Predictive Modelling for Energy Management and Power Systems Engineering [1 ed.] 0128177721, 9780128177723

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PREDICTIVE MODELLING FOR ENERGY MANAGEMENT AND POWER SYSTEMS ENGINEERING

PREDICTIVE MODELLING FOR ENERGY MANAGEMENT AND POWER SYSTEMS ENGINEERING Edited by

RAVINESH DEO School of Sciences, University of Southern Queensland, QLD, Australia

PIJUSH SAMUI Department of Civil Engineering, NIT Patna, Patna, Bihar, India

SANJIBAN SEKHAR ROY School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-817772-3 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Candice Janco Acquisitions Editor: Graham Nisbet Editorial Project Manager: Leticia M. Lima Production Project Manager: Kamesh Ramajogi Cover Designer: Greg Harris Typeset by MPS Limited, Chennai, India

Contents 3. Community-scale rural energy systems: General planning algorithms and methods for developing countries

List of contributors ix About the editors xi Foreword xiii Preface xv

Alejandro Lo´pez-Gonza´lez

List of Acronyms 63 3.1 Introduction 64 3.2 Conclusion 82 Acknowledgments 82 References 83

1. A Multiobjective optimal VAR dispatch using FACTS devices considering voltage stability and contingency analysis Youssouf Amrane and Nour EL Yakine Kouba

4. Proven energy storage system applications for power systems stability and transition issues

1.1 Introduction 1 1.2 Problem formulation 2 1.3 A proposed hybrid particle swarm optimization and gravitational search algorithm 5 1.4 Stability index 10 1.5 Flexible alternating current transmission systems modeling 11 1.6 Simulation results 13 1.7 Conclusion 25 References 25 Appendix 26

Jean Ubertalli and Timothy Littler

4.1 Introduction 85 4.2 Proven energy storage for increased service provision 87 4.3 Grid functions for energy storage system 88 4.4 Energy storage characterization for digital inertia 91 4.5 Test model of the transmission system 96 4.6 Future implications of hybridized scheme to transition issues 110 4.7 Chapter summary 112 References 112

2. Photovoltaic panels life span increase by control Bechara Nehme, Nacer K M’Sirdi, Tilda Akiki, Aziz Naamane and Barbar Zeghondy

5. Design and performance of two decomposition paradigms in forecasting daily solar radiation with evolutionary polynomial regression: wavelet transform versus ensemble empirical mode decomposition

Acronyms 27 Chapter outcome 28 2.1 Introduction 28 2.2 Degradation modes of photovoltaic panels 30 2.3 Real-time simulation model 38 2.4 Thermal model of a photovoltaic panel 43 2.5 Mitigation of degradation via control 51 2.6 Conclusion 60 Acknowledgments 61 References 61

Mohammad Rezaie-Balf, Sungwon Kim, Alireza Ghaemi and Ravinesh Deo

5.1 Introduction 115 5.2 Study area and evaluation criterion

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5.3 Methodology 119 5.4 Models implementation and application 5.5 Results and discussions 128 5.6 Conclusions 138 Appendix 139 References 140

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9. Nowcasting solar irradiance for effective solar power plants operation and smart grid management

6. Development of data-driven models for wind speed forecasting in Australia Ananta Neupane, Nawin Raj, Ravinesh Deo and Mumtaz Ali

6.1 Introduction 143 6.2 Materials and methods 148 6.3 Results of short-term wind speed forecasting 158 6.4 Results of daily wind speed forecasting 6.5 Summary 184 References 187

8.5 Conclusions References 246

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7. Hybrid multilayer perceptron-firefly optimizer algorithm for modelling photosynthetic active solar radiation for biofuel energy exploration Harshna Gounder, Zaher Mundher Yaseen and Ravinesh Deo

Acronyms 191 7.1 Introduction 192 7.2 Chapter background review 196 7.3 Materials and methodology 201 7.4 Application results and analysis 214 7.5 Discussion 225 7.6 Conclusion 226 References 227 Further reading 231

8. Predictive modeling of oscillating plasma energy release for clean combustion engines Xiao Yu, Qingyuan Tan, Linyan Wang, Meiping Wang and Ming Zheng

8.1 Introduction 233 8.2 Challenges of plasma discharge under engine conditions 235 8.3 Experimental setup and methodology 238 8.4 Predictive modeling of oscillating plasma discharge 240

Marius Paulescu, Eugenia Paulescu and Viorel Badescu

9.1 Introduction 249 9.2 Solar irradiance 252 9.3 Statistical models for short-time solar irradiance 260 9.4 Performance of the solar irradiance forecast 264 9.5 Conclusions 268 References 269

10. Short-term electrical energy demand prediction under heat island effects using emotional neural network integrated with genetic algorithm Sagthitharan Karalasingham, Ravinesh Deo and Ramendra Prasad

10.1 Introduction 271 10.2 Theoretical overview 273 10.3 Study area and data 277 10.4 Predictive model development 280 10.5 Results and discussion 285 10.6 Conclusions and remarks 290 10.7 Limitations and further research 296 References 296

11. Artificial neural networks and adaptive neuro-fuzzy inference system in energy modeling of agricultural products Ashkan Nabavi-Pelesaraei, Shahin Rafiee, Fatemeh Hosseini-Fashami and Kwok-wing Chau

11.1 11.2 11.3 11.4 11.5

Introduction 299 Data collection and energy calculation 301 Artificial neural network 310 Adaptive neuro-fuzzy inference system 315 Validation of artificial neural network and adaptive neuro-fuzzy inference system model 319 11.6 Other models of machine learning 320

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11.7 Interpretation of results 323 11.8 Conclusion 329 Acknowledgment 330 References 330

12. Support vector machine model for multistep wind speed forecasting Shobna Mohini Mala Prasad, Thong Nguyen-Huy and Ravinesh Deo

12.1 Introduction 335 12.2 Literature review 338 12.3 Materials and method 342 12.4 Results and discussion 359 12.5 Conclusion 382 References 383 Appendix 388

13. MARS model for prediction of short- and long-term global solar radiation Dilki T. Balalla, Thong Nguyen-Huy and Ravinesh Deo

13.1 Introduction 391 13.2 Literature review 393 13.3 Materials and methodology 397 13.4 Results and discussion 408 13.5 Conclusion 433 References 434

14. Wind speed forecasting in Nepal using self-organizing map-based online sequential extreme learning machine Neelesh Sharma and Ravinesh Deo

14.1 Introduction 437 14.2 Literature review 439

14.3 Materials and methods 447 14.4 Short-term forecasting 457 14.5 Daily forecasting model 465 14.6 Monthly forecasting model 472 14.7 Conclusion 480 References 481

15. Potential growth in small-scale distributed generation systems in Brazilian capitals Carmen B. Rosa, Paula D. Rigo and Julio Cezar M. Siluk

15.1 Introduction 485 15.2 Distributed generation in Brazil 487 15.3 Measurement method 490 15.4 Results 495 15.5 Conclusion 500 Acknowledgments 502 References 503

16. Trend of energy consumption in developing nations in the last two decades: a case study from a statistical perspective Anshuman Dey Kirty

16.1 Introduction 507 16.2 Related work 509 16.3 Implementation 511 16.4 Conclusion 520 References 520

Index 523

List of Contributors Tilda Akiki

Holy Spirit University of Kaslik, USEK, Jounieh, Lebanon

Mumtaz Ali Deakin-SWU Joint Research Center on Big Data, School of Information Technology, Deakin University, VIC, Australia Youssouf Amrane Laboratory of Electrical and Industrial Systems, University of Sciences and Technology Houari Boumediene, Algiers, Algeria Viorel Badescu Romania

Candida Oancea Institute, Polytechnic University of Bucharest, Bucharest,

Dilki T. Balalla Australia

School of Sciences, University of Southern Queensland, Springfield, QLD,

Kwok-wing Chau Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong Ravinesh Deo

School of Sciences, University of Southern Queensland, Springfield, QLD, Australia

Alireza Ghaemi Department of Civil Engineering, Graduate University of Advanced Technology, Kerman, Iran Harshna Gounder School of Sciences, University of Southern Queensland, Springfield, QLD, Australia Fatemeh Hosseini-Fashami Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran Sagthitharan Karalasingham QLD, Australia

School of Sciences, University of Southern Queensland, Springfield,

Sungwon Kim Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, South Korea Anshuman Dey Kirty Vellore Institute of Technology, Vellore, India Nour EL Yakine Kouba Laboratory of Electrical and Industrial Systems, University of Sciences and Technology Houari Boumediene, Algiers, Algeria Timothy Littler Department of Energy, Power and Intelligent Control (EPIC), IEEE and EEECS Research Society, Queen’s Belfast University, Belfast, Northern Ireland Alejandro Lo´pez-Gonza´lez Institute of Industrial and Control Engineering, Universitat Polite`cnica de Catalunya—BarcelonaTech, Barcelona, Spain; Department of Electrical Engineering—Campus Terrassa (ESEIAAT)—BarcelonaTech, Tarrassa, Spain; Socioeconomic Centre of Petroleum and Alternative Energies, Universidad del Zulia, Maracaibo, Venezuela Nacer K M’Sirdi Aix Marseille Universite´, Marseille, France Aziz Naamane Aix Marseille Universite´, Marseille, France Ashkan Nabavi-Pelesaraei Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran; Management of Fruit and Vegetables Organizations, Tehran Municipality, Tehran, Iran Bechara Nehme Holy Spirit University of Kaslik, USEK, Jounieh, Lebanon

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Ananta Neupane Australia

School of Sciences, University of Southern Queensland, Toowoomba, QLD,

Thong Nguyen-Huy School of Sciences, University of Southern Queensland, Springfield, QLD, Australia; Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD, Australia; Vietnam National Space Center, Vietnam Academy of Science and Technology, Hanoi, Vietnam Eugenia Paulescu Marius Paulescu

Faculty of Physics, West University of Timisoara, Timisoara, Romania Faculty of Physics, West University of Timisoara, Timisoara, Romania

Ramendra Prasad Department of Science, School of Science and Technology, The University of Fiji, Saweni, Lautoka, Fiji Shobna Mohini Mala Prasad School of Sciences, University of Southern Queensland, Springfield, QLD, Australia Shahin Rafiee Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran Nawin Raj

School of Sciences, University of Southern Queensland, Springfield, QLD, Australia

Mohammad Rezaie-Balf Department of Civil Engineering, Graduate University of Advanced Technology, Kerman, Iran Paula D. Rigo Post-Graduate Program in Production Engineering, Federal University of Santa Maria (UFSM), Santa Maria, Brazil Carmen B. Rosa Post-Graduate Program in Production Engineering, Federal University of Santa Maria (UFSM), Santa Maria, Brazil Neelesh Sharma

University of Southern Queensland, Springfield, Springfield, QLD, Australia

Julio Cezar M. Siluk Post-Graduate Program in Production Engineering, Federal University of Santa Maria (UFSM), Santa Maria, Brazil Qingyuan Tan Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, Canada Jean Ubertalli

IEEE PES member, Queen’s Belfast University (QUB), Belfast, Northern Ireland

Linyan Wang Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, Canada Meiping Wang Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, Canada Zaher Mundher Yaseen Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam Xiao Yu Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, Canada Barbar Zeghondy

Holy Spirit University of Kaslik, USEK, Jounieh, Lebanon

Ming Zheng Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, Canada

About the editors Professor Ravinesh Deo is an Associate Professor at the University of Southern Queensland, Australia, the Program Director for the Postgraduate Science Program, and Research Leader in Artificial Intelligence. He also serves as an Associate Editor for two international journals: Stochastic Environmental Research and Risk Assessment and the ASCE Journal Hydrologic Engineering journal (USA). As an Applied Data Scientist with proven leadership in artificial intelligence, his research develops decision systems with machine learning, heuristic, and metaheuristic algorithms to improve real-life predictive systems especially using deep learning explainable AI, convolutional neural networks, and long short-term memory networks. He was awarded internationally competitive fellowships including the Queensland Government US Smithsonian Fellowship, Australia India Strategic Fellowship, Australia China Young Scientist Exchange Award, Japan Society for Promotion of Science Fellowship, Chinese Academy of Science Presidential International Fellowship and Endeavour Fellowship. He is a member of scientific bodies, and has won Publication Excellence Awards, Head of Department Research Award, Dean’s Commendation for Postgraduate Supervision, BSc Gold Medal for Academic Excellence, and he was the Dux of Fiji in Year 13 examinations. Professor Deo has held visiting positions at the United Stations Tropical Research Institute, Chinese Academy of Science, Peking University, Northwest Normal University, University of Tokyo, Kyoto and Kyushu University, University of Alcala Spain, McGill University, and National University of Singapore. He has undertaken knowledge exchange programs in Singapore, Japan, Europe, China, the United States, and Canada and secured international standing by researching innovative problems with global researchers. He has published books with Springer Nature, Elsevier, and IGI and over 190 publications of which over 140 are Q1 including refereed conferences, edited books, and book chapters. Professor Deo’s papers have been cited over 4000 times with a Google Scholar H-Index of 36 and a Field Weighted Citation Index exceeding 3.5. Professor Pijush Samui is currently an Associate Professor at the National Institute of Technology, Patna, India. He is an established researcher in the application of Artificial Intelligence (AI) for solving different problems in engineering. He has developed a new method for prediction of response of soil during an earthquake. He has produced charts for the prediction of the response of soil during an earthquake and has developed equations for the prediction of lateral spreading of soil due to an earthquake. He has developed equations for the determination of the seismic liquefaction potential of soil based on strain energy and prediction of seismic attenuation. He has developed efficient models for the prediction of the magnitude of reservoir-induced earthquakes. He has developed models for the determination of medical waste generation in hospitals with equations used for practical purpose. The developed models can be used for the Clean India project. He has

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determined frequency effects on liquefaction by using the Shake Table. He has applied AI techniques for the determination of bearing capacity and settlement of foundation and equations for the determination of bearing capacity and settlement of shallow foundations. He also developed equations for the determination of compression index and angle of shearing resistance of soil. He has developed equations for the prediction of uplift capacity of suction caisson. He also developed equations for the determination of fracture parameters of concrete. His active research activity is evident from his extensive citation of publications in Google Scholar (total frequency of 1280) with an H-Index of 22. Dr. Samui has published journal papers, books/book chapters, and peer reviewed conference papers with coauthors from Australia, India, Korea, and several other nations. He also holds the position of Visiting Professor at the Far East Federal University (Russia). Sanjiban Sekhar Roy is an Associate Professor in the School of Computer Science and Engineering, Vellore Institute of Technology. He joined VIT in the year 2009 as an Asst. Professor. His research interests include deep learning and advanced machine learning. He has published around 50 articles in reputed international journals (with SCI impact factors) and conferences. He also is an editorial board member for a handful of international journals and a reviewer for many highly reputed journals such as Neural Processing Letters (Springer), IEEE Access: The Multidisciplinary Open Access Journal, Computers & Security (Elsevier), International Journal of Advanced Intelligence Paradigms (Inderscience International Publishers), International Journal of Artificial Intelligence and Soft Computing (Inderscience International Publishers), Ad Hoc Networks (Elsevier), Evolutionary Intelligence (Springer), Journal of Ambient Intelligence and Humanized Computing (Springer), Iranian Journal of Science and Technology, Transactions of Electrical Engineering (Springer). He uses deep learning and machine learning techniques to solve many complex engineering problems, especially those related to imagery. He is specialized in deep convolutional neural networks and generative adversarial networks. Dr. Roy also has edited many books with reputed international publishers such as Elsevier, Springer, and IGI Global. Very recently, the Ministry of National Education, Romania in collaboration with “Aurel Vlaicu” University Arad Faculty of Engineers, Romania has awarded Dr. Roy with a “Diploma of Excellence” as a sign of appreciation for the special achievements obtained in scientific research in 2019.

Foreword The demand for electrical power has been rising globally, both at national and community scales, and there is a need to find more sustainable and newer forms of electrical power generation resources. The world is concurrently faced with the challenge of mitigating climate change, a large portion of which is due to the emission of greenhouse gases arising from the use of fossil fuels. Renewable energy is in the unique position of addressing both these issues simultaneously. The inclusion of renewable energy technologies (RETs) such as hydropower, wind, solar, and biomass to the generation mix of power grid supplies is routine practice. Such technologies currently supply some 26% of the global electrical power generation. As they displace almost the same fraction of fossil fuel power from the generation mix, these RETs reduce global greenhouse gas emissions by a comparable proportion. Renewable energy generation consists of dispatchable (synchronous) power such as hydropower and biomass, and variable (or asynchronous) generation such as wind and solar. While synchronous generation may be added seamlessly to the generation mix, the inclusion of asynchronous generation requires more care. The variable nature of such renewable sources makes the total output of the grid supply unpredictable, and their integration into the system leads to system instabilities. These two issues necessitate, amongst other things, the predictive modeling of variable renewable energy resources as well the use of new methodologies for enhancing system strength. This Edited Book considers the development of computational tools for prediction and optimization of energy production for power systems using computer-aided algorithms and energy management methodologies. The choice of the chapter contributions has been meticulously executed by the Editors. They consist of a wide range of topics specific to energy optimization and forecasting, and include the forecasting and nowcasting of wind and solar energy resources, enhancing the system viability and strength via digital inertia in the form of battery storage and providing algorithms for the management of community-scale rural energy systems. Amongst the expected ultimate outcomes of this publication is the improvement of power grid system efficiency and its performance. This will have immediate consequences on efficiency of energy distribution at the national and community levels, and make a positive impact on countries’ emissions reduction programs. The publication of this book comes in the wake of the launch of Sustainable Development Goals (SDGs) and the Paris Agreement in 2015. It synergizes well with Goal 7 of SDG, which is to ensure access to affordable, reliable, sustainable, and modern energy for all. The substantive agreement reached in Paris with regard to climate change mitigation was the undertaking by all Parties to Nationally Determined Contributions (NDCs) to greenhouse gas reductions. Following the release of the IPCC Special Report on Global Warming of 1.5 C in October 2018 and the subsequent Climate Action Summit of

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September 23, 2019, there has been vigorous debate regarding the adequacy of the NDCs in achieving the agreed goal of net zero emissions by 2050. The outcome of these deliberations was the realization that much greater reductions in emissions were necessary than those proposed originally in the Paris Agreement. The present book will go a long way toward facilitating the design and implementation of power grid systems that improve national and community-scale distribution as well as reducing overall national GHG emissions. This book is of great relevance to two of the major ongoing discussions on global issues, and is an immensely valuable and timely addition to the scientific literature on energy modeling and management. Professor Anirudh Singh Lautoka, Fiji Islands 22 March 2020

Preface Today climate advocates are advising energy industries to embed renewable energies into power grids; the role of artificial intelligence in demand side management remains paramount but the success of this vision is at the heart of latest modeling or optimization techniques. National electricity markets, energy management experts, electronic, electrical, and mechatronic engineers should be familiar with advanced optimization techniques that can be used to improve existing energy demand systems, and also to integrate renewable energy into real power grids. This book provides ideas on optimization techniques as an interdisciplinary concept. The book acts as a common platform required by practitioners to become familiar with the latest developments of energy optimization techniques based on artificial intelligence. It is written to provide a “one-spot” collective resource for practitioners to learn about predictive models in the energy sector, their practical applications, and case studies. The purpose is to provide the modeling theory in an easy-to-read format verified with onsite models (i.e., case studies) for specific geographic regions and scenarios. There is a need for this sort of text because we currently have several models in isolated contexts. Putting the theory of energy simulation models and applying those optimization techniques in a single bound book will help novice readers to grasp the concepts more easily than highly technical publications.

What problem does this book solve? Currently, technical papers and books present materials in a way such that both a beginner reader and energy experts find it too hard to grasp the ideas. A requirement for postgraduate, early and mid-career researchers is to read and understand energy modeling in a way that they can quickly relate the theory and practical applications. This book will provide such a platform whereby readers will appreciate both the theory and practical applications, and also see the comparison of different energy management and optimizations in different chapters.

Why would readers choose this book? Readers will choose this book because it contains both theory and practice related to energy demand management in a single document, has several optimization models in this area, provides easy-to-understand chapters, and supports people new to the field. For experts, the book will be appealing as it gives first-hand experience about artificial intelligence models—an area that is growing in the current phase.

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The book is written as a practical guide focused for postgraduate teaching (case studies, modeling, and simulations), early and mid-career research and teaching scholars, academics, renewable energy practitioners, electrical and electronic engineers, climate energy scientists, and future energy policy makers. It will serve as a highly summarized text on latest developments in energy, consumer energy simulations, and energy demand side management. The book provides the latest approaches for energy exploration, advanced predictive models, and case studies in geographically diverse locations, modern techniques, and demonstrations to apply artificial intelligence in decision-making for the renewable and conventional energy sector. It will therefore be a useful resource for the energy industry— particularly for engineering and energy management experts.

Rigor This book is compiled carefully with highly focused chapters that will present to the readers the modern-day optimization techniques in energy exploration (particularly a balanced account of theory and case studies) applied in the energy demand side and real-life power management system. It will make a significant contribution to the development of mathematical tools and data simulation models, and their relevance to different geographic power distributions and case studies that will support modern-day energy engineering applications. The text will be a useful resource for power systems engineering and the design of energy management platforms in complex consumer markets, for scientific application of real-time energy prediction and management systems, and for integrating artificial intelligence tools for real-time adaptive systems incorporated in energy predictions and management environments. The book will assist modern-day engineers and scientists to become familiar with advanced optimization techniques for better power systems designs, optimization techniques, and different algorithms for consumer power management. It is our hope that all readers will benefit significantly in learning about the state-of-theart machine learning models and decision support systems, including energy management science and energy policy perspectives. Happy reading and learning! Ravinesh Deo University of Southern Queensland, Towoomba, QLD, Australia June 24, 2020, Email: [email protected]

C H A P T E R

1 A Multiobjective optimal VAR dispatch using FACTS devices considering voltage stability and contingency analysis Youssouf Amrane and Nour EL Yakine Kouba Laboratory of Electrical and Industrial Systems, University of Sciences and Technology Houari Boumediene, Algiers, Algeria

1.1 Introduction Numerous complex power system planning and operations optimization problems have to be solved by the power system engineers and researchers. Optimal reactive power planning (ORPP) is one example of an optimization problem which is concerned with the security and economy of a power system operation. The ORPP is one of the most complex problems of power systems since it requires the simultaneous minimization of two objective functions. The first one deals with the minimization of operation cost by reducing real power loss and improving the voltage profile. The second objective is to minimize the allocation cost of additional reactive power sources (capacitive or inductive banks, FACTS devices, etc.). Also, the ORPP problem must satisfy a number of physical and operational limitations constraints. The latter include the load flow equations, real and reactive power generator, lower and upper limits of the tap ratios of transformers, shunt capacitor or reactor outputs, and generator voltages (Amrane et al., 2014). The ORPP is modeled as a large-scale nonlinear programming problem (NLP). To solve the ORPP problem many conventional and intelligent optimization algorithms have been proposed, such as quadratic and sequential quadratic programming (QP/SQP) (Grudinin, 1998), interior point method (IPM) (Amrane et al., 2014; Oliveira et al., 2015), particle swarm optimization (PSO) (Amrane and Boudour, 2015; Pourjafari and Mojallali, 2011), differential evolution algorithm (DEA) (Amrane et al., 2015), and bacterial foraging algorithm (BFA) (BelwinEdward et al., 2013).

Predictive Modelling for Energy Management and Power Systems Engineering DOI: https://doi.org/10.1016/B978-0-12-817772-3.00001-X

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1. A Multiobjective optimal VAR dispatch using FACTS devices considering voltage stability and contingency analysis

In this paper, a hybrid PSO and gravitational search algorithm (PSO-GSA) (Mirjalili and Hashim, 2010) is proposed to solve the ORPP problem. The PSO-GSA has been found to be robust and flexible in solving the complex optimization problem. To validate the robustness of this method 23 benchmark functions have been used to validate the performance of the PSO-GSA algorithm and compare it with standard PSO and GSA (Mirjalili and Hashim, 2010). The obtained results show that the number of functions which the PSO-GSA performs well is nearly twice that of PSO and GSA, which shows it is robust and effective. Lenin et al. (2014) applied the PSO-GSA method to solve the optimal reactive power dispatch (ORPD) problem for real power loss and the minimization of bus voltage deviations. Mangaiyarkarasi and Raja (2014) showed another use of PSO-GSA, which cartels the exploiting and exploring features of the PSO and GSA to achieve the objective of determining the optimal location and optimal size of the static volt ampere reactive compensator (SVC), and thereby minimize the voltage deviation from the nominal value. The hybrid PSO-GSA algorithm also has been applied on the economic load dispatch problem (ELD) problem considering transmission loss, prohibited zones and ramp rate limits (Ashouri and Hosseini, 2013; Hardiansyah, 2013; Jiang et al., 2014). A state-of-the-art of the proposed method in several electrical engineering domains is presented in the appendix section. The voltage instability study is considered as one of the critical issues in the electric power system. In this chapter, the voltage instability study is based on two different stability indexes. Namely fast voltage stability index (FVSI) (Amrane et al., 2014; Musinin and Abdul Rahman, 2002) and line stability index (Lmn) (Moghavemmi and Omar, 1998) are studied and used to identify the weakest bus and the most critical line in the system. The proposed approach has been tested on ORPP problems using SVCs and TCSCs devices for the equivalent Algerian electric power system 114-bus. Two stability indexes, FVSI and Lmn,