in meta-pc calculate Pp sumP += Pp endfor for all pcs in meta-pc send task * Pp / sumP endfor

Explanation: we sort all the nodes of the system and determine the power of each node, as well as in parallel determine the total power of the entire system. After that, in the loop, we divide our problem into a set of subtasks, according to the formula specified in the pseudocode. Then we send the received subtasks to the appropriate nodes. This method is very effective and allows you to parallelize the problem almost perfectly. In this case, the system will work as efficiently as possible, and accordingly the execution time will be fast. In addition, as shown the results of the experiment, nodes complete their work almost simultaneously. Which is definitely very good, because the waiting time until all the nodes are released, in this case will be minimal.

8 Conclusion and Future Works This article has systematized and briefly described such concepts as GRID system with inalienable resources. The main characteristics of GRID systems are given. The principles of construction and difficulties that may arise in the development of such systems are revealed. A separate section describes how GRID with inalienable resources differs from normal. Much attention is paid to scheduling algorithms, examples are given, and a brief description of the essence of the algorithm. The main purpose of this publication was to discover a new approach to solving the problem of scheduling task flows on GRID system processes. The found algorithm is based on the Law of Balance of Forces, or the Principle of Equilibrium.

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At present, several options for implementing this approach have been developed, each of which, under different conditions, gives its positive results. The publication presents two algorithms that allow you to efficiently distribute tasks to system nodes. Experiments have shown that these algorithms give better results than other wellknown ones. A feature of the approach is also its versatility and the possibility of using it in both static and dynamic systems. How exactly this approach was implemented, on which system, and what results were obtained in detail will be published in future publications.

References 1. Singh, M.: An overview of grid computing. Paper presented at the Proceedings—2019 ICCCIS 2019, 2019-January, pp. 194–198. https://doi.org/10.1109/icccis48478.2019.8974490 2. Research data management simplified. www.globus.org 3. Buyya, R., Broberg, J., Goscinski, A.M.: Cloud Computing: Principles and Paradigms. Wiley, New Jersey (2010) 4. Sharma, P.: Grid computing vs. cloud computing. Int. J. Inf. Comput. Technol. 3(6), 577–582 (2013). ISSN 0974-2239 5. Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. Supercomput. Appl. 15(3) (2001) 6. Foster, I., et al.: The Open Grid Services Architecture. Version 1.0. Informational Document (2005) 7. Kaur, H., Gupta, K.: Challenges in grid computing. Int. J. Sci. Res. Eng. Technol. (IJSRET) 2(3), 141–144 (2013). ISSN 2278-0882 8. Fan, W., Wu, Z., Yang, J.: Challenges of grid computing. In: WAIM 2005, LNCS, vol. 3739, pp. 25–31. Springer, Berlin, Heidelberg (2005) 9. Task Scheduler for developers—Win32 apps. https://docs.microsoft.com/en-us/windows/ win32/taskschd/task-scheduler-start-page 10. Kropyvnytska, V.B., Klim, B.V., Romanchuk, A.G., Slabinoga, M.O.: Investigation of scheduling algorithms in computer systems. Exploration and development of oil and gas fields. 2(39), 93–105 (2013). UDC 004.942, ISSN 1993-9973 11. Ankita, Sahana, S.: Evolutionary based hybrid GA for solving multi-objective grid scheduling problem. Microsyst. Technol. 26(5), 1405–1416 (2020). https://doi.org/10.1007/s00542-01904673-z 12. Carastan-Santos, D., De Camargo, R.Y., Trystram, D., Zrigui, S.: One can only gain by replacing EASY backfilling. A simple scheduling policies case study. Paper presented at the Proceedings—19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019, pp. 1–10 (2019). https://doi.org/10.1109/ccgrid.2019.00010 13. Dheenadayalan, K., Muralidhara, V.N., Srinivasaraghavan, G.: Storage load control through meta-scheduler using predictive analytics (2016). https://doi.org/10.1007/978-3-319-280 34-9_9 14. Haruna, A.A., Jung, L.T., Zakaria, N.: Design and development of hybrid integrated thermal aware job scheduling on computational grid environment. Paper presented at the 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015—Proceedings, pp. 13–17 (2016). https://doi.org/10.1109/ismsc.2015.7594020 15. Hlaing, Y.T.H., Yee, T.T.: Static independent task scheduling on virtualized servers in cloud computing environment. Paper presented at the 2019 International Conference on Advanced Information Technologies, ICAIT 2019, pp. 55–59 (2019). https://doi.org/10.1109/aitc.2019. 8920865

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16. Kaur, M.: Multi-objective evolution-based scheduling of computational intensive applications in grid environment (2017). https://doi.org/10.1007/978-981-10-1678-3_44 17. Khan, Z.F.: Novel architecture for effective load balancing and dynamic group scheduling in grid computing topology. Paper presented at the Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies (2017). https://doi.org/10.1109/iccpct.2017. 8074379 18. Kumar, P.S., Parthiban, L., Jegatheeswari, V.: Privacy and security issues in cloud computing using idyllic approach. Int. J. Netw. Virtual Organ. 21(1), 30–42 (2019). https://doi.org/10. 1504/IJNVO.2019.101146 19. Lavanya, M., Santhi, B., Saravanan, S.: Adaptive cloudlet scheduling algorithm using three phase optimization technique (2019). https://doi.org/10.1007/978-3-030-15357-1_24 20. Mathur, A.K., Charan Teja, S., Yemula, P.K.: Optimal charging schedule for electric vehicles in parking lot with solar power generation. Paper presented at the International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018, pp. 611–615 (2018). https://doi.org/10. 1109/ISGT-Asia.2018.8467916 21. Naithani, P.: Genetic algorithm based scheduling to reduce energy consumption in cloud. Paper presented at the PDGC 2018—2018 5th International Conference on Parallel, Distributed and Grid Computing, pp. 616–620. (2018). https://doi.org/10.1109/pdgc.2018.8745801 22. Pujiyanta, A., Nugroho, L. E., Widyawan: Advance reservation for parametric job on grid computing. Paper presented at the Proceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019 (2019). https://doi.org/10.1109/icic47613.2019.898 5978 23. Pujiyanta, A., Nugroho, L. E., Widyawan: Planning and scheduling jobs on grid computing. Paper presented at the Proceeding—2018 International Symposium on Advanced Intelligent Informatics: Revolutionize Intelligent Informatics Spectrum for Humanity, SAIN 2018, pp. 162–166 (2019). https://doi.org/10.1109/sain.2018.8673372 24. Ramyachitra, D., Kumar, P.P.: Frog leap algorithm for homology modelling in grid environment. Int. J. Grid Util. Comput. 7(1), 29–40 (2016). https://doi.org/10.1504/IJGUC.2016.073775 25. Sant’Ana, L., Carastan-Santos, D., Cordeiro, D., De Camargo, R.: Real-time scheduling policy selection from queue and machine states. Paper presented at the Proceedings—19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019, pp. 381–390 (2019). https://doi.org/10.1109/ccgrid.2019.00052 26. Tindell, K., Hansson, H.: Real Time Systems by Fixed Priority Scheduling. DoCS, Uppsala University (1997) 27. Audsley, N., Burns, A.: Real-Time System Scheduling. University of York, UK 28. Danilov, M.V.: Methods for scheduling tasks in real-time systems. Programs Syst. 4, 28–35 (2019) 29. Scheduling algorithms—operating systems. https://studme.org/329864/informatika/algori tmy_planirovaniya 30. Freund, R.F., Gherrity, M., Ambrosius, S., Camp-bell, M., Halderman, M., Hensgen, D., Keith, E., Kidd, T., Kussow, M., Lima, J.D., Mirabile, F., Moore, L., Rust, B., Siegel, H.J.: Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet. In: 7th IEEE Heterogeneous Computing Workshop (HCW’98), pp. 184–199 (1998) 31. Etminani, K., Naghibzadeh, M.: A Min-Min Max-Min selective algorithm for grid task scheduling. In: 3rd IEEE/IFIP International Conference in Central Asia on Internet (2007). https://doi.org/10.1109/canet.2007.4401694 32. Kokilavani, T., George D.I.: Amalarethinam: load balanced Min-Min Algorithm for static meta-task scheduling in grid computing. Int. J. Comput. Appl. (0975) (2011) 33. What is a process. https://www.computerhope.com/jargon/p/process.htm 34. Threads vs. processes. https://www.backblaze.com/blog/whats-the-diff-programs-processesand-threads/

A Concept of the Architecture and Creation for SIEM System in Critical Infrastructure Rat Berdibayev , Sergiy Gnatyuk , Yaroslav Yevchenko , and Vitaliy Kishchenko

Abstract Energy sector is important unit of critical infrastructure and the protection of its ICT infrastructure is essential for states. Cloud and other advanced technologies are implementing in various ICT infrastructures. It has led to increased attention to the problems of cyber threats, the growth of which is inseparably linked with the growth of ICT. In this paper the analysis of the existing models, systems and methods for cyber threats detection in critical infrastructure was carried out for their disadvantages defining. A model of cloud service has been developed; it allows to ensure the security of cloud service and conduct appropriate simulations. Improved method for cyber threats detection has been developed, it allows to detect cyber threats in cloud services and classify them. The developed method was experimentally investigated using NSL-KDD dataset. It was proved the correctness of its work and the possibility of application in cloud services as well as increase efficiency of cloud system security by 48.02%. Cloud service model has been developed that can be used to build cloud services based on the various architectures. Also, the structural scheme of the main concept of the architecture and creation for SIEM was constructed, that can be used in energy and other sectors of critical infrastructure. In the future, based on the proposed method and model, appropriate tools for detecting and classifying cyber threats in cloud services can be developed. It can be autonomous functional unit of SIEM as well as other instrumental tools of CSIRT/SOC in energy and other sectors of critical infrastructure. Keywords Concept · Cloud service · Cybersecurity · Critical infrastructure · Detection · Identification · Architecture · SIEM · NSL-KDD dataset R. Berdibayev Almaty University of Power Engineering and Telecommunication, Almaty, Kazakhstan S. Gnatyuk (B) · V. Kishchenko National Aviation University, Kyiv, Ukraine e-mail: [email protected] Y. Yevchenko Institute of Special Communication and Information Security of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_13

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1 Introduction Energy sector is important unit of critical infrastructure (Fig. 1) in different states (in the set with transport, communications, gas and oil industry and others). Today traditional energy technologies are becoming progressively more connected to modern digital technologies and ICT networks. This increasing digitalization makes the energy system smarter and enables consumers to better benefit from innovative energy services. Digitalization creates significant risks as an increased exposure to cyberattacks and cyberincidents potentially jeopardizes the security of energy supply and the privacy of consumer data [1]. The protection of critical energy infrastructure is essential for states because the well-being of their societies depends on its good functioning. An attack on it or a disruption can cause serious problems to the citizens and can jeopardize national security of the state. For this reason, today critical energy infrastructure protection is a key issue [2]. In various critical infrastructures cloud service using can deploy users’ applications, build their infrastructure or simply process data, but in any case, they trust their confidential data to the cloud service provider and want to be sure that their data is secure. Providing cybersecurity in a cloud environment is the responsibility of the provider, and therefore their systems must meet a number of requirements of both national and international law and international recommendations. Therefore, the main scientific and technical problem can be formulated as follows: data security may be compromised and there is a risk of mass data loss by many users due to the possibility of conducting cyber threats in cloud services. Because information is not only stored in the cloud, but is also processed, users must be confident in the security Fig. 1 Sectors of critical infrastructure by Huntsman

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and availability of their data. The solution to this problem can be provided by using various methods of cyber threat detection (MCD), IDS/IPS systems, cyber incident response modules, etc. [3]. Cyber threat is any circumstance or event that may cause a breach of cybersecurity policy and/or damage to an automated system [4–6]. The main purpose of cybersecurity is to prevent the implementation of existing cyber threats, which are the sources of the following risks [7–10]: (1) (2) (3) (4) (5) (6)

Loss of intellectual property; Violation of compliance and regulations; Compromising credentials and authentication; API threats; Hacking accounts; Improper usage of cloud services.

2 Review of Related Papers and Problem Statement Cloud computing systems have a multi-level architecture of different services and levels of management. Security issues for the SaaS platform can generally be divided into two categories: attacks on development tools and attacks on management tools. In general, all threats can be divided into three groups: (1) threats to data confidentiality; (2) attacks on the interface; (3) SSH attacks. Security issues for the IaaS and PaaS platforms are grouped into four classes: attacks on cloud services, attacks on virtualization, attacks on unified computing, and attacks on SLAs [11, 12]. In the Table 1 shows a multi-level classification of threats for the three layers of clouds, which are the first level. At the next level are cloud services, and at the third—the types of attacks on these services [13]. There are many MCDs, but they all use very similar techniques for direct detection. A significant disadvantage of most of them is that they are described only theoretically and have no practical confirmation (verification). (1)

(2)

Method for cyber threat recognition based on fuzzy feature clustering [14, 15]. The essence of splitting objects that contain signs of anomalies (cyberattacks) into a class of sets of the same type in terms of cybersecurity lies in splitting sets of objects into subsets. The method involves the usage of machine learning. Traditional fuzzy clustering algorithms use a given number of partition clusters as input parameters, and some of them also use a given cluster fuzzy index in the space of signs of vulnerabilities, anomalies, NSD threats and cyberattacks [16, 17]. Based on the information criterion of functional efficiency for the IP, a mechanism for adjusting the parameters of the algorithm for clustering threat signs can be implemented. Method for detecting cyber threats using Big Data technology [18]. Data management and expertise methods such as biometric authentication protect against cyberattacks by providing security solutions to the massive protection

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Table 1 Multilevel classification of data security threats in cloud computing Layer of cloud (service)

Cloud service

Security threat

Attack type

Risk value

SaaS

Web service

Data security

Confidentiality

Medium

Interface attack

Signatures attack

Low

Attacks on users credentials

Medium

Attacks on API keys

Medium

Attacks on users credentials

Medium

ARP spoofing on virtual switching

High

MAC spoofing on virtual switching

High

Software level virtualization

Hacking on computing

Low

Cloud software

Harmful software

Scripts

High

Computing services

Unified Attacks during data computing attacks processing

Low

SLA attacks

High

API

IaaS and PaaS

Development services

(3)

Virtualization platform

SSH attacks

Hardware level virtualization

Hacking

of data volumes. By analyzing Big Data logs, we can prevent cyber threats by monitoring data. When Big Data analysis is combined with JIT (Just in Time) analysis, it collects information on machines that have an open connection to locations outside the LAN. It also predicts future attacks and provides information about previous attacks that may have taken place on your system. IBM has developed a big data solution that protects data from threats and fraud. IBM’s solution detects risk and intrusion when analyzing structured and unstructured data. QRadar correlates in real time, detects anomalies and reports for immediate threat detection, and sends rich security data to IBM Big Data products such as IBM InfoSphere BigInsights. Large datasets need to be reduced to successfully detect anomalies. Method for detecting cyber threats using the analysis of social networks [19]. Among the main classes of methods used in ACC, we can distinguish the following: methods of graph analysis, statistical methods, data mining, methods of optimization theory and algorithm theory. It is also convenient to single out the methods of semantic analysis and text analysis. In this case, it is necessary to verify whether the system supports the language in which users of the analyzed social network communicate. These methods are used to identify the following major threats: network spam; threats to social engineering; password theft and phishing; web attacks; leakage of information and compromising the behavior of company employees; Advanced Persistent Threat (APT) attacks.

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Method for cognitive security using artificial intelligence [20]. It is the concept of a self-defense network that identifies a potential threat on the Internet and takes appropriate action to prevent “confidential data from being compromised”. At the same time, you use a combination of a number of modern technologies to identify and analyze key threats (both external and internal to the client) using special techniques for analyzing real-time data behavior. Method for detecting cyber threats using a structured behavioral model [21]. The method is based on the analogy of comparing natural language and network traffic. First, the Trace Sequence of the captured network traffic is determined with the parameters: active text, active grammar, active vocabulary, and ratio. The next step is to start processing the captured sequence using DBSCAN—a spatial data clustering algorithm where noise is present. Next, the data from the analysis of the captured piece of traffic is compared with the normal behavior of traffic that was obtained in an isolated environment, and is called the reference. Having certain differences results in detection of cyber threats. Method for “deep analytics” [22, 23]. This method is a combination of popular and effective methods (predictive analytics, descriptive analytics, graph analysis, analysis of unstructured information, optimization), which together give the desired result—the detection of cyber threats or anomalies. Examples of method implementation: detection of anomalies; statistical threat profiles; relationship analysis. Method for detecting cyber threats of Yu. Malachi [24]. The system and method of detecting a cyber threat in accordance with implementations of the present invention include automatic detection of resources in the network, resource detection unit, emulation, fake asset creation unit, at least one resource detected in the network, associating a trap sensor with malware with emulation resource and detection by malicious trap sensor, malware related to the emulated resource. The system and method may also include downloading data related to the detected malware on the server, analyzing the downloaded data on the server to obtain the analysis result and performing one or more actions based on the analysis result. Radar Services method for detecting and counteracting cyber threats [25]. The method is based on the use of: many systems for analysis of signature and behavioral analysis of network traffic and next-generation isolated software technologies for analysis of all incoming e-mails; advanced correlation engine that analyzes network traffic and events that use statistical models, recursive methods and machine learning to differentiate normal and abnormal behavior and identify patterns; the usage of a risk and safety management team that analyzes, verifies and aggregates all findings. Network streaming and threat detection approach [26]. Numerous studies have been conducted on real-time threat detection systems. The problem of processing large amounts of data on network traffic of corporate systems, while providing real-time monitoring and detection, was considered, which remain unresolved. In particular, they introduced and evaluated a flow-based

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threat detection system that can quickly analyze overly intensive real-time network traffic data using streaming flow-based clustering algorithms to detect abnormal network actions. SANS company cyber threat detection, prevention and control system [27]. This system is described only theoretically (in the form of recommendations), and contains 5 aspects: use of security measures based on end-toend visibility; avoid excess information; use security solutions that perform real-time analysis; reduce latency within the network; introduction of “deep” protection. Method for data collection, processing, analysis and storage for monitoring cyber threats and their notification to users [28]. The system collects intelligence data from multiple sources and then pre-processes the intelligence data for further analysis by the intelligence analyst. The analyst reviews the intelligence and determines if it is appropriate for the client to sign a cyber threat alert service. The system reforms and collects intelligence data and automatically transmits intelligence data through many delivery methods. Method for planning the structure of cyber threats and their application to reduce the impact of cyber threats [29]. A security system consisting of a computer, memory, data storage, containing a dictionary of the intellectual capacity of cyber-threats and a technological dictionary; and an application stored in memory. When executed by a computer, the program generates a report that identifies the intent of the cyber threat and identifies the cyber threat technology, in which the intent of the cyber threat is selected from several intentions for the cyber threat listed in the dictionary of cyber threat imposition and in which cyber threat technology is selected from the technology dictionary.

After the analyzing of known MCDs [14–29], it is clear that not all of these methods have been qualitatively and experimentally studied. Almost half of them have high requirements for computing resources and are not easy to implement, and therefore are described only theoretically. In addition, due to the technology of the method itself, not all algorithms have the ability to log new, not yet assigned to any category of cyber threats. Also, only fuzzy feature-based clustering, Cognitive Security Method, Network Streaming and Threat Detection System, MCD of SANS company, and MCD using cyber threat planning have real-time cyber threat detection. In the review of the literature for each method was not said that the study was also conducted in cloud computing systems, but indicated the possibility of such implementation for the methods: MCD based on fuzzy clustering of features, MCD using Big Data technology, the method of “deep analytics” and Network streaming and threat detection system. In general, this analysis indicates the problem of detecting cyber threats in cloud environments of any type and services of any type. The main purpose of this work is to develop a method for detecting cyber threats in cloud services. To achieve this purpose we need to solve the following tasks: 1.

Develop a model of cloud service and based on it a MCD to ensure the security of cloud services by further neutralizing the identified threats;

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Create the scheme of the main concept of the architecture and creation for SIEM that can be used in energy and other sectors of critical infrastructure; Experimentally investigate the MCD to verify its correct operation and the possibility of application in cloud services.

3 Theoretical Background of Method Development 3.1 Technological Architecture of Secure Cloud Service Based on Cloud Computing Technology Cloud environment in which the MCD will be introduced in this section. The technology architecture is based on the recommendations of Cisco, which has developed its own progression of evolution of cloud data centers: (1) consolidation and aggregation of data center assets; (2) abstraction, is a key phase, because the assets of the data center are abstracted from the services that are actually supplied; (3) automation, which is capitalized on consolidated and virtual aspects, fast backup services and automatic modeling; (4) the interaction of the corporate “cloud” with the public; (5) the final phase—“inter-cloud”, which replaces the existing types of clouds. Figure 2 shows the technological architecture, which presents the “cloud” data center of the next generation. The diagram shows examples of component blocks for the data center. In general, the completed architecture contains not only components of the structure, but also is regulated by different types of service and regulatory requirements. The architectural model offers 9 tiers of the data center network: application software; virtual machine and distributed virtual switch (virtual machine, VSwitch); storage and storage networks (storage, SAN); calculation (compute); access; aggregation; core, where there is also a module for detecting cyber threats; peering; basics of the Internet (IP-NGN backbone). Figure 3 presents proposed scheme of the main concept of the architecture and creation for SIEM that can be used in energy and other sectors of critical infrastructure. The main structural units are horizontal databases, analytical and monitoring units, cloud storage and sources (system 1—system N). Proposed scheme for SIEM (Fig. 3) can be also integrated in real infrastructures with functioning various SIEM or other incident management systems. Along with the technological component of the architecture of data centers, an important place is also occupied by the issue of trust in the infrastructure model of “cloud” computing [25]. The key to gaining an advantage from the cloud is to establish a trust approach that begins with the establishment of such attributes in cloud architecture. Trust in a “cloud” data center is based on several basic concepts: (1)

Security: traditional data issues and resource access control, encryption and incident detection;

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Fig. 2 Technological architecture of the data center based on cloud computing

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Fig. 3 Concept of the architecture and creation for SIEM

(2) (3) (4)

Control: the ability of the enterprise to directly manage the processes of deployment of applications; Compliance and maintenance at the management level: compliance with general requirements; Timely detection of cyber threats, prevention of intrusions, blocking cyberattacks.

3.2 Groups of Cyber Threats and Cyberattacks in Cloud Environments One of the most complete descriptions of all cyber threats and attacks that can be implemented is in the KDD database [30]. NSL-KDD is a data set proposed to solve some of the integral problems of the KDD’99 data set mentioned. Although this new version of the KDD dataset is still not without some problems, and is not the best guide to existing real networks, and due to the lack of available datasets for network identification systems, it is used as an effective reference dataset, which will help researchers compare different MCD. In addition, the number of entries in NSL-KDD sets and test sets is processed. This advantage makes it available to run experiments without having to accidentally pick a small portion. Thus, the results of the evaluation of different research papers will be consistent and comparable. The NSL-KDD dataset has the following advantages over the initial KDD dataset: (1) (2)

It does not include redundant entries in the data set, so classifiers will not be biased for a recurring entry. There are no duplicate records in the proposed test sets; therefore, productivity is not biased by methods that have better detection rates.

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(3)

The number of selected records from each complex group is inversely proportional to the percentage of records in the original KDD dataset. As a result, the classification indicators of different teaching methods change in a wider range, which makes it more effective to accurately assess different teaching methods. The number of entries in the set and test sets is clear, making it possible to experiment on a complete set without having to randomly select a small part.

(4)

3.3 Block Diagram of the Proposed Method for Detecting Cyber Threats Figure 4 shows a block diagram of detecting cyber threats in a cloud environment. When the host is connected to the cloud environment, network traffic begins to be generated. Next is the data processing unit (data process), where the network traffic arrives at the behavior analyzer (behavior analyzer), which contains the records of the NSL-KDD database. The analyzer compares the data captured from the network traffic with the database and begins to use classifiers to determine. The next block to which the data is transmitted is the block of identification and analysis, where the pre-classified threat is analyzed in detail on certain grounds, and it is determined to which elements the threat was directed. After that, the system issues a warning message that part of the traffic is abnormal, and at the same time begins to check the identified threat with previously found or recorded immediately in the database. If such a match is found, a notification with further actions can be issued (in case of their previous successful application). The last two modules are actually a record of detailed data about the threat (date, group, whether it was previously identified, etc.) and the formation of a mini-report for review, which shows the overall result. Taking into account the characteristics of cloud computing systems and ideal cyber threat detection systems, the developed method meets the following requirements: 1.

2.

Processing of large-scale dynamic multilevel autonomous computing systems and data processing environments. Clouds are large-scale systems based on virtual machines that are automatically created, transferred, and removed at the user’s request at runtime. It is generally assumed that the middleware provider initially reported changes in resources, but in cloud computing involving large networks and systems, it is important to automatically support these changes without human intervention. To overcome the complexity of its dynamic nature, the process of detecting cyber threats must be able to cope with it without human intervention, which facilitates the monitoring and control of network elements in real time. Identify various attacks with the least false positives. Due to the growing number of attacks, their complexity and unpredictability, the system must recognize new attacks and their vulnerable intentions to choose the best response according to the degree of risk and proper prevention. The method should be educational and improve its detection ability over time. It should also be designed to maintain the

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Fig. 4 Block diagram of the MCD in the cloud environment

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desired level of performance and security with the least computational resources, as the efficiency of cloud services is based on its computing capabilities. Quick detection and warning. Rapid detection and warning is a very important factor in the development of detection methods, as it affects the overall performance of the system and is crucial for the delivery of pre-agreed QoS. A cloud system with multiple administrators should minimize or no human intervention to avoid wasting time on the administrator’s response. It must work in real time and provide automatic responses to suspicious actions. Autonomous self -adaptation. The cyber threat detection system must be selfconfigured and adapted to configuration changes, as computing nodes are dynamically added and removed. The development of an appropriate architecture will allow you to determine how alerts should be processed and distributed from the individual detection components, while maintaining the topological model of cloud computing. It also facilitates the monitoring and control of network components. Scalability. The MCD must be scalable to efficiently handle the huge number of network nodes available in the cloud and their communication and computational load. Deterministic calculations in the cloud. They provide critical and critical functional services that have specific performance requirements in terms of retention, reliability, and resilience. MCD should not only provide real-time performance, but also ensure the negative impact of the deterministic nature of the network. Synchronization of autonomous MCD. Information and actions must be synchronized to detect widespread and simultaneous attacks, to apply appropriate responses, or to change a particular component system or configuration of the entire network and to adopt an appropriate prevention strategy. Resistance to compromise. The MCD must protect itself from unauthorized access or attacks. The IMC must be able to authenticate network devices, authenticate the administrator and verify its actions, protect its data, and block any vulnerabilities that may cause additional vulnerabilities.

4 Experimental Study and Discussion 4.1 Experimental Study in RStudio Environment Input/output of the experiment: the input data is 20% of the NSL-KDD dataset, the output data are classified data (normal or abnormal—threat). Experimental environment: open-source development environment for R (programming statistics and RStudio data visualization). RStudio includes a console, a syntax highlighting editor that supports direct code execution, and tools for scheduling, logging, debugging, and desktop management. Figure 5 shows the working environment of the RStudio tool.

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Fig. 5 Main working window of RStudio tool

The window is divided into four parts: (1) working part—for direct writing and running code, there is also a standard toolbar for all tools; (2) after viewing the data— there are tabs of the environment (you can view the loaded data sets and libraries) and history (see versions of the project); (3) console—to display the results of the written program, and data related to the environment (loading of the library); (4) field of view of visualized results (diagrams, histograms, etc.). Stages of research study Stage 1: Connection of all necessary libraries, loading of a training data set of NSL-KDD database (Figs. 6 and 7). Stage 2: Analysis of the test data set

Fig. 6 Code for data set downloading

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Fig. 7 Downloaded 20% of training data

Only 20% of the training data of the NSL-KDD database have 25,191 elements that have 43 features (Fig. 8). In Fig. 9 the distribution of types of threats and attacks is shown.

Fig. 8 Attributes of the training data set

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Fig. 9 Distribution of types of threats

As can be seen from Fig. 9 the largest number of attacks is related to the DoS [25]. Stage 3: Direct testing of the method Next, we test our method using built-in functions and data set. Initially, residual data and duplicates were separated, and separate small data sets were identified (Fig. 10). After that, the traffic is analyzed (Fig. 11) and the normal and anomalous data are determined, as well as the accuracy of the result (Fig. 12). Stage 4: The result of the experiment

Fig. 10 The results of data processing

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Fig. 11 The result of traffic analysis

Fig. 12 Test results of the MCD

During the experiment, the following results were obtained: the total percentage of threats detected—96.356%, correctly classified—95.89%, incorrectly classified— 4.11% (Fig. 12). Since the study incorrectly classified a certain percentage of threats, it is logical to apply the definition of errors of the 1st and 2nd kind.

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The first kind of error is that the null hypothesis H0 is rejected, although in reality it is correct. The second kind of error is that the null hypothesis H0 is accepted, although the alternative hypothesis Ha is actually correct. The error of the first type is equivalent to the so-called false positives. An example of an error of the first type. Let’s study a cure for a certain disease. The null hypothesis states that these drugs have no effect on the course of the disease. If we reject the true null hypothesis (we make a type I mistake) and accept the false alternative, that is, we believe that the use of these drugs affects the course of the disease (which, in fact, is not the case). By increasing the confidence level from 95 to 99%, we reduce the probability of making a type I error (is rejecting the true null hypothesis) from 5 to 1%. However, there is another danger: this increases the likelihood of making a type II error. Type II error is equivalent to false negatives. The probability of making a type II error is denoted as a. 1 − a—power of the criterion. That is, you need to determine how many of the misidentified threats were actually threats and how many were normal traffic. We have that, IC0 = 5%. Assume that with an improved method algorithm, the percentage of incorrect classifications will decrease. Assume that the value obtained IC = 4.11%, when choosing a larger data set and conducted 10 experiments. The value of the variance is 1%. ¯ criterion provided that correct hypothesis H0: IC0 = 4.11% Solution. Statistics X (Fig. 13). Result: the probability of receiving errors of 1 type—5%, 2 types—12%.

Fig. 13 Calculation of type 2 errors

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4.2 Experimental Study in the CloudSim Simulation System Input/output of the experiment: the input data is a set of NSL-KDD data and captured network traffic, the output—classified data (normal or abnormal—threat) and the value of the efficiency of the MCD. Experimental environment: CloudSim simulation system. CloudSim platform is a generalized and scalable simulation tool that allows fullfledged modeling and simulation of cloud computing systems and infrastructure, including the construction of data centers using the “cloud”. It is an extension of the basic functionality of the GridSim platform, providing the ability to model data storages, web services, resource allocation between virtual machines [31]. Let’s look at the log of the revealed threats which was written down during carrying out modulation (Fig. 14). The logs study in RStudio was done to visualize the results: distribution of identified threats and attacks (Fig. 14); diagram of the dependence of the percentage of detection on the type of threat (Figs. 15 and 16). As can be seen from Fig. 15 most detected attacks are related to DoS [25]. Comparison of the results of simulations on the CloudSim platform is shown in Table 2. The displayed results indicate that when simulating the data center model without MCD, but provided that there is a built-in threat level security network detector, the level of detected threats is at 45.87%, which indicates insufficient security of the cloud service, because it means that if only the built-in anti-attack module is present,

Fig. 14 Log of detected cyber threats

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Fig. 15 Distribution of identified threats and attacks

Fig. 16 Percentage distribution of identified threats depending on their type

Table 2 Comparison of simulation results for SIEM detection module

Experiment

MCD connection

Detected threats (%)

1

–

45.87

2

+

93.89

less than half of the attacks will be detected. And when conducting simulations with the built-in MCD, the level of detected cyber threats is at the level of 93.89%, which indicates the effectiveness of its work [32–34].

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5 Conclusions and Future Research Study In this paper was defined that the protection and cybersecurity ensuring of critical energy infrastructure is essential for states because the well-being of their societies depends on its good functioning. The analysis of the existing models, systems and MCD was carried out, which allowed to identify their main shortcomings, namely: lack of data on experimental research, the impossibility of its use in cloud services (for the most part), some MCDs do not implement real-time cyber threat detection etc. A model of cloud service has been developed, which uses technological architecture, high-speed communication, unified structures and calculations. It allows to ensure the security of cloud service based on cloud computing and conduct appropriate simulations of cloud service functioning. Improved MCD has been developed, which due to dynamic resources, autonomous self-adaptation and scalability and deterministic calculations allows to detect cyber threats in cloud services and classify them (for example, using to the NSL-KDD classifier or other datasets of cyber threats and classifiers). The developed MCD for cloud services was experimentally investigated using NSL-KDD data base. It has proved the correctness of its work and the possibility of application in cloud services as well as increase efficiency of cloud system security by 48.02% (the efficiency of detecting cyber threats in cloud service is 93.89%, and without the application of the proposed MCD—45.87%). In addition, a cloud service model has been developed that can be used to build cloud services based on the various cloud computing architecture as well as structural scheme of the main concept of the architecture and creation for SIEM was constructed, that can be used in energy and other sectors of critical infrastructure (the main structural units are horizontal databases, analytical and monitoring units, cloud storage and sources). In the future, based on the proposed MCD and model, appropriate tools for detecting and classifying cyber threats in cloud services can be developed [35, 36]. It can be autonomous functional unit of effective SIEM or other instrumental tools of CSIRT/SOC for cybersecurity events correlation and cyberincidents response. Acknowledgements This research study was conducted with support of research grant №AP06851243 “Methods, models and tools for security events and incidents management for detecting and preventing cyber attacks on critical infrastructures of digital economics” (2020–2022), funded by Ministry of Digital Development, Innovation and Aerospace Industry of the Republic of Kazakhstan.

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References 1. Recommendations on the importance of critical energy infrastructure (CEI) stakeholder engagement, coordination and understanding of responsibilities in order to improve security. NATO Energy Security Centre of Excellence, Vilnius, 116 pp. (2018) 2. Cybersecurity in the energy sector. European Commission, Energy Security. Online access mode. https://ec.europa.eu/energy/topics/energy-security/critical-infrastructure-andcybersecurity_en?redir=1 3. De Jong, M., Hughes, L.: Critical energy infrastructure: identification and protection. Energy Security: Operational Highlights, issue 11 (2017) 4. Abidar, R., Moummadi, K., Moutaouakkil, F., Medromi, H.: Intelligent and pervasive supervising platform for information system security based on multi-agent systems. Int. Rev. Comput. Soft. 10(1), 44–51 (2015) 5. Active security for advanced threats counteraction. Online access mode. http://www.itsec.ru/ articles2/target/aktivnaya-zaschita-kak-metod-protivodeystviya-prodvinutym-kiberugrozam 6. The 6 major cyber security risks to cloud computing. Online access mode. http://www.adotas. com/2017/08/the-6-major-cyber-security-risks-to-cloud-computing/ 7. Google security whitepaper for Google cloud platform. Online access mode. https://habrahabr. ru/post/183168/ 8. Dokas, P., Ertoz, L., Kumar, V.: Data mining for network intrusion detection. Recent Adv. Intrusion Detect. 15(78), 21–30 (2014) 9. Ahmed, P.: An intrusion detection and prevention system in cloud computing: a systematic review. J. Net. Comput. Appl. 11, 1–18 (2016) 10. Anderson, J.P.: Computer security threat monitoring and surveillance. Tech. Rep. Cont. 36, 179–185 (1982) 11. Carl, G., Kesidis, G., Brooks, R.R., Rai, S.: Denial-of-service attack-detection techniques. Int. Comput. IEEE 8–89 (2006) 12. How to build physical security into a data center. Online access mode. http://www.csoonline. com/article/2112402/physical-security/physical-security-19-ways-to-build-physical-securityinto-a-data-center.html?page=3 13. Chatzigiannakis, V., Androulidakis, G., Maglaris, B.: A distributed intrusion detection prototype using security agents, pp. 14–25. HP OpenView University Association (2004) 14. Abraham, T.: IDDM: Intrusion detection using data mining techniques. In: DSTO Electronics and Surveillance Research Laboratory, vol. 9, pp. 30–39 (2001) 15. Zaliskyi, M., Odarchenko, R., Gnatyuk, S., Petrova, Y., Chaplits, A.: Method of traffic monitoring for DDoS attacks detection in e-health systems and networks. In: CEUR Workshop Proceedings, vol. 2255, pp. 193–204 (2018) 16. Chouhan, M.: Adaptive detection technique for cache-based side channel attack using Bloom Filter for secure cloud. In: Conference on Computer and Information Sciences, vol. 1, pp. 293– 297 (2016) 17. Hu, Z., Gnatyuk, S., Koval, O., Gnatyuk, V., Bondarovets, S.: Anomaly detection system in secure cloud computing environment. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 9(3), 10–21 (2017) 18. Li, H.-H., Wu, C.-L.: Study of network access control system featuring collaboratively interacting network security components. Int. Rev. Comput. Softw. 8(2), 527–532 (2013) 19. Dilek, S., Çakır, H., Aydın, M.: Applications of artificial intelligence techniques to combating cyber crimes: a review. Int. J. Arti. Intel. Appl. 6(1), 21–39 (2015) 20. How big data can improve cyber security. Online access mode. https://csce.ucmss.com/cr/ books/2017/LFS/CSREA2017/ABD3239.pdf 21. Kirichenko, L.: Cyber threats detection using social networks analysis. Int. J. Inf. Technol. Know. 11, 23–32 (2017) 22. Cisco creates self-defending networks for cyber threats detection. Online access mode. https://nag.ru/news/newsline/30762/v-cisco-sozdayut-samooboronyayuschuyusya-setdlya-vyiyavleniya-kiberugroz.html

242

R. Berdibayev et al.

23. Xiaohua, Y.: Early detection of cyber security threats using structured behavior modeling. ACM Trans. Inf. Syst. Secur. 5, 10–35 (2013) 24. Methods for deep analytics to counteract of modern threats. Online access mode. http://bisexpert.ru/sites/default/files/archives/2016/bis9_konovalov.pdf 25. Bondarovets, S., Koval, O., Gnatyuk, S.: System for anomalies detection for cellular networks provider based on Big Data concept. Inf. Technol. Secur. 4(1), 44–53 (2016) 26. Malachi, Y.: System and method for cyber threats detection; release date 22.10.2015. Pat. № WO2015159287 27. Hassan, Z., Odarchenko, R., Gnatyuk, S., Zaman, A., Shah, M.: Detection of distributed denial of service attacks using snort rules in cloud computing & remote control systems. In: Proceedings of the 2018 IEEE 5th International Conference on Methods and Systems of Navigation and Motion Control, October 16–18, 2018, pp. 283–288. Kyiv, Ukraine (2018) 28. Byrski, A, Carvalho, M.: Agent-based immunological intrusion detection system for mobile Ad-Hoc networks. In: Bubak, M., van Albada, G., Dongarra, J., Sloot, P. (eds.) Computational Science, ICCS 2008, 5103, pp. 584–593. Springer, Berlin/Heidelberg (2008) 29. Zhang, Z.: Hide: a hierarchical network intrusion detection system using statistical preprocessing and neural network classification. In: IEEE Workshop on Information Assurance and Security, No. 16, pp. 85–90 (2001) 30. Charles, E., Samuel, M., Roger, N., Daniel, O.: System and method of data collection, processing, analysis, and annotation for monitoring cyber-threats and the notification thereof to subscribers; release date 23.03.2012. Pat. № US20020038430 A1 31. John, P., Frederick, D., Henry, P., Keane, M.: System and method of cyber threat structure mapping and application to cyber threat mitigation; release date 03.04.2013 Pat. № US9749343B2 32. Vipin, K.: K-means clustering approach to analyze NSL-KDD intrusion detection dataset. Int. J. Soft Comput. Eng. 3, 332–340 (2013) 33. Buyya, R., Ranjan, R., Calheiros. R.: Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: International Conference on High Performance Computing Simulation, pp. 1–11. IEEE, USA (2009) 34. Oksiiuk, O., Chaikovska, V., Fesenko, A.: Security technique for authentication process in the cloud environment. In: Proceedings of the 2019 IEEE International Scientific-Practical Conference: Problems of Infocommunications Science and Technology (PIC S&T 2019), №9061248, pp. 379–382 (2019) 35. de Carvalho, R.S., Saleem, D.: Recommended Functionalities for Improving Cybersecurity of Distributed Energy Resources, pp. 226–231. 2019 Resilience Week (RWS), San Antonio, TX, USA (2019) 36. Hupp, W., Hasandka, A., de Carvalho, R.S., Saleem, D.: Module-OT: a hardware security module for operational technology. In: Power and Energy Conference (TPEC) 2020, pp. 1–6. IEEE Texas (2020)

Application of Virtual and Augmented Reality at Nuclear Power Plants Oleksandr Popov , Anna Iatsyshyn , Dmytro Sokolov , Maksym Dement , Ihor Neklonskyi , and Alexander Yelizarov

Abstract Development of nuclear energy is an important area of functioning of developed world countries. Nuclear power plants are objects of increased danger, so their development prospects are closely related to issues of safe operation and protection of territories, personnel, the environment on the territory of the station. Use of virtual and augmented reality technologies for energy sector received positive approval from the world’s leading countries. The research examined publications on positive aspects of virtual and augmented reality technologies application for various industries, in particular for nuclear energy. Practical examples of application of virtual and augmented reality technologies for operation of nuclear power plants are described. As a result of the study, it is determined that currently virtual and augmented reality technologies for nuclear power plants are used in the following areas: modeling of various nuclear energy processes; operation, repair and maintenance of equipment; presentation of activities, construction of the station; staff training and education. Use of virtual and augmented reality technologies for nuclear power plants confirms its economic efficiency through the reduction of cost and time costs of staff travel to facilities; elimination of design errors before the beginning of the stage of construction and installation works; increasing the level of industrial safety; improving the management of nuclear power plants. Keywords Energy · Virtual nuclear power plant · Augmented reality · Virtual reality · Digitalization · Advanced training · Staff training

O. Popov · A. Iatsyshyn (B) State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”, Kyiv, Ukraine e-mail: [email protected] O. Popov G.E. Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, Kyiv, Ukraine Interregional Academy of Personnel Management, Kyiv, Ukraine D. Sokolov · M. Dement · I. Neklonskyi · A. Yelizarov National University of Civil Defence of Ukraine, Kharkov, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_14

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1 Introduction Development of nuclear energy is an important area of functioning of developed world countries. The publication [1] states that nuclear energy created new round in the history of human development. Unfortunately, three major nuclear accidents in the history of nuclear power plants (NPPs) (Tri-Mile Island, Chernobyl, Fukushima1) caused global impact and led to significant radioactive contamination, causing significant damage to public health, natural and agro-environmental systems, etc. Therefore, safety is a necessary condition for the nuclear energy development [1, 2]. NPPs are objects of increased danger. So, their development prospects are closely related to issues of their safe operation and protection of territories, civilians and the environment on plant territory. Under various negative circumstances (violations of technological processes, safety and operating conditions, man-made accidents and incidents, natural phenomena, terrorist sabotage, hostilities, etc.), various emergencies can happen at NPPs. It pose a significant risk to environment, health of staff and population of the surrounding areas. Analysis of man-made emergencies by threat to human life, nature of action, scale of destruction of buildings, amount of material and economic damage, etc., shows that the most dangerous are emergencies causing radioactive and chemical contamination of the environment [2]. Currently, Ukraine has developed nuclear energy industry. It is based on four existing nuclear power plants: Rivne, Khmelnytsky, South Ukraine and Zaporizhia. It is planned to increase capacity of this industry for the next decades according to the “Energy Strategy of Ukraine until 2035” [2]. Development of most analog systems becomes impractical in the new technological era due to availability of digital alternative. Digital state becomes usual state of functioning and development of many systems, spheres, organizations, industries and economies. Main purpose of digitalization is to achieve digital transformation of existing and creation of new economy sectors, as well as the life spheres transformation into new more efficient and modern. Such growth is only possible when digitalization-related ideas, actions, initiatives and programs are integrated into national, regional, sectoral and development strategies. High-tech production and modernization of industry with the help of digital technologies, scale and pace of digital transformations should be priority of economic development. Sectors of the economy using digital technologies growths faster, cheaper and better [3]. So, digital technologies and successful projects of foreign developers need to be introduced more widely in the field of energy. The work [1] state that NURESAFE7 simulation platform based on NURSIM was created after the Fukushima-1 nuclear accident. It is a platform for safety analysis, operation and engineering of nuclear reactor design. Virtual Nuclear Power Plant (Virtual4DS) is an integrated simulation platform covering NPP environment, based on digital reactor consisting of digital traffic, digital meteorology and data on processes in the earth’s crust. Virtual NPP gives opportunity to perform modeling of multiactive operations, consider the evolution of nuclear accidents, use to support management decisions, anticipate emergencies and others based on big data, mobile

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internet, artificial intelligence, cloud computing platform and other advanced digital technologies [1]. Therefore, issues of “nuclear energy safety” have gained new importance both for NPP personnel and for the training of future specialists in the energy sector thanks to digital technologies. Indeed, new evolutionary stage of society is called technological era. For this era it is important to train professionals who will be competitive and will be able to quickly master the professions of the future. We believe that it is important to use digital technologies, in particular, virtual reality (VR) and augmented reality (AR) in specialists training for the new technological era [4]. Enterprises and large industries can’t operate without digital technology in the digital era. So, process of staff training needs should be improved. VR and AR technologies are functional and affordable. It is possible to model complex tasks that require adaptive thinking and real skills by their help. Therefore, these technologies are an ideal tool for learning in the digital era. Today, global VR and AR spending will grow by 100% or more every year. Rising costs reflect willingness of companies to provide their employees with virtual environments for learning new skills and abilities. VR and AR technologies will become basis of training in an industrial environment, as training will become more effective, interesting and safe. Also, widespread use of immersion technologies requires cooperation between industrial companies and developers of VR and AR technologies, which must ensure their compliance with training and safety requirements for different organizations [5].

2 Literature Analysis and Problem Statement Various problems aspects related to potentially dangerous objects functioning are considered in number of publications [2, 6–25]. Introduction of digital tools in energy companies work and in staff training is studied in [26–33]. Peculiarities of VR and AR technologies application for training were subject of consideration by many scientists [34–45]. VR and AR technologies are constantly improved and they are a powerful tool to ensure operation of NPPs and training/education of personnel. Therefore, there is a need to continue research on these technologies application for safe operation of potentially dangerous objects, including nuclear power plants. The research aim—is to research features and best practices of using VR and AR technologies to support work and training of NPP personnel.

3 Research Results The state policy of Ukraine [3] stipulates that it is important to encourage businesses and citizens to consume and use digital technologies, i.e. to make technologies available in Ukraine. Also, digital infrastructures should arouse the desire and motivation of people to connect to them and use them. It is important to modernize, optimize,

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scale and accelerate development of own business using digital technologies. They are digital economy basis. We agree with the work [46] that an important place is occupied by AR and VR technologies within the new stage of innovative development of society. It was named Industry 4.0. In essence, these technologies have both common and distinctive features, which are reflected in their use specifics by companies in process of relevant products creating. VR and AR technologies involve creation of thematic visualized content that can be used by target audience to meet specific needs with the help of modern electronic devices. Currently, AR and VR technologies are actively implemented in production processes, in the medical field, in educational processes, etc. In publications [39, 45] the difference between the concepts of AR and VR is considered in detail. Humanity faced a problem called cognitive overload with improvement of digital technologies and the general digitalization of public life. That is, a situation when number of operations that need to be performed on the human brain exceeds its capacity. AR is technology that can unload human brain, release some of the cognitive effort and help optimize their use [47]. Using AR technology, you can project digital information off-screen devices and combine virtual objects with the real world. Device’s processor, camera and screen will be used to combine virtual objects and elements with real ones. AR technology is easy to use, you just need to point the camera at a certain place and the result will appear on the screen. It can be three-dimensional object, an animation or something else [47]. We are impressed by the opinion expressed in the publication [34] that smart technology should be one of the main topics of research in modern conditions. It is important to meet society needs by means that do not harm environment and do not deplete natural resources. AR is new technology that combines three-dimensional virtual objects with reality. Also in the article [34] an analysis of the potential benefits of using AR technology in universities in Saudi Arabia is performed. Results of study show that of Saudi universities staffs believe that AR use in higher education has positive environmental and economic benefits. AR technologies have great potential for many applications. The publication [48] states that the US Navy tests AR goggles Magic Leap One for personnel training. “Tactically reconfigurable artificial combat enhanced reality (TRACER)” system was specially designed for military. This system includes directly Magic Leap glasses attached to processor in backpack behind military. It is a weapon model developed by Haptech (formerly known as StrikerVR) which supports simulation of recoil when firing, hand tracking system, as well as special software which provides various simulation scenarios. Great advantage of virtual training is less prediction for participants. It allows creating of learning scenarios much faster and cheaper than learning in the real world. Also, NASA contractors use Microsoft AR HoloLens augmented reality headsets for quick and correct assemble of Orion spacecraft elements. Therefore, engineers began to use AR equipment on daily basis to perform their current work duties.

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With help of the AR headset specialists can see holographic models of the spacecraft’s elements, which are created for engineering design using a special software product Scope AR. Virtual models of details and schemes of marking are visually superimposed on already collected parts of design. Engineers from the American aerospace company Lockheed Martin use Microsoft HoloLens AR goggles for auxiliary purposes during assembling of crew cabin capsule elements. It saves a lot of time, as there is no need to read thousands of pages of paper instructions for preparation and production [49]. The study [35] emphasizes that technological progress through digitalization provides basis for a new format of human life. Focus on future work, automation and digitalization of many technological processes led to modernization of jobs, especially in industry. This changes requirements for employees with mandatory mastery of digital competencies. Various technologies, in particular AR, can be used to support employees in developing the necessary competencies. Also in [35] the potential of AR as an innovative learning environment was investigated. It can be applied to various professional cases in the work of industrial enterprises. A similar conclusion was made in a study [36], namely that it is important to use AR applications in industry. They meet demands of industry, namely developed assistance system for maintenance of wind energy based on AR and welding simulator based on AR. Approaches and directions of application of AR solutions in industrial scenarios are outlined. We will consider various aspects of AR and VR technologies application for energy sector, in particular for potentially dangerous objects operation. Advances in immersion technology go far beyond consumer solutions. They have potential to turn complex industrial scenarios into “living and realistic experiences”. AR and VR technologies were used for many years as part of dive technologies development with excellent results in following areas: pilot training, astronauts and military specialists. Advances in IT made creation of virtual worlds more accessible to a wide range of industries. Currently, such industries as oil refining, oil and gas, energy generating turned to AR and VR to learn how to work with hazardous systems [5]. These industries need to store and standardize knowledge and experience of their staff. For example, Energoholding DTEK (Ukraine) has VR simulator to improve skills of DTEK Kyiv Electric Networks electricians. It was stated on the company’s official website in 2019 [49]. This VR-simulator in real time simulates production and potentially life-threatening situations. So, workers can hone their skills and hone their skills without risking their lives. The company has serious training base and uses modern educational innovations for training with VR-simulator help. DTEK is one of the leaders in VR technology implementation in Ukraine’s power industry. Energetics is considered as foundation of digital world future. The European Education Foundation is also interested in the VR complex developed by DTEK [50]. Also, in 2019 Energoholding DTEK (Ukraine) signed a cooperation agreement with the smart suit developer Teslasuit (Great Britain). Innovative development will provide opportunity to train employees using VR tools. It increases occupational safety in enterprises. The smart suit is equipped with special sensors. The system

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transmits tactile and motor sensations, different temperature regimes and simulates vibration. Combined with VR technology (virtual reality glasses), Teslasuit allows you to create feeling of full presence in a thermal power plant unit. It allows the company’s staff to learn new skills without being exposed to danger. Digital technologies can significantly increase the safety of employees. They include prevention of careless movements, a biometric system for analyzing performance and fatigue indicators. The Teslasuit smart suit (Fig. 1) today creates opportunities that were not previously used in industry. It takes occupational safety to a new level, which is one of DTEK’s priorities [51]. Indeed, advanced industrial enterprises from near abroad also integrated AR and VR technologies into business projects. They develop full-fledged strategies for staff work and training based on these technologies. Currently, smart suits are promising solution for the industry. Such smart suits are interesting for staff training. Smart suits solve problems with tactile sensations, due to which the immersion in virtual reality is more complete. A person, for example, not only realistically sees fires and hears sounds, but also feels flame temperature. The smart suit has motion tracking, so it is possible to perform and analyze movement of whole human body. Biometrics allows you to get data on acceleration of respiration, heart rate, sweating. These signs may indicate onset of employee panic. Such simulations should become an integral part of personal protective equipment for large industrial companies. It is important to use smart suits during work with possible emergencies. Learning in regular VRhelmet can take wrong step back and not notice dangerous structure, hit or stumble. Of course, the cost of smart suits is still quite high, but when these projects become widespread, their cost should decrease [52]. VR technology became a global tool for nuclear power plants studying. In 2019, a large-scale VR simulation of the Fukushima-1 emergency power plant was presented to the world public. With its help engineers and scientists can assess requirements for

Fig. 1 The Teslasuit smart suit for a thermal power plant [51]

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work to be carried out in the area of radiation contamination. Canadian university has virtual nuclear reactor simulator. It is used to train emergency response personnel. Ability to simulate different situations and stop events allows lecturers to adjust and objectively evaluate staff and students actions [53]. Chinese scientists at the Chinese Academy of Sciences developed Virtual4DS simulator with virtual reality support to simulate the life cycle events of nuclear power plants and extreme scenarios such as reactor destruction. Using this platform, you can analyze new reactors safety, predict changes in radiation levels and long-term environmental impacts. Virtual4DS can also be connected to new NPP management system for training, staff training, accident simulation. It can test effectiveness of contingency plans. The developed software use will be carried out for design and safety assessment of object through the analysis of large data from digital reactor along with information about the climate, tectonic movement in certain areas [53]. The “digital reactor” (VisualBUS) was developed to provide high accuracy integrated modeling of multiphysical processes for nuclear energy [1]. VR and high computing performance, the operation of the digital reactor is aimed at achieving full range and integrated modeling of reactor behavior in full cycle mode due to integration of digital technologies such as big data, cloud platforms, visualization. Virtual NPP is based on a digital reactor. It is an integrated simulation platform for entire NPP environment. Virtual4DS is an integrated simulation platform for modeling accidents and emergency situations at NPPs. It performs following tasks: modeling reactor design and operation, accident modeling and warning, full-scale migration of radionuclides and environmental impact assessment, public health risk assessment. Virtual4DS can be connected to NPP information system and get access to its status in real time. Also, Virtual4DS has wider application. It is used during wells drilling, testing nuclear weapons and nuclear power plants and more. It was accredited by The Nuclear Energy Agency of the Organization for Economic Co-operation and Development [1]. Let’s consider more detailed tasks of Virtual4DS [1]: • large-scale diffusion of radionuclides in the environment. Research of long-term diffusion processes, forecasting of consequences, estimation of dose load of radiation on population health and environment; • accident warning and simulation of various accidents. It is possible to predict the risks of emergencies at NPPs based on forecasting of NPP equipment malfunctions and changes in the external environment. Thus it is possible to predict the most probable accidents; • nuclear emergency and personnel training. Timely and correct rescue plays an important role in overcoming consequences of nuclear accident. Nuclear accidents are very dangerous and cannot be simulated in the laboratory. It is possible to simulate different scenarios of nuclear accidents and perform training and education of personnel in near-real-world environments due to VR technologies. Training effect in the simulation scene for NPP personnel is important and has advantages of safety, savings (money and time) and repeatability possibility.

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Virtual Fusion Reactor (FUSION-V) is customized applications of Virtual4DS for fusion reactor design and safety assessment. It supports simulation and prediction of new phenomena based on multiphysics coupling with neutron transport in media of high-gradient attenuation radiation, new coolant, tritium breeding and cycle, etc. Safety design and evaluation in the whole space with high fidelity can be done including radiation safety design and evaluation, operation and control safety, repair safety, accident evolution, etc. FUSION-V is a collaborative research platform, supporting collaboration-based research tasks management, cloud-based research such as co-design, intelligent analysis-based performance evaluation, and decision support. FDS team developed virtual reactor (CLEAR-V) based on Visual4DS (Fig. 2). It is possible to anticipate potential problems during the design, construction and operation of the reactor to speed up the design and construction of the reactor and provide operator training [1]. Augmented Reality APP—Chornobyl NPP ARCH AR application was officially launched in 2018. According to the State Agency of Ukraine on Exclusion Zone Management, this application allows to visit new safe confinement and look in detail at the design of the Arch and the Shelter by smartphone. With this tool you can see absolutely all details to the size of the exhibition stand. It is possible to get real picture of object “Shelter” details without risk to human health [54]. In the future, applications of this type can be used to improve efficiency of emergency preparedness and response system and emergencies at potentially hazardous sites. But this requires development of new methods, approaches and information systems to address emergency prevention. These systems should be based on adequate mathematical models of development of various emergencies and meet modern requirements in the field of civil protection [4].

Fig. 2 Three-dimensional neutronics accelerator driven subcritical system model [1]

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Virtual tour or interactive 3D application is a software product that allows you to visualize, navigate, interact with a 3D model. It allows you to move on threedimensional model representing real or projected object, to perform various actions. High degree of 3D model realism contributes to effect of user immersion in presented object. Such objects can be industrial sites, buildings, oil rigs, nuclear power plants, etc. Interactive 3D applications can be effectively used at all stages of the life cycle of an industrial facility: from design to dismantling. Also, applications can be used to solve highly specialized problems or be a part of automated enterprise management system. It can be used by different groups of professionals: engineers, operators, firefighters, repair specialists, security service. Using of 3D-applications provides managers of industrial facility with ample opportunities to monitor progress of various processes at the enterprise, assess the current state of the object, forecast possible accidents [55]. Let’s analyze various aspects of 3D applications use in the operation of nuclear power plants [55]. 1.

2.

3.

Operation, repair and maintenance. Carrying out repair work on complex and dangerous man-made objects is associated with risk of abnormal situations. For this reason, great attention is paid to careful planning of such works. Safety is a priority for operation of industrial facilities. An interactive 3D application based on an up-to-date and accurate three-dimensional model allows you to easily plan repairs, in particular, assess the possibility of safe work, plan the placement and movement of personnel and equipment taking into account safety zones, work with maintenance engineers to perform technological operations. Forecasting and elimination of emergencies. Use of interactive 3D applications increases level of industrial safety, especially on dangerous objects. Such applications can be a system part for monitoring and managing engineering systems of buildings and structures used in potentially hazardous, especially hazardous, technically complex and unique objects. Virtual tour allows to estimate conformity of object to norms of chemical, fire, radiation safety, to develop such documents, as the plan of localization and liquidation of emergency situations. The 3D application allows not only to simulate the development of an emergency situation, but also to assess possible consequences. Also, the user can observe the process both from the side and from inside the virtual object to increase efficiency. For example, assess extent of smoke spread at specified time after the start of the fire or check capacity of emergency exits when playing scenario for the evacuation of personnel. Safety. Presence of 3D model of NPP contributes to effective development of protection scheme. It takes into account individual infrastructure of industrial facility. It is determined by the size and configuration of production site, geometric characteristics of buildings and structures, peculiarities of their location on the territory. Using interactive 3D-application it is possible following: to choose means and schemes of protection and control of situation on object, effectively to place cameras of video surveillance systems, to organize pass mode,

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to provide physical and antiterrorist protection of objects. Also it is possible to visualize in real time. Visualization of processes. Interactive 3D application allows you to visualize modeling results—from spread of smoke in case of fire to assess convenience of repair work in terms of staff access to object. Online visualization involves demonstration of real-time events; offline visualization shows calculations results, such as spread of toxic substances in the event of an emergency. Online visualization involves use of sensors system. It is based on information from which mathematical model of technological process is built and its state at current time is visualized. This visualization helps control the process. Alternative is to visualize different states in pre-determined scenario. Therefore, we emphasize that AR and VR technologies application at nuclear power plants, where the human factor and staff training play a significant role, has significant potential.

We can note following regarding the cost-effectiveness of using interactive 3Dapplications on dangerous objects: reduction of cost and time for staff travel to object; design errors elimination before beginning of construction and installation works stage; increasing level of object industrial safety; improving efficiency of dangerous objects management [55]. Rapid development of VR and AR technologies and their scope expansion led to number of scientific and technical studies on VR and AR technologies development. It is also important to analyze best practices in VR and AR technologies use for NPP personnel training and education. It is necessary to introduce such best practices in higher education institutions, where future specialists are trained to work at nuclear power plants. In [56] it was emphasized that reliable training is a key to safe and productive activities of NPP personnel: “cost of the slightest mistake in the energy sector can be incredibly high!”. In order to minimize risks, special attention should be paid to training for work at NPPs. VR technology is a reliable and effective simulator, with which you can easily design any situation and work out procedure to solve possible problems. By means of VR-system: actions order in the emergency mode is fulfilled; optimization of temporary costs for urgent repairs is achieved; dismantling/installation of equipment elements, etc. is being worked out. Using VR technology you can visualize project for collective acquaintance, further adjustment and joint decision-making in the framework of corporate activities [56]. There is no doubt that energy infrastructure is critical. So, reliability and safety of its facilities, and hence the training of the highest requirements is also critical. VR technology allows you to safely organize such training in conditions close to real. Interactive 3D applications allow you to organize training for remote or hazardous industries. Having 3D model of NPP, there is no need to go to facility for training: training can be done in the office, staff will not only be able to virtually explore the facility, but also with the instructor to play different scenarios [55]. The American Electric Power Research Institute (EPRI), namely the Nuclear Maintenance Applications Center, prepared interactive manual with VR interface

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for working with the Terry Turbine pump turbine. Wearing VR helmet connected to the computer and running the program (Figs. 3, 4 and 5) user enters learning environment [57]. There the user has access to following modes: (1) arbitrary—you can in any order remove and replace pipeline nodes; (2) workshop—the user disassembles and assembles unit with help of prompts, rogram alternately highlights nodes; (3) instruction—an animated video is launched, which shows the procedure for disassembly

Fig. 3 EPRI virtual reality training for terry turbine maintenance (Example 1)

Fig. 4 EPRI virtual reality training for terry turbine maintenance (Example 2)

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Fig. 5 EPRI virtual reality training for terry turbine maintenance (Example 3)

and assembly of the pipeline; (4) test—only text instructions are available. The Terry Turbine was handed over to energy company Dominion Energy, which tested it at three of its nuclear power plants and also praised it. NPP management hopes that virtual training will interest young people in working at NPPs and thus help to improve the staffing situation [57]. In [58] it is noted that VR complex served several purposes. It was created at the Rostov NPP during the construction of its third and fourth power units. According to the developer, VE Group controls (supports) station construction, presentation and staff training. The complex was used primarily as visualization tool in solving complex optimization problems, such as schedule adjusting to minimize delays caused by late deliveries of equipment from contractors and subcontractors. VRsystem belongs to the CADWall type. Image (stereoscopic or normal) is projected on a flat screen. The VE Group solution implements Blending, i.e. stitching images where joints are invisible. Switching and control system allows both in normal and stereo mode to display on top of background image windows of various applications, scaling them as needed. For training, the complex includes system of interactive interaction (tracking). It monitors movement of person in special suit in front of virtual stage. To Developers increased number of infrared cameras from the standard four to ten to increase the accuracy of tracking. Special VR-gloves allow users to work out installation processes on virtual objects, check assembly level of structures and interchangeability of their parts. The complex also includes video conferencing system and acoustics for conferences and meetings [58]. Currently, there are various organizations, such as NPP “Educational Technology”—a research and production company [58] with 40 years of experience that develops and manufactures components of modern high-tech educational environment for most sectors of Russia’s industrial economy. Transition of nuclear energy to higher technological way contributed to the creation in the system of vocational

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Fig. 6 Areas of use of VR and AR technologies for nuclear energy

education of high-tech educational tools (simulators, simulators) as the most effective tools for the formation of knowledge and professional competencies. Such interactive educational equipment and visual aids allow with minimal material and resource costs to identify and consolidate in student appropriate causal links in studied objects, phenomena and processes. On the web site [58] in the section “Training equipment for nuclear energy” it is presented following: nuclear power plants (blocks of nuclear power plants); facilities and complexes with industrial nuclear reactors. Atomic energy training equipment complete with digital tools and VR technologies are designed to comprehensively support all forms of educational process [58]. Thus, based on the analysis of the scientific literature [1, 31, 32, 43] and publications on the Internet [5, 50–58], it is determined that currently VR and AR technologies for nuclear energy are used in the following areas (Fig. 6): modeling of various nuclear energy processes; operation, repair and maintenance of NPP equipment; presentation of activities, NPP construction; staff training and education.

4 Conclusions Analysis of the world experience of VR and AR technology for nuclear energy defined that powerful energy companies actively use these technologies for various needs of industry, namely: staff training, presentation of new equipment (exhibition complexes), etc. It is determined that currently VR and AR technologies for NPPs are used in the following areas: • modeling of various nuclear energy processes. Digital reactor and virtual NPP is used for high-precision integrated modeling of multiphysical processes of nuclear energy. It can analyze new reactors safety, predict changes in radiation levels and long-term environmental impact;

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• operation, repair and maintenance of NPP equipment. Application of VR and AR for planning of repair works on the basis of actual and exact three-dimensional model for estimation purpose of possibility of safe carrying out of works, planning of placement and personnel and equipment movement taking into account safety zones, to work with maintenance engineers; • presentation of activities, construction of NPPs. Application of VR and AR technology for developments or projects visualization. It can be used as a visualization tool in solving complex optimization tasks, such as calendar plan adjusting to minimize delays caused by late deliveries of equipment from contractors and subcontractors during the NPPs construction; • staff training and education. VR and AR technologies can be the most effective tool for skills and professional competencies developing of NPP personnel. Employees can improve their skills (simulating accidents to test the effectiveness of contingency plans) and hone their skills without risking their lives. Thus, various aspects of training and advanced training of NPP personnel using AR and VR technologies are considered. Also, use of VR and AR technology confirms its cost-effectiveness due to the reduction of cost and time spent on business trips to facilities; elimination of design errors before beginning of construction and installation works stage; increasing the level of industrial safety; improving the efficiency of NPP management.

References 1. Wu, Y.: Development and application of virtual nuclear power plant in digital society environment. Int. J. Energy Res. 1–13 (2019). https://doi.org/10.1002/er.4378 2. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Yatsyshyn, T., Matvieieva, I.: Analysis of possible causes of NPP emergencies to minimize risk of their occurrence. Nucl. Radia. Saf. 1(81), 75–80 (2019). https://doi.org/10. 32918/nrs.2019.1(81).13 3. On approval of the concept of the development of the digital economy and society of Ukraine for 2018–2020 (2018). https://zakon.rada.gov.ua/laws/show/67-2018-%D1%80 4. Iatsyshyn, A.V., Kovach, V.O., Romanenko, Ye.O., Deinega, I.I., Iatsyshyn, Andrii V., Popov, O.O., Kutsan, Yu.G., Artemchuk, V.O., Burov, O.Yu., Lytvynova, S.H.: Application of augmented reality technologies for preparation of specialists of new technological era. In: Kiv, A.E., Shyshkina, M.P. (eds.) Proceedings of the 2nd International Workshop on Augmented Reality in Education (AREdu 2019), Kryvyi Rih, Ukraine, March 22, 2019, CEUR Workshop Proceedings, vol. 2547, pp. 181–200 (2019). https://ceur-ws.org/Vol-2547/paper14.pdf 5. Virtual reality in industrial automation. https://ua.automation.com/content/virtualnaja-rea lnost-v-promyshlennoj-avtomatizacii 6. Kyrylenko Y., Kameneva I., Popov O., Iatsyshyn A., Artemchuk V., Kovach V.: Source term modelling for event with liquid radioactive materials spill. In: Babak V., Isaienko V., Zaporozhets A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 261–279 (2020). https://doi.org/10.1007/978-3-030-48583-2_17 7. Mokhor, V., Gonchar, S., Dybach, O.: Methods for the total risk assessment of cybersecurity of critical infrastructure facilities. Nucl. Rad. Saf. 2(82), 4–8 (2019). https://doi.org/10.32918/ nrs.2019.2(82).01

Application of Virtual and Augmented Reality …

257

8. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Hurkovskyi, V., Nikolaiev, K., Yatsyshyn, T., Dimitriieva, D.: Physical features of pollutants spread in the air during the emergency at NPPs. Nucl. Radia. Saf. 4(84), 88–98 (2019). https://doi.org/10.32918/nrs.2019.4(84).11 9. Uvayeva, O, Vakaliuk T., Kostromin, D.: Environmental monitoring and recommendations on decreasing the levels of pesticide pollution in Zhytomyr region of Ukraine. E3S Web Conf. 166, 01004 (2020). https://doi.org/10.1051/e3sconf/202016601004 10. Maevsky, O., Artemchuk, V., Brodsky, Y., Pilkevych, I., Topolnitsky, P.: Modeling of the process of optimization of decision-making at control of parameters of energy and technical systems on the example of remote earth’s sensing tools. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 111–122 (2020). https://doi.org/10.1007/978-3-030-48583-2_7 11. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Yatsyshyn, T.: Conceptual approaches for development of informational and analytical expert system for assessing the NPP impact on the environment. Nucl. Radia. Saf. 3(79), 56–65 (2018). https://doi.org/10.32918/nrs.2018.3(79).09 12. Myrontsov, M.: The method to research equivalent solutions zones for inverse problem of well logging electrometry. In: Monitoring 2019 Conference—Monitoring of Geological Processes and Ecological Condition of the Environment, pp. 1–5 (2019). https://doi.org/10.3997/22144609.201903217 13. Yatsyshyn, T., Shkitsa, L., Popov, O., Liakh, M.: Development of mathematical models of gas leakage and its propagation in atmospheric air at an emergency gas well gushing. East. Eur. J. Enterp. Technol. 5/10(101), 49–59 (2019). https://doi.org/10.15587/1729-4061.2019.179097 14. Iatsyshyn, A., Iatsyshyn, A., Artemchuk, V., Kameneva, I., Kovach, V., Popov, O.: Software tools for tasks of sustainable development of environmental problems: peculiarities of programming and implementation in the specialists’ preparation. E3S Web Conf. 166, 01001 (2020). https://doi.org/10.1051/e3sconf/202016601001 15. Popov, O., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Pomaza-Ponomarenko, A.: Emergencies at potentially dangerous objects causing atmosphere pollution: peculiarities of chemically hazardous substances migration. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 151–163 (2020). https://doi.org/10.1007/978-3-030-48583-2_10 16. Myrontsov, M.: Problem of equivalence in inverse electrometry problems of oil and gas wells. In: 18th International Conference on Geoinformatics—Theoretical and Applied Aspects, 1–5 May 2019. https://doi.org/10.3997/2214-4609.201902070 17. Shkitsa, L.E., Yatsyshyn, T.M., Popov, A.A., Artemchuk, V.A.: The development of mathematical tools for ecological safe of atmosfere on the drilling well area. Neft. Khozyaystvo Oil Ind. 11, 136–140 (2013) 18. Popov, O.O., Iatsyshyn, A.V., Kovach, V.O., Artemchuk, V.O., Kameneva, I.P., Taraduda, D.V., Sobyna, V.O., Sokolov, D.L., Dement, M.O., Yatsyshyn, T.M.: Risk assessment for the population of Kyiv, Ukraine as a result of atmospheric air pollution. J. Health Pollut. 10(25), 200303 (2020). https://doi.org/10.5696/2156-9614-10.25.200303 19. Deineka, D., Kobziev, O., Avina, S., Grin, S., Deyneka, V., Taraduda, D., Sobina, V.: Studying the photocatalytic oxidation of hydroxybenzene in aquatic medium on the photocatalizers SnO2, ZnO, TiO2. East. Eur. J. Enterp. Technol. 5/6(95), 59–67 (2018). https://doi.org/10. 15587/1729-4061.2018.145198 20. Iatsyshyn, A., Artemchuk, V., Zaporozhets, A., Popov, O., Kovach, V.: Mathematical approaches for determining the level of impact of ash-slag dumps of energy facilities on the environment. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 1–13. (2020). https://doi. org/10.1007/978-3-030-48583-2_1 21. Kovach, V., Lysychenko, G.: Toxic soil contamination and its mitigation in Ukraine. In: Dent, D., Dmytruk, Y. (eds.) Soil Science Working for a Living. Springer, Cham (2017). https://doi. org/10.1007/978-3-319-45417-7_18

258

O. Popov et al.

22. Mergner, R., Janssen, R., Kovach, V., et al.: Fostering sustainable feedstock production for advanced biofuels on underutilised land in Europe. Eur. Biomass Conf. Exhib. Proc. 2017, 125–130 (2017) 23. Okhariev, V.: Improvement of statistical method for environmental monitoring datasets interpretation. In: Monitoring 2019 Conference—Monitoring of Geological Processes and Ecological Condition of the Environment, pp. 1–5 (2019). https://doi.org/10.3997/2214-4609.201 903237 24. Trysnyuk, V., Okhariev, V., Trysnyuk, T., Zorina, O., Kurylo, A., Radlowska, C.: Improving the algorithm of satellite images landscape interpretation. In: 18th International Conference Geoinformatics: Theoretical and Applied Aspects, pp. 1–5 (2019). https://doi.org/10.3997/ 2214-4609.20190208 25. Myrontsov, M.: The method to solve the inverse problem of lateral logging sounding and lateral logging. In: Monitoring 2019 Conference—Monitoring of Geological Processes and Ecological Condition of the Environment, pp. 1–5 (2019). https://doi.org/10.3997/2214-4609. 201903244 26. Zaporozhets, A.: Development of software for fuel combustion control system based on frequency regulator. In: CEUR Workshop Proceedings, vol. 2387, pp. 223–230 (2019). https:// ceur-ws.org/Vol-2387/20190223.pdf 27. Zaporozhets, A.: Analysis of control system of fuel combustion in boilers with oxygen sensor. Period. Polytech. Mech. Eng. 63(4), 241–248 (2019). https://doi.org/10.3311/PPme.12572 28. Zaporozhets, A., Kovtun, S., Dekusha, O.: System for monitoring the technical state of heating networks based on UAVs. In: Shakhovska, N., Medykovskyy, M. (eds.) Advances in Intelligent Systems and Computing IV. CCSIT 2019. Advances in Intelligent Systems and Computing, vol. 1080. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33695-0_61 29. Kyrylenko, O.V., Blinov, I.V., Parus, Y.V., Ivanov, H.A.: Simulation model of day ahead market with implicit consideration of power systems network constraints. Techn. Electrodyn. 5, 60–67 (2019). https://doi.org/10.15407/techned2019.05.060 30. Blinov, I.V., Parus, Ye.V., Ivanov, H.A.: Imitation modeling of the balancing electricity market functioning taking into account system constraints on the parameters of the IPS of Ukraine mode. Techn. Electrodyn. 6, 72–79 (2017). https://doi.org/10.15407/techned2017.06.072 31. Gurieiev, V., Sanginova, O.: Simulation and study of modes for full-scale mode simulator for Ukrainian energy systems. In: Proceedings of the 2nd International Conference on Intelligent Energy and Power Systems (IEPS’2016), Kyiv, Ukraine, 7–11 June 2016. IEEE. https://doi. org/10.1109/ieps.2016.7521848 32. Gurieiev, V., Sanginova, O.: Distributed simulation environment of modes for full-scale mode simulator for Ukrainian energy systems. Tech. Electrodyn. 5, 67–69 (2016). https://doi.org/10. 15407/techned2016.05.067 33. Kutsan, Y., Gurieiev, V., Iatsyshyn, A., Iatsyshyn, A., Lysenko, E.: Development of a virtual scientific and educational center for personnel advanced training in the energy sector of Ukraine. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 69–84 (2020). https://doi.org/10.1007/ 978-3-030-48583-2_5 34. Alahmari, M., Issa, T., Issa, T., Nau, S.Z.: Faculty awareness of the economic and environmental benefits of augmented reality for sustainability in Saudi Arabian universities. J. Clean. Prod. 226, 259–269 (2019). https://doi.org/10.1016/j.jclepro.2019.04.090 35. Sorko, S.R., Brunnhofer, M.: Potentials of augmented reality in training. Procedia Manuf. 31, 85–90 (2019). https://doi.org/10.1016/j.promfg.2019.03.014 36. Quandt, M., Knoke, B., Gorldt, C., Freitag, M., Thoben, K.-D.: General requirements for industrial augmented reality applications. Procedia CIRP 72, 1130–1135 (2018). https://doi. org/10.1016/j.procir.2018.03.061 37. Sahin, D., Yilmaz, R.M.: The effect of augmented reality technology on middle school students’ achievements and attitudes towards science education. Comput. Educ. 144, 103710 (2020). https://doi.org/10.1016/j.compedu.2019.103710

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38. Arici, F., Yildirim, P., Caliklar, S, ¸ Yilmaz, R.M.: Research trends in the use of augmented reality in science education: content and bibliometric mapping analysis. Comput. Educ. 142, 103647 (2019). https://doi.org/10.1016/j.compedu.2019.103647 39. Syrovatskyi, O.V., Semerikov, S.O., Modlo, Ye.O., Yechkalo, Yu.V., Zelinska, S.O.: Augmented reality software design for educational purposes. In: Kiv, A.E., Semerikov, S.O., Soloviev, V.N., Striuk, A.M. (eds.) Proceedings of the 1st Student Workshop on Computer Science & Software Engineering (CS&SE@SW 2018), Kryvyi Rih, Ukraine, 30 Nov 2018, pp. 193–225. https:// ceur-ws.org/Vol-2292/paper20.pdf 40. Popel, M.V., Shyshkina, M.P.: The cloud technologies and augmented reality: the prospects of use. In: Kiv, A.E., Soloviev, V.N. (eds.) Proceedings of the 1st International Workshop on Augmented Reality in Education (AREdu 2018), Kryvyi Rih, Ukraine, 2 Oct 2018, CEUR Workshop Proceedings, vol. 2257, pp. 232–236 (2018). https://ceur-ws.org/Vol-2257/paper23. pdf 41. Scaravetti, D., Doroszewski, D.: Augmented reality experiment in higher education, for complex system appropriation in mechanical design. Procedia CIRP 84, 197–202 (2019). https://doi.org/10.1016/j.procir.2019.04.284 42. Garzón, J., Acevedo, J.: Meta-analysis of the impact of augmented reality on students’ learning gains. Educ. Res. Rev. 27, 244–260 (2019). https://doi.org/10.1016/j.edurev.2019.04.001 43. Chauliac, C., Aragones, J.-M., Bestion, D., et al.: NURESIM—a European simulation platform for nuclear reactor safety: multi-scale and multi-physics calculations, sensitivity and uncertainty analysis. Nucl. Eng. Des. 241(9), 3416–3426 (2011) 44. Pochtoviuk, S.I., Vakaliuk, T.A., Pikilnyak, A.V.: Possibilities of application of augmented reality in different branches of education. In: Kiv, A.E., Shyshkina, M.P. (eds.) Proceedings of the 2nd International Workshop on Augmented Reality in Education (AREdu 2019), Kryvyi Rih, Ukraine, March 22, 2019, CEUR Workshop Proceedings, vol. 2547, pp. 92–106 (2019). https://www.ceur-ws.org/Vol-2547/paper07.pdf 45. Iatsyshyn, A.V., Kovach, V.O., Lyubchak, V.O., Zuban, Y.O., Piven, A.G., Sokolyuk, O.M., Iatsyshyn, Andrii V., Popov, O.O., Artemchuk, V.O., Shyshkina, M.P.: Application of augmented reality technologies for education projects preparation. In: Kiv, A.E., Shyshkina, M.P. (eds.) Proceedings of the 7th Workshop on Cloud Technologies in Education (CTE 2019), Kryvyi Rih, Ukraine, 20 Dec 2019, CEUR Workshop Proceedings, vol. 2643, pp. 134–160 (2020). https://ceur-ws.org/Vol-2643/paper07.pdf 46. Chubukova, O.Yu., Ponomarenko, I.V.: Augmented reality technology use for study of disciplines in Ukraine’s higher education institutions. Problemy innovatsiino-investytsiinoho rozvytku 16, 20–27 (2018) 47. What is augmented reality and how is it different from virtual reality? https://blog.comfy.ua/ ua/shho-take-dopovnena-realnist-i-chim-vona-vidriznyaehtsya-vid-virtualnoyi-realnosti 48. Sailors use augmented reality to train for combat. https://www.photonics.com/Articles/Sai lors_Use_Augmented_Reality_to_Train_for_Combat/a64948 49. NASA is using HoloLens AR headsets to build its new spacecraft faster. https://www.technolog yreview.com/s/612247/nasa-is-using-hololens-ar-headsets-to-build-its-new-spacecraft-faster 50. DTEK has developed a VR simulator for power engineers. https://reform.energy/news/dtekrazrabotal-trenazher-vr-simulyator-dlya-elektroenergetikov-12018 51. DTEK will use Teslasuit smart suits. https://reform.energy/news/dtek-budet-ispolzovat-smartkostyumy-teslasuit-11309 52. VR platform and “smart suit”: why the Russian industry is increasingly immersed in virtual reality. https://news.rambler.ru/other/41591943/?utm_content=news_media&utm_med ium=read_more&utm_source=copylink 53. Chinese scientists have developed a VR simulator of a nuclear power plant. https://holograph ica.space/news/virtual4ds-20583 54. Chornobyl Arch online: Stalker application launched online. https://znaj.ua/society/175005chornobilska-arka-onlayn-u-merezhu-zapustili-dodatok-dlya-stalkeriv 55. Interactive 3D-applications creation. https://trimetari.com/ru/uslugi/sozdanie-interaktivnyh3d-prilozhenij-i-sistem-virtualnoj-realnosti

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56. How VR technology is used in energy. https://cutt.ly/4fNjrMZ 57. Virtual and augmented reality technologies are used in energy. https://energy.s-kon.ru/tehnol ogii-virtualnoj-i-dopolnennoj-realnosti-nahodyat-primenenie-v-energetike/ 58. Virtual stands on nuclear energy. https://labstand.ru/catalog/atomnaya_energetika

Application of Material Measure in Measurements: Theoretical Aspects Vitaliy Babak , Artur Zaporozhets , Yurii Kuts , Leonid Scherbak , and Volodymyr Eremenko

Abstract The issues of using material measures to assess the results of measurement of physical quantities are considered. The concept of matching the material and probability measures with the aim of forming a unified approach to the assessment of the result and characteristics of measurement uncertainty is proposed. An example of using a set of material and probability measures in hardware and software modules of information-measuring systems is shown. Keywords Material measure · Probability measure · Measurement result · Information-measuring systems

1 Introduction The basis of the theory and practice of measurement are the fundamental concepts of models and measures. On their basis, measuring instruments were developed, the use of which contributed to the acquisition of new knowledge. The precisely deterministic and probabilistic models of measured values and processes, signals as carriers of information, as well as material and probabilistic measures allow to form a measurement result, to provide the properties of objectivity and reliability for it. Therefore, the issues of improving and developing models and measures in measurement methodology play an important role in achieving the required measurement accuracy and expanding their application areas. The concept of measure is widely used in various applied aspects, including metrology [1–5]. A significant contribution in the development of the using of measures in measurements made in [3, 4, 6]. V. Babak · A. Zaporozhets (B) · L. Scherbak Institute of Engineering Thermophysics of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] Y. Kuts · V. Eremenko National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnical Institute”, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_15

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This paper is devoted to the issues of generalization and research into the use of measures in measurements.

2 Main Part At the beginning of the twenty-first century, the number of publications on the theory of measurement was significantly reduced in comparison with the 50–80 years of the XX century. This result is explained by the fact that in the field of measurements their commercial and business nature is becoming more and more apparent, which contributes to the secretiveness of the latest achievements in informationmeasuring technologies. At the same time, the current stage of development of science, technology and society, requires a wider range of scientific research.

2.1 The Role of the Measure in Evaluating Measurement Results In the process of creating measurement tools, the measure is a fundamental element on the basis of which quality is quantified. Due to this, fundamentally new quantitative information is formed, the uncertainty of the characteristics of the objects, phenomena and processes decreases. This fact makes it possible to adjust the wellknown triad of research methodology “model—algorithm—program” to a more reasoned and constructive—“model—measure—algorithm—program”. The effectiveness of the practical implementation and use of measures is determined by their uncertainty in the reproduction of units of physical quantities. The smaller the uncertainty, the higher the class of the standard or measuring instrument during implementing the corresponding measure. It is known that each country has national standards of units of physical quantities. The nomenclature of the standards and their accuracy characteristics are assessed at the level of the gross product and considered the national treasury of each country. The most developed countries of the world have the largest nomenclature of national standards of physical units [7, 8]. The implementation of physical measures [9, 10] in metrology will be discussed below.

2.2 Material Measures The following definition is generally accepted in metrology: a measure is a measuring device that implements playback and (or) preservation of the physical quantity of a given size. There is another definition: measure (degree of physical quantity, measure

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of quantity) is a measuring instrument in the form of any body, substance or device, intended to reproduce and store the physical quantity of one or more specified dimensions, whose values are expressed in terms of units and are known with the necessary accuracy [3, 4]. In these definitions, the following initial concepts are used as a quantity and a physical quantity. In a broad sense, quantity is a generalization of specific concepts, for example, length, plane angle, area, mass, and the like; a physical quantity is understood as a property that is common in a qualitative sense to many physical objects (their states), and is individual for each of them in a quantitative sense [2]. Usually the physical quantities have corresponding dimensions. Values as separate independent objects do not exist, but are certain characteristics of a group of objects of the material world. Therefore, by the measure in the above definitions means the material carrier of a physical quantity of a given size. The given definitions of a measure are oriented on the practical implementation of measurement, and don’t reflect its physical and mathematical essence. In this sense, a measure of a physical quantity is a certain numerical function μ(·), which associates with each subset of a set A of values of a physical quantity X an integral (generally called) number N = μ( A). For the material measure, the axiomatic of the measure of sets is preserved. Some difference is due to the need to measure the mentioned and negative physical quantities. The latter feature is determined by the choice of a zero measurement scale and can be formally taken into account with the help of artificial introduction, by a certain logical condition, of the “–” sign or with the help of some constant C: μ( A) = μ(A) − C. For example, in the case of transition from absolute temperature to temperature on the Celsius scale, we have C = 273.15 K. A characteristic property of physical quantities is the possibility of their measure  ment, that is x j , x j+1 comparison with a certain value of the same kind and a certain size, which by agreement is taken as a unit of measurement as a measure. Methodologically the use of set measures for measurements can be justified as follows. The size of a physical quantity, as a result of the action of a combination of various burdening factors, could not be determined with arbitrarily high accuracy. Because the entire range of measurement, for example, a scalar physical quantity X is advisable to divide into a number of intervals of the form X j , j = 1, 2, 3, . . .. Each with such intervals that do not overlap is a certain set of dimensions of a physical quantity. On the totality of such sets, one can construct a numerical function with the property of measure. As a result of measurement, a non-named number N is obtained that expresses the ratio of the size x of the measured value X to the measure µ(1x ) defined for a given unit set 1x of values X . The choice 1x and µ(1x ) is established by agreement and justified within the framework of the adopted system of physical quantities units. The main purpose of the measurement is to establish the value of a physical quantity as a quantitative estimate of its size [4]. Y = N · µ(1x )

(1)

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is realized on the basis of reproducing the material degree of the size of a physical quantity. Equation (1) does not take into account the errors of measurement and reproduction of the size of a physical quantity.

2.3 The Concept of Harmonization of Material and Probabilistic Measures in Measurements Metrology, as the science of measurement, in its development has passed a difficult path from the deterministic to the probabilistic approach. Even over the past decades, the concept and content of measurements has been significantly transformed. To confirm this fact, it suffices to give the following definitions of measurements. In work [3] it is given: “Measurement is a reflection of the properties of an object that find themselves in relation to equivalence, order and additivity by a limited number of named natural numbers”. The international dictionary on metrology VIM-3 [2, 8] contains the following broader definition, which emphasizes the probabilistic nature of the measurement result: “Measurement is the process of obtaining one or more values of a quantity that can be reasonably attributed to a quantity”. In the same dictionary, the measurement result is explained as a set of values attributed to the measured quantity along with any other available and significant information (for example, it can be a probability distribution law, standard or extended uncertainty, embracing probability, etc.). The stochastic nature of measurement as a process is emphasized another characteristic—the probability of embracing, that is, the probability that the totality of the true values of the measured quantity is in the indicated interval of embracing. Often the measurement result is presented in the form of coverage interval, which is determined by its own limits and the corresponding probability of embracing [4, 6]. The basis of the measurement definitions is the displaying of values set of the measured quantity, the quality of the study objects in the number—named or nonnamed. Such displaying is convenient and allows you to formalize the measurement result, and to apply mathematical methods for their processing. Next, the logical-mathematical concepts of isomorphism and homomorphism and their use in measurement theory are briefly considered. These concepts express the sameness (isomorphism) or similarity (homomorphism) of the structure of systems (sets, processes, structures, etc.). Two systems, considered abstractly from the nature of their constituent elements, are isomorphic to each other, if each element of the first system corresponds to only one element of the second and each link in one system corresponds to a link to the other and vice versa (Fig. 1). Such a one-to-one correspondence is called an isomorphism. Complete isomorphism occurs only between abstract, idealized objects (for example, the correspondence between a geometric figure and its analytical expression in the form of a formula; a straight line segment as a set of points and a set of real numbers). Often

Application of Material Measure in Measurements … Fig. 1 Graphic illustration of the principle of isomorphism

Set of model properties

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Set of electrical signals

isomorphism is associated with not all, but only with certain cognitively fixed properties and relations of compared objects, which in other relationships may be different. The isomorphism property allows to investigate the model relation that exists in the object of research (OR). In the case when the action of physical laws is investigated under certain conditions, a theoretical analysis of mathematical formulas, measurement equations in the macrocosm is performed, the display of the sets of values of the qualities of the studied quantities or processes and the sets of numbers can be considered isomorphic. Evidence or justification of isomorphism for measurement problems play a fundamental role. This is due to the following. In the general case, the measurement task is the inverse problem of the theory of signals and systems. In the case of proof of isomorphism, the solution of such a problem exists. Unlike isomorphism, “homomorphism” is the correspondence of objects (systems) unambiguously only in one direction. Therefore, the homomorphic image is an incomplete, approximate display of the structure of the original. Homomorphism relationships are more general (and weaker). Therefore, any homomorphism is an isomorphism, but not vice versa. In a measurement experiment, the set M of the properties of the object of study is homomorphic to the set of M parameters and the characteristics of the received information signals. The nonuniformity of reflection inherent in a homomorphism in the general case makes it possible to solve direct problems of measurement theory (modeling) and complicates the solution of inverse problems (actually obtaining measurement results). The goal of the direct task is to find the consequences of known or given causes (information signals as a function of certain OR properties), i.e. “along” causal relationships (Fig. 2). The inverse problems consist in finding the causes (properties of the OR by known signals), i.e. in the direction “against” cause-effect relationships. Direct tasks arise at the design stage, analysis of the measuring instrument, and inverse problems—during the measurement (control).

Fig. 2 Schematic illustration of the formation of the measurement result (gl , gu —lower and upper bounds of uncertainty)

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Measurement process K(g) Measure of Sensor physical quantity

g

Property G

Measure of time y(g,x,y,z,t)

Measure of spatial coordinate

Probability measure

p

P0

Fig. 3 The use of material and probabilistic measures in measurements

The need to share material and probabilistic measures to form a measurement result is shown in Fig. 3. A physical measure gives only one number (or a vector in the case of multidimensional measurements). A measurement result, as a vector, formed by a specific set of prescribed values together with other relevant information, is formed using a probability measure. In the structure in Fig. 3 displayed: 1. 2.

A sensor can perform the conversion of a physical quantity g to another—K(g), for which it’s much easier to create a measure K o (g). Applying a measure of a physical quantity K o (g) to the size K(g), in general, measuring the field at a point in space with Cartesian coordinates (x, y, z) at time t gives a number: +   y(g, x, y, z, t) = K (g, x, y, z, t) K o (g)

3. 4.

5.

(2)

where [·]+ —is the designation of the operation of extracting the integer part of a number. Due to the action of the vector p¯ of unaccounted influencing factors, the inverse transforms y → g gives not only one value, but a certain neighborhood. The result of the measurement is obtained not only as a consequence of technical means of comparing the size of the measured physical quantity with the value, taken as its unit of measurement, but also as the result of applying a probabilistic measure, a specific mathematical apparatus and mathematical statistics. Conversion of various physical quantities to an angular value (plane angle, phase shift of signals) is a convenient type of conversion, because the unit of measure—radian (or π ), is reproduced by means of computer technology with almost unlimited accuracy regardless of the place and time of measurement.

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The stochastic approach in the theory of measurements has particular importance in the case of measurements of physical quantities that have a pronounced probabilistic nature, for example, in the case of nano-dimensions, the study of quantum effects, and the others.

Thus, obtaining a meaningful measurement result is based on the use of a consistent, inextricably connected set of material and probabilistic measures.

2.4 Example of the Use of Measures in Information-Measuring Systems (IMS) The solution of the problem of ensuring homogeneity and stationarity of a measure in the theory of IMS in each specific measurement case has its own specifics and characteristic features [6, 7]. At the same time, it is possible to distinguish general properties of use and types of measures: M1—a measure of a unit of quantity; M2—a measure of the unit of spatial coordinates of the measurement site; M3—a measure of a unit of measurement time; M4—a probabilistic normalized measure of the result and accuracy of measurements in the statistical processing of measurement data; M5—the degree of protection of information of a specific measurement process. In Fig. 4 shows a schematic representation of the use of these measures in the conversion of measurement information by functional modules of IMS. A special case of information protection—improving the noise immunity of measurements, is used for almost all measurement processes. In the structure of IMS, as a rule, there is a signal filtering module, which provides an increase in the signal-to-noise ratio at its output. In the case of measurements of n (n > 1) quantities, there are n measures of the units of the quantities under study. The first three degrees are material measures

Fig. 4 Schematic representation of the use of different measures in the structure of IMS

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(in the general case, we have n + 2 measures), which are implemented by the corresponding standards of measures with a given accuracy. The homogeneity and stationarity of such measures are determined by the characteristics of technical devices and their formation systems. This makes it possible with a certain extent to manage the assurance of uniformity and stationarity of material measures, when carrying out the measurement process in different places of space and at different times, while ensuring a comparison of measurement results and fulfilling the requirements of measurements uniformity. A probabilistic normalized measure is a nonmaterial measure, but a measure of the totality of the effect of various random factors on the value and characteristics of the data and the results of measurements during their conduct. The use of a probabilistic measure in the statistical processing of measurement data makes it possible to improve the accuracy of the measurement result as compared with the accuracy of the measurement data. The measure of information security during measurements is complex. The measure forms by a large number of factors, the action of most of which is random. This makes it possible to determine such a measure as a probabilistic one, which can be applied both for individual operations, such as transmitting measurement data via communication channels, recording the measurement result, and for the whole measurement process as a whole. The accuracy value of these five measures transforms respectively into the accuracy value of the data and measurement results in the corresponding modules of IMS.

3 Conclusions Currently, the use of the international system of SI units at the quantum level and the concept of uncertainty to evaluate measurement results, which are the foundation of measurement practice, require a wide range of theoretical and simulation studies of measurement processes in various subject areas to form a unified measurement methodology. The paper presents the fundamentals of the modern methodology of using measures to assess the results of measuring physical quantities, which reflect only one of the main scientific and technical problems of measurement, as a complex process of combining theory and practice. The concept of matching material and probabilistic measures with the aim of a unified approach to the assessment of the result and characteristics of measurement uncertainty is proposed. An important example for the theory and practice of measurement of the using a combination of material and probabilistic measures in hardware and software modules of IMS is considered.

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References 1. Halmosh, P.: Measure theory. Faktorial Press, Moscow (2003) 2. JCGM 200:2012 International vocabulary of metrology. Basic and General Concepts and Associated Terms (VIM), 3rd edn. (2012) 3. Dorozhovets, M., Motalo, V., Stadnik, B.: Fundamentals of Metrology and Measurement technology. Lviv Polytechnic, Lviv (2005) 4. Chyntsov, V.M.: Fundamentals of Metrology and Measuring Instruments: A Manual. NTU “KhPI”, Kharkiv (2005) 5. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Methods and models for information data analysis. In: Diagnostic Systems for Energy Equipments. Studies in Systems, Decision and Control, vol. 281, pp. 23–70 (2020). https://doi.org/10.1007/978-3030-44443-3_2 6. Eremenko, V., Zaporozhets, A., Babak, V., Isaienko, V., Babikova, K.: Using Hilbert transform in diagnostic of composite materials by impedance method. Periodica Polytechnica Electri. Eng. Comput. Sci. 64(4). https://doi.org/10.3311/PPee.15066 7. Babak, V., Eremenko, V., Zaporozhets, A.: Research of diagnostic parameters of composite materials using Johnson distribution. Int. J. Comput. 18(4), 483–494 (2019) 8. JCGM 100:2008. Evaluation of measurement data. Guide to the expression of uncertainty in measurement. Joint Committee for Guides in Metrology (2008) 9. Crowder, M.: Analysis of Repeated Measures, 272 pp. Routledge, New York (2017). https:// doi.org/10.1201/9781315137421 10. Pereira-da-Silva, L., Virella, D.: Accurate direct Measures are required to validate derived measures. Neonatology, 113(3). https://doi.org/10.1159/000485667

Environmental Safety

Innovative Developments to Solve Major Aspects of Environmental and Radiation Safety of Ukraine Yurii Zabulonov , Oleksandr Popov , Volodymyr Burtniak , Andrii Iatsyshyn , Valeriia Kovach , and Anna Iatsyshyn

Abstract In countries with developed nuclear energy industries there are various problems of environmental and radiation safety that require constant attention and to find optimal ways to solve them. For Ukraine, nuclear energy is a strategically important element of energy supply: today its contribution is about 60%, and according to the Energy Strategy till 2035 there is a plan to increase production. Such prospects for the development of the energy sector are closely related with solving the urgent issues of safe and efficient operation of nuclear fuel cycle facilities. Employees of the State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine” make significant scientific and practical contribution to solve the issues of environmental and radiation safety in Ukraine. This study presents innovative developments that were created by the scientific team of the Department of Nuclear, Radiation and Technogenic-Ecological Safety of the State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”. The results of scientific research and development in the following areas are presented: nuclear physics technologies and technogenic-ecological safety. During 2017–2020, a number of research developments were created and implemented, namely: a plasma-chemical unit to clean nuclear power plants wastewaters; system of operative radiation control (high speed) on the basis of the aircraft; system of radiation control and identification of ionizing radiation on the basis of a solid-state detector with high metrological and operational characteristics; identifierspectrometer based on a solid-state detector for nuclear fuel cycle facilities; portable workstation for comprehensive radiation monitoring of environmental objects «Food light»; information and technical methods of emergencies prevention related to air pollution on the territories of NPPs in Ukraine; new mathematical models of air pollution by emissions from stationary point sources, which allow to determine the zones of technogenic loading at different emission modes, meteorological characteristics and modes of operation of technogenic objects; new technology for the production of effective sorbent based on thermally expanded graphite and many others. Prospects Y. Zabulonov · O. Popov (B) · V. Burtniak · A. Iatsyshyn · V. Kovach · A. Iatsyshyn State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_16

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and directions of further innovative activity of the State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine” are determined. Keywords Nuclear energy · Nuclear fuel cycle · Environmental safety · Nuclear physics technologies · Comprehensive ecological monitoring · Emergency prevention · Software and modeling complexes

1 Introduction Nuclear energy sector is quite developed in Ukraine. Share of nuclear energy generation in total electricity production will increase according to the Energy Strategy of Ukraine for the period up to 2035 “Security, Energy Efficiency, Competitiveness”. Prospects for this industry development are closely related with solving urgent problems of safe and efficient operation of nuclear fuel cycle (NFC) facilities. Scientific support for the solutions of the mentioned issues is provided by the units of the Department of Nuclear Physics and Energy of the National Academy of Sciences of Ukraine, including the State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine” (SI “IEG NASU”). SI “IEG NASU” [1] is one of the leading scientific institutions in Ukraine in the field of environmental and radiation safety. It is known worldwide, recognized and works closely with many domestic and foreign scientific organizations. The Institute is legal successor of the State Research Center for Environmental Radiogeochemistry. It was established in 1996 on the basis of the Department of Environmental Radiogeochemistry and the Department of Metallogeny of the Institute of Geochemistry, Mineralogy and Ore Formation of the National Academy of Sciences of Ukraine. Since 2004, SI “IEG NASU” is a member of the Department of Nuclear Physics and Energy of the NAS of Ukraine [2]. Currently, director of the SI “IEG NASU” is a corresponding member of the NAS of Ukraine, Doctor of Technical Sciences, Professor, Yurii Zabulonov. Employees of the Department of Nuclear, Radiation, Technogenic and Environmental Safety (Department) deal with solution of tasks on civil protection, complex monitoring and scientific-technological basis of radiation and environmental safety. The Department consists of the Division of Nuclear Physical Technologies, the Division of Civil Defense and Innovation and the Division of Environmental Protection and Radiation Safety. In January 2021 SI “IEG NASU” celebrates its 25th anniversary. Therefore, it is important to present its work, namely analysis, systematization and generalization of the experience of the Department over the past few years.

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2 Literature Analysis and Problem Statement Experience of SI “IEG NASU” was partially summarized and described in the publication [3]. Publications of the employees of the Department present obtained scientific results, which are systematized in the following areas: • civil protection of territories, population and environment [4–8]; • comprehensive environmental monitoring of technogenic loaded territories [4–6, 9, 10]; • radiation, technogenic and ecological safety [11–28]. However, a work on the analysis, generalization and illustration of scientific achievements and other aspects of the Department of Nuclear, Radiation, Technogenic and Environmental Ecological Safety of the SI “IEG NASU” was not performed before. Therefore, the authors decided to present work of the Department for the period 2017–2020. The research aim—is to summarize and to systematize work of the Department of Nuclear, Radiation, Technogenic and Environmental Safety; to highlight interaction with other institutions and to outline perspective areas for further work.

3 Research Results Scientific topics and results of scientific research of the Department correspond to: prior directions of science and technology development in Ukraine; national tasks aimed to integrate domestic science into the world‘s scientific space and the European Research Area; concepts of development of the National Academy of Sciences of Ukraine for 2014–2023; goals and priorities of the Energy Strategy of Ukraine for the period up to 2035; goals of sustainable development of Ukraine for the period up to 2030; development strategy implementation of domestic research infrastructure; tasks provided by the current legislation in the field of informatization of science, education; strategies for system development of involvement and preparation of student youth for scientific and scientific-technical activities; etc. Basic research results of the Department are main scientific and technological base of innovative developments of methods, techniques, technologies and control means of radiation and sanitary and hygienic condition of environment, cleaning of environmental components from technogenic pollution. Therefore, basic researches over the past few years were aimed to solve sustainable development issues of nuclear energy, environmental problems of NFC, handling with radioactive materials at all stages of their life cycle: 1.

Environmental safety issues of NFC: new principles and creation of technologies of nanoscale multi-purpose materials usage to improve environmental safety of NFC enterprises; development of scientific bases for stochastic analysis of radiation fields to determine criteria for pre-emergency condition of nuclear

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facilities and remote monitoring; improvement of scientific and technological bases of monitoring, forecasting, modeling and assessment of radiation and sanitary and hygienic condition of natural environment of Ukraine, emergencies prevention of natural and technogenic nature. Scientific support of works that are aimed to overcome consequences of the Chornobyl catastrophe, to return the Exclusion Zone and the Zone of obligatory guaranteed resettlement territories to the national economic use. Applied research was aimed at:

1.

2.

3.

4. 5.

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Solving environmental safety issues due to the operation of nuclear facilities (NPPs, LRW (liquid radioactive wastes) storage facilities, plants on spent nuclear fuel reprocessing), development of devices of radioactive aerosols emissions monitoring, measures to minimize radioactive contamination of the hydrosphere and rehabilitation of radioactive sites due to contamination and radiation accidents. Development of technologies for wastewater treatment from organic and radioactive contaminants generated during the operation of NPPs and NFC facilities. Development of technology for creation of solid-state detectors based on cadmium tellurium for X-rays and gamma radiation detection with high efficiency and energy resolution. Development of mathematical and software tools to optimize network of air pollution monitoring stations in technogenic loaded areas in Ukraine. Creation and introduction of the newest technologies of elements manufacturing of anode and cathode lithium-ion accumulators of increased capacity for tasks of peak loadings conversion on power system. Method development for synthesis of highly dispersed nanosorbent based on magnetically sensitive thermally expanded graphite to eliminate spills of oil and petroleum products in water areas.

The SI “IEG NASU” formed comprehensive approach to the research and as a result innovative scientific and technical projects were developed. Therefore, for the period 2017–2020, scientific and technical (experimental) developments of the Department were aimed at: 1.

2.

creation of: plasma chemical treatment plant prototype for liquid radwaste generated during the operation of NPPs and NFC facilities (Fig. 1); mobile installation prototype for highly dispersed magnetically sensitive thermally expanded graphite synthesis; prototype of thermochemical cavitation unit for liquid radwaste treatment of NFC facilities (Fig. 2); development of: rapid response systems based on unmanned aerial vehicles for effective control of radiation situation at industrial sites of NFC facilities and radioactively contaminated areas; systems of radiation control and identification of ionizing radiation based on solid-state detector with high metrological and operational characteristics for effective control of specific activity

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Fig. 1 Plasma chemical plant for purification of contaminated drinking water by cold plasma

Fig. 2 View of mobile laboratory installation for thermally expanded graphite synthesis

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of liquid, viscous, bulk food and non-food samples at the level of permissible concentrations and below, identification of their radionuclide composition; prototypes of mobile systems for neutron and gamma radiation detection, including from “hidden” sources of ionizing radiation, in real time, to control movement of radioactive materials; prototype installation of rapid measurement of the specific content of strontium-90 for radioecological monitoring in laboratory and field conditions; software-modeling system for impact assessment of potentially dangerous objects of Ukraine emissions on the air and the population of adjacent territories. Also, during 2017–2019, “top secret” research work was carried out, related to solve urgent problems of emergency prevention at critical infrastructure facilities. Employees of the Department carried out scientific and scientific-technical examination. Basis for expert activities conducting is scientific, technical and experimentalapplied base of the National Academy of Sciences of Ukraine and many years of employees experience. Scientific and expert activities are carried out in various committees, councils, commissions, working groups, in particular: The National Commission for Radiation Protection of the Population, Verkhovna Rada of Ukraine, the Ministry of Energy and Environmental Protection of Ukraine. Employees of SI “IEG NASU” are experts at the international organizations as Pure Earth (USA) consulting on solving pollution by minimizing the impact on health, especially children; research and production company Pegasus Environmental and Nuclear Services, Inc. (USA) on air monitoring; Ministry of Energy and Environmental Protection of Ukraine for the analysis of pesticide residues in the environment, food and agricultural raw materials, etc. There was also an examination of dissertations for degree of Candidate of Science (Doctor of Philosophy) and Doctor of Science, review of scientific articles, abstracts of dissertations, monographs, works nominated for various awards, scholarships and other awards, master’s and bachelor’s theses, curricula and more. The divisions stuff of the Department are qualified scientists with many years of positive experience in research conducting. Extensive experience, high qualification and professionalism of employees allow effectively redistribute their workload in implementation of fundamental, applied and innovative projects. Significance of the scientific results obtained by the employees and their recognition on the territory of Ukraine is confirmed by received prizes, scholarships and other awards. SI “IEG NASU” completed full cycle of training and certification of highly qualified scientific personnel. During 2017–2020, three specialized scientific councils functioned at the Institute: geological sciences (specialties “Geology of metallic and non-metallic minerals” and “Environmental safety”), technical sciences (specialties “Civil protection” and “Environmental safety”), technical science (specialties “Civil Protection” and “Environmental Safety”, “Secret”). Employees of SI “IEG NASU” work at universities, which is a source of involving young people into the scientific work from the student audience. Agreements on cooperation were signed with the leading Ukrainian universities and on the basis of the SI “IEG NASU” students have their internships.

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In order to increase efficiency and to ensure optimal results of activities, agreements on cooperation and joint scientific and technical activities were concluded with leading enterprises and organizations of Ukraine and the world, among which are: State Enterprise “NNEGC Energoatom”, State Enterprise “State Scientific and Technical Center on Nuclear and Radiation Safety” of the State Inspectorate for Nuclear Regulation of Ukraine, State Concern “Nuclear Fuel”, “Pegasus Environmental and Nuclear Service, Inc.” (USA), “UTR Engineering & Trade Gmbh” (Austria), “PLEJADES Independent Experts” (Germany), “Indra Scientific SA” (Belgium), “Mavinci” (Turkey), “EU-China Vision & Action” (China), “ANSeeN” (Japan), and foreign universities: Blacksmith Institute (USA)), Clemson University (USA), Vilnius University (Lithuania), Khazar University (Azerbaijan), Maritime Academy in Szczecin (Poland), Lublin Polytechnic (Poland), etc., and institutions of NAS Ukraine. Every year various meetings and conferences are held with foreign partners as part of the implementation of these agreements. Employees of the Department completed internships in the world’s leading research and educational organizations, including: Blacksmith Institute (USA), Harbin Institute of Technology (China), Pegasus Environmental and Nuclear Services, Inc. (USA), the Food and Agriculture Organization of the United Nations (Italy). Scientists of SI “IEG NASU” are involved in international organizations working under the auspices of the UN: UNESCO, the Red Cross, the OSCE, the IAEA, FAO, as experts. Representatives of IEG NAS of Ukraine participate in projects implementation funded by the 7th EU Framework Program, EU Horizon 2020 Framework Program “Euratom”, NATO, IAEA, UK funds, USA, etc. The employees of the Division of Nuclear, Radiation, Technogenic and Environmental Safety have more than half a century of experience in eliminating environmental consequences of radiation accidents in Ukraine (Chernobyl disaster, radiation accidents at Kyiv and Kharkiv UDO “Radon”), Russia (Chelyabinsk-40), in Semipalatinsk, Kazakhstan), Japan (Fukushima), Latvia (Baldone). After the Chornobyl catastrophe, the Interdepartmental Commission on Radioactive Waste Management of Ukraine was established. It currently functions on the basis of SI “IEG NASU”. During the period 2017–2020, the staff of the Division of Nuclear, Radiation, Technogenic and Environmental Safety published more than 120 scientific papers, including monographs, articles in scientific journals indexed in Scopus and Web of Science Core Collection, articles in scientific journals of Ukraine, abstracts reports in the materials of scientific Ukrainian and international conferences. Let’s present the most significant results of research and development achieved in recent years in the following areas: 1.

Nuclear physical technologies:

– a model of automated operational remote control and monitoring system based on an aircraft for rapid response and effective control of radiation situation in the area of influence of natural and tehnogenic sources of ionizing radiation was developed. Software and hardware channel testing the for scanning radioactive radiation was performed;

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– a method of manufacturing X-ray and gamma radiation sensors with high resolution based on cadmium tellurium was developed. A laboratory sample of the dosimeter was created; – it was developed plasma-chemical technology for the synthesis of nanocomposites for purification of water from radioactive contamination, heavy metals, organic compounds. The technology is intended for use at the stage of liquid radwaste processing; – an experimental sample of identifier-spectrometer based on solid-state detector designed for operational control and identification of radioactive isotopes at NFC facilities was developed; – developed and manufactured an experimental sample of low-threshold (20– 3000 keV) “Mobile monitoring station for radiation and meteorological conditions of the environment”—“MeteoSpectrum”. It provides real-time information on the state of environment in real time. 2.

Technogenic and environmental safety:

– it was developed software-modeling system that allows comprehensive assessment of chemical impact of energy facilities on air and population of adjacent areas with appropriate accumulation, storage, systematization, processing, analysis, exchange and visualization of monitoring data, as well as modeling and forecasting layer of the atmosphere in the controlled areas (Fig. 3). The modeling module is based on a new mathematical model of air pollution by emissions from stationary technogenic objects, developed by the authors. Unlike existing models, it is a set of three analytical dependencies that describe the distribution of pollutants in space and time, respectively, in instantaneous (volley), short-term and continuous emissions. This allows to solve a wider class of problems related to the

Fig. 3 Created software to provide safety

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control of atmospheric air in the areas of pollution sources under different emission conditions, meteorological characteristics and modes of their operation. Analytical dependences that form a mathematical model of air pollution in nonstationary and stationary modes of emissions from technogenic objects under the condition of complete reflection of pollutants from the underlying surface have the following form: (1)

for immediate emission: q(x, y, z, t) = A1 (B1 + C1 + D1 ),

where A1 , B1 , C 1 , D1 —functions of the time variable, which have the following form: 

Me

A1 (t) =

−

2 x cos α+y sin α−u H t +(−x sin α+y cos α)2 ef 4K t

− λ(x cosu α+y sin α)



He f

8π π K 2 K z t 3

B1 (t) = e−

(z−He f −wg t )2 4K z t

wg D1 (t) = Kz

∞

, C1 (t) = e− e−

(

wg He f −z 0 Kz

(

wg He f −z 0 Kz

,

) − (z+He f −2z0 −wg t )2

) − (z+He f −2z0 −wg t+ξ )2 4K z t

4K z t

,

dξ ;

0

(2)

for a source of short-term emission duration t 1 : t1 q(x, y, z, t) =

A2 (B2 + C2 + D2 ) dτ, 0

where A2 = A1 (t − τ ), B2 = B1 (t − τ ), C2 = C1 (t − τ ), D2 = D1 (t − τ ); (3)

for a continuous emission source: t A2 (B2 + C2 + D2 ) dτ = A3 (B3 + C3 + D3 ),

q(x, y, z) = lim

t→∞ 0

(

where A3 =

Me

u H (x cos α+y sin α) wg z−He f ef + 2K 2K z

4π K

√

Kz

) − λ(x cos α+y sin α) uH ef

,

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B3 =

e

− 21

x 2 +y 2 K



+

(z−He f )2 · Kz

x 2 +y 2 K

D3 =

wg Kz

∞ 0

e

u 2H ef K

2 z−H + ( K ze f )

 wg 1 x 2 +y 2 + ξ − 2 2K z K x 2 +y 2 K

, C3 =

 2

Kz

+

e

− 21

x 2 +y 2 K



+

x 2 +y 2 K

(z+He f −2z0 −ξ )2 · u He f





 w2

+ Kgz

K

(z+He f −2z0 −ξ )2

wg2 +K z

(z+He f −2z0 )2 · Kz

u 2H ef K

2 z+H −2z + ( eKf z 0 )

w2

+ Kgz

,

dξ ,

Kz

where q(x, y, z, t)—the concentration of the pollutant as a function of the coordinates of space and time, [mg/m3 ]; λ—parameter that takes into account the interaction of impurities with the environment (chemical transformation, leaching by precipitation, absorption by the underlying surface), [s−1 ]; K x , K y , K z —turbulent diffusion coefficients along the axes, respectively Ox, Oy and Oz (when building the model it was assumed that K x = K y = K), [m2 /s]; H ef —effective lifting height of the emission torch, [m]; z0 —the roughness parameter of the underlying surface, [m]; t—the time of impurity spread, [s]; α—the angle between the wind direction and the axis Ox; u He f —wind speed at the effective height of the emission source, [m/s]; wg —gravitational deposition rate for heavy impurities, [m/s]. It is also should be noted the world-class experimental technical devices created by the staff of the Division. It provides real-time operational search, detection, localization and identification of nuclear radiation materials for limited time or even single observation in uncertain and adverse conditions and interference, namely distributive integrated automated system for control and monitoring of nuclear radiation materials, radwaste and sources of ionizing radiation at NFC facilities. There are following elements of it: 1.

2.

3.

4.

“ASPEK” aero-gamma spectrometry system which provides remote detection of spatially dispersed sources of nuclear radiation materials from the aircraft, visualization of their spatial distribution, identification of isotopic composition, construction of a map of radioactive contamination of area to geographic reference; “ASPEK-O” spectrometric complex (SC) for radiation detection and monitoring based on unmanned aerial vehicle type octocopter. It allows remote measurements of the equivalent dose power of gamma radiation and to determine radionuclide composition of the radiation trace and surface density. This SC is the first system in Ukraine that is able to perform entire cycle of work—from the detection of local inhomogeneities in soil and search for radioactive sources and their contouring to determination of radiation pollution levels; portable workstation “Vector-M” is designed to detect moving and stationary sources of ionizing radiation in real time with definition of their geographical coordinates and identification of sources and reliable assessment of their activity; automated complex of spectrometry of human internal radiation “G-Screen” is designed to measure content of incorporated radionuclides in the human body and their identification;

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6.

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portable workstation “FoodLight is designed to measure activity of gammaemitting radionuclides Cs-137, Cs-134, K-40, Ra-226, Th-232, liquid, in viscous, loose food and non-food samples at the level of permissible concentrations and below without preparation of samples by chemical isolation and concentrationin the field of volumetric (specific); comprehensive automated system for control and monitoring of rapid response. Assessing of radiation situation in the shortest possible time contributes to making the most optimal decisions to eliminate radiation accidents consequences.

Figure 4 shows the block scheme of the system for remote radiation monitoring. Note the radio channel is always active during flight but the Wi-Fi channel is only active when stationary and serves mainly for transmission of stored data. Figure 5 shows the map with radioactive spots along the flight route. Employees of the Department are founders and members of two scientific schools. First one is “Technogenic and environmental safety of potentially dangerous objects” in the areas of comprehensive environmental monitoring of potentially dangerous objects; mathematical modeling, forecasting and impact assessment on environment

Fig. 4 Block scheme of the system for remote radiation monitoring

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Fig. 5 Overlaying the flight routes on the map of the area

and population from technogenic objects. It was founded by the former director of SI “IEG NASU”, corresponding member of NAS of Ukraine Georgii Lysychenko. Second one—“Nuclear physics technologies and systems” in areas of comprehensive monitoring, scientific and technological bases of radiation and technogenic environmental safety; technologies for purification of liquid radwaste and technogenic contaminated liquids. This scientific school was founded by the current director of the SI “IEG NASU”, corresponding member of the NAS of Ukraine Yurii Zabulonov. Development of these scientific schools provides significant potential of highly qualified world-class specialists in solving fundamental and applied problems of sustainable development of NFC. Uniqueness of the Institute is marked by the following due to activities of the Department staff: 1.

2.

It is the only one Institution in Ukraine which developed and applied technology and equipment for remote (on board of any aircraft, including unmanned aerial vehicle) radiation mapping of territories, including the Chernobyl Exclusion Zone. It is world leader in the development of plasma chemical technology: developed innovative plasma chemical reactors, primarily for treatment of liquid radwaste from radionuclides of NFC facilities, technogenic wastewater from pesticides, for disposal of persistent organic pollutants (pesticides, etc.).

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5.

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It is only one Institution in Ukraine that deals systematically and comprehensively with radiation monitoring of the territories of NFC facilities. It is one of the basic scientific organizations in Ukraine, where research of the Chornobyl catastrophe consequences for the environment and the population, started in 1986, continues. Vast majority of scientific research is carried out at intersection of such branches of science: nuclear physics, physical chemistry, geochemistry and plasma physics, thermophysics and molecular physics, solid state physics, spin chemistry. It allows getting unique scientific results, implement innovative projects, be attractive for investment and competitive in world science.

Developments of the Department have great practical importance for ensuring environmental safety of the state. It is confirmed by received acts of implementation in various enterprises and organizations. Thus, during 2017–2020 many research developments were implemented, including: plasma chemical plant for sewage treatment of NPPs (Research and Production Association “Energochim”); system of operational radiation monitoring of rapid response based on the aircraft (Corporation “Ukrainian nuclear devices and systems:); radiation monitoring and identification system of ionizing radiation based on solid-state detector with high metrological and operational characteristics (LLC “NIG Mitron”); identifier-spectrometer based on solid-state detector for NFC facilities (LLC “Indra Scientific Ukraine”); Portable workstation for complex radiation monitoring of environmental objects “Food light” (Indra-Scientific, Belgium); information and technical methods of emergencies prevention related to air pollution at sites of Ukrainian NPPs (Department of Civil Protection Events Organization of the State Service of Ukraine for Emergencies); new mathematical models of air pollution by emissions from stationary point, linear and site sources, which allow to determine areas of technogenic load at different emission modes, meteorological characteristics and modes of operation of man-made objects (State Ecological Academy of Postgraduate Education and Management); rotary-pneumatic piston machine and concept of building autonomous efficient energy generating complexes based on renewable energy sources (“Bioenergy Association of Ukraine”); new technology for production of efficient sorbent based on thermally expanded graphite (“Perechyn district sector of the State Service of Ukraine for Emergencies in Zakarpattia region”) and others. Also 5 utility model patents and 1 application for utility model patent were received. Scientific authority of the employees of the Department is confirmed by the following achievements: 1.

Participation in implementation of following international projects: within the program HORIZONT 2020—SME INSTRUMENT Phase I-OILCS “Oil catchpot system” (2018), “FORBIO—use of abandoned lands in Europe for growing bioenergy crops” EU project FORBIO-REP-691,846 (2016–2018), and SfP 985,094 “Reliable nuclear materials identification technology from spectrometry data” (2017–2019); in cooperation with International Laboratory for Analysis of Outbreaks of Disease and Poisoning in the international project

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3.

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“Development of Autonomous Mobile Complex (AMK-1X) and compact technology for utilization of toxic substances and their precursors by method of plasma sublimation and magnetic classification” (2015–2017).); in cooperation with Japanese-German colleagues (University of Munich, PLEJADES and Quantum Systems)—international project “Remote mapping of temporary disposal points of radioactive waste and assessment of radioactive contamination levels by methods of lidar and radiation scanning of the research area” (2017); joint Ukrainian-German project initiated by the German Office for Radiation Protection “Creation of new sensitive detectors for radiation monitoring of aerosols” (2017). In January 2020 an international grant was received under funding from Innovate UK, UK Research and Innovation—“Advanced cold plasma technology for the removal of persistent organic pollutants from fresh water” (2020–2023). Number of agreements for implementation of economic contract scientific and technical work with various organizations, won and appropriate funding in competitions of the NAS of Ukraine under various target programs and grants of the President of Ukraine for young scientists. The Department staff held many meetings with representatives of domestic and international scientific institutions and organizations as a part of joint projects and cooperation: Clemson University (USA), NATO (Belgium), LudwigMaximilians University of Munich (Germany), The Food and Agriculture Organization of the United Nations (Italy), Blacksmith Institute (USA), research organizations PLEJADES (Germany), Quantum Systems (Germany), Mavinci (Turkey), ASD GROUP Corporation (Turkey), UTR Engineering & Trade Gmbh (Austria), Indra Scientific SA (Belgium), Joint Research Center of the European Commission (EU countries), Research Institute of Natural Sciences and Technology (Ukraine, China), “EU-China Vision & Action” (China), Nuclear Power Institute of China (China), Academic Committee of Shandong Academy of Sciens (China), Biology Institute of Shandong Academy of Sciens (China), Environmental Technology Engineering Co., ltd (China), Beijing Huahang Beidou Energy Technology Co., Ltd. Ltd (China), “Environmental Resources & Technology (Beijing) Inc.” (China), GET (Japan), Tokyo Institute of Technology (Japan), Korea Atomic Energy Research Institute (South Korea), Asociatia de Prietenie Japan, Ukraine, Moldova ONG, Global Technologies (Singapore), Oak Ridge National Laboratory (USA), NNEGC Energoatom (Ukraine) and others. The employees of the Department constantly take part in various scientific mass events, namely international conferences, forums, seminars, exhibitions and presentations. Also, they are members of program and organizing committees of international and national scientific events, namely: workshop “Energetic crop production: use of Miscanthus hybrid perspectives of planting in Ukraine” in the framework of the EU project Horizon 2020 “FORBIO use of abandoned land in Europe for growing bioenergy cultures” EU project FORBIOREP-691846 (Lincoln, Great Britain, 2018); workshop at the German Institute “Forschungsinstitut für Bergbaufolgelandschaften” (2017); panel discussion at

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4.

5.

6.

287

the Blacksmith Institute retreat (New York, USA, 2018); International scientificpractical conference “Current issues and prospects for postgraduate education development” (2020). Development of scientific potential and training of scientific youth is an important aspect of the Department work. This is evidenced by number of awards received by young scientists working in the Department, awards, distinctions, scholarships, honorary diplomas, namely: Prize of the President of Ukraine for young scientists in 2018 (Popov O.O., Kovach V.O., Iatsyshyn A.V., Artemchuk V.O.); Award of the Verkhovna Rada of Ukraine to the most talented young scientists in the field of basic and applied research and scientific and technical developments for 2017 (Popov O.O., Artemchuk V.O.), Scholarship of the President of Ukraine (Popov O.O., Kovach V.O.), Scholarship of the National Academy of Sciences of Ukraine (Kovach V.O.), nominal scholarship of the Verkhovna Rada of Ukraine for young scientists-doctors of sciences (Popov O.O.), Distinction of the NAS of Ukraine “Youth. Inspiration. Work” (Kovach V.O.). Also, young scientists received 2 grants from the National Academy of Sciences of Ukraine to research laboratories/groups of young scientists of the National Academy of Sciences of Ukraine to conduct research in priority areas of science and technology and a grant from the President of Ukraine to support research of young scientists in 2018. High level of scientific achievements of employees is marked by responses and reviews of scientific works received from leading scientists of Clemson University (USA), Vilnius University (Lithuania), Khazar University (Azerbaijan) and other organization. Numbers of appreciation letters were also received from foreign partners for providing consultations and expert assessments in solving important scientific problems. Scientists of the Department were invited to television programs, briefings, interviews to promote scientific achievements, coverage of research activities, providing expert assessments, namely: Georgii Lysychenko took part in the program “Characters” on the radio “Vesti”. It was dedicated to the Day of Remembrance of the Chernobyl accident (2017), he gave an interview to the information site “Ukrainian Choice” about the Chernobyl accident, its consequences and area affected by radioactive pollution due to the Chernobyl tragedy (2017); Yurii Zabulonov gave an interview to representatives of the Japanese company “GET” (2018) on joint projects possibility in the field of consequences overcoming of the Chernobyl and Fucushima accidents (spectrometry of radioactive objects, radioecology in decontamination of equipment, radioecology of the Red Forest) and gave an interview to NATO Liaison Office in Ukraine on implementation of the Institute’s research project “Reliable nuclear materials identification technology from spectrometry data” under the program

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“The NATO Science for Peace and Security Program, SPS” (2017). The interview is timed to the 20th anniversary of the NATO-Ukraine Strategic Partnership; an interview to South Korean public television on the impact of the Chernobyl accident on human health (2017); Yurii Zabulonov and Volodymyr Burtniak took part in programs on the TV channel “1 + 1” as well. They presented the most important achievements and developments of the Institute (2019) there. SI “IEG NASU” has reputation of leading scientific institution of Ukraine for development of nuclear and physical technologies and radwaste management. It promotes foreign investors interest in possible cooperation. Thus, during 2017– 2020 number of meetings took place with potential investors from Bulgaria (2019) and Turkey (2019). As a result of the meetings, an agreement was reached and all aspects of future cooperation were agreed and relevant documents were prepared for signing of agreements on obtaining funding for projects implementation. Also, the employees of the Department are participants in the development of the conceptual foundations of the scientific park “Academ.City”.

In 2019 due to the change in the leadership of the Institute, the general strategy of its development and the Department in particular was updated. It is aimed at increasing role of domestic science as an important factor in country modernizing, increasing of the Institute’s contribution to ensuring effective economic and social development of Ukraine, its defense capabilities and national security by: – raising level of basic and applied research, scientific and technical (experimental) developments, strengthening their interdisciplinary nature in order to be widely implemented in state and international organizations; – intensification of research and development aimed at increasing of scientific intensity and competitiveness of scientific results, creating an effective innovation infrastructure; – development of research infrastructure, improvement of their logistics and information support; – strengthening human resources, creating new and developing existing scientific schools, attracting talented young people to the scientific process; – development of educational activity, expansion of participation in training of scientific personnel of the highest qualification; – further integration into the international scientific community. The strategy describes tasks and measures to improve activities of the Institute in such important areas as: research, innovation, educational, international, research and publishing, staffing, logistics and financial support, science promotion, image development. Let’s present special measures to support positive trends of activity: – structure improving of the Department of Nuclear, Radiation, Technogenic and Environmental Safety by creating of department of innovation and international activities (for innovation, improving international cooperation, ensuring participation in international projects);

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– transition to the model of new type of institution—research HUB-institute, which works closely with business structures, authorities, leading companies, venture and investment funds, international partners and is a driver of innovative development of the region and the country as a whole; – introduction of new interdisciplinary directions of scientific research; – holding conferences, seminars, trainings, desktops, lectures, etc. for young scientists and awarding them prizes to motivate further research; – launch an international workshop on environmental issues in the framework of the International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters, which results will be published in the journal E3S Web of Conferences (France); – in 2021 to take part in organization of the International Symposium of Sciences on the separation of substances, which will be held in Ukraine, to promote establishment and development of large number of scientific ties, to launch promising research projects; – holding an international scientific-practical conference once every two or three years on the basis of SI “IEG NASU” with coverage of its work in media and publication of results in special issue of scientific publication of the same name; – involvement of well-known scientists in conducting lectures (online, offline), increasing academic mobility by signing memoranda with higher education institutions of Ukraine and abroad; – increasing of English language proficiency level of employees through appropriate training courses introduction; – promoting of joint departments and laboratories establishment in order to integrate academic and university sectors of science, popularization of research results; – wider implementation of the project approach in practice of scientific activity of the Department in particular on the basis of project and grant national and international funding; – formation of “startup incubator” and a business accelerator for further implementation of innovative ideas in the national and international business environment; – implementation of the IPR strategy: improvement of intellectual property protection system and work on commercialization of intellectual property rights, intensification of patent activity, economic attractiveness ensuring of scientific developments. Generally strategic goals of the Institute for 2019–2023 are following: to ensure high world level of research and development, to obtain significant scientific results and their widespread implementation in various spheres of public life, to develop innovation, to train young generation of scientists in tradition of the best scientific schools. Thus, in view of the above, it can be argued that human and technical potential of the Department of Nuclear, Radiation, Technogenic and Ecological Safety allows to solve current problems of energy and environmental safety of Ukraine.

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4 Conclusions Today, State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine” is one of the leading scientific institutions in Ukraine in the field of environmental and radiation safety. It is world-renowned and works closely with many scientific organizations nationally and internationally. The Department of Nuclear, Radiation Technogenic and Environmental Safety is a part of the Institute. It provides solutions of urgent issues in such important areas of Ukraine’s national security as civil protection of territories, population and environment, comprehensive environmental monitoring of technogenic-loaded territories, radiation and technogenic and ecological safety. Over the 25-years history the State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine” with its activities and unique developments gained prestige at the national and international levels. Young promising scientific personnel is trained thanks to the active work of scientific schools in the Department of Nuclear, Radiation Technogenic and Environmental Safety. They demonstrate achievements in work, successfully defend dissertations, receive awards and scholarships from the President and the Verkhovna Rada of Ukraine for young scientists. We would also like to note the activity in approbation of scientific results through speeches at international and Ukrainian conferences, publication of articles in international publications, cooperation with universities, research institutions of Ukraine, as well as with foreign colleagues. Results of scientific research are practice-oriented and are constantly implemented in enterprises and organizations. Updated development strategy of the Institute takes into account current requirements and trends in science, industry and education. The Institute continues to develop and to create positive image of Ukrainian science in the international arena.

References 1. State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”. www.igns.gov.ua 2. Department of Nuclear Physics and Energy. https://www.nas.gov.ua/EN/NASUDepartment/ Pages/default.aspx?DepartmentID=0002169 3. Institute of Environmental Geochemistry the NAS of Ukraine and MES of Ukraine (1996– 2011). Logos, Kyiv, 172 pp. (2011) 4. Burtniak, V., Zabulonov, Y., Stokolos, M., Bulavin, L., Krasnoholovets, V.: Application of a territorial remote radiation monitoring system at the Chornobyl nuclear accident site. J. Appl. Remote Sens. 12(04), 046007 (2018). https://doi.org/10.1117/1.jrs.12.046007 5. Zabulonov, Yu.L., Burtnyak, V.M., Odukalets, L.A.: System for effective remote control and monitoring of radiation situation based on unmanned aerial vehicle. Sci. Innov. 13(4), 40–45 (2017). https://doi.org/10.15407/scine13.04.040 6. Burtniak, V., Zabulonov, Y., Stokolos, M., Bulavin, L., Krasnoholovets, V.: The remote radiation monitoring of highly radioactive sports in the chornobyl exclusion zone. J. Intell. Robot. Syst. Theory Appl. 90(3–4), 437–442 (2018). https://doi.org/10.1007/s10846-017-0682-7

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7. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Hurkovskyi, V., Nikolaiev, K., Yatsyshyn, T., Dimitriieva, D.: Physical features of pollutants spread in the air during the emergency at NPPs. Nucl. Radiat. Saf. 4(84), 88–98 (2019). https://doi.org/10.32918/nrs.2019.4(84).11 8. Popov, O., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Pomaza-Ponomarenko, A.: Emergencies at potentially dangerous objects causing atmosphere pollution: peculiarities of chemically hazardous substances migration. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 151–163 (2020). https://doi.org/10.1007/978-3-030-48583-2_10 9. Zabulonov, Yu.L., Boychenko, S.G., Zholudenko, O.O., Buhera, M.A.: The tendencies of climate change and water regime of the middle-lower part in the basin of Southern Bug River (in the region of location of the South-Ukrainian Nuclear Power Plant). Geofizicheskiy Zhurnal 40(5), 268–283 (2018). https://doi.org/10.24028/gzh.0203-3100.v40i5.2018.147498 10. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Yatsyshyn, T.: Conceptual approaches for development of informational and analytical expert system for assessing the NPP impact on the environment. Nucl. Radiat. Saf. 3(79), 56–65 (2018). https://doi.org/10.32918/nrs.2018.3(79).09 11. Litinas, A., Geivanidis, S., Faliakis, A., Courouclis, Y., Samaras, Z., Keder, A., Krasnoholovets, V., Gandzha, I., Zabulonov, Y., Puhach, O., Dmytriyuk, M.: Biodiesel production from high FFA feedstocks with a novel chemical multifunctional process intensifier. Biofuel Res. J. 7(2), 1170–1177 (2020). https://doi.org/10.18331/BRJ2020.7.2.5 12. Zabulonov, Y., Burtnyak., V., Odukalets, L., Alekseeva, O., Petrov, S.: Plasmachemical plant for NPP drain water treatment. Sci. Innov. 14(6), 86–94 (2018). https://doi.org/10.15407/sci ne14.06.086 13. Rodionov, V.E., Vlaskina, S.I., Svechnikov, G.S., Mishinova, G.N., Vlaskin, V.I.: Nanograin boundaries and silicon carbide photoluminescence. Semicond. Phys. Quantum Electron. Optoelectron. 20(3), 344–348 (2017). https://doi.org/10.15407/spqeo20.03.344 14. Vlaskina, S.I., Svechnikov, G.S., Mishinova, G.N., Vlaskin, V.I., Rodionov, V.E., Lytvynenko, V.V.: Nano silicon carbide’s stacking faults, deep level’s and grain boundary’s defects. J. NanoElectron. Phys. 10(5) (2018). https://doi.org/10.21272/jnep.10(5).05021 15. Danilov, S., Zabulonov, Yu., Kadoshnikov, V., Odukalets, L.: Plasma-chemical destruction of persistent organic contaminants in liquids. Model. Inf. Technol. 79, 71–80 (2017). https://nbuv. gov.ua/UJRN/Mtit_2017_79_13 16. Danilov, S., Zabulonov, Yu., Kadoshnikov, V., Odukalets L.: Nanotechnologies in sewage treatment by physicochemical methods. Model. Inf. Technol. 82, 19–26 (2018). https://nbuv. gov.ua/UJRN/Mtit_2018_82_5 17. Petrov, S.V., Zabulonov, Yu.L., Yashar Katirdzhyoglu, T.: System of plasma illumination of low-reaction fuels for heating units. Scientific notes of Tavriya National University named after VI Vernadsky. Series: Tech. Sci. 1, 91–100 (2018). https://nbuv.gov.ua/UJRN/sntuts_ 2018_29_1%282%29__19 18. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Yatsyshyn, T., Matvieieva, I.: Analysis of possible causes of NPP emergencies to minimize risk of their occurrence. Nucl. Radiat. Saf. 1(81), 75–80 (2019). https://doi.org/10. 32918/nrs.2019.1(81).13 19. Kyrylenko, Y., Kameneva, I., Popov, O., Iatsyshyn, A., Artemchuk, V., Kovach, V.: Source term modelling for event with liquid radioactive materials spill. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 261–279 (2020). https://doi.org/10.1007/978-3-030-48583-2_17 20. Iatsyshyn, A., Iatsyshyn, A., Artemchuk, V., Kameneva, I., Kovach, V., Popov, O.: Software tools for tasks of sustainable development of environmental problems: peculiarities of programming and implementation in the specialists’ preparation. E3S Web Conf. 166, 01001 (2020). https://doi.org/10.1051/e3sconf/202016601001

292

Y. Zabulonov et al.

21. Popov, O.O., Iatsyshyn, A.V., Kovach, V.O., Artemchuk, V.O., Kameneva, I.P., Taraduda, D.V., Sobyna, V.O., Sokolov, D.L., Dement, M.O., Yatsyshyn, T.M.: Risk Assessment for the population of Kyiv, Ukraine as a result of atmospheric air pollution. J. Health Pollut. 10(25), 200303 (2020). https://doi.org/10.5696/2156-9614-10.25.200303 22. Yatsyshyn, T., Shkitsa, L., Popov, O., Liakh, M.: Development of mathematical models of gas leakage and its propagation in atmospheric air at an emergency gas well gushing. East. Eur. J. Enterp. Technol. 5/10(101), 49–59 (2019). https://doi.org/10.15587/1729-4061.2019.179097 23. Iatsyshyn, A., Artemchuk, V., Zaporozhets, A., Popov, O., Kovach, V.: Mathematical approaches for determining the level of impact of ash-slag dumps of energy facilities on the environment. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 1–13. (2020). https://doi. org/10.1007/978-3-030-48583-2_1 24. Kovach, V., Lysychenko, G.: Toxic soil contamination and its mitigation in Ukraine. In: Dent, D., Dmytruk, Y. (eds.) Soil Science Working for a Living. Springer, Cham (2017). https://doi. org/10.1007/978-3-319-45417-7_18 25. Mergner, R., Janssen, R., Kovach, V., et al.: Fostering sustainable feedstock production for advanced biofuels on underutilised land in Europe. In: European Biomass Conference and Exhibition Proceedings, pp. 125–130 (2017) 26. Popov, O., Yatsyshyn, A.: Mathematical tools to assess soil contamination by deposition of technogenic emissions. In: Dent, D., Dmytruk, Y. (eds.) Soil Science Working for a Living: Applications of Soil Science to Present-Day Problems, pp. 127–137. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-45417-7_11 27. Maevsky, O., Artemchuk, V., Brodsky, Y., Pilkevych, I., Topolnitsky, P.: Modeling of the process of optimization of decision-making at control of parameters of energy and technical systems on the example of remote earth’s sensing tools. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 111–122 (2020). https://doi.org/10.1007/978-3-030-48583-2_7 28. Kutsan, Y., Gurieiev, V., Iatsyshyn, A., Iatsyshyn, A., Lysenko, E.: Development of a virtual scientific and educational center for personnel advanced training in the energy sector of Ukraine. In: Babak, V., Isaienko, V., Zaporozhets, A. (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 69–84 (2020). https://doi.org/10.1007/ 978-3-030-48583-2_5

Ecological Situation of Post-mining Regions in Ukraine Oleksandr Trofymchuk , Yevhenii Yakovliev , Yevheniia Anpilova , Mykyta Myrontsov , and Viacheslav Okhariev

Abstract The growth of market requirements for socio-economic and environmental parameters of coal resources in most developed (“old”) mining areas (MA) of Ukraine has led to the closure of mines and quarries, including by the method of “wet conservation”. This process is most actively developing in the MA of Donbas, including in areas affected by armed conflict. In most developed EU countries, the decommissioning of coal mines, which have large depths and area of minefields, is a research and production complex of post-mining (PM), which is based on scientific and technical measures to prevent dangerous changes in the geological environment (GE)—subsidence of the earth’s surface, flooding of lands, release and surface outgassing of explosive and toxic gases, limiting the leakage of polluted water. In general, the set of PM measures is aimed at maintaining the balance and protective potential of the GE as a mineral and landscape base of the biosphere. In this regard, the article compares the protective potential of the GE of the Chernobyl accident zone and the Donbas region, as well as performs an expert assessment of the environmental protection potential of the PM in the leading coal MA of Ukraine. It is shown that a new model of mineral resources use, structure of geological exploration works, scientific substantiation of maximum allowable changes of GE at the closing of “old” and formation of new MA, improvement of environmental monitoring based on GIS technologies and remote sensing of the Earth is needed. Keywords Mining area · Geological environment · Post-mining · Flooding · Regional aquicludes · Maximum allowable changes · Mining · Surface subsidence

O. Trofymchuk · Y. Yakovliev · Y. Anpilova (B) · M. Myrontsov · V. Okhariev Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine, Kiev, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_17

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1 Introduction The market transformation of the Mining Industrial Complex (MIC) of Ukraine led to the substantial demands severization in both technico-economic and eco-social parameters of the natural resources management. Critical acceleration (since the 1990s) of winding up and liquidation of the mining objects usually through the wet method of lay-up (auto-rehabilitation submersion) of mines and pits (Donbas, Kryvbas, Lviv-Volyn coal basin, etc.) conditioned the insufficient consideration of object-territorial and regional changes of the environmental parameters of the natural and technogenic geosystems (NTGS) “mining region-geological environment”. The balancing interference between the growth of resources and extraction (the processing of which constitutes more than 30% GDP and 50% of export proceeds) under the market economy conditions in many regions manifested itself as a factor of territorial ecological hazards and national economic threat [1, 2]. To our point of view, during the restructuring of the leading mining areas (MA— Donbas, Lviv-Volyn coal basin, Western Donbas, Pre-Dnieper lignite basin) the experience of EU countries [3–27] (England, Germany, France, Poland, etc.) has not been considered enough with regard to the formation of the regulatory and legal basis and grounding the environmental hazard parameters of the new NTGS condition. It was mostly due to the non-regulated submersion of most coal mines and pits with the inappropriate consideration of a new environmental condition of the geological environment (GE) due to the restructuring of its accumulated long-time changes within different NTGS [1, 2, 28–32]. Noteworthy that within the recent years on the part of the public authorities and academic community much attention has been given to the issue of increasing the socio-economic and eco-technogenic safety of the land resources management efficiency and restructuring the MA in Ukraine or implementation of post-mining principles following the experience of the developed EU countries. The criteria of open market economy and armed conflict in Eastern Ukraine condition the reconsideration of, first of all many traditional eco-technogenic parameters of restructuring the developed (“old”) MA in Ukraine based on the post-mining principle. Grounding and introduction of the post-mining principles and technologies under conditions of most MA in Ukraine is connected with the substantial challenges due to the imperfection of environmental legislation i.e. of the Soviet and Independence times, and large complex of accumulated irreversible and dangerous changes of subsoils ecological condition.

2 Literature Analysis and Problem Statement According to the specialists V. Mishchenko, G. Rudko, M. Fesyk, M. Korzhnev etc. the imperfection of the market approaches towards managing the mineral and raw materials potential with regard to the depletion of many deposit, aggravation of the

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eco-geological conditions of the developed MA, growth of post-mining demands (renovation and socio-ecological stabilization), in particular, orientation towards the preparation of investment-attractive objects is still felt in Ukraine [28, 29, 33–48]. Generally speaking, the eco-technogenic and geologically-economic mechanisms of post-mining of the developed MA in the structure of the geological surveyance field remain underdeveloped, including, due to the constant financial deficit.

3 Research Results According to the environmental condition, all mining areas of Ukraine can be subdivided into three groups: 1—partial, 2—substantial, 3—critical aggravation of the environment (Table 1). The mining areas with partially aggravated environmental condition includes Oleksandria lignite basin where the ecological hazards for the population and environment before its winding up appeared on the limited areas and were mostly short-time. Mining areas and districts with the substantially aggravated environmental condition include the Lviv-Volyn and Western Donbas basins where the aggravation of eco-parameters or the environment is quite stable and at some sections or objects exceeds the norms established for its components (geo-environment, landscapes, soils, hydrographic network, ground waters, air, bio-diversity) within the impact zones of certain mining companies and their complexes. Thus, the transformation of environmental parameters of natural and technological geosystems (PTGS) “mining complex-environment” in the conditions of modern auto-rehabilitation flooding of mines and quarries in mining areas of Ukraine (Donbass, Carpathian salt mining complex, etc.) has a significant complexity and uncertainty in the implementation of post-mining measures [1, 2, 30, 32, 49, 50]. For example, the Solotvino rock salt mine, located in the Tyachiv district of Zakarpattia region, is a part of the right-bank catchment area of the transboundary Tisza river. Table 1 Classification of mining areas according to the regional environmental condition within the impact zones

Coal-mining areas (CMA)

Regional environmental condition within the MA impact zone Partially aggravated

Substantially aggravated

Donetsk

X

Lviv-Volyn Oleksandria lignite basin Western Donbas

Critical

X X X

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The mine fields are located within the first dry terraces, which contributes to the outcropping and active infiltration feeding of aquifers, as well as to the development of flood processes that complicate the mining and geological and environmental conditions of mine opening, development and decommissioning. Within the fields of the flooded mines, karst-breakdown processes and subsidence of the ground surface, which have a long-term character in the conditions of increasing deformations of mine workings and humidification of the saline body, are actively expanding [33]. With the help of modern space images processing services, a retrospective analysis was conducted and the volume-balance dynamics of the destructive karst-breakdown phenomena and sediments of the land surface progress were studied by the authors. This material was obtained from the Wayback Digital Archive and used in the construction of surface deformation models in ArcGIS Fig. 1. On the whole, the dramatic increase in the freshwater infiltration inflow and the related active development of technogenic karst caused the formation of destructive deformations of the land surface and rocks of the residential and industrial buildings foundation, roads and water-pumping networks of Solotvin village. The activation of the deformation field of the industrially wasted part of the salt stem occurs mainly in the following directions: south to the Solotvin agglomeration and the Tisza river floodplain and north to the landslide slope of the Magura Mountain Fig. 2. Taking into account structural-geological and geodynamic heterogeneity of the Solotvyno structure, the authors have created a map of the main zones of geological structures Fig. 3. The mining basin with the critical environmental condition includes Donbas, which is one of the biggest in the world: its total area is around 15 ths km2 with the land settlement manifestations of about 7.5 ths km2 . With regard to the eco-technogenic condition Donbas is the “oldest” and spatially developed mining geological complex with the coal mining (up to 0–15 km deep) being underground at most. Main factors defining the environmental condition of the mining areas of Ukraine include the following: 1.

2. 3.

disturbances in the geo-mechanic and hydrogeofiltration balance of the rock mass due to the mining works in extracting the large amounts of mineral resources, underground waters and greenhouse gases (up to 6 bln. m3 /year), formation of the water-permeable zones of drilling induced fractures; accumulation of mining and processing wastes; disturbance of hydro-geological pattern of the territory.

The remaining factors (development of the hazardous geological processes, pollution of the underground atmosphere, soils, ground and surface waters, reduction of biodiversity, etc.) are the derivatives of these three. The most eco-technogenic, social and economic threats for the population appear during the ecologically imbalanced winding-up of the mining companies because of the accelerated submersion of the mining allotments, additional deformations of the land surface, intensification of the ascendant migration of the polluted mineralized

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Fig. 1 Space survey data of the Solotvyno territory as of 25/10/2017 (up) 23/09/2020 (down) (Source https://livingatlas.arcgis.com/wayback/, 2019)

waters and explosive and toxic gases (methane, radon, hydrogen sulphide, etc.), which is connected with the displacement of the regional water-permeable (low permeability) layers [28, 30–33, 49, 50]. Besides, the surface soil pollution resources (industrial and household waste fields) and ground waters, which can actively move towards the rivers, springs and water reservoirs, can come within the active exchange zone thus expanding the ecological risk zone. From practical point of view this can result in the malfunctioning of the surface and underground potable water supply systems, flooding and destruction of the residential and industrial objects and communications (including ecologically hazardous—oil

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Fig. 2 The map of ground deformation Solotvyno mining area map

and gas pipelines, chemical enterprises, etc.), complications of the farming activity. Non-regulated development of these processes which in the coal mining areas of Ukraine are mostly irreversible, including on condition of the additional aggravation caused by the armed conflict in Eastern Ukraine, global climate changes, etc., may lead to the ecological disaster with extremely log-lasting and regional (trans-border) effect. Table 2 provides short characteristics of the mining areas with the highest level of environmental hazards, and the regions where the aggravation of eco-geological conditions may worsen. To our point of view, the most demonstrative with regard to the complexity of factors of shifting to the post-mining condition is the mining region of Donbas (Donetsk coal mining basin) together with the Western Donetsk coal mining areas embracing 15,000 km2 within three regions—Donetsk, Luhansk, Dnipro and forms one of the biggest and complex technogenically geological systems. High concentration of the mining, processing and other enterprises conditions the substantial disturbances in the geological environment and their defining impact upon the aggravation of the environmental conditions of the region. During the historical development of Donbas about 970 mines were built before the restructuring up to 215 with the dept of about 1,450 m were under the exploitation [2, 30, 32]. Main factors of the technogenic environmental changes include: • chemical pollution of landscapes;

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Fig. 3 Zones of geological structures of Solotvyno agglomeration

Table 2 Classification of the mining areas of Ukraine according to the criteria of the ecotechnogenic changes of the main environmental components as of the beginning of the post-mining Mining areas

Main environmental components Subsoil

Soil

Ground waters

Surface waters

Biodiversity

Ground atmosphere

Donetsk

5

5–4

4–3

4–3

3–2

3–2

Lviv-Volyn

4

3–2

2

2

2–1

1

Oleksandria Lignite basin

2

2

2

2

2

1

West-Donbas

4

3

2

3

2

1

*—scale of the criteria of eco-technogenic changes (in %) of the main environmental components as of the beginning of post-mining (in points): 1—initial (up to 10%); 2—substantial (11–20%); 3—heavy (21–40%); 4—critical (41–80%); 5—disastrous (more than 80%)

• substantial decrease of the ground water levels, replacement of the surface water reservoirs (about 600 incidents with the different levels of surface flow seizure); • discharge of highly-mineralized aggressive mine waters into the river system;

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• acceleration of the exogenous geological processes (landslides, karst, flooding), development of the land surface subsidence with the aggravation of the engineering and geological condition of the residential and industrial objects; • Reduction of the engineering and seismological stability of the rock masses impacted by the growth of the rock mobility in the areas of their replacement with the mining allotments, hydromechanical impacts manifestations; • formation of substantial amount of terricones also constituting the resource of the water, soil and ground air pollution; The malfunction of the ground water collectors in connection with the aggravation of their formation and quality conditions. Generally, the results of the performed analysis of the eco-technogenic condition of the developed (“old”) mining areas of Ukraine with regard to the critical technical and economic parameters of the natural resources potential achieved, allows drawing a conclusion on the possibility of the partial realization of the related measures with regard to the life safety improvement. This is mostly connected with the irreversible disturbances of the geological environment (subsoil) due to the extraction of substantial amounts of the mineral and rock masses with the fall of the enclosing and covering rocks. In this regard, to our point of view, the demonstrative is the comparison of the levels of renovation of the ecological and defense functions of the geological environment of Chornobyl NPP emergency area and the impact area of the mines submersion of Central Donbas (Table 3) [32]. The data in Table 3 show that the ecological condition of the geological environment within any natural and technogenic geosystem is a determinant parameter of the natural and resource potential renovation and life safety formation. To consider the impact of the leading balance parameters of mining works (area of crossing the mining allotments, F t and active porosity or insufficient water saturation μ) and mostly local formation of the ground waters mining draw-downs we suggest an additional application of the following dependence, which considers the temporal changes of afflux Qt on the level growth rate ddtSt :  2π km St Q t ≈ Ft × μd S dt ≈  ln R r

(1)

where Qt —is the water inflow into the mine as of the moment of time t with the decrease of the ground waters level S t , m3 /day; F t —is a free area of crossing the mining allotments and zones of drilling induced fractures of the breached rocks towards the horizon mark, which corresponds to St, m2 ; R—is the radius of the ground water draw-down, m. r—is the conditional radius of the planned line of the mining allotments of the cleaning works area (technogenic fractures formation), m; K m —is the water permeability of the coal-bearing rocks in the natural or partially breached condition, m2 /day;

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Table 3 Comparison of changes in the ecological status of the geological medium of the Exclusion zones and the unconditional (obligatory) resettlement of the Chornobyl NPP and the impact area of the massive closure of mines in the Donbas (Influences levels: red—critical, yellow—dangerous, green—background)

Types of environmental impact on the geological medium Landscapegeochemical

Lithospheric equilibrium Hydrological: critical changes in surface run-off quality Hydrogeological: aquifers (underground hydrosphere) Gas-geochemical, including the atmospheric surface layer Engineeringgeological Engineeringseismogeological

Level of impact on the geological medium Chornobyl NPP Area of influence of mass emergency area closure and flooding of mines Emergency radio geochemical contamination followed by auto- rehabilitating cleanup (up to 90% by 2035). No changes in the upper zone of the lithosphere. Short-term radionuclide pollution of the Dnieper River Basin

Element-by-element persistent geochemical contamination of soils and bottom sediments

Trace (at the level of regional background) nuclide flow

Regional pollution of underground horizons, loss of drinking water resources

Short-term contamination of the atmosphere with radionuclides Almost no changes in engineering-geological conditions

Saturation of the upper zone of the lithosphere with explosive gases in the mining zone

Relatively stable condition, except deformation of rocks at the base of buildings at the Chornobyl NPP industrial site

Reduction of engineeringseismogeological subsoil in the zone of influence of mining by 1-3 points

Irreversible violation of the geomechanical state of subsoils Regional sustainable pollution of surface run-off

Regional disturbance of rocks equilibrium in the coal-mining zone

μ—is the averaged value of the active porosity (insufficient water saturation) of the coal-bearing rocks within the area of the ground waters level increase (in unit fractions). During the equation simplification (1) through the division of variables, we get the following dependence:

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St −

μFt d St = St

So

t t=o

2π K m  dt ln R r

(2)

The final solution of the dependence (2) has the following structure:  2π K m μFt ln So St =  t ln R r

(3)

With relatively equal values of F t , K m , R, μ and r within the intervals of the cleaning (extraction) works horizons and between them, the Eq. (3) may be transformed as follows:  lg So St =

2π K m  t μFt lg R r

(4)

Considering the relative equality within the mining works horizons and filtration parameters their unification into the complex parameter would be reasonable. A=

2π K m  μFt lg R r

(5)

This allows for the dependence structuring (4) convenient for its application in the form of graphical and analytical dependence:  lg So St = At

(6)

The researches performed by the authors testified that the leading factor of the formation of NTGS parameters of Donbas “NPS mining complex” at the PM stage is the auto-rehabilitation increase of the ground waters level and formation of the complex technogenic geo-filtration system within the area of the mining water removing impact, where the following processes are dominating. Submersion of the mining allotments and adjoining rock masses with the ground water levels increase and the spatial and temporal reduction of their draw-down; Additional rocks moving and subsidence; Formation of the new ways of the planned upwards migration of the explosive and toxic gases (methane, radon, etc.), including towards the acting mines, neighboring settlements, tectonic zones and relief lowering; Diffusion of deep horizons mineralized waters migration within the mining fields with the following inflow into the local ground and surface discharge outlet.

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4 Conclusions The study and prediction of the state of individual elements of the natural environment under the conditions of anthropogenic load is one of the most important tasks of modern environmental research. As a result of the studies, the features of the transport of pollutants in the zone of influence of enterprises of the fuel and energy complex, which provides the possibility of mathematical modeling of the migration of pollutants in soils and in the surface layer of air from ash and slag dumps, were established. The assessments above testify that the complex of both eco-technogenic and socio-economic post-mining measures in most coal-mining areas of Ukraine may be partial considering the irreversibility of most changes of the ecological parameters of geological environment which are at the stage of active development or already exist [1, 5, 8–10]. With this in mind, due to the complex imbalance in the subsoil during the extraction of large amounts of mineral and rock crude materials and formation of the “mass deficiency” in the upper lithosphere of practically all developed mining areas at the post-mining stage most ecological functions of the ecological environment are lost (landscape and geochemical, water and ecological, engineering and geological, etc.). Thus, main task of the implementation of the separate post-mining measure aimed at the increase of the local population life safety in the mining areas of Ukraine may be as follows: – improvement of the predictions with regard to the changes of the basic lifesupporting environmental components (soils, hydrosphere, subsoil, etc.); – elaboration of the compensation model of stable socio-economic and ecotechnogenic development of the mining areas of Ukraine taking into account the mining complexes restructuring experience in the EU states (Germany, England, etc.); – improvement of the monitoring structure through extension of the assessment parameters complex, introduction of the technogenic system models of the coal mining areas, application of GIS and ERS technologies (interferometry, spectrometry, etc.); – scientific grounding of the maximum permissible levels of the environmental components ecological parameters and long-lasting life safety; – insurance of the stable exploitation of the critical infrastructure objects (complex of energy, water, and heat supply, transport networks, etc.); – intensified application of the fresh groundwater resources as a resource of potable and household water supply protected against the technogenic pollution; – detection of the farmlands chemically hazardous for growing the agricultural products. Generally speaking, the stage of the formation of bases and development of the post-mining of the coal mining areas of Ukraine is a strategic sci-tech and socioeconomical task for the economy and substantial part of population, considering the

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leading role of the coal feedstock component in the GDP formation and insurance of the resource and energy security of the state.

References 1. Rudko, G.I., Bondar, O.I., Yakovlev, Y.O., Ermakov, V.M., et al.: Ecological safety of coal deposits of Ukraine, 608 pp. Kyiv, Bukrek (2016) (In Ukrainian) 2. Gavrilenko, J.N., Ermakov, V.N., Ulickij, O.A. et al.: Technogenic consequences of the coal mines closure in Ukraine, 632 pp. Nord-Press, Donetsk (2004) (In Russian) 3. Ache, P.: Cities in old industrial regions between local innovative Milieu and urban governance—reflections on city region governance. Eur. Plan. Stud. 8(6), 693–709 (2000) 4. Albrecht, H.: Industrial landscape and world heritage project “Montanregion Erzgebirge”. TICCIH Congress Terni-Roma (2006). http://www.ticcihcongress2006.net/paper/Paper%20B/ Albrecht%20B.pdf 5. Dale, B.: An institutionalist approach to local restructuring: the case of four Norwegian mining towns. Eur. Urban Region. Stud. 9(1), 5–20 (2002) 6. Eckart, K., et al.: Social, economic and cultural aspects in the dynamic changing process of old industrial regions. Ruhr District (Germany), Upper Silesia (Poland), Ostrava Region (Czech Republic). LIT Verlag, Münster (2003) 7. Gorzelak, G.: Regional development and planning in East Central Europe. In: Kean, M. (ed.) Regional development and employment policy. Lessons from Central and Eastern Europe, pp. 62–76. ILO, Geneve (1998) 8. White, M.O.: Regional innovation and regeneration—an Irish experience. In: Good (Best) Practice Cases in Regional Development after Mining and Industry, pp. 39–46. Grazer Schriften der Geographie und Raumforschung. Universität Graz (2007) 9. Wirth, P., Lintz, G.: Rehabilitation and development of mining regions in Eastern Germany— strategies and outcomes. Moravian Geograph. Rep. 14(2), 69–82 (2006) 10. Kirkwood, N. (ed.): Manufactured sites. In: Rethinking the Post-Industrial Landscape, 1st edn. Taylor & Francis, London, New York (2001) 11. McManus, P.: Mines, wines and thoroughbreds: towards regional sustainability in the Upper Hunter, Australia. Region. Stud. 42(9), 1275–1290 (2008) 12. Ježek, J.: Mining regions and development strategies in the Czech Republic. In: Good (Best) Practice Cases in Regional Development after Mining and Industry, pp. 15–22. Grazer Schriften der Geographie und Raumforschung. Universität Graz (2007) 13. Harfst, J., Wirth, P., Bieberstein, C.: Strengths, Weaknesses, Opportunities and Threats of European Mining Regions (SWOT Report I), Output 3.3.1. Leibniz Institute of Ecological and Regional Development, Dresden (2010) 14. Müller, B., Finka, M., Lintz, G. (eds.): Rise and Decline of Industry in Central and Eastern Europe. Springer, Berlin (2005) 15. Rey, S.J., Janikas, M.V.: Regional convergence, inequality, and space. J. Econ. Geogr. 5(2), 155–176 (2005) 16. Steiner, M.: From Old Industries to New Regions: Policies for Structural Transformation in Accession Countries, 1st edn. Leykam (2003) 17. Wirth, P., Lintz, G.: Strategies of rehabilitation and development in European mining regions. Grazer Schriften für Geographie und Raumforschung 42, 75–85 (2007) 18. Zimmermann, F.M., Pizzera, J., Janschitz, S.: The role of actors in regional development. In: Good (Best) Practice Cases in Regional Development after Mining and Industry, pp. 63–74. Grazer Schriften für Geographie und Raumforschung (2007) 19. Healey, P.: Collaborative Planning: Shaping Places in Fragmented Societies. University of British Columbia Pr (1997)

Ecological Situation of Post-mining Regions in Ukraine

305

20. Hudson, R.: Rethinking change in old industrial regions: reflecting on the experiences of North East England. Environ. Plan. A 37(4), 581–596 (2005) 21. Pearman, G.: 101 Things to Do with a Hole in the Ground. Eden Project (2009) 22. Kaufmann, A., Tödtling, F.: Systems of innovation in traditional industrial regions: the case of Styria in a comparative perspective. Reg. Stud. 34(1), 29–40 (2000) 23. Jolliffe, L., & Conlin, M.: Lessons in transforming mines into tourism attractions. In: Mining Heritage and Tourism: A Global Synthesis. Routledge Advances in Tourism, pp. 242–247. Routledge, Oxon, New York (2011) 24. Jones, C., Munday, M.: Blaenavon and United Nations world heritage site status: is conservation of industrial heritage a road to local economic development? Reg. Stud. 35(6), 585–590 (2001) 25. Horváth, G.: Landscape and human effect: recent changes in the Medves Area, North Hungary. In: Vaishar, A., Zapletalová, J., Munzar, J. (eds.): Regional geography and its applications, pp. 60–65. Frenštát pod Radhoštˇem (2003) 26. Horváth, G.: Problems of the transition in the Medves area (North Hungary). In: Ili´c, M. (ed.) Problemi regionalnog razvoja Hrvatske i susjednih zemalja. Regional development problems in Croatia and neighbouring countries, pp. 125–133. Zagreb (2005) 27. Karancsi, Z., Mucsi, L.: Human impact on the Medves region, N-Hungary. Zeitschrift für Geomorphologie 41(8), 247–253 (1997) 28. Yakovlev, Y., Yermakov, V., Ulytskyi, O.: Ecological consequences atomic explosion chamber flooding of mine “Yuncom” (Central Donbas). Mineral resources of Ukraine, 2019, N 2, 35–39. https://doi.org/10.31996/mru.2019.1.38-44 29. Yakovliev, Y.O.: Critical changes of ecological status of entrails within Donbas. Mineral resources of Ukraine, N 3, 34–39 (2017) (In Ukrainian) 30. Yakovliev, Y., Chumachenko, S., with contribution from HD staff. Ecological Threats in Donbas, Ukraine. Assessment of ecological hazards in Donbas impacted by the armed conflict in eastern Ukraine. Centre for Humanitarian Dialogue (2017) 31. Risk Assessment Report: Solotvyno salt mine area. Advisory Mission to Ukraine. Union Civil Protection Mechanism (EUCPT). European Commission (2016) 32. Anpilova, Y., Yakovliev, Y., Drozdovych, I.: Landscape and geological factors of water and ecological conditions technogenesis of Donbas at the post-mining stage. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects. Extended Abstracts (2020). https://doi.org/10.3997/2214-4609.2020geo136 33. Trofymchuk, O., Anpilova, Y., Yakovliev, Y., Zinkiv, I.: Ground deformation mapping of Solotvyno mine area using radar data and GIS. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects. Extended abstracts (2020). https://doi.org/10.3997/ 2214-4609.2020geo138 34. Myrontsov, M.L.: Electrometry effective inverse problem solving method. In: 19th International Conference Geoinformatics—Theoretical and Applied Aspects. Extended abstracts (2020). https://doi.org/10.3997/2214-4609.2020geo090 35. Myrontsov, M.L.: Lateral logging sounding and lateral logging complex effective inverse problem solving method. In: 19th International Conference Geoinformatics—Theoretical and Applied Aspects. Extended abstracts (2020). https://doi.org/10.3997/2214-4609.2020geo092 36. Myrontsov, M.L.: The method to research equivalent solutions zones for inverse problem of well logging electrometry. In: XIII International Scientific Conference “Monitoring of Geological Processes and Ecological Condition of the Environment”. Extended abstracts (2019). https:// doi.org/10.3997/2214-4609.201903217 37. Myrontsov, M.L.: The method to solve the inverse problem of lateral logging sounding and lateral logging. In: XIII International Scientific Conference “Monitoring of Geological Processes and Ecological Condition of the Environment. Extended abstracts (2019). https:// doi.org/10.3997/2214-4609.201903244 38. Myrontsov, M.L.: A new method and program for multiprobe electric logging quantitative interpretation. In: Geoinformatics 2012—11th International Conference on Geoinformatics: Theoretical and Applied Aspects. Extended Abstracts (2012)

306

O. Trofymchuk et al.

39. Okhariev, V.: Improvement of statistical method for environmental monitoring datasets interpretation. In: Conference Proceedings, Monitoring 2019. Extended abstracts (2019). https:// doi.org/10.3997/2214-4609.201903237 40. Trofymchuk, O., Okhariev, V., Trysnyuk, V.: Environmental security management of geosystems. In: Conference Proceedings, 18th International Conference of Geoinformatics—Theoretical and Applied Aspects (2019). https://doi.org/10.3997/2214-4609.201902083 41. Okhariev, V., Trysnyuk, V.: Environmental aspects of Ukrainian energy sector in connection with transition to low-carbon development. In: Conference Proceedings, Monitoring 2019. Extended abstracts (2019). https://doi.org/10.3997/2214-4609.201903236 42. Trofimchuk, A.N.: Dynamic interaction of a rigid slab with a water-saturated poroelastic foundation. Int. Appl. Mech. 32(1), 60–64 (1996). https://doi.org/10.1007/BF02084849 43. Trysnyuk, V., Okhariev, V., Trysnyuk, T., Zorina, O., Kyrylo, A., Radlowska, C.: Improving the algorithm of satellite images landscape interpretation. In: Conference Proceedings, 18th International Conference of Geoinformatics—Theoretical and Applied Aspects (2019). https:// doi.org/10.3997/2214-4609.201902084 44. Riznyk, T.O., Beseda, M.I., Polinovskyi, V.V., et al.: Scientific report “Creation of a computer database of factual and cartographic data to assess the impact of mining operations on the geological environment of Donbass”, 251 pp. State Committee of Geology of Ukraine, UkrDGRI (2005) (In Ukrainian) 45. Liutyi, H.H., Riznyk, T.O.: Estimates of the coal enterprises impact on river runoff in the Donbass. Zb. naukovykh prats UkrDGRI, N 1. 96–101 (2006) (In Ukrainian) 46. Liuta, N.H.: Ekolohichnyi stan dovkillia ta yevropeiski perspektyvy Ukrainy. Ecological state of the environment and European prospects of Ukraine. Mineral resources of Ukraine, N 1. 6–11 (2011) (In Ukrainian) 47. Udalov, I.V.: Transformation of the geological environment under the influence of man-made processes (in the conditions of the north-eastern Donbass), 176 pp. KhNU, Kharkiv. Karazina (2016) (In Russian) 48. Grebenkin, S.S., Ermakov, V.N., Ulitskiy, O.A., et al.: Geomechanical and technological problems of mines closing of Donbass, 264 pp. DonNTU, Donetsk (2002) (In Russian) 49. Lukianova, V., Trofymchuk, O., Anpilova, Y.: Environmental safety of motor transport enterprises within the urban areas. J. Ecol. Eng. 21(4), 231–236 (2020). https://doi.org/10.32347/ 2411-4049.2019.1.54-64 50. Trofymchuk, O., Yakovliev, Y., Klymenko, V., Anpilova, Y.: Geomodeling and monitoring of pollution of waters and soils by the Earth remote sensing. 19th Int. Multidis. Sci. GeoConference SGEM 19(1.4), 197–204 (2019). Extended abstracts. https://doi.org/10.5593/sgem2019V/1.4/ S02.025

Development of Teaching Methodology in the Field of Environmental Monitoring of Atmosphere Andrii Iatsyshyn , Volodymyr Artemchuk , Artur Zaporozhets , Oleksandr Popov , Valeriia Kovach , and Dmytro Taraduda

Abstract The article substantiates the importance of conducting environmental monitoring of the surface layer of the atmosphere using specialized softwaremodeling systems. Information software is described for the tasks of monitoring and controlling the ecological status of urbanized areas (AISEEM system), which was developed by the authors. The features of the special course for training future specialists in the field of ecology and environmental protection using specialized software and modeling systems are considered. Keywords Environmental monitoring · Teaching methodology · Atmosphere · Software

1 Introduction Polluted air intensively affects not only humans and biota, but also the hydrosphere, soil and vegetation cover, the geological environment, buildings, structures and other man-made objects. Therefore, the protection of atmospheric air and the ozone layer A. Iatsyshyn · V. Artemchuk · O. Popov · V. Kovach State Institution “The Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”, Kyiv, Ukraine A. Iatsyshyn · V. Artemchuk · O. Popov G.E. Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, Kyiv, Ukraine A. Zaporozhets (B) Institute of Engineering, Thermophysics of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] O. Popov · V. Kovach Interregional Academy of Personnel Management, Kyiv, Ukraine V. Kovach National Aviation University, Kyiv, Ukraine D. Taraduda National University of Civil Defence of Ukraine, Kharkiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_18

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is a priority environmental problem. This problem is given attention in all developed countries [1], in particular, the issue of air monitoring is important. Monitoring in the field of atmospheric air protection in the modern sense can be considered as an information-analytical system, which is used for the purpose [2–4]: • receipt, collection, storage, analysis of information on the state of atmospheric air and factors affecting its quality; • assessment and analysis of information on the level of air pollution; • predicting changes in pollution levels and real threats; • development of scientifically based recommendations for making decisions on the protection of atmospheric air; • operational information services to users at all levels; • promoting the development of international cooperation in the field of atmospheric air protection, the rational use of natural resources and environmental safety; • improving the quality of substantiation of environmental protection measures and the effectiveness of their application. The priority environmental monitoring tasks of the environmental monitoring of the surface layer of the atmosphere (EMSLA) are considered [5–7]: • long-term systematic observations of the surface layer of the atmosphere; • analysis of the ecological state of atmospheric air and forecasting its changes; • assessment of the levels and degree of danger of pollution to the environment and the life of the population; • assessment of the composition and volume of emissions of pollutants; • generalization of data on the level of pollution in a certain territory for a certain period of time; • information and analytical support for decision-making on the protection of atmospheric air, the rational use of natural resources and environmental safety; • information services of state authorities, local governments, as well as providing environmental information to the population of the country and international organizations. The most important task of EMSLA is to provide information to determine strategies and to make decisions on managing the environmental safety of atmospheric air. The solution of this task will allow to determine the amount of damage suffered from pollution of the surface layer of the atmosphere, the level of morbidity of the population, to allow to select areas of promising development, to determine the state of pollution of certain areas of the city and the level of background pollution of the atmosphere [1, 8]. In modern conditions, the purposes of training future specialists in the field of ecology and environmental protection are: 1.

the training of highly qualified professional ecologists capable of solving specialized tasks and practical problems in the field of ecology, environmental

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protection and environmental management, that involves the application of theories and methods of sciences related to ecology, environmental protection and environmental management, characterized by interdisciplinary [9]; training highly qualified and professional ecologists who are able to solve scientific problems and questions to reduce the level of anthropogenic impact on the environment, develop effective environmental protection measures, and also solve practical problems in the field of ecology and environmental protection [10].

Also, the need to prepare future specialists in the field of ecology and environmental protection is the use of information and communication technologies (ICT) to help carry out modeling and forecasting man-made pressures on the environment, and therefore the ability to apply these technologies is important for further professional activity [11]. Considering the fact that ICTs are constantly being improved and new specialized systems are being developed for solving EMSLA tasks, it is important in training students to familiarize them with the latest developments, systems and software, and to develop the skills of future specialists in the field of ecology and environmental protection in further professional activities [12].

2 Main Part Studies have shown that today the system of EMSLA of Ukraine is ineffective, has significant problems and disadvantages. The air quality monitoring network was established in the 1970s in accordance with the standards of the former USSR. The layout of the monitoring stations (posts) and their quantity, methods of sampling and analysis are implemented in accordance with the provision adopted in the USSR in 1989 [13, 14]. The existing network of stationary observations in modern conditions is no longer optimal for detecting and comparing background, average and maximum levels of air pollution. Such a position of the network of monitoring the state of atmospheric air of Ukraine does not make it possible to effectively solve the problems of EMSLA, and therefore needs to be significantly modernized in many aspects [15, 16]. Also the training of highly qualified specialists in the field of ecology and environmental protection is important to improve the efficiency of the EMSLA system. This, in turn, requires the development of new or improvement of existing programs for the training of such specialists using modern specialized software and modeling systems aimed at developing of practical skills and abilities to solve EMSLA problems. For this purpose, the special course “Methods and means of EMSLA” was developed, consisting of 4 modules: • “Regulatory and legal support in the field of ecology and environmental protection”; • “Environmental monitoring of the surface layer of the atmosphere”, • “Methods and means of solving the problems of EMSLA”;

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• “Information systems in the field of ecology and environmental protection”. The purpose of the special course is to train future specialists to develop their knowledge of regulatory support in the field of ecology and environmental protection, in particular, on EMSLA and develop their ability to solve EMSLA problems using special methods and tools, as well as the ability to use specialized software and modeling systems in the field of ecology and environmental protection in further professional activities [17]. The special course consists of lectures and practical classes, provides for consultations and independent extracurricular work of students on the study of additional and scientific literature [18]. There are 60 h for studying the discipline (16 h of lectures, 24 h of practical training, 20 h of independent work). The main objectives of the special course are: • to acquaint students with regulatory support in the field of ecology and environmental protection, in particular, about EMSLA; • to teach to assess the current state of the EMSLA, the main and priority tasks of the EMSLA in different regions of Ukraine; • to teach how to predict the state of the surface layer of the atmosphere using the methods of mathematical modeling; • to acquaint students with the basic concepts and methods that are used in assessing the impact of man-made objects on the atmospheric air and to form the ability to use them; • to form the skills for solving the problems of EMSLA using special methods and means; • to form the ability to use specialized software and modeling systems in the field of ecology and environmental protection in future professional activities; • to develop students’ thinking and environmental literacy; • to develop the skills of independent work. During the lecture course, an outline of the main issues of regulatory support in the field of ecology and environmental protection, in particular about EMSLA, is provided. During the practical classes, acquaintance with domestic and foreign modern specialized software-modeling systems is offered. The feasibility of using these systems is due to the fact that they significantly expand the possibilities of solving the problems of atmospheric air quality management. To date, for solving the problems of EMPSA, domestic and foreign informationbased software-modeling systems are used, for examples: the EOL-2000 environmental monitoring system [h], “Air” system, the ARM ECO, EkoGIS-Kiev, Ecotrans, EcoStat, “ERA-Air”, “EPK ROSA”, UPRZA “Ekolog”, “Magistral-city 2.3”, “GIS-Atmosphere”, ZONE complex of operational analysis of the dosimetric situation KADO, “RADExpert”, RODOS (European system), RECASS and NOSTRADAMUS (Russia), ARGOS (Denmark, Sweden), JSPEEDI (Japan), NARAC, MEPAS (USA), etc.

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An important practical aspect in this course is the use by future specialists, developed by the authors, an information and software for the tasks of monitoring and controlling the ecological state of urbanized territories (AISEEM system), including a block of statistical analysis and preliminary assessment of technogenic loads on atmospheric air; block of mathematical modeling and forecasting of atmospheric pollution levels and risks for the population; block visualization and construction of environmental maps. In Fig. 1 examples of the operation of some developed software modules of the AISEEM system are shown. To date, information and software has been developed and implemented at the Office of Information and Analytical Support of the Ministry of Emergencies of Ukraine, the All-Ukrainian Research Institute of Civil Protection of the Population and Territories from Technological and Natural Emergencies, the State City Enterprise “Ivano-Frankivskteplokommunenergo”, the Dnipropetrovsk Regional Center for Hydrometeorology, Department of the organization of civil protection measures of the State Emergency Service of Ukraine, a separate unit “Scientific and technical center” of the state enterprise “NAEC” “Energoatom”. Consider the main modules of the developed special course “Methods and means EMSLA”. The first module “Regulatory support in the field of ecology and environmental protection” envisages consideration of the following regulatory legal acts: Law of Ukraine “On Atmospheric Air Protection” (2007), Resolution of the Cabinet of Ministers of Ukraine “On Approval of the State Target Ecological Program for Environmental Monitoring natural environment” (2007), Directive No. 2008/50/EU of the European Parliament and of the Council “About atmospheric air quality and clean air for Europe”, CORINAIR Inventory of atmospheric emissions in Europe; IPPC EMEP/EEA emission inventory guidebook 2013, Law of Ukraine “On Environmental Impact Assessment” (2017), National Plan for Reducing Emissions from Large Combustion Plants (2017), Draft Law on Strategic Environmental Assessment (No. 6106 of March 20, 2018), Regional publications of the World Health Organization and the like. The second module, “Environmental monitoring of the atmospheric surface layer,” highlights the role of environmental monitoring in environmental safety tasks, examines the theoretical foundations of the EMSLA and the main and priority tasks of the EMSLA in Ukraine. The tasks that need to be implemented to ensure the protection of the air basin of the city as a component of its environmental safety are determined. The methodology of assessing the territory of the city by the degree of air pollution and the direction of research by which monitoring in the field of atmospheric air protection in the modern sense can be implemented as an information-analytical system is considered. The stages of the process of managing atmospheric air quality indicators are distinguished. The main and priority tasks of the EMSLA are considered and it is determined that the most important task of the EMSLA is to provide operational information for determining strategies and making decisions on the prevention of air pollution. The basic requirements for the organization of the EMSLA network,

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Fig. 1 Examples of some modules of the developed AISEEM system

sources of air pollution and the influence of meteorological factors on air pollution are considered. The current state of EMSLA in Ukraine is analyzed. The third module “Methods and means of solving environmental monitoring problems of the atmospheric surface layer” covers the study of the main methods and tools for solving the problems of the EMSLA. Comparative assessments of the EMSLA capabilities and modeling methods used to solve the basic problems of ensuring

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environmental safety of the atmosphere are given. Various approaches to the mathematical modeling of the distribution of impurities in the atmosphere are analyzed, their main advantages and disadvantages are determined. Methods are considered for predicting air pollution, based on the results of theoretical and experimental studies of the laws governing the propagation of impurities from their source. The models for assessing atmospheric air quality indicators (models of atmospheric air pollution and models of dispersion of impurities in the atmosphere) and conditional classification of models of atmospheric air pollution are considered. The concept of risk, domestic and foreign models for assessing environmental risks are considered. The fourth module “Information systems in the field of ecology and environmental protection” describes the characteristics of the most famous domestic and foreign environmental information systems that solve the problems of quickly identifying current changes in the state of atmospheric air, predicting the spread of pollution in space, identifying places of increased concentration of harmful substances in order to make informed administrative decisions on sanitary-hygienic, design-technical and other measures to ensure environmental safety. Modern geographic information systems and the advantages of their use in the tasks of atmospheric air quality management are considered. The advantages of using modern computer-based environmental monitoring systems for air quality management tasks are examined: Government Information and Analytical System for Emergencies, EOL-2000 [h] Environmental Monitoring System, AISEEM Environmental and Energy Monitoring and Information Monitoring System, Air system, AWP ECO, EcoGIS-Kiev, Ecotrans, EcoStat, ERA-Air, EPK ROSA, URZA Ecolog, “Magistral-city 2.3”, “GIS-atmosphere”, ZONE complex of operational analysis of the dosimetric situation of KADO, geoinformation expert-modeling complex “RADExpert” and others. Systems and software systems using nuclear power plants are considered, namely: RODOS (pan-European system), RECASS and NOSTRADAMUS (Russia), ARGOS (Denmark, Sweden), JSPEEDI (Japan), NARAC and MEPAS (USA). The above described systems and software systems are compared according to certain criteria, and recommendations are given on the selection of these systems for the optimal solution of EMPS problems. The developed information and software are aimed at solving many scientific and practical problems of the EMSLA. The main ones are [1, 19, 20]: • collection, storage and processing of environmental monitoring data; • sampling of environmental monitoring data, their graphic visualization and statistical analysis; • construction and definition of urgent tasks of the EMSLA; • determination of distributions of pollutant concentrations under various scenarios; • calculation of environmental and technological risks; • justification of the choice of coordinates for the rational placement of observation points of the network for monitoring the state of atmospheric air; • study of the stability of territorial systems (study of the dynamics of risks during different periods of time); • determination of the dependence of the effect of pollutants on other factors;

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• visualization of various environmental data using graphs, charts, electronic maps, etc. Review of the content of the main modules of the developed special course “Methods and means of EMSLA” was made in article [21]. Evaluation of students’ learning outcomes is proposed to be carried out in the form of a test, as well as intermediate examinations, practical work, oral answers, educational research tasks. The main learning outcomes of the special course “Methods and means of EMSLA” using specialized software-modeling systems are presented in Table 1.

3 Conclusions The current state of the EMSLA of Ukraine is characterized by significant shortcomings; it does not allow Ukraine to fully fulfill its obligations to international organizations in the field of air protection. With the technical re-equipment and improvement of the regulatory and methodological support of the EMSLA system of Ukraine, it is important to train highly qualified specialists in the field of ecology and environmental protection. It provides for the passage of various special courses in this area. The paper proposed the content and technology of training of one of these special courses, which was called “Methods and means of EMSLA”. An important practical aspect in this course is the use by future specialists of information and software for the tasks of monitoring and controlling the ecological state of urbanized territories (AISEEM system), developed by the authors. This will allow them to acquire knowledge and practical skills for solving problems of analysis, modeling, forecasting and visualization of monitoring data on the state of atmospheric air. As a result of the study and on the basis of own experience of using specialized software-modeling systems for solving the problems of the EMSLA, the following conclusions can be drawn: • it is important to familiarize students with regulatory support in the field of ecology and environmental protection, in particular, about EMSLA; • it is important to acquaint students with the basic concepts and methods that are used in assessing the impact of man-made objects on the environment and to form the ability to apply them; • it is important to teach students how to solve the problems of EMSLA with the use of special methods and means, as well as to develop the ability to use specialized program-modeling systems in the field of ecology and environmental protection in their future professional activities.

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Table 1 The main learning outcomes of the special course “Methods and means of Emsla” Knowledge

Skills

Attainments

Regulatory and legal framework in the field of ecology and environmental protection, basic terms, basic knowledge about the monitoring of atmospheric air

Orientation in regulatory support in the field of ecology and environmental protection

The use of regulatory support in professional activities

Concept and methods used in calculating air pollution

Evaluation of the state of the EMSLA, the main and priority tasks of the EMSLA in various regions of Ukraine

Systematization of the EMSLA tasks on the territory of Ukraine, calculation of air pollution indices

Approaches, methods and models for assessing the state of atmospheric air

Approaches, methods and models for determining the spatial distribution of pollutant concentrations

Classification, advantages and disadvantages of models of atmospheric air pollution and models of dispersion of impurities in the atmosphere

Environmental risks

Environmental risk assessment Risk assessment of chronic under man-made conditions intoxication and instantaneous effects of toxic effects on the population, individual carcinogenic risk and hazard ratio of exposure of i-substance

Information systems EOL-2000 [h], AISEEM, Air, ARM ECO, EkOGIS-Kiev, Ecotrans, EcoStat, ERA-Air, EPK ROSA, UPRZA Ecolog, KADO, RADExpert, ARGOS, NARAC, MEPAS,JSPEEDI etc.

Software systems for modeling and forecasting the spread of pollution in space, identifying areas of elevated concentrations of harmful substances

The use of domestic and foreign information systems for assessing man-made pressures on the environment, skills in computer networks, the use of modern information technologies and software

Knowledge of general principles of modeling and predicting the state of the environment

Forecasting the state of the elements of the environment using mathematical modeling methods

Ability to choose and use methods of mathematical modeling and prediction of the state of the environment

Environmental information processing methods

Methods for processing environmental information, practical skills for obtaining and visualizing information about the current state of various environmental components

Assessment of the state of natural objects by the results of monitoring, collection, integration, processing, analysis and assessment of environmental information using ICT

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Directions for further research should focus on the creation of specialized advanced training courses for decision makers in the management of environmental safety of technogenically loaded areas and the substantiation of relevant techniques.

References 1. Kuklinska, K., Wolska, L., Namiesnik, J.: Air quality policy in the U.S. and the EU—a review. Atmos. Pollut. Res. 6(1), 129–137 (2015). https://doi.org/10.5094/apr.2015.015 2. Chassidim, H., Almog, D., Mark, S.: Fostering soft skills in project-oriented learning within an agile atmosphere. Eur. J. Eng. Educ. 43(4), 638–650 (2018). https://doi.org/10.1080/030 43797.2017.1401595 3. Johnston, F.H., Wheeler, A.J., Williamson, G.J., Campbell, S.L., Jones, P.J., Koolhof, I.S., Lucani, C., Cooling, N.B., Bowman, D.M.J.S.: Using smartphone technology to reduce health impacts from atmospheric environmental hazards. Environ. Res. Lett. 12(4), 044019 (2018). https://doi.org/10.1088/1748-9326/aab1e6 4. Camarillo-Naranjo, J.M., Alvarez-Francoso, J.I., Limones-Rodriguez, N., Pita-Lopez, M.F., Aguilar-Alba, M.: The global climate monitor system: from climate data-handling to knowledge dissemination. Int. J. Digit. Earth 12(4), 394–414 (2019). https://doi.org/10.1080/17538947. 2018.1429502 5. Barakeh, Z.A., Breuil, P., Redon, N., Pijolat, C., Locoge, N., Viricelle, J.-P.: Development of a normalized multi-sensors system for low cost on-line atmospheric pollution detection. Sens. Actuators B: Chem. 241, 1235–1243 (2017). https://doi.org/10.1016/j.snb.2016.10.006 6. Chang, Y., Liu, X., deng, C., Dore, A.J., Zhuang, G.: Source apportionment of atmospheric ammonia before, during, and after the 2014 APEC summit in Beijing using stable nitrogen isotope signatures. Atmos. Chem. Phys. 16(18), 11635–11647 (2016). https://doi.org/10.5194/ acp-16-11635-2016 7. Santos, V.D.N., Ferreira, N.M.F., Santos, J.C.B., Santos, F.M., Moita, F.D., Ferreira, J.P., Silva, M. Project-Based Learning Methodology for Robotics Education. In: Tsitouridou, M.A., Diniz, J., Mikropoulos, T. (eds.) Technology and Innovation in Learning, Teaching and Education. TECH-EDU 2018. Communications in Computer and Information Science, vol. 993, pp. 377– 387. https://doi.org/10.1007/978-3-030-20954-4_28 8. Shupranova, L.V., Khlopova, Kharytonov, V.M.: Air pollution assesment in the DNEPROPETROVSK industrial megapolice of Ukraine. In: Air Pollution Modeling and its Application XXII. NATO Science for Peace and Security Series C: Environmental Security, pp. 101–104 (2015). https://doi.org/10.1007/978-94-007-5577-2_17 9. Ratcheva, D.P., Collins, P., Goldstone, M.: A Methodology for assessing environmental risks associated with air quality. In: 2012, International Conference on Health, Safety and Environmental in Oil And Gas Exploration and Production, 11–13 September 2012, Perth, Australia. https://doi.org/10.2118/156745-ms 10. Oliveira, L.M., Rodriques, J.J.: Wireless sensor networks: a survey on environmental monitoring. JCM 6(2), 143–151 (2011) 11. Zaporozhets, A., Babak, V., Isaienko, V., Babikova, K.: Analysis of the air pollution monitoring system in Ukraine. In: Babak, V., Isaienko, V., Zaporozhets, A (eds.) Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, vol. 298, pp. 85–110 (2020). https://doi.org/10.1007/978-3-030-48583-2_6 12. Hrebjonkina, O.S.: Professional orientation of teaching higher mathematics students of environmental specialties. Pedagogical Educ. Theory Prac. 15, 171–176 (2013) 13. Iatsyshyn, A., Iatsyshyn, A., Artemchuk, V., Kameneva, I., Kovach, V., Popov, O.: Software tools for tasks of sustainable development of environmental problems: peculiarities of programming and implementation in the specialists’ preparation. E3S Web Conf. 166, 01001 (2020). https://doi.org/10.1051/e3sconf/202016601001

Development of Teaching Methodology in the Field …

317

14. Shkitsa, L.E., Yatsyshyn, T.M., Popov, A.A., Artemchuk, V.A.: The prediction of contaminators distribution in the atmosphere on the territory of the drilling unit. Neftyanoe hazyaystvo 11, 136–140 (2013) 15. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Technical provision of diagnostic systems. In: Diagnostic Systems For Energy Equipments. Studies in Systems, Decision and Control, vol. 281, pp. 91–133 (2020). https://doi.org/10.1007/978-3030-44443-3_4 16. Rudyshyn, S.D.: Concept of professional training of an ecologist in the high school of Ukraine. Creat. Pedag. 4, 101–104 (2011) 17. Rudyshyn, S.D.: Biological Preparation of Future Ecologists: Theory and Practice (2009) 18. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Kameneva, I., Taraduda, D., Yatsyshyn, T.: Risk assessment for the population of Kyiv, Ukraine as a result of atmospheric air pollution. J. Health Pollut. 10(25), 1–11 (2020). https://doi.org/10.5696/2156-9614-10.25.200303 19. Safranov, T.A., Lukashov, D.V., Shelest, Z.M., Vladymyrova, O.H., Chuhay, A.V.: Professional orientation of teaching higher mathematics students of environmental specialties. Visnyk KHNU imeni V.N. Karazina. Ecology 16, 141–149 (2017) 20. Iatsyshyn, A.V., Popov, O.O., Kovach, V.O., Artemchuk, V.O.: The methodology of future specialists teaching in ecology using methods and means of environmental monitoring of the atmosphere’s surface layer. J. Inform. Technol. Educ. 66(4), 217–230 (2018) 21. Yachina, N.P., Khuziakhmetov, A.N., Gabdrakhmanova, R.G.: Formation and development of the regional system of continuous environmental education of a teacher. Ekoloji 106, 1315–1322 (2018)

A Transdisciplinary Analytical System for Supporting the Environmental Researches Oleksandr Trofymchuk , Mykyta Myrontsov , Viacheslav Okhariev , Yevheniia Anpilova , and Vasyl Trysnyuk

Abstract The subject of the research is the creation of information technologies for the analysis and interpretation of data about the level and specifics of man-made environmental impact. The relevance of the development and implementation of such information technologies was substantiated. The main aspects of man-caused impact and efficiency of contact and remote methods of data acquisition on the ecological situation within the framework of environmental monitoring were analyzed. The method of calculation of multi-parametric dynamic correlation is developed for the analysis and interpretation of ecological datasets. Mathematical framework of the proposed approach is Pearson correlation coefficient calculating. The proposed method is implemented in the form of information technology and tested on the example of a long-term array data analysis on water pollution in Ukraine. Test locations for method implementation are situated in the river basin of Ustia, Rivne region and river basin of Prut, Chernivtsi region. Proposed approach and developed software for its use can become a part of analytical system for support of environmental researches. The concept of transdisciplinary integration of information about environmental situation and their scientifically substantiated interpretation in problems of ecological management on the basis of geographic information technologies were offered. This concept provides a scientific basis for the structure of a promising system of information support for management decisions in the field of environmental safety and nature management. Such a system should include both subsystems for collecting environmentally important information based on monitoring data and methods for its interpretation and complex assessment of the environmental situation in the researched territory. The implementation of such a system is optimal based on the use of a wide range of geographic information systems technology. Keywords Environmental safety · Man-caused impact · Multi-parameter dynamic correlation · Environmental monitoring · Remote sensing technology · Geographic information system O. Trofymchuk · M. Myrontsov · V. Okhariev (B) · Y. Anpilova · V. Trysnyuk Institute of Telecommunications and Global Information Space, National Academy of Sciences of Ukraine, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_19

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1 Introduction The high level of man-caused environmental impact in Ukraine and other countries all over the world is mainly due to the irrational nature of nature management and the imperfection of the environmental safety management system [1, 2]. Improving the management system requires effective means of collecting, analyzing and displaying information about the current environmental situation. The creation of a system that will provide such tools will significantly increase the efficiency of environmental research [3–5]. Therefore, the subject of the research is the creation of information and analytical methods for its using as part of a complex system to support environmental research. One of the directions of scientific researches in Institute of Telecommunications and Global Information Space is development actual mathematic models and information technologies, including ecological safety and rational nature management. Some scientific results in this areas were presented for problem of such form of mass wasting as landslides [6, 7]. Some mathematical and geoinformatic models of landslides in different locations of Ukraine were presented in [8–11]. An original approach to solve the inverse problem of well logging electrometry for oil and gas extraction was proposed in [12–16]. Research and assessment of environmental footprint of such man-caused activities as mining industry, motor transport development in urban areas, water use and others were described in [17–20]. In anthropogenic systems, the methodological principles of the environmental safety within “human—technosphere” system are the decisive factors during the transformation of the ecosystems. The prediction mechanism involves the use of a large number of variables and factors, which leads to a rapid accumulation of experimental data in the field of research and complicated the complex integration. Anomalies dynamics forecasting and obtaining the possible consequences assessment on its basis requires not only the regular observations, but also the availability of effective methods for their treatment [21]. The structure of man-caused impact is presented on Fig. 1. There are good reasons to complement contact methods of monitoring [22–26] with remote sensing technologies. Such a decision should be considered appropriate, taking into account the following factors. First, conducting direct field observations throughout the region is quite expensive. Also, monitoring only through field methods precludes the use of a single system method, which complicates further assessment and zoning of the territory. It is advisable to identify two main tasks that can be solved using remote sensing methods: – localization of pollution sources for their further inventory as part of environmental models; – determination of spatial areas of environmental pollution and differentiation of pollution intensity within these areas.

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Fig. 1 Structure of man-caused impact to environment

The remote sensing processing component solves the problem of primary data processing at the input (binding, additional binding, calibration, detection of the most contaminated areas, segmentation of the proposed decryption areas) [27–35]. At this stage of the system, the coordinates of the expected boundaries of the spread of pollutants, the selection of areas of their greatest concentration.

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2 Method, Theory and Examples One of the problems of environmental safety is the search and determination of new man-caused impact factors or unaccounted events that have led to changes in structure of environmental pollution [36]. In fact, all indicators recorded within the environmental monitoring system are parameters of man-caused impact model. We can research and identify a trend that indicates the presence of additional component using research of fluctuations of such indicators. This way, the processes of operational environmental monitoring and the prevention of ecological emergencies can be improved [21, 36]. For analysis, it is necessary to have a massive of long-term monitoring data, for example, pollution indexes for water objects. Parameters describing the process are pollution indexes in a particular spatially determined point in determined moment of time. A monthly interval between each new value was chosen to be more definite and reliability of the analysis results. Data interpretation was performed by statistical analysis methods, particularly, by dynamic correlation based on Pearson correlation coefficient calculating method [37]:  rXY =



cov X Y =  σ X σY 

 − − X − X (Y − Y ) −

2



X− X

(1) − 2

(Y − Y )

− −   where r is correlation coefficient for X and Y; X = n1 nt=1 X t and Y = n1 nt=1 Yt are means of sample. Process can be describing more properly using Eq. (2), which proposed in [18]:



m



DC lm X 1, X 2, . . . X n, =  2

n

j=m−l

i=1

 n m i=1

j=m−l

j

Xi −



j Xi

−

m



m

2 ,

k k=m−l X i

k k−m−l X k

(2)

where Xi —researched ecological process, xj i —pollution index value on the date on j. The window method was used to describe the dynamics of correlation coefficient value changes and, therefore, to determine the trend in the interrelations between environmental pollution processes. The size of “window” depends on the parameter I, which determines the amount of time interval for which the dynamic correlation coefficient was calculated. In this case, a correlation coefficient was calculated for each measurement. The limited number of proximate measurements for each process was taken into account. In such a way, we can analyze the dynamics of the relationship between the measurement results.

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It was used the software to calculate the dynamic correlation coefficients for large data sets. The algorithm was created in Delphi™ by Embarcadero Tech, results visualization was presented in Origin™ by OriginLab Corporation. Program window is presented in Fig. 2: – suspended materials—various solid particles, which, depending on the physical parameters, can settle to the bottom of the reservoir or float to the surface, are an integral part of wastewater; – pH—the degree of acidity of water, a dimensionless indicator that characterizes the concentration of free hydrogen ions in water; – dissolved oxygen. High levels may indicate a high concentration of photosynthetic biota in the water, and oxygen levels usually increase with rain or snowfall. The level of oxygen can be reduced by the presence in the water of metal compounds that accelerate the oxidation process; – 5-day biochemical oxygen demand (BOD). The indicator is related to the previous one, is a parameter that characterizes the amount of oxygen that can be spent on the oxidation of those substances to be oxidized, they include most pollutants. High BSCs indicate possible water pollution; – ammonium nitrogen. Contaminant contained in wastewater. Changes in its concentration are mainly related to agriculture, namely the functioning of livestock complexes. Also, exceeding its MPC may indicate discharges from the food industry. May be contained in domestic wastewater; – nitrate nitrogen and nitrite nitrogen. Indicators of the content of these substances indicate fresh contamination;

Fig. 2 Program window for correlation rate counting

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– phosphates. The main sources of water pollution by phosphates are household chemicals and phosphate fertilizers, which are washed away together with soils from arable land (plane washout). – chlorides. They degrade water quality, but their harmful effects on human health have not been proven. As example, in Figs. 3, 4, 5, 6, 7 results of analysis using described approach are presented. Data set of water pollution indexes in Rivne region (since 2004 till 2014) and a section of Prut river situated up-stream of city of Chernivtsi (since 1996 till 2020) was created and analyzed. Sudden changes of correlation graphics at particular short moments can show a high probability of pollutants discharge in the researched period of time. Also, it can be unauthorized. Consequently, described approach and software can be used for operational monitoring and prevention of emergency. Analyzing of graphs, adding or deleting some metrics shows that described approach can be used in a regional environmental safety system. Particularly, sudden changes in correlation dynamics can indicate releases or emissions (may be unauthorized) [37]. The proposed method makes possible the analysis of large statistical datasets. These data are mostly getting using contact monitoring methods, but combination of contact monitoring with remote sensing technologies can increase monitoring effectiveness. Environmental safety management system for geosystems is provided by creating rational “structures” which include: – establishing of territorial organization; – choice of forms and types with taking into account the peculiarities of environmental risks for ecosystems;

Fig. 3 Correlation coefficients dynamics for pollution indicators in Ustia River inside Rivne city territory: suspended substances, acidity, biochemical oxygen consumption, ammonium nitrogen, nitrate nitrogen, nitrite nitrogen and phosphates

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Fig. 4 Correlation coefficients dynamics for pollution indicators in Ustia River inside Rivne city territory: suspended substances, acidity, dissolved oxygen, biochemical oxygen consumption, ammonium nitrogen, nitrate nitrogen, nitrite nitrogen, phosphates and chlorides

Fig. 5 Correlation coefficient dynamics for indicators in Prut river up-stream of Chernivtsi: dissolved oxygen and nitrates

– variable actions to prevent environmental threats. Figure 8 shows the proposed model of complex system of environmental researches information support [36, 38].

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Fig. 6 Correlation coefficient dynamics for indicators in Prut river up-stream of Chernivtsi: ammonium, dissolved oxygen and nitrates

Fig. 7 Correlation coefficient dynamics for indicators in Prut river up-stream of Chernivtsi: ammonium, 5-day biochemical oxygen demand, suspended materials, dissolved oxygen, nitrates, sulfates and phosphates

A Transdisciplinary Analytical System for Supporting the Environmental Researches

Fig. 8 Complex system of environmental researches information support

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For evaluation risks created by stationary sources of anthropogenic impact, actual process can be planned both for sources on the territory of monitoring and sources of transboundary pollution. For more effective management it is necessary to present the results of research as comprehensive maps of the polluted territories. Estimating of uncertain risk, characterized by indicators such as coefficient of variation, mean square deviation, confidence interval needs a drawback calculating. In order to ensure environmental safety of geosystems, the environmental management is activity of rational cooperation between the economy, technology and environment. For goals of predictive management, ecological situation in geosystems is forecasting, as well as the permissible consequences are determining. Operational management of environmental safety means the control of accordance of real ecological situation to normative indicators, as well as the maintenance of goal situation by various technological processes. The main sources for decision making in the field of environmental safety and natural resource us are comprehensive monitoring data, mathematical modelling of anthropogenic impact processes, integral assessment of such impact for regions, classification of territories and realization of the above listed as part of interactive territorial geographic information system [39–48]. Before development of GIS-technology in the field of environmental safety management, it is necessary to determine a variety of quantitative and other means to achieve a certain goal. The final index of ecological classification of the territories is the relative integral indicator of anthropogenic impact per unit of selected territory. Man-caused impact should be considered as any negative influence of human activity on the geosystem components which can be quantitatively measured. There are integral indexes of atmosphere pollution, hydrosphere and land resources [38]. Proper objectivity during the classification is provided by a comprehensive synthesis of on-ground measurements, methods of mathematical modelling [49, 50] and remote sensing technologies in conjunction with active use of geographic information technologies. It should be attending to the implementation of remote sensing and geography information technologies, as far as the methodology of space images decoding is universal. Use of these capabilities allows automating the process of assessment and forecasting the current ecological situation within a specialized geographic information system of a region.

3 Conclusions Decision making in the field of nature management and environmental safety requires the provision of complete and up-to-date information on the state of the environment. This possibility is provided by a combination of contact and remote monitoring methods. The approaches proposed in this study, in particular, the method of multiparameter dynamic correlation, provide a scientifically reasonable interpretation of

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environmental data that can be used in the challenges of comprehensive evaluation of the ecological condition for territories.

References 1. United Nations Convention on the Agenda for the 21st Century (1992). Last accessed 3–14 June 1992. https://www.un.org/ru/documents/decl_conv/conventions/agenda21.html 2. Johannesburg Plan of Implementation—Resolution of the World Summit on Sustainable Development. Last accessed 4 Sept 2002. https://www.un.org/russian/conferen/wssd/docs/plan_w ssd.pdf 3. Dolishny, M.I., Kravtsiv, V.S.: Environmental safety strategy (regional context): monograph. Lviv, NASU Institute of Regional Economy (1999) 4. Rudko, G.I.: Man-caused ecological safety of the geological environment: monograph. Ivan Franko Lviv National University, Lviv (2001) 5. Dovhiy, S.O., Bidyuk, P.I., Trofymchuk, O.M., Savenkov, O.I.: Forecasting methods in decision support systems: a monograph. Naukova Dumka, Kyiv (2011) 6. Baum, R.L., Miyagi, T., Lee, S., Trofymchuk, O.M.: Introduction: hazard mapping. landslide science for a safer geoenvironment. Methods of Landslide Studies, vol. 2. Springer, Cham (2014), pp. 395–396 7. Kaliukh, I., Senatorov, V., Marienkov, N., Trofymchuk, O., Silchenko, K., Kalyukh, T.: Arrangement of deep foundation pit in restricted conditions of city build-up in landslide territory with considering of seismic loads of 8 points. In: Geotechnical Engineering for Infrastructure and Development—Proceedings of the XVI European Conference on Soil Mechanics and Geotechnical Engineering, ECSMGE, vol. 3, pp. 955–959 (2015) 8. Trofimchuk, A.N.: Unsteady oscillations of a liquid-saturated poroelastic soil layer. Int. J. Fluid Mech. Res. 29(1), 124–133 (2002) 9. Trofymchuk, O., Kaliukh, I., Klymenkov, O.: TXT-tool 2.380-1.1: Monitoring and early warning system of the building constructions of the Livadia palace, Ukraine (Book Chapter). In: Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools, vol. 1. Fundamentals, Mapping and Monitoring (2017) 10. Trofymchuk, O., Kalyukh, Y., Hlebchuk, H.: Mathematical and GIS-modeling of landslides in kharkiv region of Ukraine. Spatial Analysis and Modelling, Landslide Science and Practice (2013) 11. Trofymchuk, O., Kalyukh, Yu., Trofimova, I., Hlebchuk, H.: Modelling of landslide hazards in Kharkov region of Ukraine using GIS. In: Landslides: Global Risk Preparedness. Springer, Berlin, Heidelberg. pp. 267–276 (2013) 12. Myrontsov, M.L.: Electrometry effective inverse problem solving method. In: 19th International Conference Geoinformatics—Theoretical and Applied Aspects, Extended Abstracts (2020). https://doi.org/10.3997/2214-4609.2020geo090 13. Myrontsov, M.L.: Lateral logging sounding and lateral logging complex effective inverse problem solving method. In: 19th International Conference Geoinformatics—Theoretical and Applied Aspects, Extended Abstracts (2020). https://doi.org/10.3997/2214-4609.2020geo092 14. Myrontsov, M.L.: The method to research equivalent solutions zones for inverse problem of well logging electrometry. In: XIII International Scientific Conference “Monitoring of Geological Processes and Ecological Condition of the Environment”, Extended Abstracts (2019a). https:// doi.org/10.3997/2214-4609.201903217 15. Myrontsov, M.L.: The method to solve the inverse problem of lateral logging sounding and lateral logging. In: XIII International Scientific Conference “Monitoring of Geological Processes and Ecological Condition of the Environment”, Extended Abstracts (2019b). https:// doi.org/10.3997/2214-4609.201903244

330

O. Trofymchuk et al.

16. Myrontsov, M.L.: A new method and program for multiprobe electric logging quantitative interpretation. In: Geoinformatics 2012–11th International Conference on Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2012) 17. Anpilova, Y., Yakovliev, Y., Drozdovych, I.: Landscape and geological factors of water and ecological conditions technogenesis of donbas at the post-mining stage. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2020) https://doi.org/10.3997/2214-4609.2020geo136 18. Lukianova, V., Trofymchuk, O., Anpilova, Y.: Environmental safety of motor transport enterprises within the urban areas. J. Ecol. Eng. 21(4), 231–236 (2020). https://doi.org/10.32347/ 2411-4049.2019.1.54-64. 19. Trofymchuk, O., Yakovliev, Y., Klymenko, V., Anpilova, Y.: Geomodeling and monitoring of pollution of waters and soils by the Earth remote sensing. In: 19th International Multidisciplinary Scientific GeoConference SGEM 2019, Extended Abstracts vol. 19, Issue 1.4, pp. 197–204 (2019). https://doi.org/10.5593/sgem2019V/1.4/S02.025. 20. Trofymchuk, O., Anpilova, Y., Yakovliev, Y., Zinkiv, I.: Ground deformation mapping of solotvyno mine area using radar data and GIS. In: 19th International Conference Geoinformatics: Theoretical and Applied Aspects, Extended Abstracts (2020). https://doi.org/10.3997/ 2214-4609.2020geo138. 21. Trofymchuk, O., Okhariev, V., Trysnyuk, V.: Environmental security management of geosystems. In: Conference Proceedings, 18th International Conference of Geoinformatics - Theoretical and Applied Aspects, p. 1 (2019). https://doi.org/10.3997/2214-4609.201902083. 22. Galitsyn, V.K.: Monitoring systems. Kyiv National Economy University, Kyiv (2000) 23. Kysyl, N.M.: Theoretical principles of environmental monitoring in Ukraine under conditions of sustainable development. NLTUU Collection of Scientific and Technical Papers (2008). https://www.nbuv.gov.ua/portal/chem_biol/nvnltu/15_4/208_Kysil_15_4.pdf 24. Stow, C.A., Carpenter, S.R., Webster, K.E., Frost, T.M.: Long-term environmental monitoring: some perspectives from lakes. Ecol. Appl. 8, 269–276 (1998) 25. Urquhart, N.S., Paulsen, S.G., Larsen, D.P.: Monitoring for policy-relevant regional trends over time. Ecol. Appl. 8, 246–257 (1998) 26. Kovalchuk, I.P.: Regional environmental monitoring—expert assessment of the situation and functioning: monograph. Lviv, Opillya-L (2009) 27. Krasovsky, G.Y.: Introduction to space environmental monitoring methods. National Aerospace University, Kharkiv (1999) 28. Dovhiy, S.O., Kopiyka, O.V., Prusov, V.A.: Modelling of gas aerosol cloud propagation in the atmosphere. Informatization of aerospace science about Earth, Kyiv, Naukova Dumka (2001) 29. Krasovsky, G.Y.: Information technologies for space monitoring of aquatic ecosystems and forecast of urban water consumption. Naukova Dumka, Kyiv (2003) 30. Krasovsky, G.Y.: Space monitoring of aquatic ecosystem safety with the use of geoinformation technologies. Institute of National Security Problems, Kyiv, Naukova Dumka (2007) 31. Popov, M.O.: Ways to obtain space information in the interests of national security and defense. Sci. Def. 2, 38–50 (2003) 32. Klymenko, V.I.: Cartographic model of land pollution assessment along highways. Environ. Saf. Nat. Resour. Using 1, 62–67 (2012) 33. Krasovsky, G.Y.: Modern information technologies for space monitoring of the Black Sea: monograph. Naukova Dumka, Kyiv (2010) 34. Klymenko, V.I.: Modern information technologies for ecological safety of soils: monograph. Naukova Dumka, Kyiv (2012) 35. Dovhiy, S.O.: Environmental monitoring using NOAA satellite remote sensing images. Kyiv (2003) 36. Okhariev, V., Trysnyuk, V.: Environmental aspects of Ukrainian energy sector in connection with transition to low-carbon development. In: Conference Proceedings, Monitoring 2019, p. 1 (2019). https://doi.org/10.3997/2214-4609.201903236 37. Okhariev, V.: Improvement of statistical method for environmental monitoring datasets interpretation. In: Conference Proceedings, Monitoring 2019, p. 1 (2019). https://doi.org/10.3997/ 2214-4609.201903237

A Transdisciplinary Analytical System for Supporting the Environmental Researches

331

38. Trysnyuk, V., Okhariev, V., Trysnyuk, T., Zorina, O., Kyrylo, A., Radlowska, C.: Improving the algorithm of satellite images landscape interpretation. In: Conference Proceedings, 18th International Conference of Geoinformatics—Theoretical and Applied Aspects, p. 1 (2019). https://doi.org/10.3997/2214-4609.201902084 39. Serdutskaya, L.F.: Technogenic ecology: mathematical and cartographic modeling. IPME, Moscow (2009) 40. Serdutskaya, L.F.: System analysis and mathematical modeling of ecological processes in water ecosystems. Librocom, Moscow (2009) 41. Arkhipova, L.M.: Modeling and assessment of spatial patterns of characteristics of the Dniester hydroecosystem within the Carpathian region. Ecol. Saf. Balanc. Nat. Manag. (1), 22–27 (2010) 42. Haupt, S.E., Beyer-Lout, A., Long, K.J.: Assimilating concentration observations for transport and dispersion modeling in a meandering with field. Atmos. Environ. 43(6), 1329–1338 (2009) 43. Akimenko, V.V.: Mathematical modeling of the ecological state of the boundary layer of the region’s atmosphere. Luhansk (1998) 44. Tymkiv, D.F., Onatsko, R.G., Naslednikova, M.A.: Analysis of a mathematical model of the distribution of emissions from reservoirs into the atmosphere. Math. Mach. Syst. 2, 117–121 45. Fedra, K.: Environmental decision support systems: a conceptual framework and application examples. Imprimerie de l’Universitè de Genéve, pp. 387–393 (2000) 46. Bracht, M.J.: Geohydrological information systems for water management. International Conference on Integrated Water Resource Management. Amsterdam, pp. 687–690 (1994). Last accessed 26–29 Sept 1994 47. Sokolov, Y.N.: Dynamic processes in ecological systems: monograph. Odesa, ODEU (2012) 48. Polumienko, S.K., Rybakov, S.O.: Indicative analysis of technological development sustainability. Kyiv, Logos (2015) 49. Popov, O., Yatsyshyn, A.: Mathematical tools to assess soil contamination by deposition of technogenic emissions. In: Dent, D., Dmytruk, Y. (eds.) Soil Science Working for a Living: Applications of Soil Science to Present-day Problems, pp. 127–137. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-45417-7_11 50. Yatsyshyn, T., Shkitsa, L., Popov, O., Liakh, M.: Development of mathematical models of gas leakage and its propagation in atmospheric air at an emergency gas well gushing. East.-Eur. J. Enterp. Technol. 5/10(101), 49–59 (2019). https://doi.org/10.15587/1729-4061.2019.179097

A Conceptual Approach to the Development of Software Tools for the Analysis and Synthesis of Geophysical Monitoring Systems Models Oleksandr Maevsky , Volodymyr Artemchuk , Yuri Brodsky , Lesia Makarenko , and Yurii Shpylovyi Abstract Nowadays, the energy potential of mankind has begun to exceed the energy of some processes of natural origin, and the technology of application of powerful technical systems and complexes can play the role of a trigger for the manifestation of large-scale catastrophic phenomena. As known, there is a contradiction between the “goals” of man and nature, and according to the law of necessary diversity William Ashby, you can increase the efficiency of control in the system “nature-society” in two ways: the first—to reduce the diversity of the control object (environment) or increase the diversity of the control system—society. The main purpose of this study is the presentation of the conceptual approach to the development of software tools for the analysis and synthesis of a geophysical monitoring systems model. To achieve this goal, two approaches are proposed to describe the interaction “humanity—the environment”. The first approach is based on the use of “game principles”. The “game principle” of humanity and the environment can be described using a system of ordinary differential equations, pre-determined with the phase coordinates and “controls” of the players, as “control” affects the phase coordinates. At the heart of the second approach, the environment is considered as a system characterized by fluctuations under the influence of anthropogenic load. Depending on the level of external influence, the system either returns to equilibrium or as a result of increasing the amplitude of oscillations is destroyed and not restored, which is equivalent to a catastrophic state. The present paper substantiates the model of the geophysical monitoring system based on the Lagrange equations for an oscillatory O. Maevsky · Y. Brodsky Polissia National University, Zhytomyr, Ukraine V. Artemchuk (B) G.E. Pukhov Institute for Modelling in Energy Engineering of NAS of Ukraine, Kyiv, Ukraine e-mail: [email protected] State Institution, “the Institute of Environmental Geochemistry of National Academy of Sciences of Ukraine”, Kyiv, Ukraine L. Makarenko · Y. Shpylovyi National Pedagogical Dragomanov University, Kyiv, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_20

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system with two or more degrees of freedom in the “factor space”, and the model based on “game principles”. The authors also show the block diagram algorithm of the analysis and synthesis of geophysical monitoring systems models. Further development of this concept involves a computational experiment for a specific system using appropriate software tools. Keywords Software · Geophysical monitoring systems model · Factor space · Simulation model · Game principle · Environment

1 Introduction The rapid growth of scientific and technological potential in various fields of human activity is constantly opening up new opportunities and vast prospects for the transformation of the world around us. However, along with the progressive development of modern society, there is an exacerbation of the problem of environmental safety: any civilization seeks to achieve maximum prosperity in spite of the natural environment, which is always trying to return to a stable state. There is a contradiction between the “goals” of man and nature. Therefore, according to the law of necessary diversity William Ross Ashby [1], you can increase the efficiency of control in the system “nature-society” in two ways: the first—to reduce the diversity of the control object (environment), or increase the diversity of the control system—society (morality, culture, responsibility, human intellect). A good illustration of this idea is Ashby space (Fig. 1), which has two axes: the horizontal axis represents the variety of responses, and the vertical axis represents the variety of stimuli. Today, the energy potential of mankind has begun to exceed the energy of some processes of natural origin, and the technology of application of powerful technical systems and complexes can play the role of a trigger for the manifestation of large-scale catastrophic phenomena. For example, the use of geophysical weapons, the object of influence of which is the geosphere (from the lithosphere to outer space), and the purpose—to create mechanisms for artificial induction of dangerous natural phenomena: earthquakes, tsunamis, atmospheric catastrophes, ozone depletion, climate change with terrible irreversible consequences, violation of the structure of the ionosphere, magnetosphere, etc. The global trend of catastrophes of both natural and man-made origin carries a real threat to the existence not only of individual territories, countries, peoples but also of all mankind.

2 Literature Analysis and Problem Statement This article is a continuation of previous research by the authors [3–5] and their colleagues [6–8] in the field of improving the efficiency of management decisions, environmental safety issues, development of appropriate software, etc.

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Fig. 1 Ashby Space [2]

Some environmental, information, and other aspects of geophysical monitoring systems models are presented in papers [9–21]. For example, the author of [13] writes, that the technosphere metabolizes not only energy and materials but information and knowledge as well. The article first examines the history of knowledge about large-scale, long-term, anthropogenic environmental change. Also, the author proposes that knowledge infrastructures for the Anthropocene might not only monitor and model the technosphere’s metabolism of energy, materials, and information but also integrate those techniques with new accounting practices aimed at sustainability. This paper [17] presents a variational approach to solving direct and inverse problems based on the joint use of mathematical models and data monitoring of processes of geophysical hydro-thermodynamics. This approach is used to solve problems related to environmental protection. A good illustration of interconnected geophysical processes in the environment (Fig. 2) on the example of the Arctic region is shown in paper [21]. Climate change drives changes in regional temperature and precipitation, and these exogenous changes interact with regional topography and landscape patterns of permafrost and plant communities and are mediated and propagated by hydrology (blue text indicating hydrologic variables) in pushing Arctic ecosystems toward directional ecosystem shifts (black arrows). Ecosystem shifts occur at the local landscape scale, but the consequences can feedback to regional and global scales (gray arrows), through, e.g., reshaping of hydrology (changes in evapotranspiration (ET), water

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Fig. 2 Interactions and feedbacks from three general types of shifts in Arctic terrestrial and freshwater ecosystems [21]

balance, and surface water connection/fragmentation) and by altering albedo, and carbon (CO2 and CH4 ), energy fluxes. If these changes accumulate across large geographic areas, they can also affect the global climate. A lot of game models for particular issues of human-environment systems are shown in papers [22–29] and others. For example, authors [24] to illustrate the applicability of their results analyze stochastic models of evolutionary games, Lotka–Volterra dynamics, trait evolution, and spatially structured disease dynamics. Analysis of these models demonstrates environmental stochasticity facilitates the coexistence of strategies in the hawk–dove game, but inhibits coexistence in the rock–paper–scissors game and a Lotka–Volterra predator-prey model. The analyzed publications allow us to conclude that the unsolved part of the general problem is the construction of advanced mathematical models using information technology in the interests of state security in its key areas, systematic analysis of their role and place, essence, and content at the present stage of science and technology.

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3 Purpose and Objectives of the Study The main purpose of this study is the presentation of the conceptual approach to the development of software tools for analysis and synthesis of geophysical monitoring systems model. To achieve this goal, two approaches are proposed to describe the interaction “humanity—the environment”. The first approach is based on the use of “game principles”. At the heart of the second approach, the environment is considered as a system characterized by fluctuations under the influence of anthropogenic load. Depending on the level of external influence, the system either returns to equilibrium or as a result of increasing the amplitude of oscillations is destroyed and not restored, which is equivalent to a catastrophic state.

4 Research Methods The implementation of both the first and second approaches requires a clear description of the phase variables in the first case and the establishment of the physical content of the generalized coordinates in the second case. This study begins with a brief analysis of publications devoted to describing the interaction “humanity - the environment”, and focuses mainly on the description of the two authorial approaches. The following methods were used in this study: the method of comparative analysis; mathematical modeling; simulation modeling; system analysis, etc.

5 Research Results 5.1 The Game Principle The “game principle” of humanity and the environment can be described using a system of ordinary differential equations, pre-determined with the phase coordinates and “controls” of the players, as “control” affects the phase coordinates. The environment tries to minimize anthropogenic impact and has control φ1 , φ2 , . . . φi , which can be stochastic. Mankind maximizes its impact on the environment and has control ψ1 , ψ2 , . . . ψ j . The choice of control depends on the situation of the player “humanity”. Assume that x1 is some environmental resource (for example, the amount of oil in the bowels of the planet in the appropriate units), and let x2 characterize the number of consumers who use this resource for their own purposes. Resource x1 can be reduced due to anthropogenic impact by building oil complexes. Let x3 be the total number of oil-producing complexes, and let ψ1 (0 ≤ ψ1 ≤ 1) be the share of the total number of oil-producing complexes in operation at some

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point in time. It should be noted that ψ1 can be a functional dependence. In the next step, it is necessary to empirically or otherwise establish the dependence of resource losses x1 on ψ1 x3 . For certainty, we assume that this dependence is logarithmic, i.e. x1 = ln(ψ1 x3 ). Resource x1 is restored at speed V [units/time]. Based on these considerations, we have an equation of the following type: d x1 (t) = V − ln(ψ1 x3 ) ± · · · dt

(1)

In the given Eq. (1) after the signs “±“, there can be components that characterize other actions of the player “humanity”. Denote by x4 —some non-renewable natural resources required for the formation of resource x1 . Then “s“ is the maximum speed of obtaining this resource, and φ1 (0 ≤ φ1 ≤ 1) is the fraction of “s“, which can be a functional dependence or be stochastic in nature (i.e. the player’s control “environment”). Then we have another equation: d x4 (t) = − sφ1 , (x4 ≥ 0) dt

(2)

Equations (1) and (2) is the equation of “motion” in the n-dimensional Euclidean space of the game. It is clear that within the framework of the conceptual approach proposed in the article, no specific differential equations can be compiled, which requires consideration of the real system and experimental data processing. For a real system, i = 1 . . . n differential equations of the form (3) can be compiled after determining the phase variables and player controls. d xi (t) = f (x1 , x2 . . . xn , ψ1 , ψ2 . . . ψν ) dt

(3)

5.2 The Approach Based on the Energy Properties The second approach to creating a model of the geophysical monitoring system is based on the “energy properties” of the studied system in the “factor space” under the condition of external (including anthropogenic) load. For certainty, consider two factors influencing the selected system x1 and x2 , respectively. Then the state of the system in the factor space will be determined by a point with radius—vector r = r(x1 , x2 ), Fig. 3. Hence, it logically follows that the control of the states of the selected system is carried out by external influence on the parameters x1 and x2 . To preserve the physical content of the quantities, it is proposed to move to the same units of factors x1 and x2 , as well as the energy parameters of the system. Then the dimensionality of energy parameters [J] and unknown dimensions x1 and x2 will be presented in combinations [m] and [c], i.e. in spatial and temporal units.

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Fig. 3 Example of factor space with radius vector

The transition to these dimensions is carried out by transferring the units of mass [kg] in [m] and [s].   m3 As is known, 1 kg = 6.67×10−11 [[s 2 ]] , then the dimensions of energy quantities [J] and forces [N] will look like this: 

 m5   ; [1 J ] = 6.67 × 10 s4  4 −11 m   [1 N ] = 6.67 × 10 s4 −11

To describe the behavior of a system with two degrees of freedom using Lagrange equations for small oscillations: ⎧  d ∂W ⎪ ⎪ ⎨ dt ∂ x  −  1 ⎪ d ∂W ⎪ ⎩ − dt ∂ x2

∂W = S1 ; ∂ x1 ∂W = S2 ; ∂ x2

(4)

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where W —reflects the kinetic energy of a point in the factor space, S1 , S2 — generalized forces that include potential forces, resistance forces, and perturbation forces. It is known that in order to investigate small oscillations of the system in the vicinity of a stable equilibrium, it is necessary to decompose into power series W and S1 , S2 and the dissipative function. For further research, we assume that the radius vector r = r(x1 , x2 ) is a function only of x1 and x2 , which in turn are functions of time. N

m k rk(2) It is known that the kinetic energy of the system W = . For a system 2 with two degrees of freedom, given that rk =

∂ rk  x ∂ x1 1

+

k=1 ∂ rk  x , we ∂ x2 2

obtain for W :

     d x1 (t) (2) d x1 (t) d x2 (t) (2) d x2 (t) 1 + M22 W = + 2M12 , M11 2 dt dt dt dt (5)

where M11 =

N



mk

k=1

∂ rk ∂ x1

2

, M12 =

N

k=1

mk

∂ rk ∂ rk ∂ x1 ∂ x2



2 N

, M22 = m k ∂∂ xrk2 . k=1

Thus, the kinetic energy W in (5) is determined in the equilibrium neighborhood of a definite positive quadratic form, the coefficients of which correspond to known conditions. We do similar calculations for the potential energy P of the system which is a function of only x1 and x2 . Recall that the schedule in a number of potential energies in the vicinity of the equilibrium state gives the following dependence:  P(x1 , x2 ) = (P0 ) +

   ∂P ∂P 1 ∂2 P ∂2 P 2 x1 + x2 + x1 + x1 x2 + ∂ x1 0 ∂ x2 0 2 ∂x2 ∂ x1 ∂ x2 1 0 0

 1 ∂2 P x22 + · · · 2 ∂x2 2 0

(6)

Holding in (6) the components of the second-order, we obtain: P(x1 , x2 ) =

 1 H11 x12 + 2H12 x1 x2 + H22 x22 2

(7)

2 

2 

2  where H11 = ∂∂ xP2 , H12 = ∂ x∂1 ∂Px2 , H22 = ∂∂ xP2 . 1 2 0 0 0 A detailed mathematical description of these transformations is set out in most classical works on theoretical physics. After substitution in (4), we finally obtain a system with two degrees of freedom:

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⎧  2   2 ⎪ ⎪ M11 d x1 + H11 x1 + M12 d x2 + H12 x2 = 0; ⎪ ⎨ dt 2 dt 2   2   2 ⎪ d x1 d x2 ⎪ ⎪ + H12 x1 + M22 + H22 x2 = 0. ⎩ M12 dt 2 dt 2

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(8)

Then for a system with an arbitrary finite number of degrees of freedom, we have:      ⎧ d 2 x1 d 2 x2 d 2 xn ⎪ ⎪ ⎪ M11 + H11 x1 + M12 + H12 x2 + · · · + M1n + H1n xn = 0 ⎪ ⎪ dt 2 dt 2 dt 2 ⎪ ⎪ ⎪ ⎪       ⎪ ⎪ 2 2 2 ⎪ ⎪ ⎨ M21 d x1 + H21 x1 + M22 d x2 + H22 x2 + · · · + M2n d xn + H2n xn = 0 dt 2 dt 2 dt 2 ⎪ ⎪ ⎪ ⎪ ............................................................................................. ⎪ ⎪ ⎪       ⎪ ⎪ ⎪ ⎪ d 2 x1 d 2 x2 d 2 xn ⎪ ⎪ M x x x + H + M + H + · · · + M + H = 0 nn nn n ⎩ n1 n1 1 n2 n2 2 dt 2 dt 2 dt 2

(9) After solving system (8) or (9), phase trajectories in factor space are constructed. The final step is to study the probability distributions of factors x1 and x2 in order to determine which of the factors has the greatest impact on the behavior of the system. To do this, it is proposed to use Bayesian dependencies. That is, having a significant amount of statistical data on the values of factors x1 and x2 , which have no large or zero levels of correlation, we empirically establish their distribution laws. Let the event A be that the phase trajectory of the evolution of the system under certain controls went beyond the established limits. Then the question arises, which of the factors x1 and x2 to a greater extent contributed to the emergence of such a situation, and with what probability. For the answer we apply Bayesian dependences of a posteriori confirmation of hypotheses. There is a complete group of incompatible hypotheses: U1 —event A occurred largely due to a factor x1 ; U2 —event A occurred largely due to a factor x2 . Then the required probability Q(U1 /A) for the factor x1 : Q(U1 /A) =

Q(U1 )Q(A/U1 ) Q(U1 )Q(A/U1 ) + Q(U2 )Q(A/U2 )

(10)

Probability Q(U2 /A) for the factor x2 : Q(U2 /A) =

Q(U2 )Q(A/U2 ) Q(U1 )Q(A/U1 ) + Q(U2 )Q(A/U2 )

(11)

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5.3 Algorithm of the Analysis and Synthesis of Geophysical Monitoring Systems Models In general, the algorithm for analysis and synthesis of models of geophysical monitoring systems consists of the following stages: 1. 2. 3. 4. 5. 6. 7.

Selection of the object (system) of research. Study of the basic properties of the system. Development of the concept of control system (reasonable choice of phase variables and controls). Construction of a mathematical model of control. Conducting a computational experiment. Assessment of the adequacy of the constructed model. In case of inadequacy of the received model, to carry out its improvement from point 4. The block diagram of this algorithm is shown in Fig. 4.

6 Discussion and Conclusions Further development of this concept involves a computational experiment for a specific system using appropriate software tools. The paper substantiates the model of the geophysical monitoring system based on the Lagrange equations for an oscillatory system with two or more degrees of freedom in the “factor space”, and the model based on “game principles”. The coefficients in systems (8) and (9) are controls that affect the phase coordinates x1 , x2 , . . . xn , which makes it possible to build a phase portrait and study the selected system, as well as to influence its evolution.

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Fig. 4 Block diagram algorithm of analysis and synthesis of geophysical monitoring systems models

References 1. Ashby, W.: Effect of controls on stability. Nature 155, 242–243 (1945). https://doi.org/10.1038/ 155242b0 2. Harish’s Notebook. When a Machine Breaks. Access mode. https://harishsnotebook.wordpr ess.com/tag/ashby/ 3. Maevsky, O., Artemchuk, V., Brodsky, Y., Pilkevych, I., Topolnitsky, P.: Modeling of the process of optimization of decision-making at control of parameters of energy and technical systems on

344

4.

5.

6.

7. 8.

9.

10.

11.

12. 13. 14.

15.

16.

17.

18.

19.

O. Maevsky et al. the example of remote earth’s sensing tools. Stud. Syst. Decis. Control 298, 111–122 (2020). https://doi.org/10.1007/978-3-030-48583-2_7 Popov, O.O., Iatsyshyn, A.V., Kovach, V.O., Artemchuk, V.O., Kameneva, I.P., Taraduda, D.V., Sobyna, V.O., Sokolov, D.L., Dement, M.O., Yatsyshyn, T.M.: Risk assessment for the population of Kyiv, Ukraine as a result of atmospheric air pollution. J. Health Pollut. 10(25), 200303 (2020). https://doi.org/10.5696/2156-9614-10.25.200303 Iatsyshyn, A., Iatsyshyn, A., Artemchuk, V., Kameneva, I., Kovach, V., Popov, O.: Software tools for tasks of sustainable development of environmental problems: peculiarities of programming and implementation in the specialists’ preparation. E3S Web Conf. 166, 01001 (2020). https://doi.org/10.1051/e3sconf/202016601001 Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Simulation and software for diagnostic systems. Stud. Syst. Decis. Control 281 (2020). https://doi.org/10. 1007/978-3-030-44443-3_3 Eremenko, V., Zaporozhets, A., Isaenko, V., Babikova, K.: Application of wavelet transform for determining diagnostic signs. CEUR Workshop Proc. 2387, 202–214 (2019) Zaporozhets, A., Kovtun, S., Dekusha, O.: System for monitoring the technical state of heating networks based on UAVs. Adv. Intell. Syst. Comput. (2020). https://doi.org/10.1007/978-3030-33695-0_61 Alvarez, J.K., Sutjipto, S., Kodagoda, S.: Validated ground penetrating radar simulation model for estimating rebar location in infrastructure monitoring. In: Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications (2018). https://doi.org/10.1109/ iciea.2017.8283069 Azpiroz-Zabala, M., Cartigny, M.J.B., Sumner, E.J., Clare, M.A., Talling, P.J., Parsons, D.R., Cooper, C.: A general model for the helical structure of geophysical flows in channel bends. Geophys. Res. Lett. 44(23), 11932–11941 (2017). https://doi.org/10.1002/2017gl075721 Dammeier, F., Moore, J.R., Hammer, C., Haslinger, F., Loew, S.: Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models. J. Geophys. Res. Earth Surf. 121(2), 351–371 (2016). https://doi.org/10.1002/2015jf003647 Di Fiore, V., Punzo, M., Tarallo, D., Cavuoto, G.: Application of innovative geophysical techniques in coastal areas. Geophys. Princ. Appl. Emerg. Technol 47–59 (2016) Edwards, P.N.: Knowledge infrastructures for the anthropocene. Anthrop. Rev. 4(1), 34–43 (2017). https://doi.org/10.1177/2053019616679854 Greco, F., Giammanco, S., Napoli, R., Currenti, G., Vicari, A., Spina, A.L., Sicali, A.: A multidisciplinary strategy for in-situ and remote sensing monitoring of areas affected by pressurized fluids: application to mud volcanoes: a multidisciplinary environmental monitoring strategy. In: SAS 2016—Sensors Applications Symposium, Proceedings, pp. 291–296 (2016). https:// doi.org/10.1109/sas.2016.7479861 Li, S., Hu, R.: Numerical simulation of ground penetrating radar based on advanced prediction of adverse geological bodies. IOP Conf. Ser. Earth Environ. Sci. 371(2) (2019). https://doi.org/ 10.1088/1755-1315/371/2/022038 Marullo, S., Minnett, P.J., Santoleri, R., Tonani, M.: The diurnal cycle of sea-surface temperature and estimation of the heat budget of the Mediterranean sea. J. Geophys. Res. Oceans 121(11), 8351–8367 (2016). https://doi.org/10.1002/2016jc012192 Penenko, V.V., Penenko, A.V., Tsvetova, E.A.: Variational approach to the study of processes of geophysical hydro-thermodynamics with assimilation of observation data. J. Appl. Mech. Tech. Phys. 58(5), 771–778 (2017). https://doi.org/10.1134/s0021894417050029 Schattan, P., Baroni, G., Oswald, S.E., Schöber, J., Fey, C., Kormann, C., Achleitner, S.: Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing. Water Resour. Res. 53(5), 3615–3634 (2017). https://doi.org/10.1002/2016wr020234 Schlosser, J.S., Braun, R.A., Bradley, T., Dadashazar, H., MacDonald, A.B., Aldhaif, A.A., Sorooshian, A.: Analysis of aerosol composition data for western united states wildfires between 2005 and 2015: Dust emissions, chloride depletion, and most enhanced aerosol constituents. J. Geophys. Res. Atmos. 122(16), 8951–8966 (2017). https://doi.org/10.1002/ 2017jd026547

A Conceptual Approach to the Development of Software …

345

20. Schroeder, J.R., Crawford, J.H., Fried, A., Walega, J., Weinheimer, A., Wisthaler, A., Mattson, E.: Formaldehyde column density measurements as a suitable pathway to estimate near-surface ozone tendencies from space. J. Geophys. Res. 121(21), 13088–13112 (2016). https://doi.org/ 10.1002/2016jd025419 21. Wrona, F.J., Johansson, M., Culp, J.M., Jenkins, A., Mård, J., Myers-Smith, I.H., Wookey, P.A.: Transitions in arctic ecosystems: ecological implications of a changing hydrological regime. J. Geophys. Res. G: Biogeosci. 121(3), 650–674 (2016). https://doi.org/10.1002/2015jg003133 22. Bao, X., Li, H.: Study on the evaluation method of subgrade slope green protection effect in dry-hot valley of sichuan-tibet railway. Math. Prob. Eng. (2020). https://doi.org/10.1155/2020/ 7159582 23. Bauch, C.T., Sigdel, R., Pharaon, J., Anand, M.: Early warning signals of regime shifts in coupled human-environment systems. Proc. Natl. Acad. Sci. U.S.A. 113(51), 14560–14567 (2016). https://doi.org/10.1073/pnas.1604978113 24. Benaim, M., Schreiber, S.J.: Persistence and extinction for stochastic ecological models with internal and external variables. J. Math. Biol. 79(1), 393–431 (2019). https://doi.org/10.1007/ s00285-019-01361-4 25. Chen, I.: An evolutionary game study of an ecological industry chain based on multi-agent simulation: A case study of the poyang lake eco-economic zone. Sustainability 9(7) (2017). https://doi.org/10.3390/su9071165 26. Dan, L.: Research on innovation-synergy driven model for improving ecological competitiveness in resource-based city. Nat. Environ. Pollut. Technol. 15(2), 515–520 (2016) 27. He-Si, P., & Hong-Zhi, L.: The evolutionary game analysis of cross-regional ecological compensation—based on the perspective of the main functional area. E3S Web Conf 53 (2018). https://doi.org/10.1051/e3sconf/20185304043 28. Sigdel, R., Anand, M., Bauch, C.T.: Convergence of socio-ecological dynamics in disparate ecological systems under strong coupling to human social systems. Theor. Ecol. 12(3), 285–296 (2019). https://doi.org/10.1007/s12080-018-0394-z 29. Vermeulen, P.J., van Ruijven, J., Anten, N.P.R., van der Werf, W.: An evolutionary game theoretical model shows the limitations of the additive partitioning method for interpreting biodiversity experiments. J. Ecol. 105(2), 345–353 (2017). https://doi.org/10.1111/1365-2745. 12706

Estimated Efficiency of Biogenic Elements Removal from Waste Water in the Ideal Displacement Photobioreactor Sergii Shamanskyi , Sergii Boichenko , and Lesia Pavliukh

Abstract Surface water bodies eutrification problem caused by their pollution by nutrients coming together with wastewater is considered in the article. It is proposed to use a technological scheme at municipal and other sewage treatment plants, which provides for the organization of additional wastewater treatment by using them as a medium for the cultivation of energy microalgae. The use of the absorption process of nutrients by microalgae organisms due to the metabolism of the latter is proposed. To implement the process, the use of closed photoioreactors of ideal displacement is proposed. To design such photobioreactors, nutrients removal efficiency calculating model, namely nitrogen and phosphorus compounds from wastewater during the operation of such a photobioreactor, is proposed. The exponential form of the model is used and equations system for finding the concentration of phosphates, nitrates and nitrites along the length of the working zone of the photobioreactor is derived. Nutrient absorption constants concept is used, which is determined by the activation energy of absorption of each element according to the Arrhenius formula. The proposed model allows determine the required residence time of wastewater in the working area of the reactor to achieve the required degree of nutrients removal, or their desired final concentration in treated wastewater. According to the proposed model, the efficiency of the photobioreactor design previously developed by the authors in specific conditions (for specific input nutrients concentrations in treated wastewater and the use of the microalgae strain Clorella vulgaris) was calculated. Wastewater eutrophication potential concept is used and the level of its reduction due to the use of the proposed method of their additional treatment is calculated. Keywords Treatment · Wastewater · Phosphates · Nitrates · Nitrites · Microalgae · Eutrophication potential

S. Shamanskyi · S. Boichenko (B) · L. Pavliukh National Aviation University, Kyiv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Zaporozhets and V. Artemchuk (eds.), Systems, Decision and Control in Energy II, Studies in Systems, Decision and Control 346, https://doi.org/10.1007/978-3-030-69189-9_21

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1 Introduction The most surface water bodies in Ukraine are classified as polluted or very polluted. This condition significantly increases environmental risks. It is the use of water that does not meet quality requirements that causes and spreads many infectious and non-infectious diseases. Surface water may contain specific contaminants, the presence of which is associated with objects located in their catchment basins. One of the most important sources of surface water pollution is sewage, which is insufficiently treated. Along with effluents, nutrient compounds deliver to surface waters, except other things. Among the main components are nitrogen and phosphorus compounds. They are present in high concentrations due to the ingress of detergents, effluents from food, processing, agricultural, medical and other enterprises, as well as due to the imperfection of existing treatment technologies [1–5]. Their presence in reservoirs leads to eutrophication—the rapid reproduction of blue-green algae, resulting in negative changes in the flora and fauna of the reservoir, as well as the appearance of toxic compounds in the water. An important and urgent task is to develop effective nutrients removing from wastewater methods at the stage of treatment with waste minimization and the possibility of returning the removed nutrients to economic circulation, as they are valuable elements of mineral fertilizers, especially phosphorus compounds, which are limited in nature [6].

2 Analysis of Literature Data and Problem Statement Various methods can be used to remove phosphorus and nitrogen compounds. One of the promising methods can be considered the use of wastewater for the microalgae cultivation [7, 8]. Due to their natural metabolism, they are efficient absorbers of nutrients and carbon dioxide, while increasing their biomass. However, the biomass of many strains of microalgae can be used to manufacture many types of products [9]. When using this method of wastewater treatment at municipal treatment plants, excess biomass can be directly processed into biogas at treatment plants [10–14]. This can be done in methane tanks together with excess activated sludge. Biomass can also be processed into liquid motor biofuels, or into phosphate and nitrate fertilizers [15–17]. To reduce the wastewater eutrophication potential discharged into surface water bodies, it is proposed to use the technological scheme of drainage proposed in [18]. The scheme provides traditional wastewater treatment, after that the wastewater is delivered to additional treatment in a photobioreactor. Microalgae for cultivation are also fed into the photobioreactor, as well as carbon dioxide, which is supposed to be obtained in the anaerobic fermentation plant of excess activated sludge and residual biomass of microalgae obtained after cultivation. It is assumed that the microalgae

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biomass removed from the photobioreactor is fed to the oil removal plant and liquid biofuel production [19]. Part of this biofuel is burned at the local thermal power plant, the rest is considered as a commercial biofuel. The purified flue gases of the thermal power plant are also delivered to the photobioreactor to enrich the culture medium with carbon dioxide [20]. The biomass of microalgae remaining after processing is fed to the anaerobic fermentation plant, which works according to the technology proposed in [21]. For additional wastewater treatment from nutrients, it is proposed to use a closedtype photobioreactor of continuous action, the design of which is proposed in [22]. Schematically, its design is presented in Fig. 1. The design can be considered as a closed-type photobioreactor of ideal displacement. The structure is made in the form of a flowing rectangular container (reactor vessel) 3, inside which are transparent tubes 4, interconnected and fixed in the housing by the knee holders 5 and 13 so as to form a solid coil. A mixture of wastewater with microalgae prepared in the mixer 1 is fed into the coil by means of a supercharger 2. Carbon dioxide is fed into the coil from the carbon dioxide storage tank 9 through the supply pipes 7 and the injection nipple 6 to saturate the wastewater as a culture

Fig. 1 Structural Scheme of a Photobioreactor for Wastewater Treatment From Nutrients [23]: 1— the mixer; 2—supercharger; 3—reactor vessel; 4—transparent tube; 5—knee holder; 6—nipple; 7—CO2 supply tube; 8—distribution tray; 9—capacity for CO2 ; 10—guiding tray; 11—separator; 12—valve; 13—knee holder; 14—receiving tray; 15—drainage tray

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medium. Transparent tubes 4 are fixed at an angle to the horizon so that placed on one side of the coil of the knee 13 are located above the knees 5, placed on the opposite coil. On these knees there are valves for release of the gases saved up there. After the treatment process, the mixture of wastewater with microalgae is sent to the microalgae separator 11. The clarified wastewater through the guiding tray 10 and the distribution tray 8 is sent inside the reactor vessel 3. It acts as a temperature stabilizer in the cold season, increasing the thermal inertia of the whole structure. scattering medium of direct solar radiation in the warm season to reduce the effect of photoinhibition of photosynthesis. Through the collection tray 14 and the discharge tray 15, the treated and clarified wastewater can be discharged into the reservoir. Experience shows that the use of closed photobioreactors for the cultivation of microalgae is more efficient than the use of open water for this purpose. However, despite the large number of studies in this area, it can be argued that today there are no generally accepted methods for calculating reactors of this type when used to treat wastewater from nutrients [24–26]. The current task is to develop methods for determining the efficiency and algorithms for calculating such reactors to enable their wider use.

3 The Purpose and Objectives of the Study The aim of the study is to develop a mathematical model for calculating the efficiency of nutrients removal from wastewater by microalgae in the closed type photobioreactor of ideal displacement. The use of such a model will determine the residence time of wastewater in the working area of the photobioreactor to achieve the required degree of nutrients removal, or the required concentration at the outlet. To achieve this goal, the following tasks were formulated: – to develop a mathematical model for calculating phosphates, nitrates and nitrites concentration change along the length of the photobioreactor working zone; – to calculate the efficiency of the previously developed design of the closed-type photobioreactor for predetermined phosphates nitrates and nitrites concentrations in wastewater using a strain of microalgae Chlorella vulgaris; – to calculate the wastewater eutrophication potential reduction degree during the implementation of the treatment technological scheme using the developed design of the photobioreactor.

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4 Materials and Methods of Research Research methods are based on systematic analysis of theoretical research and mathematical modeling and forecasting. The paper uses known exponential models, as well as methods for calculating the environment eutrophication potential. The experimental results obtained during the cultivation of Chlorella vulgaris microalgae in model solutions were used.

5 Modelling of Concentration Change of Some Nutrients in Wastewater The main task of the photobioreactor is to remove nitrogen and phosphorus compounds from wastewater, namely nitrates, nitrites and phosphates [27–29]. But along with the absorption of nitrates there is also residual ammonium nitrogen decline. An exponential model can be used to model the change in their concentration. An equations system can be written for phosphates, ammonium nitrogen and nitrates: ⎧ d S P O4 ⎪ ⎪ = −α P O4 · S P O4 inlet. ⎪ ⎪ ⎪ dt ⎪ ⎨ d S N H4 (1) = −α N H4 · S N H4 inlet. , ⎪ dt ⎪ ⎪ ⎪ ⎪ dS ⎪ ⎩ N O3 = −α N O3 · S N O3 inlet. dt where: S PO4 , S NH4 , S NO3 —current phosphorous compounds (equivalent PO4 ) ammonium nitrogen and nitrates concentration respectively; α PO4 , α NH4 , α NO3 —phosphorous compounds, ammonium nitrogen and nitrates adsorption constants respectively; S PO4 inlet. , S NH4 inlet. , S NO3 inlet. —inlet phosphorous compounds, ammonium nitrogen and nitrates concentration respectively. We write a separate equations system that will find nutrients concentration change along the length of the photobioreactor: ⎧ d S P O4 d S P O4 ⎪ ⎪ = −v · ⎪ ⎪ ⎪ dt dL ⎪ ⎨ d S N H4 d S N H4 , = −v · ⎪ dt dL ⎪ ⎪ ⎪ ⎪ ⎪ d S N O3 = −v · d S N O3 ⎩ dt dL

(2)

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where: L—length of the photobioreactor working zone; v—waste waters stream speed in the photobioreactor working zone. To get a complete model of the process, we add the Eqs. (1) i (2). So ⎧ d S P O4 d S P O4 ⎪ ⎪ = −α P O4 · S P O4 inlet − v · ⎪ ⎪ ⎪ dt dL ⎪ ⎨ d S N H4 d S N H4 . = −α N H4 · S N H4 inlet − v · ⎪ dt dL ⎪ ⎪ ⎪ ⎪ d S N O3 dS ⎪ ⎩ N O3 = −α N O3 · S N O3 inlet − v · dt dL

(3)

We assume that the nutrients concentration at each point of the photobioreactor does not change over time: d S N H4 d S N O3 d S P O4 = 0, = 0, = 0. dt dt dt

(4)

Taking into account formula (4) we have obtained: ⎧ d S P O4 d S P O4 ⎪ ⎪ = −α P O4 · S P O4 inlet. − v · =0 ⎪ ⎪ ⎪ dt dL ⎪ ⎨ d S N H4 d S N H4 = −α N H4 · S N H 4 inlet. − v · = 0. ⎪ dt dL ⎪ ⎪ ⎪ ⎪ dS d S N O3 ⎪ ⎩ N O3 = −α N O3 · S N O3 inlet. − v · =0 dt dL

(5)

After simplification we have received: ⎧ d S P O4 ⎪ ⎪ v = −α P O4 · S P4 inlet. ⎪ ⎪ ⎪ dL ⎪ ⎨ d S N H4 = −α N H4 · S N H4 inlet. . v ⎪ dL ⎪ ⎪ ⎪ ⎪ dS ⎪ ⎩ v N O3 = −α N O3 · S N O3 inlet. dL

(6)

Let’s divide both parts of the formulas by speed. As a result: ⎧ dS ⎪ ⎪ P O4 = − α P O4 · S P O4 inlet ⎪ ⎪ ⎪ dL v ⎪ ⎨ α N H4 · S N H 4 inlet. d S N H4 . =− ⎪ dL v ⎪ ⎪ ⎪ ⎪ ⎪ d S N O3 = − α N O3 · S N O3 inlet. ⎩ dL v

(7)

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To take into account the energy component and obtain a more complete model of the process, we express in the equations system (7) phosphorous compounds, nitrogen compounds adsorption and carbon dioxide constants in terms of activation energy according to Arrhenius.   E exp − RTi

αi = αo i

,

(8)

where: Ei—activation energy of corresponding element; Taking into account formula (8) we have obtained:  E ⎧ PO exp − RT 4 ⎪ ⎪ ⎪ d S P O4 · S P O4 inlet. α0 P O4 ⎪ ⎪ ⎪ ⎪ dL = − ⎪ v ⎪  E ⎪ ⎪ NH ⎨ exp − RT 4 α0 N H4 · S N H4 inlet. . d S N H4 =− ⎪ ⎪ ⎪ dL v ⎪  E ⎪ ⎪ NO ⎪ exp − RT 3 ⎪ ⎪ ⎪ α · S N O3 inlet. ⎪ ⎩ d S N O3 = − 0 N O3 dL v

(9)

To determine the wastewater residence time in the working zone of the reactor t, it is necessary to determine the required time for a given reduction of the concentrations of all types of compounds and to determine the dictating type. Consider a photobioreactor as a reactor of ideal displacement, in which the ratio of the length of the working pipe to its diameter is large (Fig. 2). Wastewater with microalgae seeds is continuously fed into the working area of the reactor. Photosynthesis processes occur during the movement of wastewater along the working area of the reactor. The material balance of the compounds, which are removed in the cylindrical photobioreactor for the elementary volume will be recorded as [30]:   2 ∂ Si ∂ Si ∂ Si ∂ Si ∂ Si ∂ 2 Si ∂ 2 Si = −υ Xi · − υY i · − υ Zi · + kd · + μi , + + ∂t ∂x ∂y ∂z ∂x2 ∂ y2 ∂z 2

(10)

Fig. 2 Scheme of a closed photobioreactor of ideal displacement

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where: Si—concentration of the i-th type of nutrients compounds in the culture medium (wastewater); x, y, z—spatial coordinates; υ x , υ y , υ z —spatial components of wastewater flow rate; k d —diffusion coefficient; μi —specific rate of nutrients absorption by microalgae; t—time. The left side of formula (10) is the nutrients change concentration over time for the elementary volume of wastewater. The first term of the right part characterizes the concentration change due to the transfer of nutrients by wall waters in the flow direction. The second term—characterizes the concentration change due to diffusion transfer of nutrients. The third term is the concentration change due to absorption by microalgae. The peculiarity of ideal displacement reactors is that each particle of the flow moves only in the axial direction. Stirring and diffusion under these conditions are absent. Each elementary volume of liquid moves along the reactor tube and does not mix with the others. The concentration of nutrients decreases from the initial to the final length of the working zone of the reactor, due to the absorption of microalgae. Due to these conditions, it can be assumed that the residence time of each elementary volume of wastewater in the working area of the reactor is the same. Since all elementary volumes move only in the axial direction from the beginning of the working zone of the reactor to its end, the first term of the right-hand side of formula (10) is written as: −υ Xi ·

∂ Si ∂ Si ∂ Si ∂ Si = −υ · , −υY i · = 0, −υ Zi · = 0, ∂x ∂L ∂x ∂x

(11)

where υ—the velocity of wastewater elementary volume along the working area of the reactor; L—working zone length of the reactor. Since under the conditions in the reactor there is no diffusion and mixing, the second term of formula (10) will be written as:  kd ·

∂ 2 Si ∂ 2 Si ∂ 2 Si + + ∂x2 ∂ y2 ∂z 2

= 0.

(12)

Taking into account formulas (11) and (12), formula (10) takes the form: ∂ Si ∂ Si = −υ · + μi , ∂t ∂L

(13)

During the start-up of the reactor, when the cultivation process is just beginning, the parameters of the medium in the working area of the reactor change both in its length and time. That is, the process is non-stationary. Cultivation in the established mode can be considered a stationary process, because inside the reactor, in each of

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its elementary volumes, the parameters of the environment remain unchanged over time. The parameters change only in length. For this case we can write: ∂ Si = 0. ∂t

(14)

Taking into account formula (14), formula (13) will be simplified and takes the form: −υ ·

∂ Si + μi = 0, dL

(15)

The volume of the culture medium in the reactor can be considered constant. You can write for such conditions: ∂ Si = Si

inlet.

· d Ki ,

(16)

дe: S i inlet. —initial concentration of nutrients in wastewater (at the reactor inlet); K i —nutrient compound removal rate from the wastewater in the reactor. The velocity of wastewater in the reactor can be written by the formula: υ=

dL . dt

(17)

Taking into account formulas (16) and (17), formula (15) can be written: −

d L Si · dt

· d Ki = μi , dL

inlet.

(18)

where of dt = Si

inlet.

·

d Ki . −μ

(19)

d Ki . −μ

(20)

The integration of formula (19) shows:

K i t = Si inlet. · 0

Nutrients absorption rate as a chemical reaction can be written as: β

−μi = αi · Si , where α i —constant of the nutrient compound absorption rate;

(21)

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β—reaction order. Taking into account formula (21), formula (20) will take the form:

K i t = Si inlet. ·

d Ki β

0

αi · Si

.

(22)

З вpaxyвaнням фopмyли (16), фopмyлy (22) мoжнa зaпиcaти

K i t = Si inlet. · 0

d Ki αi ·

β Si inlet.

· (1 − K i )

β

=

Si inlet. αi ·

β Si inlet.

K i · 0

d Ki . (1 − K i )β

(23)

For the first-order reaction, the formule (23) will be written: 1 t= · αi

K i 0

d Ki . 1 − Ki

(24)

Integrating the variable replacement method we have obtained: t=

1 1 · ln . αi 1 − Ki

(25)

and by the formula (25) it is possible to determine the residence time of wastewater in the working area of the reactor to achieve the required nutrients removal rate.

6 Results and Disscusion To determine the residence time of wastewater in the working area of the reactor, it is necessary to determine the rate constants of nitrogen and phosphorus compounds under specific conditions. To determine them, we use experimental data and the exponential law of concentration change. Let’s write it in the form: Si = Si inlet · exp(−αi t).

(26)

Hence, absorption rate constant of the nutrient can be determined by the formula: αi =

ln Si inlet − ln Si . t

(27)

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According to experimental data [31] when using Clorella vulgaris to remove nitrogen and phosphorus compounds at an initial concentration of ammonium nitrogen—42 g/m3 , phosphates—12 g/m3 , as well as the duration of microalgae in wastewater 24 h, the final concentration was: ammonium nitrogen—4 g/m3 , phosphates—2 g/m3 . At an initial nitrate concentration of 4.4 g/m3 and a residence time of microalgae in wastewater of 32 h, the final concentration was 1.2 g/m3 . According to formula (27), absorption rate constants: ln 42 − ln 4 ln 12 − ln 2 = 0.098; α P O4 = = 0.075; 24 24 ln 4.4 − ln 1.2 = 0.041. = 32

αN H 4 = α N O3

Calculate this time to remove phosphorus and nitrogen compounds. Municipal waste waters can contain 5…20 g/m3 of phosphorus compounds, and 50…60 g/m3 of total nitrogen (according to the Rules of receiving sewage of subscribers into the sewerage system of Kyiv city, the permissible content is: phosphates—8 g/m3 , ammonium nitrogen—20 g/m3 , nitrates—40 g/m3 ). After biological purification with denitrification, the concentration of ammonium nitrogen is 1.8 g/m3 , nitrates—9.5 g/m3 . According to the regulatory requirements of the European Union, the concentration of phosphorus compounds in wastewater discharged into reservoirs should be 1…2 g/m3 , total nitrogen—10…15 g/m3 . Take the concentration of phosphorus compounds in wastewater at the inlet to the reactor 8 g/m3 , and at the outlet— 1 g/m3 , the concentration of ammonium nitrogen at the inlet—1.8 g/m3 , and at the outlet—0.5 g/m3 (as for discharge into fishery reservoirs), nitrate concentration at the inlet—9.5 g/m3 , and at the outlet—2 g/m3 . Thus, the phosphorus compounds required removal degree will be КPO4 = 0.88, ammonium nitrogen—КNH4 = 0.72, nitrates—КNO3 = 0.79. Under such conditions, the residence time of wastewater in the reactor is required to remove phosphorus t P and t N respectively: 1 1 1 1 · ln = 28.26 hour s; t N H4 = · ln = 12.98 hour s; 0.075 1 − 0.88 0.098 1 − 0.72 1 1 = · ln = 38.06 hour s, 0.041 1 − 0.79

t P O4 = t N O3

Since the most important element for removal from wastewater is phosphorus and its compounds (from an ecological point of view, it has the greatest specific eutrophication potential, from an economic point of view—a valuable element, the reserves of which are limited in nature) asssume the residence time of wastewater in the photobioreactor as a basis, required to achieve the required phosphorus compounds removal degree –28.26 h We have accepted the residence time of wastewater in the working area of the photobioreactor –30 h. Then the concentration of phosphorus compounds, ammonium nitrogen and nitrates can be determined by formula (26). Output concentration will be:

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concentration change of phosphorus compounds (РО 4 ) concentration change of nitrates (NO3) concentration change of ammonium nitrogen (NH4)

Fig. 3 Biogenic elements concetration variation in time

S P O4 = 8 · exp(−0.075 · 30) = 0.84 · g/m 3 ; S N H4 = 1, 8 · exp(−0.098 · 30) = 0.10 · g/m 3 ; S N O3 = 9, 5 · exp(−0.041 · 30) = 2.78 · g/m 3 .

Concentration change of phosphorus, nitrate and ammonium nitrogen compounds in wastewater during the operation of the photobioreactor, calculated by formula (26), is shown in Fig. 3. As can be seen from Fig. 3 during retention time of wastewater in the working zone of the photobioreactor, is equal to 30 h, the concentration of phosphorus compounds decreases by 9.5 times, ammonium nitrogen—by 18 times, nitrates—by 3.4 times. Decreasing the concentration of these elements reduces the eutrophication potential of wastewater. This potential can be calculated by the formula: Pwastewater =

n 

Pn ×n .

(28)

1

дe: EPwastewater —eutrophication potential of wastewater; EPn —eutrophication index of the n-th nutrient, expressed as the equivalent of phosphate ion; M n —mass of n-th nutrient in some volume of wastewater; n—the amount of nutrients in wastewater. The reduction of the eutrophication potential of wastewater can be calculated by the formula:

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inlet

Pww 1 Pww = outlet = n Pww 1

Pn × Sninlet .

(29)

Pn × Snoutlet

where: ΔEPww —reduction of the eutrophication potential of wastewater; S n inlet , S n outle . —mass of n-th nutrient in some volume of wastewater before and after treatment respectively. Eutrophication indexes of nutrients [32]: EPPO4 = 1.0; EPNH4 = 0.33; EPNO3 = 0.1. After treatment by the proposed technology, the reduction of the eutrophication potential of wastewater will be: Pww =

1.0 × 8 + 0.33 × 1.8 + 0.1 × 9.5 = 8.27. 1.0 × 0.84 + 0.33 × 0.1 + 0.1 × 2.78

Thus, the eutrophication potential will decrease by 8.27 times.

7 Conclusions Surface water bodies eutrophication problem is a consequence of their pollution by nutrients. One of the main sources of their entry into surface water bodies is insufficiently treated sewage with high eutrophication potential. To reduce this potential, an effective method can be considered the use of technological schemes of sewage treatment plants with photobioreactors for the removal of biogenic compounds from wastewater by microalgae due to their metabolism. The method is promising due to the possibility of using the microalgae cultivated during purification for the production of various products, including biogas during the fermentation of this biomass together with excess activated sludge in anaerobic fermentation plants. Promised types of photobioreactors for purification can be considered closed continuous reactors. Modeling the operation of such a photobioreactor, provided it is considered as a reactor of ideal displacement, allows determine the required residence time of the wastewater with microalgae mixture in the working area of the reactor to achieve the required nutrients removal rate from wastewater. The use of experimental data on the cultivation of the microalgae strain Clorella vulgaris in wastewater, as well as the proposed model shows that in wastewater treatment with initial concentrations: phosphates—8 g/m3; nitrates—9.5 g/m3; ammonium nitrogen—1.8 g/m3, in the photobioreactor it is possible to reduce the eutrophication potential of wastewater by 8.27 times. This will significantly reduce the eutrophication load on reservoirs when discharging sewage.

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References 1. Danilovich, D.A.: The best available technology for community sanitation. Water Supply Sanit. Tech. 3, 6–13 (2012) 2. Tevjashev, A.D., Korinko, I.V., Esilevskiy, V.S., Dolgobrod, A.G., Kobylinskiy, K.V., Jaroshenko, U.V., Nikitenko, G.V.: Progressive informational resource-saving and environmentally friendly technologies in sewage systems. East. Eur. J. Appl. Technol. 2(14), 50–61 (2005) 3. Kozlov, M.N., Charkina, O.V., Pachomov, A.N., Streltsov, S.A., Chamidov, M.G., Ershov, B.A., Belov, N.A.: Operating experience of biological wastewater treatment plants from nitrogen and phosphorus compounds. Water Supply Sanit. Tech. 10–1, 35–41 (2010) 4. Charkin, S.V., Charkina, O.V.: Implementation of technologies for removal of nitrogen and phosphorus from wastewater: role of design and operation. Best Available Technol. Water Supply Sewerage 1, 4–15 (2014) 5. Capodaglio, A.G., Olsson, G.: Energy issues in sustainable urban wastewater management: use, demand reduction and recovery in the urban water cycle. Sustainability 12, 266 (2020) 6. Shamanskyi, S.I., Boichenko, S.V.: Innovative environmentally friendly technologies in Sewarage. Monograph. Кyiv: Publishing House Center for Educational Literature, 320 7. Copoкинa, КH., Якoвлeв, B.A., Пилигaeв, A.B., и дp. : The potential use of microalgae as raw material for bioenergy. Catal. Ind. 2, 63–72 (2012) 8. Sasikanth, K., Jyotsna, T., Anjali, P., Sharma, M.C.: Studies on cultivation of lipid accumulating Botryococcus Braunii from North Gujarat inland waters for generation of 3rd generation biofuels. Indian J. Appl. Res. 4(9), 31–35 (2014) 9. Naruka, M., Khadka, M., Upadhayay, S., Kumar, S.: Potential applications of microalgae in bioproduct production: a review. Octa J. Biosci. 7(1), 01–05 (2019) 10. Magdalena, J.A., Llamas, M., Tomбs-Pejy, E., Gonzбlez-Fernбndez, C.: Semicontinuous anaerobic digestion of protease pretreated Chlorella biomass for volatile fatty acids production. J. Chem. Technol. Biotechnol. 94, 1861–1869 (2019) 11. Głowacka, N., Gaduš, J.: Verification of the green microalgae biomass use for biogas. Acta Reg. Et Environ. 1, 15–49 (2019) 12. Gonzalez-Fernandez, C., Barreiro-Vescovo, S., de Godos, I., Fernandez, M., Zouhayr, A., Ballesteros, M.: Biochemical methane potential of microalgae biomass using different microbial inocula. Biotechnol. Biofuels 11, 184 (2018) 13. Koyandea, A.K., Show, P.L., Guob, R., Tangc, B., Oginod, C., Chang, J.S.: Bio-processing of algal bio-refinery: a review on current advances and future Perspectives. Bioengineered 10(1), 574–592 (2019) 14. Milledge, J.J., Nielsen, V., Maneein, S., Harvey, P.J.: A brief review of anaerobic digestion of algae for bioenergy. Energies 12, 1166 (2019) 15. Raheem, A., Prinsen, P., Vuppaladadiyam, A.K., Zhao, M., Luque, R.: A review on sustainable microalgae based biofuel and bioenergy production: recent developments. J. Clean. Prod. 181, 42–59 (2018) 16. Bhagea, R., Bhoyroo, V., Puchooa, D.: Microalgae: the next best alternative to fossil fuels after biomass. Rev. Microbiol. Res. 10(7936), 12–23 (2019) 17. Saad, M.G., Dosoky, N.S., Zoromba, M.S., Shafik, H.M.: Algal biofuels: current status and key challenges. Energies 12, 1920 (2019) 18. Shamanskyi, S., Boichenko, S.: Development of environmentally safe technological water disposal scheme of aviation enterprise. East. Eur. J. Appl. Technol. 6/10(84), 49–57 (2016) 19. Shamanskyi, S.I., Boichenko, S.V.: Chap. 11. Environment-friendly technology of airport’s sewerage. advances in sustainable aviation. —under general editorship of Tahir Hikmet Karakoҫ, C. Ozgur Colpan, Yasin Söhret. ¸ —Springer International Publishing, 161–175 (2018) 20. Shamanskyi, S., Boichenko, S.: Energy efficient and environmental friendly technology of stabilizing of airline enterprises’ wastewater sludges. East. Eur. J. Appl. Technol. 5/8(77), 39–45 (2015)

Estimated Efficiency of Biogenic Elements …

361

21. Shamanskyi, S., Boichenko, S.: Environmentally friendly biogas production. Energy Smart Systems. In: IEEE 6th International Conference on Energy Smart Systems (2019 IEEE ESS), pp. 243–248. Kyiv (2019). Last accessed 17–19 April 2019 22. Shamanskyi, S., Boichenko, S., Adeniyi, C.: Photobioreactor for microalgae cultivation for ´ biofuel production with simultaneous sewage water treatment. Systemy i Srodki Transportu Samochodnego. Wybrane Zagadnienia. Monografia nr. 16. Seria: Transport. Rzeszów: Politechnika Rzeszowska, 61–67 (2019) 23. Shamanskyi, S.I., Boichenko, S.V., Adeniyi, C.O.: Photobioreactor for wastewater treatment. Patent of Ukraine no. 133752, (2019.01) C02F 1/00, C02F 3/32 (2006.01), 201810274. Declared 16.10.2018; Published 25.04.2019, 8, 4. (2019) 24. Chen, Y., Wang, J., Zhang, W., Chen, L., Gao, L., Liu, T.: Forced light/dark circulation operation of open pond for microalgae cultivation. Biomass Bioenerg. 56, 464–470 (2013) 25. Pruvost, J., Van Vooren, G., Le Gouic, B., Couzinet-Mossion, A., Legrand, J.: Systematic investigation of biomass and lipid productivity by microalgae in photobioreactors for biodiesel application. Biores. Technol. 102(1), 150–158 (2011) 26. Pruvost, J., Le Borgneb, F., Artua, A., Legranda, J.: Development of a thin-film solar photobioreactor with high biomass volumetric productivity (AlgoFilm©) based on process intensification principles. Algal Res. 21, 120–137 (2017) 27. Manea, R.G., Ardelean, I.I.: Nitrogen and phosphorus removal from municipal wastewater using cinsortia of photosynthetic microorganisms. Sci. Bulletin. Ser. F. Biotechnol. 11, 286–292 (2016) 28. Singh, R., Birru, R., Sibi, G.: Nitrogen removal efficiencies of chlorella vulgaris from urban wastewater for reduced eutrophication. J. Environ. Prot. 8, 1–11 (2017) 29. Ruiz-Martinez, A., Serralta, J., Romero, I., Secco, A., Ferrer, J.: Effect of intracellular P content on phosphate removal in scenedesmus sp. Experimental study and kinetic expression. Biores. Technol. 175, 325–332 (2015) 30. Brjankin, К.V., Utrobin, N.P., Orechov, V.S., Djachkova, T.P.: General chemical technology: tambov, publishing house, Tambov State Technical University, 175 (2006) 31. Abdel, H.M., S. : Effect of algal density in bead, bead size and bead concentration on wastewater nutrient removal. Afr. J. Bot. 1(3), 029–034 (2013) 32. Guinee, J.D., Gorree, M., Heijungs, R., Huppes, G., Klejn, R., Koning, A., Van Oers, L., Sleeswijk, A.W., Suh, S., Udo de Haes, H.A., De Brujin, H., Van Duin, R., Huijbrects, J.M.: Life cycle assessment: an operational guide to the ISO standards. Dordrecht, Kluwer Academic Pulishers, 30 (2001)