Proceedings of the 5th International Conference on Clean Energy and Electrical Systems: Proceedings of CEES 2023 (Lecture Notes in Electrical Engineering, 1058) 9819938872, 9789819938872

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Lecture Notes in Electrical Engineering 1058

Hossam Gaber   Editor

Proceedings of the 5th International Conference on Clean Energy and Electrical Systems Proceedings of CEES 2023

Lecture Notes in Electrical Engineering Volume 1058

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Napoli, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, München, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, University of Karlsruhe (TH) IAIM, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Dipartimento di Ingegneria dell’Informazione, Sede Scientifica Università degli Studi di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Intelligent Systems Laboratory, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, Department of Mechatronics Engineering, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Intrinsic Innovation, Mountain View, CA, USA Yong Li, College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Subhas Mukhopadhyay, School of Engineering, Macquarie University, NSW, Australia Cun-Zheng Ning, Department of Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Bijaya Ketan Panigrahi, Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Federica Pascucci, Department di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, University of Stuttgart, Stuttgart, Germany Germano Veiga, FEUP Campus, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Haidian District Beijing, China Walter Zamboni, Department of Computer Engineering , Electrical Engineering and Applied Mathematics, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA Kay Chen Tan, Dept. of Computing, Hong Kong Polytechnic University, Kowloon Tong, Hong Kong

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Hossam Gaber Editor

Proceedings of the 5th International Conference on Clean Energy and Electrical Systems Proceedings of CEES 2023

Editor Hossam Gaber Ontario Tech University Oshawa, ON, Canada

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-3887-2 ISBN 978-981-99-3888-9 (eBook) https://doi.org/10.1007/978-981-99-3888-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The International Conference on Clean Energy and Electrical Systems (CEES) is an annual conference that aims to bring together people working on the design, use, and evaluation of technologies that will be the foundation of the development of clean energy and electrical systems. The 5th International Conference on Clean Energy and Electrical Systems (CEES 2023) was held successfully in Tokyo, Japan, during April 1–4, 2023. This year, CEES was featured with five keynote speeches and three invited speeches. In addition, it was structured in seven oral parallel sessions on various thematic topics and one poster display session; the topics include: Battery Performance Simulation and Clean Energy, Design and Development of Electronic Devices, New Energy Utilization, Pollution and Environmental Monitoring, Calculation Model, and Data Analysis in Power Electronic System. All submissions were peer-reviewed through a double-blind review process by an international panel of two or more expert referees, and acceptance decisions were made based on the research quality of the submitted papers. We are grateful for the authors and the participants, as they provide an ideal forum to exchange research results and innovative ideas. We also would like to express our sincere gratitude to the colleagues in the disciplines who have kindly volunteered in the review process. In summary, there are 31 papers included in the conference proceedings, which contains topics related to Power System Planning, Control, and Analysis, Power Electronics Technology and Electrical Equipment Condition Monitoring, Hybrid Power System and Battery Technology, Electric Power Construction, and Power Market Analysis. We would like to express our sincere gratitude for the invaluable assistance of Conference Chairs, Conference Co-Chair, Program Chairs, Program Co-Chairs, Publicity Chair, Steering Committee Chair, Publicity Co-Chair, and Technical Committee members. Most reviewers provided detailed and constructive comments which were valuable for the authors to continue improving their papers and making further research. Given the high-quality works done by authors and reviewers, we are confident that the CEES 2023 proceedings captures the current state of the research

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in the clean energy and electrical systems field and will have significant impact to the research community in the longer term. Finally, we wish all the participants had a wonderful experience in CEES 2023 and benefit from the volume of the proceedings and sincerely look forward to your participation again next year! Oshawa, Canada

Hossam Gabbar CEES 2023 Organizing Committee Conference Chair

Organization

Conference Chairs Masayuki Morimoto, Tokai University, Japan Hossam Gaber, Ontario Tech University, Canada

Conference Co-chair Zhenyuan Zhang, University of Electronic Science and Technology of China, China

Program Chairs Kei Eguchi, Fukuoka Institute of Technology, Japan Songgang Qiu, West Virginia University, USA Yutian Liu, Shandong University, China

Program Co-chairs Mingcong Deng, Tokyo University of Agriculture and Technology, Japan Pierluigi Siano, University of Salerno, Italy

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Organization

Publicity Chair M. A. K. Lodhi, Texas Tech University, USA

Steering Committee Chair Tanakorn Wongwuttanasatian, Khon Kaen University, Thailand

Publicity Co-chair Wanglok Do, Fukuoka Institute of Technology, Japan

Technical Committee Aidil Azwin Bin Zainul Abidin, Universiti Tenaga Nasional, Malaysia Alexander N. Ndife, Thammasat University, Thailand Amit Sant, Pandit Deendayal Energy University, India Amr S. Zalhaf, University of Electronic Science and Technology of China, China Andres Annuk, Estonian University of Life Sciences, Estonia Andres Elias Feijoo Lorenzo, University of Vigo, Spain Aref Afsharfard, Pusan National University, Republic of Korea Azharudin bin Mukhtaruddin, Universiti Pertahanan Nasional Malaysia, Malaysia Bhargav Appasani, Deemed to be University, India Bing Zhang, Nanjing University, China Boyu Qin, Xi’an Jiaotong University, China Chaiyut Sumpavakup, King Mongkut’s University of Technology North Bangkok, Thailand Chan Kar Tim, Universiti Putra, Malaysia Chaohui Zhao, Shanghai Dianji University, China Chew Kuew Wai, Universiti Tunku Abdul Rahman, Indonesia Chew Sue Ping, Universiti Pertahanan Nasional Malaysia, Malaysia Chong Wen Tong, University of Malaya, Malaysia Chunhua Liu, City University of Hong Kong, HKSAR, China Daniel Serrano García, University Carlos III of Madrid, Spain Edison Mojica, University of Perpetual Help System Dalta Las Pinas Campus, Philippines Giedr˙e Streckien˙e, Vilnius Gediminas Technical University, Lithuania Gordon Huang, University of Regina, Canada

Organization

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Guangchen Liu, Inner Mongolia University of Technology, China Haihong Qin, Nanjing University of Aeronautics and Astronautics, China Hamid Gualou, University of Caen Normandy, France Hassan A. Youness, Minia University, Egypt Hazir Farouk Abdelraheem Elhaj, Sudan University of Science and Technology, Sudan Hiroshi Kikusato, Fukushima Renewable Energy Institute, AIST (FREA), Japan Hongbo Ren, Shanghai University of Electric Power, China I-Soon Raungratanaamporn, Suranaree University of Technology, Thailand J. H. C. Pretorius, University of Johannesburg, South Africa Jarrn-Horng Lin, National University of Tainan, Taiwan Javier Menendez, Hunaser Energy, Spain Jien Ma, Zhejiang University, China Jubao Gao, University of Science and Technology Beijing, China Kreangkrai Maneeintr, Chulalongkorn University, Thailand Le Zheng, North China Electric Power University, China Lim Boon Han, Universiti Tunku Abdul Rahman, Indonesia Lorant Szolga, Technical University of Cluj-Napoca, Romania M. A. K. Lodhi, Texas Tech University, USA Maged Nashed, Electronics Research Institute, Egypt Mohammad Salah, The Hashemite University, Jordan Muhammad Ammirrul Atiqi Bin Mohd Zainuri, Universiti Kebangsaan Malaysia, Malaysia Paravee Maneejuk, Chiang Mai University, Thailand Pavel Tcvetkov, Saint Petersburg Mining University, Russia Pawinee Iamtrakul, Thammasat University, Thailand Prasenjit Dey, Mahidol University, Thailand RK Jena, Institute of Management Technology, India Rusu Liliana, Dunarea de Jos University of Galati, Romania Saeed Peyghami, Aalborg Universitet, Denmark Samson Yu, Deakin University, Australia Santanu Koley, Bits-Pilani, Hyderabad Campus, India Sebastian Werle, Silesian University of Technology, Poland Shahril Irwan Sulaiman, Universiti Teknologi MARA, Malaysia Shukuan Zhang, Dalian Maritime University, China Siti Rohani Sheikh Raihan, University of Malaya, Malaysia Somboon Sukpancharoen, Khon Kaen University, Thailand Souad Abderafi, Mohammadia School of Engineering, Morocco T. Hikmet Karakoc, Anadolu University, Turkey Tek-Tjing Lie, Auckland University of Technology, New Zealand Tsung-Mou Huang, Taiwan Power Company, Taiwan, China U. K. Sinha, National Institute of Technology Jamshedpur, India Varun Thangamani, University of Southampton Malaysia, Malaysia Wahyu Mulyo Utomo, Universiti Tun Hussein Onn Malaysia, Malaysia Warayut Kampeerawat, Khon Kaen University, Thailand

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Organization

Wongkot Wongsapai, Chiang Mai University, Thailand Wonsuk Ko, King Saud University, Saudi Arabia Woranee Mungkalasiri, Thammasat University, Thailand Xiangfei Kong, Hebei University of Technology, China Xiaomei Huang, Chongqing University, China Xu Bo, Huanggang Normal University, China Xun Liu, Wuhan University of Technology, China Yang Han, University of Electronic Science and Technology of China, China Yasin Bekta¸s, Aksaray University, Turkey Yu-Chung Tsao, National Taiwan University of Science and Technology, Taiwan Yu-Jen Liu, National Chung Cheng University, Taiwan Zhenzhi Lin, Zhejiang University, China

Contents

Power System Planning, Control and Analysis Intelligent Pre-warning Method of Transmission Line Icing Based on Stack Denoising Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunyi Wang, Wei Liu, Heng Zhou, and Yutian Liu

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Smart Contract Planning for Micro Grid Using Virtual Power Plants-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu-Chung Tsao and Wen-Hsiang Chiu

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Smart Grid System Cooperative Output Control Method Based on Distributed Compensation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qu Yanhua, Wang Haiyang, and Lin Sheng

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Optimizing the Location and Capacity of Charging Stations for a Public Electric Bus System Considering Actual Operating Service Conditions Based on Differential Evolution Algorithm . . . . . . . . . Kittiphan Nawakaittikorn and Warayut Kampeerawat High-Severity N-x-k Contingency Ranking and Screening Based on Deep Learning and Heuristic Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guang Li, Yanran Li, Yuanzhen Zhu, Li Li, Changtao Kan, Zhenya Dai, and Yutian Liu

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Power Electronics Technology and Electrical Equipment Condition Monitoring Fault Analysis and Feature Extraction of Rotary Rectifier of Aviation Three-Stage Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shukuan Zhang, Fachen Wang, Dongjie Sun, and Huacai Lu

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Research on Failure Warning of Substation Equipment Based on Gaussian Hybrid Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin lu Li and Yu fang Wang

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Contents

Sensorless Control for Contra-Rotating Permanent Magnet Synchronous Machine Based on MRAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Shukuan Zhang, Fachen Wang, Yuling Liu, and Huacai Lu Hybrid Power System and Battery Technology Research on Hybrid Cooling Circuit Control in Hybrid Vehicles . . . . . . . 127 Junchao Jing, Yiqiang Liu, Weishan Huang, Qi Li, and Zhentao Wang Optimized Power Sharing Models for HyForce: A Hydrogen-Powered Harbor Craft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Nirmal Vineeth Menon and Siew Hwa Chan Battery Management System Using Relay Contactor by Arduino Controller for Lithium-Ion Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Thitiwut Sathapornbumrungpao, Donwiwat Moonjud, Natthapon Donjaroennon, Uthen Leetond, Suphatchakan Nuchkum, and Thanatsorn Chaisirithungnaklang Electric Power Construction and Power Market Analysis Analysis of the Impact of Unit Output and Quotation on Locational Marginal Price of Unit Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Dong Liu, Fan Li, Ke Sun, Hanqing Liang, Kexin Zhang, and Yong Xing Research on Bayesian Game Strategy of Multi-agent Demand Response in Industrial Parks Based on Incomplete Information . . . . . . . . 177 Xiao Hu, Huifeng Wang, Houhe Chen, Yong Sun, Jiarui Wang, Xiangdong Meng, and Baoju Li Research on Investment Decision of Power Transformation Digital Demonstration Project Based on B-S Option Pricing Model . . . . . . . . . . . 197 Yang Sun, Xiaodong Xie, Teng Feng, Jianya Pan, and Liu Han Grid Investment Performance Portfolio Forecasting Model Based on PLS-VIP-GA-ELM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Yizheng Li, Dong Peng, Lang Zhao, Cong Liu, and Yawei Xue Research on Early Warning of Cost Deviation of Electricity Transmission and Transformation Engineering Based on MCS-SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Junqiang Sha, Yanhui Lu, Rui Xia, Huiting Dong, and Linpeng Nie

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Power Demand Side Management and Electricity Consumption Behaviours Exploring Employees’ Electricity-Saving Intentions in the Workplace Based on the Extended NAM: A Case Study in Rwanda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Yaxin Wu, Umwere Virginie, Di Bao, Iradukunda Aline Banashenge, and Yi Wang Research on Clustering Method of Deferrable Load . . . . . . . . . . . . . . . . . . 251 Lishi Du, Chang Liu, Liang Yue, and Long Yu Clean Energy Technology, Low-Carbon Transformation and Energy Consumption Analysis Energy-Related CO2 Emissions and Urbanization in Peri-Urban, Pathum Thani Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Pawinee Iamtrakul, Sararad Chayphong, I.-Soon Raungratanaamporn, and Nuwong Chollacoop Value Creation of Flue Gas for Hydrogen and Power Production Using RSOFC System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Woranee Mungkalasiri and Jitti Mungkalasiri Simulation on Technology Comparison for CO2 Enhanced Oil Recovery in the Gulf of Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Pariwat Wongsriraksa, Truong Sinh Le, and Kreangkrai Maneeintr Simulation and Optimization of High Heating Value for Rice Husk Biomass in Torrefaction Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Somboon Sukpancharoen, Rachaya Sirimongkol, Sujira Khojitmate, Nopporn Rattanachoung, Nitikorn Junhuathon, and Natacha Phetyim Implementation of Hybrid Energy Sources with Grid Interaction for Modern Net-Zero Energy Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Supanida Kaewwong, Panida Thararak, Peerapol Jirapong, Sirawit Hariwon, Sekthaphong Chaisuwan, and Churat Thararux Use of Mine Tailings as a Substrate in Microbial Fuel Cells for Electric Energy Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 F. Silva-Palacios, A. Salvador-Salinas, S. Rojas-Flores, M. De La Cruz-Noriega, R. Nazario-Naveda, M. Gallozzo-Cardenas, D. Delfin-Narciso, and Félix Díaz Energy Consumption Characteristics of Wall-Hanging Gas Boilers in Hot Summer–Cold Winter Zone of China . . . . . . . . . . . . . . . . . . . . . . . . . 343 Yubo Zhou, Xiaomei Huang, and Zhuojun Hu

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Engineering Example of Vertical Zoning Reconstruction of Municipal Solid Waste Landfill for Solidified Fly Ash Landfill . . . . . . . 359 Yanxue Chen, Xuren Zhou, Xingjian Wang, Zhijie Tan, Taihui Xiao, Hailin Chen, and Tianyu Qin Energy Harvesting and Power Transmission Effect of Metamaterial Application on Coupling Coefficient of Wireless Power Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Pharida Jeebklum, Prasenjit Dey, Phumin Kirawanich, and Chaiyut Sumpavakup Effects of Flow Velocity and Length-to-Depth Ratio on Low-Speed Rectangular Cavity Flow Oscillations for Clean Energy Harvesting . . . . 387 Abhishek Singh, Varun Thangamani, Foo Ngai Kok, and Christina Vanderwel

Power System Planning, Control and Analysis

Intelligent Pre-warning Method of Transmission Line Icing Based on Stack Denoising Autoencoder Chunyi Wang, Wei Liu, Heng Zhou, and Yutian Liu

Abstract In order to reduce the impact of transmission line icing on the secure operation of power grid in extreme weather, an online pre-warning method based on stack denoising autoencoder (SDAE) is proposed in this paper. First, considering deicing phenomenon, geographical factors and current thermal effect under different operating conditions, sample selection, data revision and sample cluster are carried out. Then, the prediction model of ice thickness growth based on SDAE is established and trained off-line. Finally, the icing trend is evaluated according to the real-time weather information, and combining with the current data as well as the cumulative effect of time, the line ice thickness is predicted online so as to conduct ice severity pre-warning. The simulation results demonstrate that the proposed method can improve the noise adaptability and prediction accuracy. Keywords Ice thickness growth prediction · Line icing · Pre-warning · Sample cluster · SDAE

1 Introduction On the background of the intensified global climate change, extreme weather such as cold wave and freezing rain occurs frequently in recent years, making the problem of line icing more and more prominent. Ice coating of power lines is often accompanied by galloping, physical overloading and other phenomena, which contributes to ice flash and line trip in light cases, tower invert and line damage in severe cases. C. Wang Shandong Electric Power Company, State Grid, Jinan 250001, Shandong, China W. Liu Weihai Power Supply Company, State Grid, Weihai 264200, Shandong, China H. Zhou (B) · Y. Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_1

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C. Wang et al.

Furthermore, the secure and stable operation of power system, as well as the reliable power supply of users are also faced with challenges. Hence, it is necessary to establish a pre-warning system for line icing, so as to provide warning information to the relevant personnel in time. Transmission line pre-warning is usually divided into three parts: icing prediction, evaluation based on predicted results and pre-warning based on evaluation results. There are two main methods to establish icing prediction models. The first one is the growth prediction model based on the microscopic process of ice coating. By establishing a physical model, the growth of the shape, density and weight of line ice coating can be predicted. Traditional models include Makkonen model, Imai model, Goodwin model and so on. In addition, the thermal effect of current is considered in many current models. Reference [1] establishes an ice growth analysis model based on the heat balance theory, in which the direct relationship between ice growth and power transmission losses is built. However, a lot of microscopic parameters are involved in the ice-coated growth model, and some of them are difficult to obtain. Moreover, the model considering the thermal effect of current is often used for deicing, but the application is limited in some cases [2]. The other one is the ice thickness prediction model based on macro weather and other factors. And the model is usually divided into two parts: data processing and ice thickness prediction. The high complexity and nonlinearity among various factors are considered in data processing. Wavelet transform [3, 4], SDAE [5] are often used to denoise so as to improve the accuracy of the prediction model, while phase-space reconstruction [4], PCA, AHP [6] and factor analysis [7] are used for feature extraction to simplify the dimension of the model. In [4], the data preprocessing method based on wavelets is adopted to prefilter spiking noise within the obtained field signals and the phase-space reconstruction theory is applied to find the minimum embedding dimension of influencing factors, thus reducing the computational complexity of the multivariate model. Considering the nonlinearity among all factors, the regression model of ice thickness is often established by intelligent algorithm, including SVM [8], ARVM [9], BP neural network [8] and other methods. And some modified methods are also studied. Reference [10] proposed a combined prediction model of BPNN-SVM-KELM based on variance–covariance (VC) weight determination method, in which better accuracy and stability are achieved compared with the single prediction models. In addition, considering the time accumulative effect of line ice coating, some researches have been conducted. A combined prediction model based on ARIMA-CSSVR is proposed in [11] to improve the prediction accuracy, in which the linear growth of transmission line icing is predicted by ARIMA, and the nonlinear errors in ARIMA are fitted by SVR based on CS-optimization. In addition, staged prediction model can achieve better effect [3]. However, for ice thickness prediction model, the operation condition of transmission line is often considered constant and the thermal effect of the line current is ignored. On the other hand, the model is often a shallow network with limited performance. The evaluation of transmission line icing is usually based on the actual operation experience, the designed ice thickness and the maximum ice thickness in history. And the accuracy of the pre-warning based on icing evaluation can provide important

Intelligent Pre-warning Method of Transmission Line Icing Based …

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guidance for the subsequent prevention and control measures. Based on the analysis of key factor mechanism and disaster conditions, a dual dynamic pre-warning model of online monitoring of icing on transmission lines and trend pre-warning is built in [5], and in-depth research and analysis on the key technologies of the model are then conducted. Reference [12] proposes a dual pre-warning scheme of transmission line icing, in which both the predicted ice thickness and a three-level pre-warning based on parameters of meteorological data obtained by online monitoring devices are considered. However, the pre-warning division method is relatively simple while the scheme is quite complex, which may bring about nonoptimal pre-warning effect. In recent years, machine learning has made remarkable achievements in dealing with multi-dimensional and nonlinear relationships. Considering the thermal effect of current, the anti-noise ability, as well as division-refined pre-warning procedure, an intelligent pre-warning method based on SDAE for transmission line icing in extreme weather is proposed in this paper. In Sect. 2, considering deicing phenomenon, geographical factors, and current thermal effect under different operation conditions, sample selection, data revision and sample cluster are carried out. For each subsample set, the ice thickness growth prediction model based on SDAE is established respectively and trained off-line in Sect. 3. In Sect. 4, the icing trend is evaluated according to the real-time meteorological data, and the ice thickness is predicted online based on the prediction model with the current data as well as the cumulative effect of time combining so as to conduce the icing severity pre-warning. The simulation results are listed and described in Sect. 5. Finally, conclusions are drawn in Sect. 6.

2 Sample Preprocessing In this section, samples are first selected to reduce the influence of invalid samples, and then data is preprocessed. Finally, samples are clustered so that icing states under similar operating conditions are classified into one category.

2.1 Sample Generation and Selection The sample is generated firstly based on historical data. Current data and meteorological data, including temperature, humidity, wind speed, wind direction, light intensity, atmospheric pressure, precipitation, etc. are obtained to represent the sample input. And then based on historical ice thickness, a first-order difference sequence reflecting ice thickness growth is constructed to represent the sample output, which can be expressed as: di = Di+1 − Di

(1)

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where, Di and Di+1 are the ice thickness at time i and 2 h after time i respectively, and d i is the ice growth thickness within 2 h. And the time interval between d i and d i+1 is 15 min. The training of the prediction model depends much on the quality of the samples, thus it is necessary to eliminate the invalid samples, which can be realized through ice thickness features. On the one hand, a sample is invalid unless the basic ice coating conditions are met and ice growth is experienced. The corresponding ice thickness feature is d i = 0, and the basic ice coating conditions can be set to temperature below 0°C and humidity above 85%. On the other hand, considering the deicing phenomenon of the line, the samples with d i < 0 are also classified as invalid samples. These two types of samples above should be eliminated.

2.2 Data Preprocessing The data collected by the monitoring device may not be used directly. Data preprocessing consists of two parts. Considering the influence of height difference and terrain factors, especially on wind speed and direction, the meteorological data is revised in the first part. Wherein, wind speed revision can be expressed as: U = (h/ h s )α U S

(2)

where, U and U S are the average wind speed of actual and standard height, h and hS are the height of the transmission line and the standard height respectively, and α is the roughness coefficient of the ground. For open plain, forest street and urban center, the values can be set to 0.167–0.125, 0.250 and 0.333, respectively. In addition, the wind direction is also revised. If the monitoring device is in the open, the revision is not needed. If the terrain is complex, the revision should be made according to the actual experience. The second part is to normalize the revised data, so as to reduce the influence of different data dimensions. And zero-mean normalization is adopted, which is specifically expressed as follows: xi∗ = (xi − xi )/σi

(3)

where, x i * is normalized sample data, x i is the actual value of sample data, xi and σ i are the mean value and the standard deviation of sample data respectively.

2.3 Sample Cluster For a line under icing state, different current will bring about different joule heat, thus influencing the ice thickness. Hence, the thermal effect of line current should be

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considered in line icing prediction. In this paper, k-means clustering is proposed for sample cluster, so that the samples under similar operating conditions are classified into one group, and the accuracy of prediction model will be improved. The specific steps are as follows: Step (1): Based on the current features of samples, determine the number of cluster centers and conduct random initialization; Step (2): Based on the principle of minimum distance, all samples are allocated to the nearest cluster center; Step (3): Recalculate each new cluster center; Step (4): Repeat Steps (2) and Steps (3) to minimize the objective function. The least variance function is adopted as the objective function, which is defined as follows: E(c1 , c2 , ...ck ) =

k 1 ∑∑ || p − ci ||2 n i=1 p

(4)

where, E is the average error sum of all samples, k is the number of clustering centers, p is each sample data; ci is the sample center of the ith category, n is the number of samples.

3 Ice Thickness Growth Prediction Model In this section, the SDAE model for feature extraction and the feedforward network model for ice thickness growth regression are established based on the sub-sample sets after clustering, which is used for short-term prediction of line ice cover thickness growth and off-line training.

3.1 Feature Extraction Based on SDAE SDAE is composed of denoising autoencoder (DAE) stacked, which has powerful feature extraction capability. By introducing denoising technology during model training, SDAE performs well in the face of actual data that may contain noise. Considering the strong nonlinearity among meteorological factors, as well as the errors and noise interference in real-time data monitoring, SDAE feature extraction model has better robustness and stability. SDAE models are usually pre-trained first, and then fine-tuned. The training process is as follows: Step (1): Given sample set {x i }(x i ∈ Rm , i = 1, 2… n), the input of each sample is xi } is obtained m-dimension, and the total number of samples is n. The sample set {~ by random noise pollution.

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Step (2): Establish the DAE model. The number of output neurons is equal to the number of input neurons, and a hidden layer is contained. Sigmoid function is selected as activation function, which is shown in (5). Since the input data features are all real type, the square error with L2 regularization added is selected as the training cost function, as shown in (6). The single-layer DAE model training process is shown in Fig. 1. f (x) = 1/(1 + e−x ) Ld =

(5)

1 ||y − x||2 + λ||ωd ||2 2

(6)

where, L d is the cost function used in DAE training, λ is the coefficient of L2 regularization term, and ωd is the weight vector of DAE. Step (3): Conduct greedy and unsupervised training for the DAE model. The hidden layer output of the previous DAE is used as the input of the next DAE. The original input features are transformed layer by layer, and the SDAE model is built by stacking DAEs, as shown in Fig. 2. The initial connection weights are obtained through layer-by-layer training. Step (4): Based on the sample set of initial connection weights and noise pollution, the neural network is trained and the connection weights are fine-tuned using error back propagation. Fig. 1 DAE model training process

h Ld

ge fe

qD ...

...

x%

Fig. 2 SDAE pre-training process

...

x

y

...

hl fel

...

hl-1 ...

...

h2 fe2

...

h1 fe1

x%

...

Intelligent Pre-warning Method of Transmission Line Icing Based … Fig. 3 Feedforward neural network regression model

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x1

y1

x2

y2

o

...

x3

... xn Input layer

yp Hidden layer

Output layer

3.2 Ice Thickness Growth Regression Based on Feedforward Neural Network Based on the outputs of SDAE feature extraction model, the regression of ice thickness growth can be realized by feedforward neural network, which is shown in Fig. 3. And the training process is as follows: Step (1): Construct a three-layer feedforward neural network and assign a random number in the interval (−1, 1) to each connection weight. Sigmoid function is selected as the activation function and the calculation accuracy and maximum learning times M is set. The mean square error is selected as the loss function, which is expressed as: n ) 1 ∑( yi − yi MSE = n i=1 ⌃

(7)

⌃

where, yi and yi represent expected and actual output of the network respectively, n is the number of samples. Step (2): The output of the last layer of SDAE feature extraction model is used as the input of the feedforward network to calculate the input and output of each neuron in the hidden layer and the output layer. Step (3): Calculate the loss function and the partial derivative of the loss function to the connection weight of each neuron based on the chain rule. According to error back propagation and gradient descent method, the value of the connection weight is fine-adjusted until the loss function coverages.

4 Procedures of Ice Pre-Warning In this section, the evaluation method of icing trend is first introduced, then the ice coating severity pre-warning method based on the evaluation results is established, and finally the flow chart of the procedure is drawn.

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Table 1 Evaluative criteria of icing trend Evaluation level

Meteorological conditions

Icing trend

Measures needed

Level-I

Not satisfied with Level-II

No icing trend

Safe

Level- II

The temperature is below 0 °C and the humidity is above 85%

General icing Observing trend

Level- III

Based on Level-II, the wind speed is above Strong icing 3 m/s trend

Continuously monitoring

Level- IV

Based on Level-III, the wind direction is below 30°or above 150°

Prepare counter-measures

Very strong icing trend

4.1 Evaluation of Icing Trend Since there are many transmission lines in power grid and the operation environment is quite complex, the pre-warning result based on simple division method may not be very accurate, and complex scheme such as dual pre-warning will bring difficulty to decision-making. On the premise of simplifying the pre-warning procedure as far as possible, the evaluation of icing trend can be carried out first, and based on the evaluation result, different pre-warning levels are reasonably divided. Based on meteorological factors, different levels of evaluation can be set, which are used in subsequent pre-warning procedure. The evaluation of icing trend is divided into four levels, corresponding to four states: no icing trend, general icing trend, strong icing trend and very strong icing trend. Furthermore, the evaluative criteria is shown in Table 1.

4.2 Ice Coating Severity Pre-Warning Procedure The ice coating severity pre-warning procedure are divided into two parts: the division method of pre-warning levels based on icing trend evaluation and online update of pre-warning grade based on the time accumulation effect. According to the designed ice thickness of the line or the maximum ice thickness in history, the maximum allowable thickness of ice coating is set to Hm. And according to the evaluation of icing trend, the ice severity pre-warning threshold is set and the warning levels are divided based on the ice trend evaluation and actual operating experience. With the predicted ice thickness combined, the pre-warning is conducted. Moreover, pre-warning levels are shown in Table 2. As shown in Table 2, in general, the higher the icing trend evaluation level is, the smaller the pre-warning threshold is, thus reflecting the degree of ice coating severity. It is worth noting that the data in Table 2 is not set in stone and can be determined according to the actual operation.

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Table 2 Classification of ice severity pre-warning levels Pre-warning level Icing trend evaluation

Pre-warning state

Level-I Level- II

Level- III

Level- IV

Level-0

Hm

65% Hm

Serious ice coating

For Level-I in icing trend evaluation, there is no icing trend, so the pre-warning result is Level-0. In Level-0 state, transmission lines keep normal operation without taking measures. In Level-1 state, the effect of icing is not significant in the short term, and decisions can be made based on the results of subsequent pre-warning time points, whose interval is 15 min. In Level-2 state, the arc of the line increases significantly, which may gallop under the transverse wind. Thus, the pre-icing measures can be taken, and the decision can be made based on the warning of the subsequent warning time points. In Level-3 state, there may be accidents such as line damage, causing power flow shift or chain fault. Hence, de-icing measures should be prepared immediately and implemented at the corresponding time. It is approximately believed that the ice thickness will increase linearly in the short term. Considering the cumulative effect of time, the ice thickness can be expressed as: Dt = H0 + t

ΔH T

(8)

where, Dt is the predicted ice coating thickness after time t, H 0 is the initial ice coating thickness, T is the prediction time scale of 2 h, ΔH is the predicted ice coating growth thickness within time T. Furthermore, if the pre-warning result of ice coating severity is Level-2 or Level3, the prediction result and pre-warning level of ice coating severity at the forward pre-warning time point will be updated online based on (8) and Table 2.

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4.3 Flow Chart of Pre-Warning Procedure The pre-warning procedure is divided into offline and online parts, as shown in Fig. 4. In offline part, data and sample preprocessing is realized, and ice thickness growth prediction model is built and trained, which are introduced in Sect. 2 and Sect. 3 respectively. And in online part, icing pre-warning process is achieved, as introduced in Sect. 4. In addition, historical training dataset is updated online through new sample data obtained real-time and the connection weights of SDAE are updated offline through model training.

Transmission line ice early warning process

Offline

Online New sample data

Online updating

Historical training dataset

Data preprocessing

Sample selection and data preprocessing

Evaluation of ice coating trends

Sample cluster

Ice coating trends?

Multi-group SDAE feature extraction model training

N

Y Pre- warning result Level-0

Model selecting and ice cover growth prediction

Offline updating

Multi-group feedforward neural network regression model training

Ice severity prewarning

N Level-2 or Level-3? Y Pre-warning result updating

Pre-warning result output

Fig. 4 Flow chart of pre-warning procedure

Pre- warning result Level-1

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5 Simulation Results In this section, to verify the pre-warning model of transmission line icing, a transmission line in the middle and western regions of a province in China with high incidence of ice coating is selected as the research object. According to the historical data, the sample set is generated with sampling interval of 15 min, and the time scale of pre-warning is set as 2 h. And 2500 samples are generated, among which 100 samples are randomly selected as the test set. The input of the sample includes meteorological feature and current feature, while the output feature is constructed based on (1). Based on Sect. 3, the SDAE model is established for ice thickness growth prediction. Considering the small number of samples, the model is constructed with three hidden layers, as well as 8-dimensional input and 1-dimensional output. The model is trained through the methods introduced in Sect. 3. According to (2) and (3), the sample data are preprocessed. And the k-means clustering method with 3 cluster centers is adopted, which can classify the icing states under similar working conditions into one group, thus improving the accuracy of the model. And to verify the validity of sample cluster, simulation is conducted, as shown in Fig. 5. Three predictive indicators are then adopted to evaluate the prediction result, which is express as: M AE =

n | 1 ∑ || qi − q f i | n i=1

(9)

n | 1 ∑ || qi − q f i |/ max(qi ) n i=1 [ | n |1 ∑| | |qi − q f i |2 RMSE = | n i=1

M RE =

(10)

(11)

where, MAE is the mean of absolute error; MRE is the mean of relative error; RMSE is the root mean square error; n is the amount of samples; qi is the expected result; qfi is the prediction result. And the indicators are calculated in Table 3. Based on the definition of each indicator, the smaller the value is, the more accurate the prediction will be. Thus, it is shown that the model with clustering will improve the accuracy of prediction. The reason for the phenomenon is that the current features of each sample after clustering are close to each other, and the training of the subsample Table 3 Prediction indicators of two cases

Case

MSE

MRE

RMSE

Without clustering

0.4098

0.1161

0.4927

With clustering

0.2553

0.0925

0.3136

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Fig. 5 Simulation results for the validity of sample clustering

set can approximately ignore the influence of the current thermal effect. For the model without clustering, the default condition is that the current features are all similar, thus increasing the error of the prediction results. However, after clustering, multiple models need to be trained, which will increase the training load. Since the model training process is conducted offline, the actual impact can be is ignored. Considering that the SDAE model can reduce the influence of noise in the process of feature extraction, the noise reduction performance is simulated with 10% and 20% noise added respectively. The simulation results are shown in Fig. 6 and the prediction indicators are calculated in Table 4. Based on the results, it is shown that with the increase of noise proportion, the deviation of prediction will also increase. In addition, the impact of 10% noise proportion on the results remains at a small level, while the impact of 20% noise proportion is reluctantly acceptable, reflecting the antiinterference ability of the model against low noise. The reason for the phenomenon is that in the training process of SDAE, noise-polluted samples are used for training, but considering the small number of samples, the noise introduced in the training is small. Four typical samples are selected from the sample set to explain the process of ice severity pre-warning, as shown in Fig. 7. The maximum allowable ice thickness of the line is set to 20 mm, so as to determine the threshold value for different pre-warning levels. For pre-warning point t1 , the real-time meteorological, current data and the Fig. 6 Simulation results for the anti-noise ability of SDAE

Intelligent Pre-warning Method of Transmission Line Icing Based … Table 4 Prediction indicators of three cases

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Case

MSE

MRE

RMSE

No noise

0.2553

0.0925

0.3136

10% noise

0.2913

0.0997

0.3762

20% noise

0.4182

0.1208

0.5362

line ice thickness H 0 are firstly obtained. Then based on the meteorological features, the icing trend evaluation is Level-IV, and the ice thickness growth prediction model is selected. The ice thickness growth is predicted as 0.5 mm, and based on the time accumulation effect, the ice prediction thickness is 0.9 mm, which ranks Level-0 of ice severity pre-warning. For pre-warning point t2 , the process is similar, while the pre-warning result is Level-1, which needs observing. Pre-warning point t3 and t4 are adjacent with interval of 15 min, and the ice severity pre-warning levels are both Level-2. The next process is to update the ice prediction thickness and pre-warning level of the forward warning time point. It is also noted that the pre-warning time scales of t3 and t4 overlap, and the subsequent time points should prevail in general. At the same time, it is shown in Fig. 7 that there is a change in the pre-warning result of tx at the time point of the intersection region, indicating that the prediction value of ice thickness is close to the threshold of ice severity pre-warning. And based on (9), the ice thickness at tx is calculated as 5.9 mm and 6.1 mm respectively, which is close to the Level-2 threshold of 6 mm. Thus, further observation should be maintained, and de-icing measures can be prepared at this moment. And the practical operation results prove the effectiveness of the pre-warning method.

Time

Real-time data

Icing trend evaluation

t1

Data1 0.4mm

Level-

t2

Data2 1.4mm

t3

t4

Ice thickness growth forecast

Ice coating severity pre-warning

Update online?

Δ H=0.5mm

Safe

N

Level-

Δ H=1.6mm

Level-1

N

Data3 3.8mm

Level-

Δ H=2.4mm

Level-2

Y

Data4 4.1mm

Level-

Δ H=2.7mm

Level-2

Y

T t3

t4 T

Fig. 7 Typical samples for pre-warning procedure

Level-1

Level-1

Level-2

tx Level-2

Level-2

... Level-2

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6 Conclusion An intelligent pre-warning method based on SDAE is proposed in this paper. Considering the current thermal effect under different operating conditions, sample cluster based on k-means clustering is carried out after sample selection and data revision. For each subsample, the prediction model of ice thickness growth based on SDAE is established for the sake of noise resistance. The method of ice pre-warning proposed in this paper are relatively simplified in procedure but refined in pre-warning division. According to the real-time weather information, the icing trend evaluation is conducted, which contributes to the selection of pre-warning threshold. Based on the line ice thickness prediction, the ice severity is pre-warned. For significant prewarning results, the levels forward pre-warning time points will be updated based on the cumulative effect of time. Simulation results show that sample cluster based on k-means clustering improves the accuracy of prediction, and the model based on SDAE has good robustness. For further understanding, the pre-warning is also simulated according to typical samples. The method proposed in this paper can provide effective guidance to relevant personnel and with the development of online monitoring devices, the problem of requiring a large number of samples will be settled to some extent. Acknowledgements The authors thank the project supported by Science and Technology Project of State Grid Shandong Electric Power Company, China (520613220005).

References 1. Huang W, Hu B, Shahidehpour M, Sun Y, Sun Q et al (2022) Preventive scheduling for reducing the impact of glaze icing on transmission lines. IEEE Trans Power Syst 37:1297–1310 2. Jafarishiadeh F, Mohammadi F, Sahraei-Ardakani M (2020) Preventive dispatch for transmission de-icing. IEEE Trans Power Syst 35:4104–4107 3. Sun W, Wang C (2019) Staged icing forecasting of power transmission lines based on icing cycle and improved extreme learning machine. J Clean Prod 208:1384–1392 4. Li J, Li P, Miao A, Chen Y, Cao M et al (2018) Online prediction method of icing of overhead power lines based on support vector regression. Int Trans Electr Energy Syst 28:e2500 5. Hassan RU, Sun R, Liu Y (2022) Online static security assessment for cascading failure using stacked de-noising auto-encoder. Int J Electr Power Energy Syst 137:107852 6. Zhu Y, Wang H, Wu N, Li M, Xu X et al (2014) Icing on-line monitoring dynamic prediction model. High Voltage Eng 40:1374–1381 7. Wang W, Peng W, Tong L, Tan X, Xin T (2019) Study on sustainable development of power transmission system under ice disaster based on a new security early warning model. J Clean Prod 228:175–184 8. Xiong W, Yuan H, You L (2018) Prediction method of icing thickness of transmission line based on MEAO. Clust Comput 21:845–853 9. Zhao J, Zhang H, Zou H, Pan J, Zeng C et al (2022) Probability prediction method of transmission line icing fault based on adaptive relevance vector machine. Energy Rep 8:1568–1577

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10. Niu D, Liang Y, Wang H, Wang M, Hong W (2017) Icing forecasting of transmission lines with a modified back propagation neural network-support vector machine-extreme learning machine with kernel (BPNN-SVM-KELM) based on the variance-covariance weight determination method. Energies 10:1196 11. Li T, Shi X, Cao N, Gu Z, Zhao S et al (2021) Combined prediction method of transmission line icing based on ARIMA-CSSVR. In: 2021 China international conference on electricity distribution (CICED), pp 63–69 12. Wang J, Liu S, Shao J, Long M, Wang J et al (2018) Study on dual pre-warning of transmission line icing based on improved residual MGM-Markov theory. IEEJ Trans Electr Electron Eng 13:561–569

Smart Contract Planning for Micro Grid Using Virtual Power Plants-Based Approach Yu-Chung Tsao and Wen-Hsiang Chiu

Abstract Virtual power plants (VPPs) are recently a major trend in the development of the global power industry to promote the diversified development of energy, especially in energy storage, energy saving, and environmental protection. Renewable energy can be connected to the country grid through advanced communication technology and blockchain architecture. In this study, we introduce the VPP-based smart contract mechanism, involving information flow and power flow, and realizing the two-way interaction between power companies and consumers. The transaction model of the virtual power plant smart contract is constructed with the smart contract mechanism using blockchain technology, and based on the application experience of smart contract companies. VPP providers can provide an integrated information flow and a current platform for the exchange of information and capital flows generated by renewable energy suppliers and demand response suppliers. The result shows that our VPP-based approach could be used in smart contract planning for micro grid. Keywords Virtual power plant · Micro grid · Distributed energy resources · Blockchain technology · Smart contract

1 Introduction Currently, more than 95% of Taiwan’s energy needs are met by imports; hence, to promote energy independence, it has become necessary to transform the government’s energy policy and stimulate energy storage, energy saving, and system integration. Renewable energy volatility is a new challenge to power system operation, Y.-C. Tsao (B) Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan e-mail: [email protected] Y.-C. Tsao · W.-H. Chiu Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, Taipei 106, Taiwan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_2

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but with technological development of the electric power industry in recent years, distributed energy can share the loading of centralized power plants. However, new technology, business models, and regulatory frameworks need to be explored to meet the ever-increasing demand for energy. A virtual power plant (VPP) is an important trend in the development of the global electricity industry. With the development of the power market and technology, VPPs have developed from maintaining the stability of the power grid by removing the user load in the past to a new mode that integrates the functions of distributed power generation equipment, green energy, energy storage systems, and demand-side management. Morris [1] proposes a methodology for systematizing and representing benefits and their interrelationships, which allows complex systems to be represented in a concise, elegant format. The study attempted to minimize the cost of energy served to the community considering the fixed costs associated with microgrids and Distributed Generation, and suggests benefits to a variety of stakeholders. Pudjianto et al. [2] stated that the development trend of clean energy, DER and demand-side management will share some of the original responsibility of centralized power generation. At present, the structure of power systems is greatly changing due to the penetration of decentralized generations. Naval and Yusta [3] pointed out that the VPP might buy or sell additional energy in the real-time market or use the storage system to compensate for prediction errors, ensuring compliance with market operations. Virtual Power Plants and Microgrids concepts have not to meets all criteria necessary for a complete Smart Grid solution. To meet this challenge, new technology frameworks need to be developed. A VPP mode using smart contracts in the real-time control of technology, combined with electronic information and communication technology, and marketing management strategies, can be used to gather several types of distributed power supply and energy storage systems, and participate in demand response.

1.1 Power Flow and Information Flow of Virtual Power Plant VPP does not change the connection mode between each distributed energy resource (DER) and the power grid, but integrates DER, energy storage, controllable load, and other types of resources through control, metering, communication, and other technologies. The central control platform coordinates and controls these resources in a unified manner and participates in the electric power market with optimal resource scheduling. The power flow and information flow of a virtual power plant are shown in Fig. 1. The International Renewable Energy Agency (IRENA) has reported that a virtual power plant can be considered one of the development modes of aggregators, and

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Fig. 1 Power flow and information flow of VPP

proposed the following operation mode for the electricity and information flow in the participating market [4]. (1) Power flow: The power flows directly from the DER equipment to the power grid. (2) Data flow: VPP operators mainly collect DER information and report it to an independent system operator (ISO). In a commercial VPP, the aggregated resources must submit their own operation parameters, marginal cost, and other information to the VPP operator, who then performs load prediction, power generation prediction, and wholesale market price prediction to implement the best resource operation mode. (3) Cash flow: Cash flows in the electricity market are generally disbursed directly to DERs after the settlement of smart contracts.

1.2 Electricity Retailing Utility Enterprise Participation in VPP Mode Participation in the VPP model can enhance user experience and market participation by increasing the share of renewable energy, reducing power supply costs, and reducing peak load to meet standby capacity obligations. The architecture of the VPP business model is shown in Fig. 2 and described as follows. (1) The retailing utility enterprise engages with VPP: The retailing utility enterprise reached a commercial agreement with VPP operators to participate in the Frequency Control Ancillary Services (FCAS) market.

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Fig. 2 VPP business model

(2) The retailing utility enterprise acts as VPP by itself: The power retailing utility enterprise who is also the operator of VPP can participate in the FCAS market independently as a market participant. With the trend of global development of renewable energy, distributed energy is highly permeated in the world’s power grid and power market. The international community has gradually focused on VPP, which can integrate distributed power generation equipment, renewable energy, energy storage system and demandside management. Also, VPP can effectively improve energy efficiency, centralized control and dispatching should be carried out by more efficient contract mechanism. However, previous studies have not considered more efficient operation mechanism, so this study added smart contract. This study employs the smart contract to obtain three goals for enhance VPP mechanism which included (1) Speed, efficiency and accuracy (2) Trust and transparency security (3) Savings through smart contracts. Virtual power plants are driven by the liberalization of electricity with the rapid expansion of decentralized energy. However, large-potion of renewable energy may cause impact on the power system. If we can promote distributed power generation system by taking advantage of existing information and communication technology and virtual power plant technology, we can reduce the risk of energy shortage. The contributions of our study include the following three main aspects. First, we introduce a VPP-based smart contract mechanism, which includes information flow and capital flow, and the use of blockchain technology smart contract mechanism to achieve two-way interaction between power companies and consumers. Furthermore, the application of smart contracts in power grids is further realized through five knowledge-sharing processes. Finally, based on the application experience of smart contract companies, a transaction model of a VPP smart contract is constructed. The remainder of this paper proceeds as follows. First, the concept of VPP is described, which illustrate the concept, power flow, and information flow of VPP, electricity retail utility enterprise participation in VPP mode. Second, we review some of the studies on VPP, DER, and micro grid to obtain a better idea of the present scenario Third, we introduce what/why smart contracts are used in VPPs.

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Fourth, we present certain cases of the application of smart contracts and the way they are applied in VPPs. In the last section, we present the conclusion of our study, which included three aspects of smart contracts for micro grid in VPPs.

2 Literature Review This study is a qualitative study with an exploratory approach. Qualitative methods are particularly useful when exploring concepts that are not known very well, and not sufficiently researched, and when learning concepts that require an in-depth examination [5]. Previous research combined nine major business considerations. They include value proposition, customer segment, customer relationships, distribution channels, key partners, key resources, key activities, revenue streams, and cost structures, which have a clear business operation logic structure, enabling managers to carefully plan and arrange various activities, processes, and resources in a synchronous manner. To achieve the maximum economic benefit, the organization’s business innovation model is crucial. Smart Energy International [6] states that grids are centralized, meaning that power is generated from the center and relayed to consumers through distributors and service providers. This model is outdated, as it does not effectively integrate new technology. It requires great effort to modernize centralized power stations. Utilities can do the same at the micro grid with fewer risks. From a review of laws and regulations, technical, social, and economic fourdimension analysis, Ropuszy´nska-Surma [7] summarized the advantages of timeline of VPP, including power market liberalization, international energy development trends and the related laws and regulations, the popularization of renewable energy and distributed power supply, intelligent power grid, and the development of information communication technology and diligence, among others. Mahmud et al. [8] conducted a review and summarized the integration of DERs and prosumers into the VPP considering their functions, infrastructure, type, and control objectives. Finally, the factors that affect the integration of DERs and prosumers into the VPP were identified. Jiao and Evans [9] mentioned that in this early stage, government support constitutes the most important trigger for battery reuse. Previous research proposed the use of a public blockchain and self-enforcing smart contracts to construct the VPP of prosumers to provide energy services. They then defined a model for capturing prosumer-level constraints in terms of available energy profiles and energy service requirements, enabling optimal aggregation in hierarchical structures. Tseng et al. [10] analyzed contemporary sustainable industrial and operations engineering in the Industry 4.0 context. Their analysis included lean manufacturing in Industry 4.0, cyber–physical production systems, big data-driven and smart communications, safety and security, artificial intelligence for sustainability, and circular economy in a digital environment.

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Table 1 The definitions of VPP literatures

The definitions of VPP

Morais et al. [13]

The same as autonomous Microgrid

Bignucolo et al. It is an aggregation of different types of distributed resources [14] Pudjianto et al. [2]

It consists of a variety of technologies with various operating modes and availability, and can be connected to different distribution networks

Morais et al. [15]

It is a heterogeneous entity with multiple technologies and multiple sites

Mai et al. [11] concluded that grid edges are a potential solution to support network operation using the inherent controllability of DER and the availability of digital transformation. Singhal and Verma [12] pointed out that the concept of grid edges is a two-way conversation that becomes the flow of energy from utility to the customer. Smart grids will provide more electricity to satisfy rising demand, increase power supply reliability and performance, increase energy efficiency, and incorporate carbon-free energy sources into power grids. This study aims to consider smart contracts as an emerging business model for the development of VPPs. The expectation is that the VPP can integrate distributed energy to achieve the micro grid to further realize the goal of the smart grid. In a fact, the concept of VPP is still not yet converged to a unique definition, which can be defined as aggregation of different types of distributed resources or of many different DER capacities. The definitions of VPP in various literatures are summarized in Table 1.

3 Methods Smart contracts promote VPPs. A communication line must be established between the virtual power plant and the DER. The DER is different from traditional large power plants. These conventional plants usually adopt an internal closed circuit for data communication, whereas DERs usually transmit communication through the Internet. Traditional power plants involve a one-time construction, long-term operation of the facility; whereas the number of DERs change at any time, and will be accompanied by policy incentives and the emergence of new equipment. According to the definition of IBM [16], smart contracts statements that are written into code on a blockchain. A network of computers executes the actions when predetermined conditions have been met and verified. The blockchain is then updated when the transaction is completed. That means the transaction cannot be changed and only parties who have been granted permission can see the results. Hence, DER must have a safe, highly stable network transmission technology. Figure 3 presents the

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Fig. 3 Methodology used to obtain and process the information of a VPP-based Smart contract

methodology used to obtain and process the information of a VPP-based Smart contract.

3.1 Resources Allocation Decision Model After a distributed energy provider submits its initial information to the virtual power plant, the information could be written and an appropriate smart contract could be made based on making a resource allocation decision. That is the limited resource should be assigned to different energy to maximize the benefit. A mathematical model (Eqs. (1) and (2)) could be used to make the allocation decision. Let Ri be the value generated (Ri ) for energy i and Ci be the cost for energy i. The objective is to determine the n optimal amount of resource for each energy while maximining the (Ri − Ci )X i ), see Eq. (1). Equation (2) shows the total resources total benefit ( i=1 be assigned should less than the upper bound U. π = Max Subject to:

n i=1

(R i − C i )X i

(1)

Ci , Xi ≤ U

(2)

n i=1

According to the above Resources Allocation Decision model, the virtual power plant (VPP) can write information into the smart contract after making the resource allocation decision. Detail operations of smart contracts are described in Sect. 3.2.

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3.2 Operations of Smart Contracts The first feature of smart contracts is internal events, which means smart contracts use an application program to implement the terms and conditions of a trading contract. Smart contracts generally operate using an “event-driven” approach. Once the smart contract program is deployed on the blockchain platform, certain conditions are established to trigger the specified function of the contract and start the program execution. As a result of this execution, assets are usually moved. Taking online purchase of digital music as an example, we can develop an intelligent contract for trading, which specifies the distribution method of each piece of digital music and the distribution ratio according to the composer, lyricist, producer, and online shopping platforms. Once the consumer completes the online payment process, the transfer function is triggered, and the fee is automatically allocated to the relevant person’s account in proportion. The second feature of smart contracts is external events, which means that several smart contracts rely on external events; their information is managed by systems outside the blockchain, from which smart contracts must request information. For example, many crops may suffer losses owing to drastic changes in the weather. We can develop an insurance smart contract for such agricultural losses and stipulate that farmers can obtain corresponding compensation under certain climatic conditions. The conditions that trigger claims transactions must rely on the weather records of the Central Weather Bureau; the smart contract must periodically request weather data from the bureau’s system. We call this authoritative external data source “Oracle.“ Secure and effective access to external Oracle data is an important basis for several smart contracts.

3.3 Contract Mechanism Knowledge management and continuous innovation can integrate current chaotic processes to enhance customer satisfaction. The concept of collecting and integrating information into useful knowledge and expanding its benefits through knowledge sharing and management is known as knowledge management. Its application involves a method to establish common definition, data, views, and reference, so that the previously divergent architectural description of unity can play a role in analyzing or building complex operations to achieve knowledge sharing, system integration, process reengineering, and reduction of operation and maintenance

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27

costs. The process structure should be based on the concepts of rationalization, standardization, intelligence, and business modeling. • Rationalization (1) Find the best conditions through block testing and make standards (For example, developing operational management methods and standards for power distribution). (2) Establish time standards for start-up of power supply conditions for distributed energy providers. (3) Strive for balance. The power supply of distributed energy providers should be improved through blockchain record management and rolling review, regardless of peak and off-peak times. • Standardization (1) Establish operating standards (Operation method and power supply schedule). (2) Standardize smart contract instructions. The purpose of smart contracts is not to save time, but to prevent mistakes. (3) Establish a unified approach to smart contracts. Introduce the establishment of an expert system that enables smart contracts to achieve 95% of the work of experts through this system. • Systematization (1) Decentralize through blockchains. Through the decentralized account book, an abnormal management system can be established to detect an abnormal system response in a timely manner and correct it. (2) Implement power supply operation target management, and achieve target management by integrating various information through blockchain. (3) Series integrate transaction processes. Each process and form is a system on its own and must be integrated into a complete system in series to support complementarity without conflict and interference. • Intelligence (1) Establish reasonable power supply conditions and specifications for various distributed energy providers. The characteristics of the informational process should be considered, and requirements and specifications (e.g., quantity and sequence of supply) should be proposed. (2) Implement all standard operations. All operations must be performed according to the standard, all data must be recorded through the blockchain, and all anomalies must be automatically detected and dealt with in time. (3) Cancel manual backup job. Manual backup operation is not safe. It only increases issues; through the distributed ledger automatic backup, there is no fear of loss of all data.

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Fig. 4 VPP-based Smart contract mechanism

(4) Screen forms to replace paperwork forms. On-screen forms can be checked and wired instantly to source inputs, eliminate errors and delays, and help consolidate the analysis. (5) Check at the end of the day. Intelligence is the main feature of daily accounts; it is required to perform timely checks for detecting abnormalities and correcting them to avoid evidence annihilation and unclear accounts. • Business Modeling Considering blockchain technology as an emerging business model for the development of VPPs, and choosing BMC as an example, the nine elements of the business model can show the total internal capabilities or skills of a company. The VPP-based smart contract mechanism is shown in Fig. 4.

3.4 Infrastructure of Virtual Power Plant Wang et al. [17] reviewed the development of VPP from the perspective of concept, practical application, and challenges, and proposed a new concept for VPP. This includes a group of decentralized power generation, energy storage systems, and controllable loads, which can run normally after summary, optimization, coordination, and control, and serve as a dispatching unit in power system operation and transaction resources in the power wholesale market. The structure is shown in Fig. 5.

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29

Energy Storage

Renewable Energy Smart Home

VPP

EV Commercial Load

Smart Building

Fig. 5 Infrastructure of virtual power plant

3.5 Type of Contract Between Virtual Power Plant and Distributed Energy A contractual relationship must be established between the VPP and the DER manager to agree on power supply obligations, payment, and liability for breach of contract, which can be divided into three types: (1) Unfixed quantity purchase of electricity: The power purchased by the VPP depends on the power generation capacity of the equipment. The amount of power generated by the equipment is the amount of power purchased, which is especially suitable for unstable power generation, such as wind, solar, and stream flow hydropower plants. Under this contract type, the virtual power plant must bear the risk of prediction error for its generation forecasting and scheduling. (2) Periodic power supply: This means that the DER manager submits the shortterm or long-term power supply curve of its power generation equipment to the VPP. This type of contract is especially suitable for energy storage equipment and thermal power generation systems with relatively stable power generation, such as biomass energy, steam electricity symbiotic equipment, and geothermal power generation. Both parties of the contract must agree on the liability of power generation equipment failure; moreover, to improve the reliability of power supply, both parties shall agree on the liability of the DER manager for breach of power supply. (3) Ancillary service market: VPPs alter the operating conditions of the grid in specific ways, such as selling spare capacity. For this purpose, the VPP must plan in advance the capacity and energy that the DER can provide, and create a schedule for each generating device to provide auxiliary services. By contrast, the DER manager must ensure that these capacities and energies are schedulable.

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4 Results and Discussion 4.1 Application of Smart Contracts in Certain Cases IBM has applied smart contracts in certain fields. We present some cases to understand the development of smart contracts in the present time (2021). Home Improvement Retailer Uses Smart Contracts to Improve Communications The Home Depot, the home improvement retailer, implemented blockchain technology to improve its communication with vendors. By design, blockchain creates a permanent, unchangeable record of real-time data; hence, no one can alter or remove it. Role-based access means that vendors see only what they need to see. No vendor can view another vendor’s information. The Home Depot and its vendors can address this issue immediately. Being able to quickly identify where the problem originated allows the retailer and suppliers to be flexible in handling situations. Pharmacists Use Smart Contracts for Increasing Supply Chain Transparency Sonoco is working to reduce issues in the transport of lifesaving medications by increasing supply chain transparency. Pharma Portal is a blockchain-based platform powered by Blockchain Transparent Supply that tracks temperature-controlled pharmaceuticals through the supply chain to provide trusted, reliable, and accurate data across multiple parties. Trade Finance Network Companies Use Smart Contracts to Ease the Trading Process Various businesses are creating an ecosystem of trust for global trade as a blockchainbased platform by joining we.trade, which is a trade finance network company convened by IBM Blockchain. we.trade uses standardized rules and simplified trading options to reduce friction and risk while easing the trading process and expanding trade opportunities for participating companies and banks.

4.2 Transaction Mode of Smart Contract in Virtual Power Plants Because smart contracts are written in code rather than natural language, they are predictable and clear. Moreover, it is expected that contract content will be more automated using computer programs, and the method to design the program will be a key consideration. However, neither party may trust the programs designed by the counterparty; moreover, the parties may not be willing to implement the terms of the contract according to the programs designed by the other party. Distributed ledger technology can solve these problems. In this way, both parties of the transaction can

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know in advance how the contract will be executed and its possible results. The VPP blockchain platform records information on the generating capacity of all equipment, electricity selling price, generation forecast, scheduling, etc. The transaction connotation of smart contracts in a VPP is as follows: The “smart contract” is driven by time and conditions: The artificial intelligence system calculates various scenarios from the decision information generated by power generation conditions, such as electricity generation information, electricity price, generation forecast and scheduling, which are imported by the VPP operator in the “virtual power plant decision management information integration platform.” Production orders are issued to guide the operations of distributed energy operators driven by “power generation smart contracts.” “Power transmission/distribution contracts” coordinate the needs of electricity purchased by consumers at any time. The power generation equipment of various distributed energy providers can also be affixed with RFID, equipment interconnection, and scheduling transactions to maximize the economic efficiency of the principle. After processing the big data collected by the virtual power plant decision management information integration platform, a new data model will be generated, which will be further studied by the research unit. The specific research results will form the business wisdom of the “virtual power plant database” and “virtual power plant decision management information integration platform.”

Fig. 6 VPP blockchain platform

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Issuance of encrypted digital virtual currency as a medium of exchange can effectively promote the exchange of value and economy over the operation platform, and avoid external factors that are affected by electricity product market price fluctuations. The transaction mode of a virtual power plant is shown in Fig. 6.

5 Conclusion This study combines smart contracts with blockchain technology, namely smart contracts on Ethereum, which is a decentralized platform that runs on a program designed to avoid the possibility of suspension, fraud, and third-party interference. We use a smart contract for the micro grid in the VPP with following advantages. (1) Speed, efficiency and accuracy There is only online work to process and there is no need to spend time reconciling errors that often result from manual filling in paper documents. This is because smart contracts are digital and automated. With this platform, developers who want to establish a decentralized app do not need to reconstruct their own blockchain. As long as blockchain resources of the platform are used, aggregation and coordination optimization of geographically dispersed DER resources can be realized in VPP through advanced communication technology and blockchain architecture. VPPs can participate in the electricity market through smart contracts, as a participating entity in the electricity market, to provide services to the grid and electricity market. The participation of the VPP in the power market greatly increases the possibility of DER participation, and the optimal scheduling of DER convergence alleviates the impact of the previous grid on the DER connection. (B) Trust and transparency security There is no third party involved, and because encrypted records of transactions are shared across participants, there is no possibility of information being altered for personal benefit. The VPP can schedule DER resources through a notification or automatic control. In addition, the upstream part of the cash flow of the business model is submitted to the VPP by the power market in terms of market rules and implementation results, whereas the downstream part is paid by the VPP according to the actual performance of the DER implementation according to the contract and program content signed by DER and VPP. (C) Savings Smart contracts eliminate the need for intermediaries to handle transactions and, by extension, their associated time delays and fees. In recent years, certain regions of the world have been paying considerable electricity charges to non-local power companies, resulting in great financial pressure on

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the region, coupled with the pressure to reduce the carbon footprint, and create new economic opportunities for the local population. Hence, these regions have begun to consider the feasibility of using the surrounding hinterland and abundant renewable energy conditions for electricity generation. Advanced technologies such as the generation and management of renewable energy will be the origin for creating a positive cycle of local economic prosperity, recognizing that this development will not only save energy costs through selfgeneration of electricity, but also allow local communities to invest the considerable resources they had paid in the past for energy in other local activities. Simultaneously, the development of smart grids, the Internet, ultra-fast broadband networks, and other technologies will create numerous high-value and high-tech jobs for local people. In addition, it is expected that locally rich renewable energy resources will be used to supply local electricity, allowing the town to maintain most of the energy expenditure at the local level. Through the concept of a regional virtual power plant, the regional energy market can be further constructed, the balance between regional electricity generation and consumption can be achieved, and the economic benefits of power generation can be retained locally. In addition to avoiding payment of transmission costs and retaining the money generated from the sale of electricity locally, the sale of excess electricity in the region will increase revenue if the regional power system is further balanced with the larger grid system.

References 1. Morris G (2011) A Framework for the Evaluation of the Cost and Benefits of Microgrids. Lawrence Berkeley National Laboratory. https://escholarship.org/uc/item/2f37v7zq 2. Pudjianto D, Ramsay C, Strbac G (2007) Virtual power plant and system integration of distributed energy resources. IET Renew Power Gener 1(1):10–16 3. Naval N, Yusta JM (2021) Virtual power plant models and electricity markets - a review. Renew. Sust. Energ. Rev. 149:111393 4. IRENA (2019) Aggregators Innovation Landscape Brief 5. Johannessen A, Arne TP, Line C (2016) Introduksjon til samfunnsvitenskapelig metode. abstrakt forlag, Oslo 6. Smart Energy International, Digital transformation at the grid edge.https://www.smart-ene rgy.com/industry-sectors/energy-grid-management/digital-transformation-at-the-grid-edge/. Accessed 6 Oct 2021 7. Ropuszynska-Surma E, Borgosz-Koczwara M (2019) The virtual power plant-a review of business models. Proc E3S Web Conf 108(2):01006 8. Mahmud K, Khan B, Ravishankar J, Ahmadi A, Siano P (2020) An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: an overview. Renew Sust Energ Rev 127:109840 9. Jiao N, Evans S (2016) Secondary use of electric vehicle batteries and potential impacts on business models. J Ind Prod Eng 33(5):348–354 10. Tseng ML, Bui TD, Lim MK, Stephen L (2021) Sustainable industrial and operation engineering trends and challenges toward industry 4.0: a data driven analysis. J Ind Prod Eng 38(8):581–598

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11. Mai TT, Nguyen PH, Tran QT (2021) An overview of grid-edge control with the digital transformation. Electr Eng 103:1989–2007 12. Singhal R, Verma R (2020) The future of new technologies for transformation of the grid edge. J Crit Rev 7(10):1331–1336 13. Morais H, Kadar P, Cardoso M, Vale ZA, Khodr H (2008) VPP operating in the isolated grid. In: Proceedings of IEEE power & energy society general meeting, Pittsburgh, PA, USA 14. Bignucolo F, Roberto C, Valter P, Silvano S, Michele V (2006) The voltage control on MV distribution networks with aggregated DG units (VPP). In: Proceedings of the universities power engineering conference, UPEC 2006. Proceedings of the 41st international Volume: 1 15. Morais H, Cardoso M, Castanheira L, Vale ZA (2005) A decision-support simulation tool for virtual power producers. In: IEEE international conference on future power systems, Amsterdam, Netherlands, pp 1–6 16. IBM. What are smart contracts on blockchain? https://www.ibm.com/topics/smart-contracts. Accessed 13 Dec 17. Wang X, Liu Z, Zhang H, Zhao Y, Shi J, Ding H (2019) A review on virtual power plant concept application and challenges. In: IEEE innovative smart grid technologies-Asia (ISGT Asia), Chengdu, China, pp 4328–4333

Smart Grid System Cooperative Output Control Method Based on Distributed Compensation Algorithm Qu Yanhua, Wang Haiyang, and Lin Sheng

Abstract In order to improve the accuracy of the regional grid following the main grid, the robust output tracking problem of the heterogeneous dual-integral dynamic system with external disturbances in the smart grid is studied. Aiming at the uncertain part of the smart grid system, an internal model compensator is designed; for the heterogeneous smart grid system, a distributed compensation algorithm is proposed and a distributed compensator is designed, and then based on the internal model compensator and distributed compensation The device provides a state feedback controller based on neighbor node information. Using the knowledge of algebraic graph theory and matrix theory, a sufficient condition for the output of all regional power grid systems to be traced to the corresponding reference output is given. The experimental results show that the regional power grid can accurately follow the output of the main power grid. Keywords Smart Grids · Robust output tracking · Distributed Compensation Algorithm · Cooperative Output Control

1 Introduction The traditional power grid uses a centralized control method, which requires the main controller to receive information from all network nodes, which requires timely, accurate, reliable and strong anti-interference capability communication between the network nodes [1, 2], with the increasing scale of the power grid, intermittent and distributed energy access, the amount of information data is too much, resulting in information path blockage, once the regional power grid system information can not be timely and reliable transmission, will make the control center to issue the wrong instructions, resulting in the whole grid system wrong action, even collapse. Q. Yanhua (B) · W. Haiyang · L. Sheng Shenyang Institute of Engineering, Shenyang 110136, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_3

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Due to the characteristics of distributed energy “plug and play” [3], can produce the external disturbance of electric network, the impact and damage the load of power grid; therefore, external disturbance are unavoidable in the practical application of smart grids. It is particularly necessary to study the cooperative control problem of second-order multi-intelligent systems containing external perturbations [4–11]. At the same time, the access of clean energy and flexible loads, will make the structure of the grid more complex and the structure of each regional grid system is highly heterogeneous, which makes mathematical modeling increasingly difficult. Based on the above conclusions, this paper presents the output tracking problem for secondorder heterogeneous intelligent systems containing external perturbations.

2 Output Tracking Algorithm Based on Full-State Information Designing a dynamic state feedback controller to solve the output tracking problem for second-order heterogeneous intelligent systems. Since each intelligence can only receive information from its neighbor nodes, the distributed dynamic state feedback controller is designed in the following form. 

u i = K 1i x1i + K 2i x2i + K 3i z i , z˙ i = G 1 z i + G 2 (yi − Q 0 x˜i0 ),

(1)

of which zi ∈ Rs , K1i , K2i and K3i are the gain matrices to be designed. Definition Ti = (0 · · · 010 · · · 0) ∈ R(1×N) , where the ith element is 1, and the remaining elements are 0. Bringing the distributed controller (1) into the double integral dynamics, one obtains. ⎧ ⎪ ⎨ x˙1i = x ͝2i ͝ ͝ ͝ x˙2i = Ai x2i + B i K ii x1i + B i K 2i x2i + B i K 3i z i + E i (Ti ⊗ Iq )x 0 ⎪ ͝ ⎩ z˙ i = G 1 z i + G 2 C i x x1i − G 2 Q 0 (Ti ⊗ Iq )x˜0

(2)

T T  Then make ζi = xT1i , xT2i , zTi and Xi = xT0 , x˜ T0 , the closed-loop system (2) has the following form. ζ˙i = Ai ζi + B i X i Among them

(3)

Smart Grid System Cooperative Output Control Method Based …

⎛

0

In

0

0

G1

37

⎞

͝ ͝ ͝ ⎟ ⎜ ͝ Ai = ⎝ B i K 1i Ai + B i K 2i B i K 3i ⎠, ͝

G2C i ⎛

⎞ 0  0  ⎠ B i = ⎝ E i Ti ⊗ Iq 0  0 −G 2 Q 0 Ti ⊗ Iq Among them Ai ∈ R(2n+s)×(2n+s) , Bi ∈ R(2n+s)×(2qN) . Thus, the closed-loop system is obtained. First, the internal stability problem is investigated, that is, driven by the distributed controller (1), the intelligent system is asymptotically stable without considering external perturbations driven. Before giving the main theorem, the following assumptions and lemmas need to be given. ͝

͝

Assume 1 that the matrix pair Bi , Ci of canonical rows is with row-full rank. Lemma 1 For matricesA, B and C,if the matrices pair (A, B) is stable, then for all λ ∈ σ(A0 ), A − λI B is row full rank, if the matrix pair (G1, G2) contains a the matrices C 0 p-weight inner module of the matrix A0. Then the matrix pair 

A 0 G 2C G 1



B 0



is stabilizable. The following theorem gives the main conclusions about internal stability. Theorem 1 Based on Assumption 1, if the matrix pair (G1, G2) contains a directed spanning tree, then the intelligent system is asymptotically robust and stable under the distributed controller (1), when no external perturbations are considered that is, the closed-loop system ζ˙si = Ai ζsi

(4)

is progressively stable. Proof: Consider a matrix with the following form Ai of canonical rows. ⎛

Anom i

⎞ 0 0 In = ⎝ Bi K 1i Ai + Bi K 2i Bi K 3i ⎠ G 2 Ci 0 G1

ˆ i + Bˆ i (K1i K2i K3i ), Among them This matrix can in turn be written as A ⎛

⎛ ⎞ ⎞ 0 In 0 0 Aˆ i = ⎝ 0 Ai 0 ⎠, Bˆ i = ⎝ Bi ⎠ 0 G 2 Ci 0 G 1

(5)

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According to hypothesis 1, since the matrix pair (G1, G2) contains a p-weight

ˆ i , Bˆ i inner module of the matrix A0. According to Lemma 1, the matrix pair A is stabilizable. Therefore, by designing a suitable Riccati equation, the matrix gain K1i , K2i and K3i can be obtained. Further, there exists an open neighborhood W around Δ = 0 such that for all Δ ∈ W, the closed-loop system ζ˙si = Ai ζsi is asymptotically stable. Remark 2 If the matrix pair (A, B) is stabilizable. for any Q > 0, R > 0, the following algebraic Riccati equation AT P + PA − PBR −1 BT P + Q = 0

(6)

has a unique solution P > 0. The gain matrix in the above theorem K i = (K 1i , K 2i , K 3i ) can be obtained by solving the Riccati equation and choosing K = R −1 BT P. In order to obtain another main theorem, we need to give the following lemma. Lemma 2 [12] Consider the Sylvester equation AX + X B = C, Among them A ∈ Rm×n , B ∈ Rm×m and C ∈ Rn×m are the given matrices. Then there exists a unique solution to this equation X ∈ Rn×m , if and only if λi (A) + λ j (B) /= 0, i = 1, 2, ..., n, j = 1, 2, ..., m. Lemma 3 [12] is based on Assumption 1, if (G1, G2)contains a p-weight inner module of the matrix A0.The matrix equation ∏A0 = G1 ∏ + G2 Ω has the solution ∏, then Ω = 0. Selecting the matrix ┌i ∈ R(2n+s)×2qN , consider the following matrix equation. ┌i (I2N ⊗ A0 ) = Ai ┌i + B i

(7)

Since all eigenvalues   of A0 are distributed in the closed right half-plane, according to Theorem 2.1, λi Ai + λ j (A0 ) /= 0, i = 1, 2, ..., 2n + s, j = 1, 2, ..., 2qN. Thus there exists a unique solution to the above matrix equation ┌i . Make ⎞ ┌i11 ┌i12 ┌i = ⎝ ┌i21 ┌i22 ⎠ ┌i31 ┌i32 ⎛

Among them ┌i j , i = 1, 2, 3 and j = 1, 2, 3 have the proper dimensions, then according to (7), we can obtain. ͝

┌i31 (I N ⊗ A0 ) = G 2 C i ┌i11 + G 1 ┌i31   ͝ ┌i32 (I N ⊗ A0 ) = G 2 C i ┌i12 + G 1 ┌i32 − G 2 Q 0 Ti ⊗ Iq

(8)

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Since (G1 , G2 ) also contains about the matrix I N ⊗ A0 of a pN-weighted inner module. According to Lemma 3, it can be obtained that ͝

C i ┌ii = 0   ͝ C i ┌12 − Q 0 Ti ⊗ Iq = 0

(9)

The following lemma is also necessary. Lemma 4 If a directed graph G contains a directed spanning tree, and the node v0 is the root. Then, the matrix Hl = A0 + Ll of all eigenvalues has positive real parts. Lemma 5 Let A ∈ R n×n , B ∈ R m×m , λi (A), i = 1, 2, …, n and λ j (B), j = 1, 2, …, m are the eigenvalues of matrices A and B, and then, the following properties can be obtained. A ⊗ Im + B ⊗ In eigenvalues of are mn real numbers λi (A) + λ j (B), i = 1, 2, · · · , n, j = 1, 2, · · · , m. Thus the following main theorem is given. Theorem 2 If the communication graph G with node v0 as the root contains a directed spanning tree. Make ζˆi = ζi − ┌xi , δ(x0 ) = x 0 − x˜0 , then the canonical system ˙ i = ϒi Ωi Ω

(10)

 T is asymptotically stable, where Ωi = ζiT , δ(x0 )T ,  ⎞ 0 Ai ┌ ⎠ ϒi = ⎝ −μHl ⊗ Iq 0 I N ⊗ A0 + μHl ⊗ Iq 

⎛

Proof: In order to prove the result ζ the derivative is as follows. ζ˙ˆi = ζ˙i − ┌ x˙i According to (3), we can obtain. 

   0  xi  0 μ Hl ⊗ Iq −μ Hl ⊗ Iq



   0  xi  0 μ Hl ⊗ Iq −μ Hl ⊗ Iq

ζ˙ˆi = Ai ζi + B i xi − ┌(I2N ⊗ A0 )xi + ┌ According to Eq. (7), ζ˙ˆi can be written as ζ˙ˆi = Ai ζi + (B i − (Ai ┌i + B i ))xi + ┌ = Ai ζˆi + ┌



  δ(x0 )  0 μ Hl ⊗ Iq

(11)

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In addition, δ(x0 ) the derivative is as follows.     ˙ 0 ) = (I N ⊗ A0 )x 0 − I N ⊗ A0 + μHl ⊗ Iq x˜0 + μ Hl ⊗ Iq x 0 δ(x = (I N ⊗ A0 + μHl ⊗ Iq )δ(x0 )

(12)

Therefore, the canonical type (10) can be obtained from (11) and (12). Obviously, if and only if Ai and I N ⊗ A0 + μHl ⊗ Iq of the eigenvalue distribution in the closed left half-plane the canonical system (10) is asymptotically stable. According to Theorem 1, we can obtain Ai is Hurwitz’s. Next, we discuss the matrixI N ⊗ A0 + μHl ⊗ Iq . According to Lemma 5, the matrix I N ⊗ A0 + μHl ⊗ Iq has the eigenvaluesλi (A0 ) + μλ j (Hl ), i = 1, 2, · · · , q, j = 1, 2, · · · , N . Since the communication topology graph G contains a directed spanning tree, the matrix Hl of eigenvalues all have positive real parts. Therefore, there exists a sufficiently small parameter μ such that all λi (A0 ) + μλ j (Hl ) of the eigenvalues are distributed in the closed left half-plane. Remark 3 by Theorem 2, we know that when t tends to infinity, Ωi → 0. This conclusion will be applied in the next theorem. Theorem 3 based on Assumption 1, if the topology diagram G takes V0 is the root and contains a directed spanning tree, then the error between the measurable output and the reference output, driven by the distributed controller (1) ei converges to 0. Proof: The output error ei can be expressed as. ͝

ei = C i x1i − Q 0 x0

  ͝ = C i 0 0 ζi − Q 0 Ti ⊗ Iq x 0

  ͝ = C i 0 0 ζˆi + ┌i Xi − Q 0 Ti ⊗ Iq x 0

      ͝ = C i 0 0 ζˆi + ┌i Xi − Q 0 Ti ⊗ Iq x˜0 + Q 0 Ti ⊗ Iq x˜0 − Q 0 Ti ⊗ Iq x 0 According to the conditions.      Q 0 Ti ⊗ Iq x˜0 = 0 Q 0 Ti ⊗ Iq Xi and       Q 0 Ti ⊗ Iq x˜0 − Q 0 Ti ⊗ Iq x 0 = Q 0 Ti ⊗ Iq δ(x0 ) Error ei , can be written as.



     ͝ ͝ ei = C i 0 0 ζˆi + ( C i 0 0 ┌i − 0 Q 0 Ti ⊗ Iq )Xi + Q 0 Ti ⊗ Iq δ(x0 ) Then you can get.

Smart Grid System Cooperative Output Control Method Based …

͝ ei = C i 0 0 ζˆi

    ͝ ͝ + C i ┌i11 C i ┌i12 −Q 0 Ti ⊗ Iq Xi + Q 0 Ti ⊗ Iq δ(x0 )

41

(13)

According to Eq. (15), we can get.

  ͝ ei = C i 0 0 ζˆi + Q 0 Ti ⊗ Iq δ(x0 )

 

͝ = C i 0 0 Q 0 Ti ⊗ Iq Ωi

(14)

Therefore, as t tends to infinity, the error between the measurable output and the reference output ei converges. The specific algorithm can be represented in detail by the following steps and the technical flowchart is shown in Fig. 1. (1) Based on the information of A0, select a suitable μ that designs the distributed compensator. (2) Find the smallest polynomial of the outer system matrix A0 , taken as βi . (3) According toβi , find the correspondingσi , and design the corresponding internal mold compensator. (4) According to the distributed compensator and the inner membrane compensator, the design consistency protocol u i . (5) Then use Sylevester’s theorem and the LOR equation to find the gain matrices K 1i , K 2i and K 3i .

Fig. 1 Technical flow chart

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3 Smart Grid Simulation Experiments With the development of economy, distributed control is particularly important to be applied in smart grids. By equating each regional grid in a large grid as a distributed generator (DG) and considering each generator as an intelligent body, the distributed control of a large grid can be seen as distributed control of multiple intelligent bodies. Whether there is a shortage of electricity in the grid or an excess of electricity will be reflected in the frequency. Usually the AC frequency is 50 Hz, Usually the AC frequency is 50 Hz, that is, 50 vibrations per second, the shortage frequency decreases and the excess frequency increases. Once the frequency exceeds the range of 49.99–50.01, the power supplier needs to control it. For example, if a thermal power plant explodes or a nuclear power plant is suddenly destroyed, a significant frequency drop will be seen on the power grid, therefore, we control the frequency deviation of the large power grid in this section. The traditional control strategy for large grids is a centralized control strategy, while the control of DG needs to be changed from centralized to distributed control in order to prevent random errors and to improve the robustness of the control system. In this example simulation, consider that all regional grid systems have different dynamics and that they are subject to external disturbances. The simulation structure diagram of distributed control is shown in Fig. 2. Distributed control uses local area network communication in the regional grid system and wide area network communication in the large grid system. Where the dynamic model of each generator is described as [13]. ˙ fi = − Δ ˙ pm i = − Δ

Di 1 Δ fi + Δpm i Mi Mi 1 1 Δpm i + Δpci Tch i Tgi

Among them, Δ f i is the frequency deviation, the Δpm i is the generator mechanical power deviation, Δpci is the power at the load reference point, Di is the damping factor of generator i, Mi is the rotational inertia of the generator, Tch i is the time constant of the turbine, and Tgi is the time constant of generator i. Make Fig. 2 Smart grid simulation system

Smart Grid System Cooperative Output Control Method Based …

43

 T xi = Δ f i , Δpm i , u i = Δpci , Then the state space expression of the above dynamic equation is x˙i = Ai xi + Bi u i , i = 1, 2, 3, 4

(15)

Among them  −D

i

Ai =

Mi

0

1 Mi −1 Tch i





, Bi =

0



1 Tgi

Consider the state space expression (15) for the system taking the value of x˙1i = x2i x˙2i = 0.5i ∗ u i + δi where accordingly, the yi = xi is the frequency deviation to be regulated, the external  perturbation can be denoted as δi = 0.5∗i 0 0 x0 , and this external perturbation is generated by the following system. 

x˙0 = A0 x0 yr = Q 0 x0

Among them ⎛

⎞ 0 1 0   A0 = ⎝ 0 0 1 ⎠, Q 0 = 1 0 0 0 −1 0 Since (G1, G2) on matrix A0 contains a p-weight inner mode. Therefore G and G2 can be chosen as ⎛ ⎛ ⎞ ⎞ 0 1 0 0 G 1 = ⎝ −1 0 −1 ⎠, G 2 = ⎝ 0 ⎠ 0 0 0

1

Based on the above internal mode compensator algorithm, due to λ1 (A0 ) = 0, λ2 (A0 ) = i, λ3 (A0 ) = −i, the parameters μ can be chosen as μ = −2.5. According to Eq. (5), the gain matrix can be calculated as K 11 = 5.3497, K 21 = 4.7327,

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  K 31 = 1.1647 0.8021 2.5789 K 12 = 4.3605, K 22 = 3.1178,   K 32 = 1.3885 0.2683 2.8027 K 13 = 3.8811, K 23 = 2.4849,   K 33 = 1.4142 0.0044 2.8284 K 14 = 3.5982, K 24 = 2.1444,   K 34 = 1.4063 −0.1491 2.8205 Thus, all frequency regulation derived based on the distributed state feedback controller (1) is given by Figs. 3, 4, 5 and 6. Figure 7 represents all frequency deviations, Fig. 8 shows the tracking of frequency deviations when there is no control input. From Figs. 3, 4, 5 and 6, it can be seen that the four intelligences can completely track the required frequency of the controller, that is, the leader, after 33.15 s. In Fig. 7, the four lines represent the frequency deviation of the four following intelligences relative to the leader intelligences, which converges to 0 after 33.15 s. In Fig. 8, the thick line represents the frequency state of the leader, and the other four lines represent the frequency states of the four following intelligences. From the figure, it can be seen that when no controller is added, the frequency following is inconsistent, and the tracking result for the frequency is no effective. Fig. 3 Frequency deviation Δ f 1 of the tracking results

Smart Grid System Cooperative Output Control Method Based … Fig. 4 Frequency deviation Δf2 of the tracking results

Fig. 5 Frequency deviation Δf3 of the tracking results

Fig. 6 Frequency deviation Δf4 of the tracking results

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Fig. 7 Frequency deviation Δfi of the tracking results

Fig. 8 Frequency deviation Δ f i simulation results in the absence of control

It can be seen from Fig. 8 that the system cannot be stabilized at the same value without control input. The paper [14] is proposed a distributed gain scheduling strategy for load frequency control in smart grid. The stabilization time of the system is 75.48 s when the control algorithm in the paper [14] is used, while the stabilization time of the system is 33.15 s when the algorithm in this section is used, 56% faster than the algorithm in the paper [14]. This simulation result verifies the effectiveness and feasibility of the consistency algorithm based on the distributed state feedback controller in the distributed control of smart grid.

4 Conclusion This paper investigates the problem of robust output control of a second-order intelligent system containing external perturbations in a practical application of smart grids. Not all regional grid systems can communicate directly with the leader grid system, and the dynamics of all intelligences are unknown. Therefore, a distributed

Smart Grid System Cooperative Output Control Method Based …

47

dynamic compensator and a distributed internal mode compensation algorithm are proposed, while a distributed dynamic feedback control is designed to solve the robust output tracking problem of the smart grid. Finally, simulation experiments of the smart grid are given to verify the effectiveness of the proposed algorithm and compare with the algorithm in Paper [14] to validate the superiority of this algorithm.

References 1. Li GM, Zhang FS, Wu H et al (2021)Design of intelligent distributed feeder topology system based on pair equality communication. Comput Appl Softw 38(11):104–108,154 2. Li R, Wang R, Tian T et al (2021) Collaborative reinforcement learning algorithm of multi-agent achieving simultaneous multi-objectives. Comput Appl Softw 38(9):199–204 3. Zhang L, Wang L, Jin L et al (2021) Design of security authentication mechanism in electric vehicle charging stations network system. Comput Appl Softw 38(11):338–343 4. Shi YJ, Li R, Teo KL (2017) Rotary enclosing control of second-order multi-agent systems for a group of targets. Int J Syst Sci 48(1):13–21 5. Song Q, Liu F, Cao J et al (2013) M-matrix strategies for pinning-controlled leaderfollowing consensus in multiagent systems with nonlinear dynamics. IEEE Trans Cybern 43(6):1688– 1697 6. Hengster-Movric K, You K, Lewis FL et al (2013) Synchronization of discretetime multi-agent systems on graphs using Riccati design. Automatica 49(2):414–423 7. Zhang H, Wang J (2017) Active steering actuator fault detection for an automatically-steered electric ground vehicle. IEEE Trans Veh Technol 66(5):3685–3702 8. Scardovi L, Sepulchre R (2009) Synchronization in networks of identical linear systems. Automatica 45(11):2557–2562 9. Li Z, Duan Z, Chen G et al (2010) Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint. IEEE Trans Circuits Syst I Regul Pap 57(1):213–224 10. Li Z, Wen G, Duan Z et al (2015) Designing fully distributed consensus protocols for linear multi-agent systems with directed graphs. IEEE Trans Autom Control 60(4):1152–1157 11. Huang J (2004) Nonlinear output regulation: theory and applications. Society for Industrial and Applied Mathematics 12. Zhou K, Doyle JC, Glover K (1996) Robust and optimal control. Prentice hall, New Jersey 13. Zheng Y, Zhang R, Li Z (2016) Optimizing distributed gain scheduling strategy for load frequency control in smart grids based on adaptive consensus protocol. New Industrialization 6(8):41–48 14. Liu S, Liu X, Saddik A (2014) Modeling and distributed gain scheduling strategy for load frequency control in smart grids with communication topology changes. ISA Trans 2:454–461

Optimizing the Location and Capacity of Charging Stations for a Public Electric Bus System Considering Actual Operating Service Conditions Based on Differential Evolution Algorithm Kittiphan Nawakaittikorn and Warayut Kampeerawat

Abstract For modern public transportation, electric bus system is one of efficient solutions to solve the problem of fossil fuel shortage and pollution from combustion engines. This paper presents a study of the investment for an electric bus system for replacing the existing transportation system that uses gasoline vehicles. In this study, the location and capacity of charging stations are optimized based on the Differential Evolution algorithm (DE). By considering suitable service schedule management and onboard battery capacity, the investment cost and operating cost can be reduced. The case study is based on the public bus system of Khon Kaen University. From the numerical results, the proposed strategy can provide the optimal location and capacity of charging stations under the required operating condition. Moreover, the results were compared with Particle Swarm Optimization (PSO), which showed that DE effectively resolved these problems. Keywords Electric bus · Charging infrastructure · Onboard battery capacity · Differential Evolution algorithm (DE)

1 Introduction Currently, people pay attention to environmental issues because of the increasing amounts of greenhouse gases. The problems, e.g., global warming and acid rain, are caused by increased energy consumption from the engagement population and the expansion of industrial sectors that requires more energy [1]. Shifting from combustion engine vehicles to electric vehicles (EVs) is one option to address the rising emissions problem. To reduce greenhouse gas emissions and the use of fossil fuels, K. Nawakaittikorn · W. Kampeerawat (B) Electrical Engineering Department, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_4

49

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K. Nawakaittikorn and W. Kampeerawat

EVs are gaining a lot of attention in the industry. There are various types of EVs such as passenger vehicles, trucks, and buses. In public transportation, electric buses are commonly used in many countries. This makes significant changes to public transport. The electric bus is one of the effective options for solving the problem of traffic jams and pollution with its excellent energy efficiency when it is employed in a repetitive route such as school buses, city buses, company shuttle buses, and airport buses. Furthermore, it costs lower investment costs per passenger and maintenance costs than private vehicles [2]. The rapid rise in fuel costs and decreasing fuel reserves are other key factors that make electric vehicles particularly attractive [3]. With a proper charging schedule, electric buses can also deliver uninterrupted transport services comparable to diesel buses [4]. However, the driving range of a fully charged electric vehicle is shorter than that of a fully charged combustion engine vehicle. One difficulty of the electric vehicle may be the long charging time taking much longer than the refuelling of engines. Nowadays, fast charging technology has come in, making the electric vehicle change charging time from hours to minutes [5] but the rate of electric vehicle development is still slow in some countries due to two main obstacles, first electric cars cost more than conventional diesel cars due to the relatively high cost of batteries, accounting for 30%–50% of total vehicle costs. Secondly, the additional investment in infrastructure has a very high investment cost per device. Therefore, it is necessary to optimize battery size and charging infrastructure. to make the cost of urban electric bus systems more competitive. There are two main approaches to solving the problem at present. The first approach is to provide buses with high battery capacity to support half-day or full-day driving and then recharge. This approach requires low charging power because it can be charged in normal charging mode for an amount of time. That makes charging infrastructure cheaper as fast charging is not required. The second approach is to keep the battery size as small as possible, but enough for one round trip or enough to go to each bus stop. It needs quick charging at the bus stop spot which reduces the cost of bus batteries and the weight of the bus, but it will increase the cost of charging infrastructure. Comparing these two approaches reveals a difference between investing in batteries and investing in charging infrastructure. Therefore, a combination of these two approaches must be optimized to reduce the total cost of ownership (TCO) [6]. Many preceding works proposed planning strategies for building electric vehicle charging stations and optimal sizing of batteries, using the vehicle routing problem (VPR) to determine the location of charging stations to establish electric bus hub storage with a case study for short running distances [2]. Using ant colony optimization to plan the establishment of charging stations covering an area of queuing access to charging stations on the IEEE-69 bus test system is proposed in [5] but it is quite difficult and complex to implement. There is an approach based on the VRP Prim algorithm and brute-force search to find the location of charging stations to set up electric bus hub storage, thus reducing the cost of installing charging stations. and the cost of recharging will increase the vehicle’s investment cost because it requires a larger battery to support all-day use. This approach may be suitable for small transportation systems to reduce battery size [7]. With considering the urban electrical system, the establishment of charging stations can not only include evaluating the impact of

Optimizing the Location and Capacity of Charging Stations for a Public …

51

placing charging stations on the distribution system but also consider the charging cue from radial unbalanced operation as energy-efficient work. Furthermore, the trend in assessing train queue uncertainty is interesting. Assessing charging stations in this manner may be suitable for the electrical distribution system, but considering the station location for a specific route needs to assess the vehicle needs before considering the electrical system [8]. Mixed-integer linear programming (MILP) is applied to study total ownership cost (TOC) reduction and there is an evaluation of the total investment cost for the business owner, which is only a model without actual usage comparison while the point of installation of the charging station is unknown [9]. Optimal power flow (OPF) is used to determine the charging station location about the effect on the power transmission system on the IEEE-33 bus test system the location obtained may not meet the needs of the charging station as it assesses the effects of real power, reactive power, voltage violence, and power flow violence [10]. Applying the affinity propagation clustering algorithm (AP clustering algorithm), the optimization of the distribution of charging stations to cover the areas according to the actual usage of electric vehicles can be achieved but regardless of the investment cost [11]. Li-ion batteries and supercapacitor are proposed to use as a hybrid energy storage system to meet the use of electric vehicles in all driving ranges and optimally size energy storage using Particle Swarm Optimization (PSO) [12]. In Thailand, some public bus routes have already been replaced by an electric bus system. To drive more use of electric buses, the 30@30 policy of Thailand’s government becomes the main objective in the development plan to be an electric vehicle production base that aims to produce Zero Emission vehicle (ZEV) vehicles at least 30% of the total production to promote the use of motor vehicles 440,000 passenger cars and pickup trucks, 650,000 motorcycles, 33,000 buses, and trucks, as well as target the promotion of 12,000 public fast-charge electric vehicle charging stations and 1,450 battery switching stations for electric motorcycles [13]. This paper presents a strategy for locating charging stations for an electric bus route based on the actual operating condition of the bus routes and determining the size of the charging stations installed at each installation point. Considering the investment cost and the onboard battery cost, an optimization problem with practical operating constraints of the bus system is solved based on Differential Evolution Algorithm. This proposed method is supposed to provide an optimal position and capacity of the charging station on the fixed route that can be used for planning in developing electric bus route.

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2 Electric Bus System Modelling 2.1 Analysis of the Bus system The construction of charging stations for electric buses is different from public charging stations for passenger cars. Since the charging station for the electric bus is located on a regular route that the electric bus travels regularly, uniformity can effectively identify the optimum location in the route [11]. The rated power of an electric bus is usually evaluated by its acceleration time, maximum speed, and gradability. The tractive effort can be expressed as Ptract = (Fg + Fw + Fr + m · a) · v

(1)

where Fg is the gradient resistance force (N), Fw is the aerodynamic drag force (N), Fr is the rolling resistance force (N), m is the mass of the bus (kg), a is the acceleration (m/s 2 ), v is the velocity obtained from recording the trajectory (m/s). The gradient resistance force can be expressed as Fg = m · g · sin θ wher e : θ = tan

−1



l h

(2)  (3)

where θ (rad) is the road angle such that the angle made by the car on the horizontal road as an example using the arctan trigonometric function of the vehicle l (m)  makes an angle with the slope h (m), g is the acceleration due to gravity m/s 2 . Aerodynamic drag can be expressed as Fw =

1 · ρ · A f · Cd · v 2 2

(4)

where ρ is the air density (kg/m 3 ), A f is the vehicle frontal area (m 2 ), Cd is the aerodynamic drag coefficient it is a specific value for the appearance of the vehicle. the rolling resistance force can be expressed as Fr = m · g · fr · cos θ where fr is the rolling resistance coefficient.

(5)

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53

2.2 Charging Demand for Electric Bus The charging demand of an electric bus is estimated from the State of Charge (SOC), which is the rate at which the battery’s energy consumption is expressed as a percentage of the operating distance in the optimal battery state during discharge. In general, the charging time will be limited by an appropriate range of SOC that does not allow the battery to discharge to the empty stage (0%) and not to fully charge (100%), causing the battery to accumulate heat and cause the battery to deteriorate as quickly as it should. To estimate the SOC, the energy of the battery at each stage will be calculated by the simulation program and SOC can be expressed as S OCi =

E batti × 100% E battmax

(6)

where E battmax is the maximum capacity of the battery (kWh), E batti is the capacity battery decreased according to time at i (kWh).

3 Problem Formulation This paper aims to use fast charging as a basis to optimize the utilization of electric bus systems by optimizing the location and size of charging stations and reducing the size of batteries to suit the needs of the established and acquired charging stations. which is a reasonable price for investing in an electric bus system.

3.1 The Desired Capacity of Charging Stations To determine the capacity of the charging station based on the actual route data, the operating condition, stop positions and speed trajectory of the bus are considered for evaluating power and energy. The objective equation is to minimize the charging energy. optimization parameter is to satisfy constraints can be expressed as Pchg,tot (Pchg,i ) =

n 

Pchg,i

(7)

i=1

where n is the total number of stops that the vehicle stops in this route, i is the individual stopping point, where i = 1 is the initial stopping point, and i = n is the last stop. The position of each stop i is the actual position at which the vehicle stops to pick up passengers and the actual time the vehicle stops, and Pchg,i is the charging power required in the charging station at the stop position i (kW)

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The investment cost of charging stations is multiplied by the fixed unit investment cost of the battery times and desired capacity of charging stations can be expressed as Cin f ra = cin f ra, f i x · Pchg,tot

(8)

where cin f ra, f i x is the fixed investment unit cost of the charging station ($/kW).

3.2 The Cost of the Desired Capacity of the Battery The battery capacity is limited to E battmax under the condition of the capacity charging station objective function from Eq. (7), thus effectively matching the capacity battery with the charging needs of the system, and considering the battery capacity as the cost of battery investment on the bus can be expressed as Cbatt = cbatt, f i x · E batt

(9)

where cbatt, f i x is the fixed investment unit cost of the battery ($/kW), and E batt is the optimal battery capacity sampling by differential evolution algorithm (kW).

3.3 The Total Cost of the Electric Bus System The investment cost of the charging station capacity combined with the cost of the battery capacity on the EV bus can be expressed as Minimi ze Ctot = Cin f ra + Cbatt

(10)

p1 ≥ S OCmin − S OCi

(11)

p2 ≥ S OCi − S OCmax

(12)

p3 = S OCend − S OCend,w

(13)

Constraints:

where S OCmin is the desired minimum state of charge (%), S OCmax is the maximum state of charge (%), S OCend is the minimum preferable at the end of the route and, S OCend,w is the state of charge of the battery at the last bus stop point.

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4 The Proposed Optimizing the Location and Capacity of the Charging Station and Onboard Battery Capacity Based on Differential Evolution Algorithm Nowadays, optimization problems are becoming increasingly complex. This necessitates the need for efficient, fast, and accurate tools. The Differential Evolution (DE) algorithm is one of the most powerful random real-parameter optimization methods. DE is a complex but extremely effective tool to solve optimization problems. Therefore, it is widely used in science and engineering [14]. This paper applies the DE algorithm to minimize the objective function within the previously listed limitations. The DE is used to find the optimal control variable starting from the initial group of random control variables. In the proposed strategy, DE will be solved the problem to determine the location of the charging station, charging station capacity, and onboard battery capacity presented in Fig. 1 and described shown below. Step 1: Input Data SOC profile, Ptrack profile. Step 2: Set parameters of the DE algorithm: scale factor F, crossover rate Cr , and the number of population size N p . Step 3: Set the generation number G = 0 randomly and initialize a population of N p individuals Mutation process

Start

Crossover process

Import Data Ptrack profile

Selection process to find the best population Yes

Set parameters of the DE algorithm

Iteration equal to max iteration?

Generate initial population No Iteration = 1

Import to objective function

Fig. 1 The flowchart of the proposed algorithm based on DE

The best solution from objective function Stop

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  X = x1,i,G , x2,i,G , x3,i,G , ..., x D,i,G

(14)

x j,i,0 = x j,min + randi, j [0, 1] · (x j,max − x j,min )

(15)

→

i,G

where i is an index of the population, x j,i,0 is the decision parameter and is generated in the i-th individual, x j,max and x j,min are the upper and lower bounds of the decision parameter for the i-th individual, respectively. randi, j [0, 1] is a uniformly distributed random number lying between 0 and 1 (actually ≤ randi, j [0, 1] ≤ 1[0, 1]≤ 1) and is instantiated independently for each component of the i-th vector. Step 4: Take the random population into Pchg , E batt to obtain the power charging. Substituting the obtained values in the objective function, check the constraints, and find the best decision variable from the population that gives the minimum objective function value. The best decision variable from the population gives the minimum objective function value. →

Pchg = X ·Pchg,max

(16)

i,G

→

E batt = X ·E battmax

(17)

i,G

Step 5: Generate a donor vector Vi,G = {υ1,i,G , ..., } {υ D,i,G } corresponding to the →

ith target vector X via the differential mutation scheme of DE as i,G

→

→

→

→

r1i

r2i ,G

r3i ,G

Vi,G = X +F · ( X − X ) →

(18)

→

where Vi,G is the mutant vector, X is the best decision variable vector from step 5, →

→

r2i ,G

r3i ,G

r1i

X , X is the decision variable vector that is a member in Step 4 which is randomly →

→

obtained with the condition of X = X . r2i ,G

→

r3i ,G

Step 6: Generate a trial vector U = {u 1,i,G , ..., u D,i,G } for the it-h target vector i,G

X i,G through binomial crossover in the following way u i, j,G =

υi, j ; randi, j [0, 1] ≤ Cr , xi, j ; randi, j > Cr

(19)

where υi, j is the crossover vector, randi, j is a random value in [0,1]. Step 7: To keep the population size constant for future generations, the next step of the algorithm calls for selection to determine whether the target or experimental

Optimizing the Location and Capacity of Charging Stations for a Public …

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vector will survive to the next generation, that is, at G = G + 1 select operation is described as ⎧ → → → ⎪ ⎨ U if f ( U ) ≤ f ( X ) → i,G i,G i,G (20) X = → → → ⎪ i,G+1 ⎩ X if f ( U ) > f ( X ) i,G+1

i,G

i,G

Step 8: After the optimum value is obtained in that cycle, this method is repeated in step 4 and follows the sequence of steps until the loop reaches the specified number of iterations thus stopping and ending the process.

5 Results and Discussion To verify the performance of the proposed algorithm, the bus route within Khon Kaen University, Blue Line 1, is selected as a case study. For simulating cases, the maximum battery capacity of the commercial bus, i.e., 105 kW, is used for calculation. The charging station location, charging station capacity, and the new capacity battery will be determined by solving the optimization problem based on the DE algorithm. Moreover, the solutions of DE will be compared with that of particle swarm optimization. The system and simulation results in that discussion are shown below. Scenario 1: Studying the usage of the Blue Line bus within Khon Kaen University in 1 round trip on maximum battery capacity. Scenario 2: Finding the location of the charging station, charging station capacity, and onboard battery capacity by using the proposed algorithm based on DE.

5.1 Scenario 1: Studying the Usage of Blue Line Buses Within Khon Kaen University in 1 Round Trip on Maximum Battery Capacity This section presents an evaluation of bus route data collected from the actual system. In Fig. 2 is the map from the route. In Fig. 2, the bus speed profile and bus stops are shown. The tractive power is evaluated based on the data as shown in Fig. 3 and the SOC of the onboard battery are calculated, which is limited to the maximum SOC of 80%, shown in Fig. 4. Route information of Blue line 1 is summarized in Table 1. From Fig. 3, the bus travels at a maximum speed of 38.1863 km/hr, and an average speed of 15.7205 km/hr. The distance travelled in one round is 5.815 km and takes 20.75 min. From Fig. 4, the bus tractive power is 751.9842 kW at the maximum speed, − 791.1183 kW at the minimum speed, and 16.9293 kW at the average speed.

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Fig. 2 The route map of the Blue line 1 bus system

Fig. 3 Speed profile and bus stops

From Fig. 5, the maximum SOC is limited to 80%, and the minimum SOC is 59.97%. For 1 round trip, 25.0375% of battery energy is consumed.

Optimizing the Location and Capacity of Charging Stations for a Public …

Fig. 4 Tractive power profile

Table 1 Route information of Blue line 1

Fig. 5 SOC of onboard battery

The blue line bus Length of route (km)

5.815

Time to travel (min)

20.87

Stop point

26

59

60 Table 2 The parameters are necessary for calculation

K. Nawakaittikorn and W. Kampeerawat

Parameter

Value

Pchg,max (kW)

400

E battmax (kWh)

105

cin f ra, f i x ($/kW) [15]

645.52

cbatt, f i x ($/kW) [16]

135

S OCmax (%) [11]

95

S OCmin (%) [11]

40

S OCend (%)

60

5.2 Scenario 2: Finding the Optimal Charging Station Location, Charging Station Capacity, and Onboard Battery Capacity by Using the Proposed Algorithm Based on DE In this section, the proposed method will be applied to the blue line route to determine the charging station location, charging station capacity, and onboard battery capacity. To verify the performance of the proposed method, a comparison between 2 different solving methods, i.e., DE and PSO will be performed and compared. The parameters of the system, DE parameters and PSO parameters used for optimization are summarized in Table 2, Table 3, and Table 4, respectively. The proposed method is implemented in MATLAB program. The simulation condition is continuously solved so that the final remaining energy must be sufficient to reach the destination point without exceeding the lower limit and the charging time at the charging station is fixed at 5 min. In this case study, the charging station is allowed to be located at the bus top only. For the charging condition, the bus must be stopped to be charged when the SOC is in a state according Table 3 The DE parameters

Table 4 The PSO parameters

Parameter

Value

Number of populations (N p )

50

Number of iterations (i)

500

Crossover (Cr )

0.75

Scaling factor (F)

0.75

Parameter

Value

Number of populations (N p )

50

Number of iterations (i)

500

Iteration factor (W )

0.9

Learning factor (C1 , C2 )

0.75

Optimizing the Location and Capacity of Charging Stations for a Public …

61

to the constraints, if the bus needs to be charged but the required stop location is not a bus stop, the bus must continue to the nearby bus stop. The results from DE and PSO are shown in Fig. 6 and Fig. 7, respectively. Figure 5 and Fig. 6 show the SOC profile and the optimal point for charging and the results are concluded in table 5. Solving by the DE algorithm, the optimal point for charging is 61.91% SOC with a total cost of 193,8013 $ and the charging station location should be located at 16. Solving by the PSO algorithm, the optimal point for charging is 62.66% SOC with a total cost of 214,730 $ and the charging station location should be located at 16. From the numerical results It was proved that the proposed method based on DE can provide a better optimal solution than PSO for the proposed route. Even though, DE and PSO provide the same location of the charging station. The DE algorithm provides lower total cost compared with PSO by 39.62%.

Fig. 6 The SOC profile and the charging point are determined by the DE algorithm

Fig. 7 The SOC profile and the charging point are determined by the PSO algorithm

62 Table 5 Comparison of the results from DE and PSO algorithm

K. Nawakaittikorn and W. Kampeerawat

Variable

DE algorithm

PSO algorithm

Solving time (s)

30.06

29.95

SOC (%)

61.91

62.66

Power charging (kW)

193.8013

329.7012

Battery capacity (kWh)

63.9138

65.3995

Total Cost ($)

129,660

214,730

Location

16

16

6 Conclusion In this study, the optimization of charging station location, station capacity and onboard battery based on minimizing total cost is presented and the solution can be considered for the establishment for investing in a fully electric bus network. The objective function is derived from infrastructure cost and battery cost with constraints based on the actual operating conditions of the bus system route. The proposed optimization problem is solved based on DE algorithm. The numerical case study was performed on the Blue Line Shuttle bus of Khon Kaen University. To compare the performance of different solving algorithms, DE and PSO were used to solve and compared. From the results, the results from DE show the potential of an optimal solution to be more efficient compared to PSO. The proposed method based on DE can reduce the investment cost for an electric bus system by as much as 41.92%. However, the initial investment assessment may be considered in practical planning. For future work, the multiple routes will be considered at the same time in conjunction with the bus schedule management. They may also assess shared charging points that some routes may share. In this study, an objective function has been established for investing in a fully electric bus network. Responding to the actual use that has begun to change from the traditional bus system to an electric bus system for environmental friendliness. By using the DE algorithm to find charging stations. The capacity of the charging station and the capacity of the battery on the car by taking the actual route in 1 round of the Blue Line of Khon Kaen University study. The simulation results using DE show the potential of an optimal solution to be extremely efficient compared to PSO. In optimizing, DE can reduce the investment cost for an electric bus system by as much as 41.92%. However, to be used in the initial investment assessment. Future work may be to always evaluate the actual route data in conjunction with the bus schedule. They may also assess shared charging points that some routes may share, which was not considered in this case study.

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References 1. Emprecha S, Pattaraprakorn W, Chutiprapat V, Bhasaputra P (2016) The study on the effect of electric bus (non-fixed route) to energy consumption in Thailand. In: 2016 13th international conference on electrical engineering/electronics, computer, telecommunications and information technology, ECTI-CON 2016. Institute of Electrical and Electronics Engineers Inc 2. Taweepworadej W, Buasri P (2016) Vehicle routing problem for electric bus energy consumption and planning. Int J Adv Agric Environ Eng 3:224–226. https://doi.org/10.15242/ijaaee. c0516010 3. Ostadi A, Kazerani M, Chen SK (2013) Optimal sizing of the energy storage system (ESS) in a battery-electric vehicle. In: 2013 IEEE transportation electrification conference and expo: components, systems, and power electronics - from technology to business and public policy, ITEC 2013 4. Zhang C (2019) Charging schedule optimization of electric bus charging station considering departure timetable. In: State grid fujian economic research institute, Fuzhou, China 5. Leeprechanon N, Phonrattanasak P, Sharma MK (2016) Optimal planning of public fast charging station on residential power distribution system. In: 2016 IEEE transportation electrification conference and expo, asia-pacific, ITEC asia-pacific 2016. Institute of Electrical and Electronics Engineers Inc, pp 519–524 6. Teichert O, Chang F, Ongel A, Lienkamp M (2019) Joint optimization of vehicle battery pack capacity and charging infrastructure for electrified public bus systems. IEEE Trans Transp Electrif 5:672–682. https://doi.org/10.1109/TTE.2019.2932700 7. Sumith DM, Nagpal S, Sarkar G (2019) A new hybrid approach for optimal location of charging station and ADVISOR software for energy consumption estimation of electric bus. In: International conference on intelligent sustainable systems (ICISS 2019) 8. Pao-la-or P, Boribun B (2019) Modeling and analysis of the plug-in electric vehicles charging in the unbalanced radial distribution system. Int J Electr Electron Eng Telecommun 8:133–138. https://doi.org/10.18178/ijeetc.8.3.133-138 9. Lotfi M, Pereira P, Paterakis N, Gabbar HA, Catalão JPS (2020) Optimizing charging infrastructures of electric bus routes to minimize total ownership cost. In: 2020 IEEE international conference on environment and electrical engineering. 2020 IEEE industrial and commercial power systems europe (EEEIC/I&CPS Europe). IEEE 10. Kunj T, Pal K (2020) Optimal location planning of EV charging station in existing distribution network with stability condition. In: 2020 7th international conference on signal processing and integrated networks (SPIN) 11. Yanhong FAN, Chunhui HE, Danxiong FEI (2020) Electric bus charging station’s location and capacity based on routes and grid AP clustering algorithm. In: 2020 Asia-Pacific international symposium on advanced reliability and maintenance modeling (2020) 12. Ostadi A, Kazerani M (2015) A comparative analysis of optimal sizing of battery-only, ultracapacitor-only, and battery-ultracapacitor hybrid energy storage systems for a city bus. IEEE Trans Veh Technol 64:4449–4460. https://doi.org/10.1109/TVT.2014.2371912 13. The Ministry of Energy (Thailand) (2022) Report of the meeting conclusion of the National electric vehicle policy committee no.3/2021 and no.1/2022 14. Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15:4–31. https://doi.org/10.1109/TEVC.2010.2059031 15. Lithium-ion battery packs average price 2022 | Statista. https://www.statista.com/statistics/104 2486/india-lithium-ion-battery-packs-average-price/ 16. Liangzheng W, Jigang Z, Shangyong W, Wen C, Yanni L, Zeyuan Y (2022) Siting and sizing optimization for electric taxi charging station based on full life cycle. In: 2022 6th international conference on green energy and applications, ICGEA 2022. Institute of Electrical and Electronics Engineers Inc, pp 233–239

High-Severity N-x-k Contingency Ranking and Screening Based on Deep Learning and Heuristic Search Guang Li, Yanran Li, Yuanzhen Zhu, Li Li, Changtao Kan, Zhenya Dai, and Yutian Liu

Abstract Contingency analysis is the important base and critical guarantee for security and reliability of hybrid AC/DC systems. This paper proposes a high-severity N-x-k contingency ranking and screening method for hybrid AC/DC systems. Firstly, the mechanism of successive commutation failures is analyzed and the minimum second voltage drop value of the commutation bus (V 2nd c) after short-circuit fault is utilized as the index to quantify the severity of line outage contingencies. To quickly evaluate the impact of multi-line outage failures on DC systems, a deep learningbased assessment network of which the output is V 2nd c and inputs are steady-state features related to network structure is built. Secondly, a two-stage heuristic search approach is proposed to screen and rank high-severity N-x-k line outage contingencies in hybrid AC/DC power systems. Stage 1 is to narrow search space and stage 2 is to evaluate the V 2nd c and rank N-x-k contingencies based on the assessment network. A bus impedance matrix formation approach based on Woodbury formula is proposed, which is used in the calculation of input features and search index. Simulation results demonstrate that the proposed method can accelerate the screening and ranking of high-severity N-x-k line outage contingencies in hybrid AC/DC power systems. Keywords AC/DC power system · cascading failure model · deep learning technology · two-stage heuristic search

G. Li · Y. Li · L. Li · C. Kan · Z. Dai State Grid Jinan Power Supply Company, Jinan, China Y. Zhu (B) State Grid Shandong Electric Power Research Institute, Jinan, China e-mail: [email protected] Y. Liu Shandong University, Jinan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_5

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1 Introduction HVDC has been widely used in power transmission systems, and it has a significant impact on the safety and stability of AC/DC power systems. The interaction between AC and DC systems brings new challenges to power system secure operation. Linecommutated converter (LCC) based HVDC systems have been extensively applied for long-distance bulk power transmission due to the superior power-handling capability of thyristors and low operating power losses [1]. For LCC based HVDC power systems, a normal commutation process depends on the support of AC systems. Multiple line outage (N-k) failures on the AC side weaken the reactive power support capacity of AC systems, which greatly increases the risk of successive commutation failure (SCF). When the number of SCF times reaches a certain value, DC system block will be further triggered, which may cause a large-scale power outage [2]. In terms of evaluating the impact of AC failures on the DC system, there have been a few of researches [3, 4], but they only consider the single commutation failure and do not pay attention to SCF. Short-circuit ratio (SCR) can evaluate the strength of the AC/DC receiving-end power system from the perspective of network structure [5], which is able to be used to identify serious failures in hybrid AC/DC power systems [6]. However, SCR is an indicator for planning which has lower accuracy. Deep learning technology is of great advantage to deal with complex and high-dimensional data. Stacked denoising autoencoder (SDAE) has been successfully applied to the power system analysis [7]. It is promising to develop a deep learning assessment network to online evaluate the impact of line outage contingencies and three-phase short-circuit fault on DC systems. There have been some studies on N-k contingency searching and screening in AC systems. Reference [8] proposes an algorithm with zero missing rate for identification of critical N-2 contingencies that result in line overloads in post-contingency equilibrium. In [9], a mixed integer linear programming model is built to screen highorder N-k line contingencies. In addition, several artificial intelligence methods such as random chemistry algorithm [10], evolutionary algorithm [11], reinforcement learning algorithm [12] are utilized in N-k contingency analysis, which are much meaningful explorations. The occurrence of initial faults could cause cascading failures, of which serious failures will affect the normal operation of DC systems. In dispatch centers, operators should be more concerned about N-x-k contingencies, which represent multiple serious failures that occur in the system after initial failures are determined. The N-1– 1 contingency is a simplified case of the N-x-k contingency, which has been studied in some references [13]. But deep explorations of complex N-x-k contingency are still insufficient. Besides, the above studies are conducted on AC systems without considering DC systems. The screening and ranking of N-x-k contingencies for AC/ DC systems need further investigations. In order to improve the efficiency of N-x-k screening and ranking, it is necessary to quickly assess the impact of AC failures on DC systems and reduce the number of N-k contingency searches.

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To address the above problems, a high-severity N-x-k contingency screening and ranking method for hybrid AC/DC power systems based on deep learning and twostage heuristic search (TSHS) is proposed in this paper. The deep learning technology is utilized to evaluate the impact of multiple line outage failures on the DC system, which saves a lot of calculation time compared with time-domain simulations. The TSHS approach is to screen and rank high-severity N-x-k AC line contingencies in hybrid AC/DC power systems, of which the first stage is to narrow search space and the second stage is to rank contingencies based on the deep-learning network. The major contributions of this paper are summarized as follows. 1) An assessment network based on SDAE is established, which can rapidly and accurately evaluate the impact of multi-line outage failures on DC systems. 2) From the perspective of power system topology, a two-stage heuristic search approach is proposed, which can improve the screening and ranking efficiency of high-severity N-x-k line outage contingencies in hybrid AC/DC power systems.

2 Mechanism of Successive Commutation Failure 2.1 Commutation Failure The voltage sag caused by short-circuit fault in the AC side of the inverter is usually the primary reason of triggering commutation failure. During the normal commutation, the extinction angle is supposed to be controlled at a certain value to avoid commutation failures. The inverter extinction angle γ is: √ 2Id X C γ = arccos ( + cos β) VC

(1)

where V C is the commutation bus voltage, X C is the equivalent commutation reactance, I d is the DC current, and β is the advance angle of the inverter. X C is a constant value in an AC/DC system. Thus, γ mainly depends on I d /V C . The active power in the receiving-side system can be expressed as Id =

Vd0r cos α − Vd0i cos γ Rcr + Rdc − Rci

(2)

where V d0r is no-load rectifier DC voltage and V d0i is no-load inverter DC voltage, α is the firing angle, Rcr is the commutation resistance of rectifier and Rci is the commutation resistance of inverter, Rdc is the resistance of DC line. When a fault occurs in the AC side of the inverter, V d0i will decrease with the drop of V C while V d0r will not change immediately. Thus, I d and I d /V C will increase after fault which leads to the reduction of extinction angle. When γ drops below γ min corresponding to the thyristor turn-off time, leading to the unexpected turn-on of a

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thyristor supposed to be off, a commutation failure occurs. Hence, if the voltage of converter bus is below the threshold, there will be a commutation failure in the DC system.

2.2 Successive Commutation Failure After the short-circuit fault of AC side is cleared, the value of γ is large due to the overshoot. The reactive power consumption will increase significantly in the initial stage of DC power recovery, which results in a second voltage drop of the commutation bus. Assuming V 2nd c is the minimum second voltage drop value of the commutation bus. The increase of I d and decrease of V c will lead to the reduction of γ . When the value of V 2nd c is smaller than the threshold, successive commutation failure will be triggered. Therefore, V 2nd c can represent the risk of successive commutation failure. N-k line outage failures will weaken the grid structure, especially when lines around the commutation bus disconnect and generators cannot provide sufficient reactive power support for the DC system, which will aggravate the magnitude of the secondary bus voltage drop after the fault. Combined with the above analysis, this paper uses V 2nd c as an index to measure the impact of N-k line outage failures on the DC system.

3 Deep Learning-Based Assessment Network This paper developed an assessment network based on deep learning, which can evaluate the impact of multi-line outage failures on DC systems. The steady-state features of power system structures are selected as inputs and V 2nd c is as the output. The SDAE is utilized to extract high-order features of inputs, which will improve the accuracy of regression prediction.

3.1 Stacked Denoising Autoencoder Autoencoder (AE) is an unsupervised neural network model that can learn the hidden features of input data, which is called encoding. At the same time, the learned new features can be used to reconstruct the original input data, which is called decoding. AE can be used for feature dimensionality reduction to extract more effective new features. In order to reduce the over-fitting of AE and improve the generalization performance of the model, denoised autoencoder (DAE) is proposed. DAE randomly sets some input units as zero. Then the over-fitting problem of the model is significantly

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improved. The decoding process of DAE is removed and yi is used as input of next DAE, then SDAE is obtained. DAEs in SDAE are treated as building blocks stacked in the deep architecture [14].

3.2 Input Features In this paper, the voltage supported stability is estimated from the perspective of network topology. The bus function of a power system network can be expressed as: I = YV,

(3)

where I is the injected current vector of buses, V is the voltage vector of buses, Y is the bus admittance matrix. The bus admittance matrix Y can reflect the structure character of the power system network, but it is too sparse to extract features. To get more information, (6) can be transformed into: V = Z I, Z = Y −1

(4)

where Z is the bus impedance matrix of buses. Z is a full matrix and elements of Z have clear physical meaning. The diagonal element Z ii can be seen as the equivalent impedance to ground seen from node i to the entire network. The nondiagonal element Z ij is the mutual impedance between bus i and j. The value of Z ij is equal to the voltage of bus j when the unit current is injected into bus i and other buses are open. A larger value of Z ij indicates a closer connection between the two buses. For any two buses i and j in the power system, when unit current is injected into bus j, the voltage difference U ij between bus i and j is defined as the electrical distance. Assuming bus g around commutation bus d is connected with some dynamic reactive power supported devices such as generators, phase modifiers, and so on, which has strong dynamic voltage supportability for commutation bus. Z gg can reflect closeness between bus g and power system. Thus, Z gg is chosen as the input feature. According to the above analysis, all the input features are steady-state features of network topology that do not need dynamic simulation to obtain. Input features and their brief descriptions are summarized in Table 1.

3.3 Assessment Network In order to evaluate the impact of the N-x-k contingencies on the DC system, a three-phase short-circuit fault is set at the commutation bus after k line outages. The output V 2nd p of the assessment network is the predictive value of V 2nd c. High order features can be obtained by the SDAE layer-by-layer. The last layer is the

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Table 1 Input features and descriptions An example of a column heading

Column A (t)

Column B (t)

Z dd

self-impedances of commutation bus d

Z dd

Z id

mutual-impedances between bus i and commutation bus d

Z id

Z ij.ed

electrical distance between bus i and j

Z ij.ed

Z gg

self-impedances of bus g that is connected with some dynamic reactive power supported devices

Z gg

regression layer, and the cost function is: 2    Fc = Vp2nd − Vc2nd 

(5)

Fine-tuning is a supervised refinement step to optimize model parameters and minimize the cost function by using the gradient descent algorithm with back-propagation.

4 Deep Learning-Based Assessment Network 4.1 The Severity Index of Line Outage Failure This paper quantitatively analyzes the impact of line outages on reactive power support capability of the receiving-side system from the perspective of the grid topology and then searches for high-severity N-x-k failures. Generator buses near the commutation bus can provide emergency reactive power support in the event of failures, which plays an important role in maintaining voltage stability. The electrical distance between generator bus g and the commutation bus c after line j outage can be expressed as: egc, j = (Z gg − Z gc ) + (Z cc − Z cg )

(6)

The electrical distance between generator bus g and load bus e near the commutation bus after line j outage can be expressed as: egl, j = (Z gg − Z gl ) + (Z ll − Z lg )

(7)

Assuming that ei gc, j and ei gl, j are the ordered sequences of egc, j and egl, j , which means e1 gc, j < e2 gc, j < · · · , e1 gl, j < e2 gl, j < · · · ,. Considering reactive power cannot be transmitted over long distances, the closer the electrical distance between the commutation bus or its nearby load buses and the generator

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bus, the more stable the transient voltage after failures. After N-x-(λ-1) failures are determined, a severity index reflecting the influence of line j outage on DC systems is established, which can be expressed as: E λj =

n g1 

eige, j +

n g1 n g2  

i=1

eigλ, j

(8)

i=1 i=1

where ng1 and ng2 are the numbers of generator buses which are closer to commutation bus c and load bus l, nl is the number of load buses near the commutation bus. The severity of an N-x-k contingency can be expressed as: Er =

k 

E λj

(9)

λ=1

Generator nodes closest to a bus can be considered as key generator nodes of this bus. During the search process, the electrical distances between generator nodes and commutation buses or load buses are dynamically changed, so key generator nodes of the same bus are dynamically adjusted. When calculating the search index, it is necessary to obtain the bus impedance matrix elements of some nodes after a line outage. In this paper, a combination of the branch-adding method and the Woodbury algorithm is used to improve the index calculation efficiency.

4.2 Searching Process Affected by natural disasters and man-made operations, there are often AC line outages. The tripped lines or lines with higher failure probabilities are set as the initial N-x failures. Then the high-severity N-x-k contingency screening and sorting is performed. The searching process is divided into two stages. Stage 1 is to narrow search space, which will reduce the number of contingencies that need to be calculated for the V 2nd c. Stage 2 is to evaluate the value of V 2nd c based on the assessment network. Then high-risk N-k contingencies are screened and ranked according to evaluation results. The searching process of TSHS is shown in Fig. 1. C (λ) is N-x-λ contingency set. All N-x-k contingencies in C (k) are ranked according to E r , then the contingency set Dk is obtained. The nth is the number of contingencies that need to be evaluated by the assessment network. nhs is the number of high-severity contingencies that need to be focused on. After ranking contingencies, top nhs contingencies are included in high-severity contingency set R. Then, the dispatch center will take appropriate preventive and control measures against these contingencies.

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Fig. 1 The searching process of TSHS

5 Case Studies The hybrid AC/DC power grid is employed to demonstrate the effectiveness of the proposed method. The 500 kV and above voltage structure of the Shandong grid is shown as Fig. 2. There are 3 DC systems fed into the Shandong power grid.

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Fig. 2 The transmission power grid of Shandong province in China

5.1 Validating the Deep Learning-based Assessment Network This section is to verify the effectiveness of the deep learning network for evaluating the impact severity of multi-line outage failures on DC systems. The model is built for DC ZY. The number of assessment network layers is 5, which includes an input layer, an output layer and 3 hidden layers. In this paper, 50,000 samples are obtained from PSS/E simulations, of which 25,000 are training samples and another 25,000 are randomly chosen as test samples. The error of predicted V 2nd c can be expressed as:     e = Vp2nd − Vc2nd ,

(10)

where V 2nd p is the prediction value. The average error of predicted V 2nd c can be expressed as:

eave =

  N  2nd 2nd  − V V i=1 p,i c,i  N

,

(11)

where V 2nd p, i and V 2nd c, i are the prediction value and actual value of failure i. Four kinds of features are chosen as network inputs: Features 1: Zid,ed , Zgg . Features 2: Zdd , Zid , Zgg . Features 3: Zdd , Zid , Zid,ed . Features 4: Zdd , Zid , Zid,ed , Zgg . Assuming emax and emin are the maximum error and minimum error of predicted V 2nd c in test samples. The p(e < 0.005) and p(e < 0.001) are propagations of prediction error less than 0.005 and 0.001, which are used to represent the accuracy of prediction. The simulation results based on these four kinds of features are shown in Table 2. It can be seen that the network with features 4 as inputs has smaller

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Table 2 Test results of four kinds of input features eave

emax

p(e < 0.005)

p(e < 0.001)

Features 1

6.21 × 10–4

0.0430

93.09%

90.12%

Features 2

5.10 × 10–4

0.0243

94.11%

91.90%

Features 3

3.36 × 10–4

0.0158

97.25%

93.37%

Features 4

3.28 × 10–4

0.0129

97.67%

93.99%

prediction errors. It indicates that using Features 4 as input features can get better convergence. Therefore, features 4 is suitable as input features.

5.2 N-1-3 Contingency Screening and Ranking After N-x failures are determined, the proposed method can quickly screen and rank contingencies with higher severity. Taking the simplified Shandong power grid as an example and setting line outages of 49–50(1), 50–54, 50–55, 52–53(1) as N-1 failures, simulation results of N-1–3 contingency screening and ranking are shown in Table 3 and Fig. 3. Figure 3 gives the distributions of prediction errors for 100 worst N-1-3 contingencies, which shows that more than 80 percent of serious contingencies prediction errors are below 10–3 p.u. max errors, average errors of prediction values and search time are listed in Table 2. The max error in these 400 worst contingencies is 0.00751 p.u. and the max average error is below 6 × 10–4 . Besides, only one contingency is missed in the 400 worst contingencies and the screening accuracy is 99.75%. It can be seen from Table 3 that the search time of each kind of contingencies is within 14.1 s. For large-scale power systems, parallel computing technology can be used to further improve computing efficiency. Table 3 N-1–3 Contingency Search Results for Different N-1 Failure line

N-1 failure line

eave

emax

49–50(1)

8.80 × 10–4

7.51 × 10–3

14.1

50–54

5.24 ×

4.28 ×

10–3

12.9

50–55

3.96 × 10–4

5.72 × 10–3

13.5

52–53(1)

5.29 × 10–4

5.15 × 10–3

13.6

10–4

Time(s)

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80

60 40 20 0

60 40 20 0

[1, 2) [2, 5) [0, 1) [5, 10) Intervals of predicted deviation / 10-3 p.u. (a)

100

100

80

80

Number of contingencies

Number of contingencies

Number of contingencies

100

Number of contingencies

100

60 40 20 0

[1, 2) [2, 5) [0, 1) [5, 10) Intervals of predicted deviation / 10-3 p.u. (c)

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[1, 2) [2, 5) [0, 1) [5, 10) Intervals of predicted deviation / 10-3 p.u. (b)

60 40 20 0

[1, 2) [2, 5) [0, 1) [5, 10) Intervals of predicted deviation / 10-3 p.u. (d)

Fig. 3 The distribution of prediction errors for 100 worst N-1-3 contingencies with different N-1 failures. (a) The N-1 failure is 49–50(1); (b) The N-1 failure is 50–54; (c) The N-1 failure is 50–55; (d) The N-1 failure is 52–53(1)

6 Conclusions In this paper, a high-severity N-x-k contingency ranking and screening method for hybrid AC/DC systems based on deep learning and TSHS is proposed. The method is extensively studied on a real-life AC/DC transmission system. Main conclusions are drawn as follows. The proposed deep learning-based assessment network can quickly evaluate the impact of multi-line outage failures on DC systems, which is suitable for a large number of contingency searches. A TSHS approach is proposed to screen and rank high-severity N-x-k contingencies in hybrid AC/DC power system. This approach can quickly screen and rank the serious contingencies after the network structure is changed, which can guide the formulation of preventive control actions and ensure the safety of AC/DC power systems. Acknowledgements The authors thank the financial support by the Science and Technology Project of State Grid Shandong Electric Power Corporation, China (520601220003).

References 1. Mirsaeidi S, Dong X (2019) An integrated control and protection scheme to inhibit blackouts caused by cascading fault in large-scale hybrid AC/DC power grids. IEEE Trans Power Electron 34(8):7278–7291

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2. Lu J, Yuan X, Zhang M, Hu J. A supplementary control for mitigation of successive commutation failures considering the influence of PLL dynamics in LCC-HVDC systems. CSEE J Power Energy Syst 3. Shao Y, Tang Y (2018) Fast evaluation of commutation failure risk in multi-infeed HVDC systems. IEEE Trans Power Syst 33(1):646–653. https://doi.org/10.1109/TPWRS.2017.270 0045 4. Shao S, Huang Z, Li Z (2018) Evaluation of dynamic influence of harmonics of AC system on HVDC commutation failure. In: Proceedings of International Conference on Power System Technology (POWERCON), Guangzhou, pp. 2549–2556 5. Zhang F, Xin H, Wu D, Wang Z, Gan D (2019) Assessing strength of multi-infeed LCC-HVDC systems using generalized short-circuit ratio. IEEE Trans Power Syst 34(1):467–480 6. Zhu Y, Li W, Liu Y (2019) Propagation model and blackout risk quantitation analysis for cascading failures in AC/DC hybrid power systems. In: Proceedings of IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, pp. 1–5 7. Sub R, Liu Y, Wang L (2019) An online generator start-up algorithm for transmission system self-healing based on MCTS and sparse autoencoder. IEEE Trans Power Syst 34(3):2061–2070. https://doi.org/10.1109/TPWRS.2018.2890006 8. Kaplunovich P, Turitsyn K (2016) Fast and reliable screening of N-2 contingencies. IEEE Trans Power Syst. 31(6):4243–4252 9. Che L, Liu X, Wen Y, Li Z (2019) A mixed integer programming model for evaluating the hidden probabilities of N-k line contingencies in smart grids. IEEE Trans Smart Grid 10(1):1036–1045 10. Eppostein MJ, Hliens PDH (2012) A “random chemistry” algorithm for identifying collections of multiple contingencies that initiate cascading failure. IEEE Trans Power Syst 27(3):1698– 1705. https://doi.org/10.1109/TPWRS.2012.2183624 11. Jia Y, Meng K, Xu Z (2015) N-k induced cascading contingency screening. IEEE Trans. Power Syst. 30(5):2824–2825 12. Ni Z, Paul S (2019) A multistage game in smart grid security: a reinforcement learning solution. IEEE Trans Neural Netw Learn Syst 30(9):2684–2695 13. AbdiKhorsand M, SahraeiArdakani M, AlAbdullah YM (2017) Corrective transmission switching for N-1-1 contingency analysis. IEEE Trans Power Syst 32(2):1606–1615 14. Han J, Zhang D, Wen S, Guo L, Liu T, Li X (2016) Two-stage learning to predict human eye fixations via SDAEs. IEEE Trans Cybern 46(2):487–498

Power Electronics Technology and Electrical Equipment Condition Monitoring

Fault Analysis and Feature Extraction of Rotary Rectifier of Aviation Three-Stage Generator Shukuan Zhang, Fachen Wang, Dongjie Sun, and Huacai Lu

Abstract With the rapid development of modern aviation industry, aero-generator is the key equipment in the main power system of aircraft. Rotary rectifier is one of the core components of synchronous generator. Because the rotary rectifier is on the rotor of the generator, the fault location and fault cause cannot be measured directly when the fault occurs. This paper analyzes the fault types of the rotary rectifier, builds the generator model and simulates the fault types of the rotary rectifier, and uses the Fast Fourier Transform (FFT) to decompose the simulation data. Simulation results show that the model fault diagnosis method has high diagnostic accuracy. Finally, the variation rule of the simulation results under different faults is collected, which provides a reference for the fault diagnosis of the rotary rectifier. Keywords aviation three-stage generator · rotary rectifier · fault diagnosis · feature extraction

1 Introduction As one of the core components of brushless AC synchronous generator, the rotary rectifier has been widely used in aviation power system, ship power system and power grid [1, 2]. The rotor of the rotary rectifier and the AC exciter rotates in the same axis, often at a high speed, and is very prone to break down due to high temperature, high centrifugal force, overvoltage and other factors. Therefore, it is of great practical significance to carry out in-depth research on fault diagnosis of rotary rectifier, make timely, accurate and rapid judgment on potential faults of rotary rectifier, and ensure the stable operation of generator. S. Zhang (B) · F. Wang · D. Sun College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China e-mail: [email protected] S. Zhang · H. Lu Key Laboratory of Electric Drive and Control of Anhui Province, Anhui Polytechnic University, Wuhu 241000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_6

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Rotary rectifier fault can be divided into two modes: open circuit fault and short circuit fault. When the open-circuit failure occurs, the armature current and field current will increase, affecting the operation of the synchronous alternator [3]. When short-circuit fault occurs, the short-circuit current in the faulty is large, which will seriously damage the generator if not handled in time. At present, the fault diagnosis technology of rotary rectifier mainly consists of three steps: data acquisition, fault feature extraction and fault classification, among which fault feature extraction is the key technology of fault diagnosis. In [4], based on the fault diagnosis method of model, a model method for field current state monitoring of rotary rectifier is proposed. The second harmonic amplitude of field current is obtained by Fourier transform to monitor the state of rotary rectifier. In [5], author conducted simulation research and analysis on brushless excitation module, and proposed a method of rotating rectifier fault monitoring for brushless excitation system based on model analysis. In [6], the detection coil is installed on the stator of the generator by using the fault diagnosis method of signal analysis. By collecting the induced electromotive force on the detection coil, Fourier analysis is conducted on the electromotive force. The amplitudes of the second harmonic component and the sixth harmonic component of the induced voltage are taken as characteristic parameters and compared with the normal parameters of the diode to determine whether the fault occurs. In [7, 8], author collects the output current signal of synchronous alternator under different fault modes, conducts harmonic analysis on the current signal to obtain the harmonic amplitude, and compares it with the harmonic amplitude under the normal operation of the rotary rectifier, so as to identify whether the fault occurs. In [9], a fault diagnosis method based on neural network was proposed for rotating rectifier faults. Fourier transform was used to process and analyze the collected output voltage signals of rotating rectifier, and various order harmonic amplitude-values were extracted as fault characteristic parameters, which were sent to BP neural network for rotating rectifier fault diagnosis. In [10, 11], author proposes a rotating rectifier fault diagnosis method based on support vector machine. By collecting exciter excitation current signals of rotating rectifier under different fault modes, feature extraction is carried out to obtain fault feature parameters, and then the obtained fault feature parameters are sent to support vector machine for fault identification. Thus, the status monitoring of the rotary rectifier is realized. In this paper, a mathematical model is established based on the three-stage generator to build synchronous alternator simulation model, and fault classification and fault feature extraction. Various faults are simulated by controlling the breaking time of breaker. The waveform recorded by oscilloscope is decomposed by Fourier in the FTT analyzer to digitize the harmonics. Finally, by analyzing the harmonic characteristics, the rules of different faults are collected, which provides a reference for the fault diagnosis of rotary rectifier.

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Permanent magnet Main Field auxiliary excitation Rotating rectifier

Main motor

Stator

Rotor

N

S

Fig. 1 Three-stage generator structure schematic diagram

2 Mathematical Model and Simulation Model of Three Stage Synchronous Generator 2.1 The Working Principle of Three-Stage Synchronous Generator The three-stage synchronous generator consists of three independent generators: permanent magnet secondary exciter, main exciter and main generator, as shown in Fig. 1, which are coaxial connected. Three-phase alternating current is generated in the armature winding. Meanwhile, the armature winding of the rotor side of the main exciter is driven to rotate and also generates three-phase alternating current, which is rectified by the rotary rectifier and turned into direct current. The excitation winding of the main generator provides excitation, and three-phase alternating current is generated in the armature winding of the main generator [12]. The excitation windings of the main generator of the three-stage synchronous generator cannot be directly adjusted on the rotor. The system adjusts the excitation current of the main generator indirectly by adjusting the excitation current of the main exciter, so as to adjust the output voltage of the main generator.

3 Mathematical Model of Synchronous Generator The physical model is shown in Fig. 2(a). Park transformation is used to replace the stationary three-phase armature winding with the direct and alternating axis armature winding which rotates synchronically with the rotor. Where axis A, B and C represents the three-phase stationary coordinate system, and axis d and q represents the twophase rotating coordinate system. In order to simplify the analysis, the physical model shown in Fig. 2(a) was simplified into the circuit diagram shown in Fig. 2(b).

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q

iA

if

uf rf

uA

rA

Lf

ZA

LA d O

θ

rd

id Ld

iA uA A

rq

iq Lq

C

(a) Physical model

rB

rC

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LC iB

ZC iC

uC

ZB uB

(b) Circuit diagram

Fig. 2 Synchronous alternator model

According to the circuit diagram of each loop of synchronous AC generator shown in Fig. 2, the voltage equation of stator windings can be calculated by combining Faraday’s law of electromagnetic induction and KVL. The voltage equation of Aphase windings can be given as follows: e A − ri A −u A = 0

(1)

According to Faraday’s law of electromagnetic induction, the induced electromotive force is generated with the change of magnetic flux. By simplifying Eq. (1), we can get: uA =

dψ A − ri A dt

(2)

Similarly, a similar voltage equation can be obtained by conducting the same operation on the two-phase stator windings, excitation windings and damping windings of B and C. d/dt is denoted as P, and the following equation can be obtained by sorting out the equation. ⎤ ⎡ Rs uA ⎢ u ⎥ ⎢ 0 B ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ uC ⎥ ⎢ 0 ⎢ ⎥=⎢ ⎢u f = 0⎥ ⎢ 0 ⎢ ⎥ ⎢ ⎣ ud = 0 ⎦ ⎣ 0 uq = 0 0 ⎡

0 Rs 0 0 0 0

0 0 Rs 0 0 0

0 0 0 Rf 0 0

0 0 0 0 Rd 0

⎡ ⎤⎡ ⎤ ⎤ ψA −i A 0 ⎢ ⎢ψ ⎥ ⎥ 0 ⎥ ⎢ B⎥ ⎥⎢ −i B ⎥ ⎢ ⎥⎢ ⎥ ⎥ 0 ⎥⎢ −i C ⎥ ⎢ ψC ⎥ ⎥⎢ ⎥ + P⎢ ⎥ ⎢ψf ⎥ 0 ⎥⎢ i f ⎥ ⎢ ⎥⎢ ⎥ ⎥ ⎣ ψd ⎦ 0 ⎦⎣ i d ⎦ iq ψq Rq

(3)

In the above equation, uA , uB and uC are the three-phase output terminal voltage of the stator winding. iA , iB and iC are three phase currents in stator windings respectively. Rs is the phase resistance of stator windings; Rf is the resistance of rotor excitation winding; Rd and Rq are the equivalent damping winding resistances of the direct axis and the alternating axis, respectively. ψ A , ψ B and ψ C are the total flux of

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each phase of the stator winding; ψ f is the total flux of the rotor excitation winding; ψ d and ψ q are the total flux of the equivalent damping winding of the straight and alternating axes respectively. Park transform is used to deal with the stator voltage equation, and the following equation can be obtained: −1 Tdq0 u ABC = Tdq0 P(Tdq0 ψdq0 ) − r Tdq0 i ABC

(4)

By further expanding Eq. (4), the following equation can be obtained: ⎧ ⎪ ⎨ u d = Pψd − ωψq − ri d u q = Pψq − ωψd − ri q ⎪ ⎩ u 0 = Pψ0 − ri 0

(5)

In Eq. (5), ω = Pθ is the angular frequency of the generator rotor, and the change of ψ d and ψ q will produce the transformer electromotive force Pψ d and Pψ q ; likewise, the generator electromotive force ωψ d and ωψ q is produced according to the high speed rotation of the ψ d and ψ q .

4 Simulation Model of Three - Stage Synchronous Generator In this section, the simulation model of the three-stage synchronous generator is established and its healthy working state is simulated and analyzed. The simulation model is shown in Fig. 3. The apparent power of the generator is selected as 111.9 kVA, the voltage is set as 762 V, and the frequency of the generator is set as 50 Hz. The stator side resistance is 0.26 Ω and the excitation resistance is 0.13 Ω. The load active power is set to 10 kW, the frequency is set to 60 Hz, and the phase-to-phase voltage is set to 762 Vrms. Under normal operating conditions, all switches are set to remain closed, and the three-phase voltage of the excitation loop is set to 10 V and the voltage frequency to 50 Hz. The waveform of the measured three-phase output current is shown in Fig. 4. The waveform of excitation current and excitation voltage is shown in Fig. 5. The excitation voltage output is DC rectifier voltage vf with certain harmonics. The excitation current if gradually tends to be stable. It can be seen that when the rectifier works normally, the three-phase power output DC excitation voltage containing certain harmonics through the bridge rectifier, and the voltage signal is relatively stable, so it can generate stable excitation current, thus generating stable magnetic potential at the rotor side. Under the action of the prime mover, the rotor is in the power generation state. The output three-phase AC voltage fluctuates in addition to the starting state, and the three-phase output voltage does not appear distorted after stability.

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Fig. 3 Simulation model of synchronous generator Fig. 4 Three-phase current output of motor simulation operation

(a) Field current waveform

(b) Field voltage waveform

Fig. 5 The field voltage and current of the motor simulation operation

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Fig. 6 Schematic diagram of rectifier circuit

if Ea

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5 Fault State Simulation of Rotary Rectifier 5.1 Fault Mode and Simulation Model of Rotary Rectifier When short-circuit occurs, the non-short-circuit diode of the exciter will flow through a large short-circuit current, and the non-short-circuit rectifier diode will burn out quickly. Therefore, it is necessary to research and analyze the characteristics of the rectifier diode fault, so as to provide a theoretical basis for the quick fault diagnosis of the rectifier. When open circuit fault occurs, the rectification coefficient of the rectifier will decrease. In order to maintain the constant output voltage of the generator, the armature current and excitation current will increase. At this time, as long as the design margin of the excitation system is large enough and the current resistance is not lower than the maximum current at this time, the generator can still run. Figure 6 shows the principle circuit diagram of the rotary rectifier for a three-stage generator. When the diode is in open circuit fault, the output current of the rectifier will be distorted if . According to schematic Fig. 6. when a + fails, the diode conduction sequence becomes: b + , c- → b + , a- → c + , b-, if waveform is distorted in the first 1/ 3 cycle, so the A-fault rectification current waveform is distorted in the middle 1/ 3 cycle than a + , c- → a + , c- → b-, if waveform. At this time, the phase of the A-fault rectification current waveform is delayed by 2π /3 than that of the a + fault rectification current waveform, and the waveform shape is similar. Other types of open-circuit fault element current waveform can be obtained by the same analysis method, as shown in Table 1. The rotary rectifier in synchronous generator adopts the structure of three-phase bridge rectifier circuit, which outputs DC excitation voltage. By controlling the on or off state of ideal switch, the fault of different types of rectifier diode is simulated and analyzed. The simulation model is shown in Fig. 7.

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Table 1 Failure modes of rotary rectifier Failure type

Typical failure positions

Other possible failure positions

Case 1

Healthy condition

–

–

Case 2

Single diode open circuit fault

a+

a−; b + ; b−; c + ; c−

Case 3

Double diode open circuit fault in the same phase

a+ and a-

b + and b-; c + and c−

Case 4

Double diode open circuit fault of upper bridge arm

c+ and b+

a+ and c+ ; b+ and c+

Case 5

Double-diode open circuit of the lower bridge arm is faulty

a− and b−

a− and c−; b− and c−

Case 6

Double-diode open circuit of the adjacent staggered bridge arm is faulty

a+ and b−

b+ and c−; c+ and a−

Case 7

Non-adjacent staggered bridge arm double-diode open circuit fault

a+ and c−

b+ and a−; c+ and b−

Fig. 7 Simulation model of rotary rectifier fault

6 Simulation Analysis of Rotary Rectifier Fault State Take the open-circuit fault of a + diode as an example, when a + diode is opencircuit, the three-phase current ia , ib and ic will affect it, so the three-phase armature current of ABC after the failure can be regarded as the normal operation armature current superimposed Δia , Δib and Δic , and the following can be obtained. ⎧ ' ⎪ ⎨ i a = i a + Δi a i b' = i b + Δi b ⎪ ⎩ ' i c = i c + Δi c

(6)

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Then ⎧ π 5π ⎪ ⎨ −Id θ ∈ ( , ) 6 6 Δi a = ⎪ ⎩ 0 θ ∈ (0, π ) ∪ ( 5π , 2π ) 6 6 ⎧ π 5π ⎪ ⎨ Id θ ∈ ( , ) 2 6 Δi b = ⎪ ⎩ 0 θ ∈ (0, π ) ∪ ( 5π , 2π ) 2 6 ⎧ π π ⎪ ⎨ Id θ ∈ ( , ) 6 2 Δi c = π π ⎪ ⎩ 0 θ ∈ (0, ) ∪ ( , 2π ) 6 2

(7)

θ is the diode conduction Angle. Fourier decomposition of Δia gives: Δi a = a0 +

∑∞

1 = − Id + 3

(an cos nωt + bn sin nωt) / an a2n + bn2 sin(nωt + arctan ) n=1 bn

n=1 ∑∞

(8)

Δia can be calculated by 1 Δi a = − Id + 0.552Id sin ωt + 0.276Id sin(2ωt + 90◦ ) + 0.138 sin(4ωt + 90◦ ) 3 (9) It can be seen that it contains DC component I d and various harmonic components. When the rotary rectifier single-tube open circuit stator current I ff harmonic characteristics analysis is consistent with the normal operation. The variation of three-phase armature current is put into the magnetic potential equation. The results show that the exciter stator current contains two and three harmonics in addition to six harmonics. The simulation time was set as 4 s, the a + diode was disconnected at 2 s, the unidirectional open-circuit fault was simulated. Figures 8, 9, 10, 11, 12, and 13 show the waveforms of excitation voltage and current under different fault conditions. Following the analysis method of open-circuit fault of one diode, Fourier decomposition is also performed on variable Δia , Δib , Δic for other open-circuit faults. ⎧ ⎪ ⎨ Δi a = 1.1Id sin ωt Δi b = 0.637Id sin(ωt − 30◦ ) + 0.425Id sin(3ωt + 90◦ ) (10) ⎪ ⎩ ◦ ◦ Δi c = 0.637Id sin(ωt + 30 ) + 0.425Id sin(3ωt − 90 )

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(a) Field voltage waveform

(b) Field current waveform

Fig. 8 Field voltage and current waveforms with a + single tube open circuit

(a) Field voltage waveform

(b) Field current waveform

Fig. 9 Field voltage and current when two diodes are open (a + a-) in the same direction

(a) Field voltage waveform

(b) Field current waveform

Fig. 10 Field voltage and current of the exciter when two diodes in the upper half bridge are open circuit (a+ b+ )

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(a) Field voltage waveform

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(b) Field current waveform

Fig. 11 Field voltage and current of exciter with two diode open circuits (a + b-) with different phases up and down

(a) Field voltage waveform

(b) Field current waveform

Fig. 12 Field voltage and current when two diodes in the lower half bridge arm are open circuit (a- b-)

(a) Field voltage waveform

(b) Field current waveform

Fig. 13 Field voltage and current of exciter when two diode open circuit faults (a + c-) occur in non-adjacent staggered bridge arms

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⎧ 2 ⎪ Δi a = − Id − 0.552Id sin(ωt − 30◦ ) + 0.276Id sin(2ωt + 90◦ ) ⎪ ⎪ ⎪ 3 ⎪ ⎪ ◦ ⎪ ⎪ +0.138I d sin(4ωt + 90 ) ⎪ ⎪ ⎪ ⎪ ⎪ 2 ⎨ Δi b = − Id + 0.552Id sin(ωt + 60◦ ) + 0.276Id sin(2ωt + 30◦ ) 3 ⎪ ◦ ⎪ +0.138I ⎪ d sin(4ωt − 90 ) ⎪ ⎪ ⎪ ⎪ 4 ⎪ ⎪ ⎪ Δi c = Id + 0.552Id sin(ωt − 60◦ ) + 0.276Id sin(2ωt − 30◦ ) ⎪ ⎪ 3 ⎪ ⎩ +0.138Id sin(4ωt + 30◦ ) ⎧ ◦ ◦ ⎪ ⎨ Δi a = −Id + 0.639Id sin(ωt + 30 ) + 0.212Id sin(3ωt − 90 ) Δi b = Id + 0.639Id sin(ωt − 30◦ ) + 0.212Id sin(3ωt + 90◦ ) ⎪ ⎩ Δi c = 0 ⎧ 2 ⎪ Δi a = − Id − 0.552Id sin(ωt − 30◦ ) + 0.276Id sin(2ωt + 90◦ ) ⎪ ⎪ ⎪ 3 ⎪ ⎪ ⎪ ⎪ sin(4ωt + 90◦ ) +0.138I d ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ Δi = − 2 I + 0.552I sin(ωt + 60◦ ) + 0.276I sin(2ωt + 30◦ ) b d d d 3 ⎪ ◦ ⎪ +0.138Id sin(4ωt − 90 ) ⎪ ⎪ ⎪ ⎪ ⎪ 4 ⎪ ⎪ ⎪ Δi c = Id + 0.552Id sin(ωt − 60◦ ) + 0.276Id sin(2ωt − 30◦ ) ⎪ ⎪ 3 ⎪ ⎩ +0.138Id sin(4ωt + 30◦ ) ⎧ ◦ ◦ ⎪ ⎨ Δi a = −Id + 0.639Id sin(ωt + 30 ) + 0.212Id sin(3ωt − 90 ) Δi b = Id + 0.639Id sin(ωt − 30◦ ) + 0.212Id sin(3ωt + 90◦ ) ⎪ ⎩ Δi c = 0

(11)

(12)

(13)

(14)

Based on the mathematical model of AC generator, the ideal model of synchronous AC generator and the basic model of rotary rectifier are constructed in this section. The health and fault state of synchronous AC generator are simulated and analyzed, and the waveforms of excitation current and excitation voltage under each fault state of the rectifier are obtained, which provides the basis for fault feature extraction based on harmonics.

7 Research on Fault Feature Extraction of Rotary Rectifier In this section harmonic changes of the excitation current and DC bus voltage caused by rotary rectifier faults are investigated. Fourier transform is used to convert the timedomain signal to the frequency domain, so as to extract the harmonic amplitude, phase and other harmonic characteristics of the excitation current.

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The sampling start time is set as 3 s, the sampling waveform is set as 2, the frequency parameter is set as 60 Hz, and the maximum sampling frequency is set as 600 Hz. The FFT fast Fourier algorithm is used to simulate the excitation current under various fault types for harmonic analysis. As shown in Fig. 14, in healthy operation, in addition to the DC component, the stator excitation current also contains the sixth harmonics and a certain amount of the second and fourth harmonics, which is the same as the theoretical analysis results. When a single diode is open circuit, the excitation current harmonics in the fault state are mainly the second harmonics, and the proportion of the sixth harmonics decreases, followed by the first harmonics and the third harmonics, as shown in Fig. 15(a). The results are similar to the predicted values. When the upper and lower double diodes are open circuit, the field current harmonics in the fault state are mainly second harmonics, among which the first and sixth harmonics are relatively large, as shown in Fig. 15(b). Because the current waveform is similar to the normal condition, it is difficult to judge, so the fault type can be determined by harmonic analysis. When adjacent double diode is open circuit, the harmonic component of excitation current in the fault state is mainly second harmonic, including a small amount of first harmonic and third harmonic, as shown in Fig. 16(a). Compared with the single diode open circuit, the 1st and 3rd harmonic content is different, so it can be judged Fig. 14 Harmonic analysis of fault-free excitation current

(a) Single diode open field current

(b) Open field current of upper and lower diode

Fig. 15 Harmonic analysis of single-bridge arm open-circuit excitation current

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according to the ratio of the 1st and 3rd harmonic content. In the case of adjacent staggered double-diode open-circuit, the harmonic component of excitation current in the fault state is mainly the second harmonic, followed by the first harmonic and a small amount of the third harmonic, as shown in Fig. 16(b). Compared with the single diode open circuit, the content of the first and third harmonics is larger, so it can be judged according to the content ratio of the first and third harmonics. When non-adjacent double diode is open circuit, the harmonic component of excitation current is shown in Fig. 17. When non-adjacent double diode is open circuit, the harmonic component is mainly second harmonic, followed by first harmonic and a small amount of third harmonic. In the non-adjacent staggered double-diode opencircuit state, the harmonics are mainly the second harmonics, and the first and third harmonics are less. In this section, each fault mode is simulated and analyzed based on the rectifier simulation model. By collecting the excitation current signals of synchronous AC generator under different faults and performing fast Fourier decomposition on them, the distribution data of various harmonic components are obtained as the fault characteristic parameters, and the horizontal comparison of various fault types is made.

(a) Open field current of adjacent double diodes (b) Staggered double diode open field current Fig. 16 Harmonic analysis of open field current of two adjacent bridge arms

(a) Double diode open field current

(b) Staggered double diode open field current

Fig. 17 Harmonic analysis of open field current of two non-adjacent bridge arms

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By referring to the above mathematical model analysis of excitation current signals, the distribution characteristics of various harmonics are verified. It can be used as the basis to judge the fault of the rotary rectifier of three-stage generator.

8 Conclusion This paper gathers the open-circuit fault diagnosis of synchronous AC generator rotary rectifier diode, uses fractional Fourier transform to process the three-phase output voltage signal of synchronous AC generator to extract fault characteristic parameters with high accuracy, and gathers the variation rule of simulation results under different faults. When there is no fault, the second harmonic component of the excitation current is less. After the fault occurs, the harmonic component of the excitation current is mainly the second harmonic, followed by the first and third harmonic. The specific fault type can be obtained according to the content of the first and third harmonic, which provides a reference for the fault diagnosis of the rotary rectifier. Acknowledgements This work was supported in part by the Key Laboratory of Electric Drive and Control of Anhui Province, Anhui Polytechnic University under Grant DQKJ202202 and in part by “the Fundamental Research Funds for the Central Universities”, under Grant 3132023114.

References 1. Xue H, Wei J, Zhou B (2018) Comparative investigation on sensorless control of three-stage synchronous motor based on high-frequency injection method at low speed. Trans China Electrotechn Soc 33(12):2703–2712 2. Zhang C, Xia L (2007) Reviews on fault diagnosis for AC brushless generators. J Hunan Univ Technol 5:103–106 3. Rahnama M, Vahedi A, Alikhani AM (2019) Numerical modeling of brushless synchronous generator for rectifier fault detection In: International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering, IEEE, Nancy, France 4. Sottile J, Trutt FC, Leedy AW (2006) Condition monitoring of brushless three-phase synchronous generators with stator winding or rotor circuit deterioration. IEEE Trans. Indus. Appl. 3(5):1209–1215 5. Zouaghi T, Poloujadoff S (1998) Modeling of polyphase brushless exciter behavior for failing diode operation. IEEE Trans Energy Convers 13(3):214–220 6. Wei Z, Zhang SZ, Zhang T (2018) Rotating rectifier fault detection method of wound-rotor synchronous starter-generator with three-phase exciter. J Eng 13:524–528 7. Wei Z, Liu SJ, Pang T (2019) Fault diagnosis of rotating rectifier based on waveform distortion and polarity of current. IEEE Trans Indus Appl 55(03):2356–2367 8. Cai BC, Wu SYJ, Zhao T (2018) The detection of open-circuit fault of rotary diode in brushless exciter using stator current harmonic method. Large Elect Mach Hydr Turb 04:61–65 9. Gray D, Zhang FZ, Xu T (2009) A neural network based approach for the detection of faults in the brushless excitation of a synchronous motor. In: International Conference on Electro/ Information Technology, IEEE, Windsor, Canada

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10. Sun K, Li FWH, Wang T (2018) Research on PCB fault diagnosis of analog circuit based on SVM expert system. Foreign Elect Measur Technol 37(09):22–26 11. Cui J, Tang FJX, Zhang SZR (2018) Fast fault classification method research of aircraft generator rotating rectifier based on extreme learning machine. Proc CSEE 38(08):2458–2466 12. Wang HY, Lan S (2022) Research on mathematical model of three-stage aviation brushless synchronous power generation system. MICROMOTORS. 55(10):60–64

Research on Failure Warning of Substation Equipment Based on Gaussian Hybrid Model Jin lu Li and Yu fang Wang

Abstract In view of the complex operation environment of substation and the uncertainty of the operation status of substation, this paper aims to study the fault warning problem of substation equipment. Based on the Gaussian hybrid model, the early warning application model is established, the active power and reactive power loss is selected as the operation characteristics of the transformer operation detection, and the distance between the Gaussian mixed model of the transformer operation characteristics is calculated by using the divergence, and the distance is transformed into probability information to represent the failure probability of the transformer. The failure probability is calculated by substituting the instance. By comparing the result data of the current operation with the historical data of the transformer operation, we can get the judgment of the current operation state, and view the historical record of the detection and the historical operation data of the state. Keywords Gaussian hybrid model · Substation · Status detection · Fault warning

1 Introduction As the key link and core hub of the whole system, substation is the most basic, critical and huge component of the power grid equipment [1]. The country proposes a new type of power system with the characteristics of safe and controllable, clean and lowcarbon, open and interactive, flexible and efficient, intelligent and friendly, which puts forward higher requirements for substations [2]. Breakthroughs in technologies such as fault warning and predictive maintenance and platform construction have become key issues, requiring a reduction in the proportion of highly repetitive and low-technical work in inspection work, and the realization of effective and precise identification of fault potential problems of specific equipment by operation J. Li (B) · Y. Wang Shanghai Dianji University, No. 300 Shuihua Road, Nanhui New Town, Pudong New Area, Shanghai, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_7

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and inspection personnel, enabling a more detailed assessment of equipment status, improving the safety and reliability of inspection work by field staff, and reducing the probability of production accidents. For the problem of characterizing the operating state of substations, researchers have used probability distributions to characterize the operating state uncertainty [3]. For example, Gaussian distribution [4], beta distribution [5], and Cauchy distribution [6] are used. But in fact, solar irradiance is a non-Gaussian variable, and using Gaussian distribution to characterize it will cause large errors. At the same time, Gaussian, beta and Coasean distributions are not applicable to the probabilistic modeling of substation operating conditions at different time scales. In addition, the operating states of different substations are often extremely correlated, and their correlations need to be considered when modeling their probabilistic distributions in order to capture more realistic uncertainty information. Therefore, a Gaussian mixture model is used in this paper. Firstly, the characteristics and advantages of Gaussian mixing model are introduced and described in terms of definition, nature and difference of probability distribution. Then, based on the Gaussian mixture model to establish the application model of substation equipment former early warning, considering the active and reactive power loss as the operational characteristics of transformer operation detection, using the sum of three-phase active injection and reactive injection of each generator to realize the calculation of operational characteristics, using the scatter to calculate the distance between the Gaussian mixture model of transformer operational characteristics, and converting the distance into probabilistic information for characterizing the transformer fault probability. Model operations are performed by example to derive fault probability results and to validate the scatter-based probability distribution difference judgments.

2 Principle of Gaussian Mixture Model Algorithm Gaussian mixture models are well known for their high accuracy in carving random variables. In recent years, some scholars have applied the GMM technique to power system uncertainty analysis and verified its superiority in modeling the stochastic power output of renewable energy and load. It can not only portray non-Gaussian variables, but also reflect the correlation between multiple random variables, and can compute the distribution of functions of random variables analytically [7–9]. GMM has many advantages, and its use in power system analysis is gradually growing. Based on these advantages, this paper investigates the use of GMM to implement uncertainty characterization in equipment testing.

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2.1 Definition of Gaussian Mixture Model Gaussian mixture model is used to describe the joint probability distribution of a random vector X, which is defined as a convex combination of multiple Gaussian distribution functions [10]. Denote the symbol Ω = {ωm,μm,σm;m = 1,2,…M} as the set of adjustable parameters of the GMM, and the mathematical expressions of the GMM are shown in Eqs. (1)–(2). f x (x) =

∑M m=1

∑M m=1

wm Nm (x; μm , σm )

wm = 1, wm > 0

(1) (2)

e− 2 (x − μm )T σm(−1) (x − μm ) 1

Nm (x; μm , σm ) =

W

1

(2π) 2 det (σm ) 2

(3)

where fx (X) denotes the joint probability density function of X; wm is the weight coefficient; w denotes the dimensionality of X; Nm denotes the multidimensional normal distribution, which is called the m th Gaussian component of the GMM; det denotes the matrix determinant; M denotes the total number of Gaussian components; μm and σm denote the mean vector and covariance matrix of the m th Gaussian component, respectively [2]. A schematic diagram of a GMM containing four Gaussian components is given in Fig. 1. In this figure, the probability density function of the GMM is a convex combination of the probability density functions of the four Gaussian distributions. By adjusting the parameter set Ω, the GMM can portray the probability density function of any random variable. In Fig. 1, the random variables take the value 0.2 as the baseline, and the probability density values are GMM, 1st Gaussian component, 2nd Gaussian component, 3rd

Fig. 1 Schematic diagram of GMM

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Gaussian component, 4th Gaussian component, W = 1,and their weight coefficients are 0.4, 0.1, 0.3, 0.2 in order. Determining the parameter set Ω of the GMM is a typical parameter estimation problem. Based on the historical data of X, the parameter set of the GMM can be obtained using a great likelihood estimation technique. Typical algorithms include the expectation maximization algorithm [11], EM.

2.2 Properties of Gaussian Mixture Model and Its Proof Mean and Second Order Moments If the joint probability density function of the random vector X is characterized by the GMM shown in Eqs. (1)–(3), then the mean value of X is shown in Eq. (4). Further, if the dimension of X is 1. then the second order moments of X can be calculated using Eq. (5). E[X ] =

∑M m=1

w m μm

(4)

Equations (4)–(5) can be proved according to the definition of the mean and second-order moments, which are omitted here. M O N (X ) =

∑M m=1

  wm σm2 + μ2m

(5)

Superposition Principle If the joint probability density function of the random vector X is characterized by the GMM shown in Eqs. (1)–(3) and the random variable Y is an arbitrary function Q(X) of X, the expression is shown in Eq. (6). Y = Q(X )

(6)

Then the cumulative distribution function of Y FY (y) and the probability density function f Y (y) can be calculated by using the following Eqs. (7)–(8), respectively. FY (y) = FY (y) =

∑M m=1

∑M m=1

wm FYm (y)

(7)

wm f Ym (y)

(8)

Nm (x)d x1 ...d x W

(9)

Among them.  FYm (y)

=

 ...

O(X )≤y

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f Ym (y) =

∂K F m (y) ∂ y1 . . . ∂ y K Y

99

(10)

Equation (9) shows that F Y m (y) is when X obeys the Gaussian distribution Nm (x; μm , σm ) when the function of X Q(X) of the cumulative distribution function; and Eq. (10) shows that f Y m (y) is exactly the function of the cumulative distribution when X obeys the Gaussian distribution Nm (x; μm , σm ) when the function of X Q(X) of the probability density function. The core idea of the GMM superpositionality principle is to combine the problem of computing a function of the GMM, i.e., computing FY (y) with f Y (y), equivalently into the problem of computing a series of functions of Gaussian distributions, i.e., computing a series of FYm (y) with f Ym (y), thus greatly reducing the difficulty of the original problem. In addition, in the above derivation, there is no restriction on the dimensionality of Y. Therefore, regardless of whether Y is a scalar or a vector, the superposition principle holds. Linear Invariance and Treatment of Degenerate Scenarios If the random variables X is characterized by the GMM shown in Eqs. (1)–(3), and the random variable Y is a random variable satisfying Y = B X + D the X the linear transformation, then the Y the distribution of is also a GMM, and each component ∑ of this GMM is a GMM with mean Bμm + D and the covariance matrix is B m B T with a Gaussian distribution with weight coefficients ωm . Specifically, the probability density function of Y is given by Eqs. (11)–(12). f Y (y) =

m=1

wm Nm (y)

e 2 (x − Bμm − D)T (W

(11)

∑

W T )(−1) (x − Bμm − D) ∑ 1 (2π ) det(B m B T ) 2

1

Nm (y) =

∑M

m

W 2

(12)

Among them, the X is the W dimensional random variables. Y is the K dimensional random variables. D and B denote the constant coefficient vectors and matrices of appropriate dimensionality, (12) holds on the ∑ T respectively. It should be noted that Eq.∑ T (B B ) / = 0 that the matrix is required to be B premise that det m B full ∑ T m ∑rank. T always full-rank: for example, when K > W B In fact, the B m B is not mB  ∑ T is not full rank, when det B m B = 0 The denominator in Eq. (12) is zero, and this equation no longer holds, which is called a “degenerate scenario”. ∑ In this case, the following correction to Eq. (12) is needed: assume that the matrix B m BT has r non-zero roots and K − r zero roots (r < K). Then the probability density function of the random variable is given by Eqs. (13)–(15) at this time. K e 2 y˜ R+ y˜ K 1 λi )(− 2 ) δ( y˜ T Ui ) r ( i=r +1 i=r +1 (2π ) 2 1

Nm (y) =

T

(13)

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y˜ = y − Bμm − D R+ =

∑r i=1

(14)

λi(−1) Ui UiT

(15)

∑ T where λ1 , . . . , λr is the matrix ∑ B T m B the r the non-zero roots of the+matrix. U1∑ , . . . , UK is the matrix A m A the K eigenvectors of the matrix. R is the B m B T the Moore–Penrose pseudo-inverse of the matrix. δ(·) is the Dirac function defined by the following equation, as shown in Eq. (16).

 2 2 (− 21 ) (− 2σt 2 ) δ(t) = lim (2ψ ) e

(16)

σ→0

Edge Probability Invariance The method for solving the edge probability density function of the GMM is as follows. Let the random variable X = [X 1 , X 2 , . . . , X W ]T be characterized by the GMM shown in Eqs. (1)–(3), then by Eqs. (17)–(20). f X (X 1 , X 2 , ..., X W ) =

∑M m=1

wm Nm (x1 , x2 , ..., x W ; μm , σm ) e(− 2 )(X −μm) 1

Nm (x1 , x2 , ...x W ; μm , σm ) =

T

σμm (−1) (X −μm)

M 2

1

(2π) det(σm ) 2

x1 x2 μm = μm , μm , ..., μmx M ⎛

σmx1 x1 ⎜ σx2 x1 m σm = ⎜ ⎝ ... σmx M x1

σmx1 x2 σmx2 x2 ... σmx M x2

(17)

⎞ σmx1 x W σmx2 x W ⎟ ⎟ ... ⎠ σmx M x W

(18) (19)

(20)

Then X the edge distribution of X 1 , X 2 , . . . , X W all satisfy the GMM, which can be written as in Eq. (21). f X i (xi ) =

∑M

Among them i = 1, 2, 3 . . . , W.

m=1

wm Nm (X i ; μm , σmx1 x2 )

(21)

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2.3 Dispersion-Based Probability Distribution Variance Judgments Break Kullback–Leibler Dispersion Kullback–Leibler divergence (KL scatter) is also known as relative entropy. For the same random variable x the two probability distributions of P(x) and Q(x), the KL scatter can be used to measure the difference between these two probability distributions and is calculated as in Eq. (22) [12]. D K L (P||Q) =

∑ x∈χ

P(x) log(

P(x) ) Q(x)

(22)

where X is the random variable x of all possible values. Since the logarithmic function is convex, the value of KL scatter is non-negative. Sometimes the KL scatter is referred to as the KL distance, but it does not satisfy the properties of distance: first, the KL scatter is not symmetric, i.e., as shown in Eq. (23). D K L (P||Q) /= D K L (Q||P)

(23)

Second, the KL dispersion does not satisfy the triangular inequality, i.e., for random variables x the probability distribution of H (x), Eq. (24) holds. D K L (P||Q) > D K L (P||H ) + D K L (H ||Q)

(24)

Jensen-Shannon Dispersion Since the value of KL scatter varies widely, in order to visualize the difference between two probability distributions, more scholars have further calculated the Jensen-Shannon scatter based on KL scatter [13] (Jensen-Shannon divergence, JS scatter). JS scatter not only solves the problem of asymmetric KL scatter, but also has a value range between 0 and 1. The JS scatter takes 0 to indicate that the two probability distributions are identical, and takes 1 to indicate that the two probability distributions are very different. the JS scatter is calculated by Eqs. (25)–(26). J S D(P||Q) = Where,

1 1 D(P||R) + D(Q||R) 2 2

(25)

1 (P + Q) 2

(26)

R=

Figure 2 shows the JS scatter of the two probability distributions compared with each other in various cases. It can be seen that when the overlap between the two probability distributions is small, the JS takes on a large value of 0.7. As the overlap between the two probability distributions gradually increases, the JS takes on a smaller value until the two probability distributions are almost identical, when the JS takes on a value close to 0.

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Fig. 2 JS scatter between two different probability distributions

3 Modeling Ideas of Gaussian Mixture Model in Substation Equipment Fault Warning Application 3.1 The Basic Idea of Gaussian Mixture Model Fault Detection Application For a fault-free transformer, the operating characteristics do not change basically from year to year. If there is a large difference in the operating characteristics of the transformer between two adjacent years, it can be judged that the transformer may fail during the two years. Therefore, the probability of transformer failure can be quantified by measuring the magnitude of the difference between the operating characteristics of two adjacent years.

3.2 Substation Fault Warning Application Modeling Process Thus, the modeling idea of the application of Gaussian mixture model in substation equipment detection and early warning research should be divided into the following steps, as shown in Fig. 3: in the first step, appropriate transformer operation characteristics are selected; in the second step, the operation characteristics are calculated based on the actual operation data of the transformer; in the third step, the Gaussian mixture model parameter set of transformer operation characteristics for each year is obtained using the EM algorithm [14] or MAP algorithm [15]; in the

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Fig. 3 Flowchart for modeling the substation fault warning application

fourth step. Calculate the distance between the Gaussian mixture models of transformer operating characteristics for two years using KL scatter or JS scatter, and convert this distance into probabilistic information for characterizing the probability of transformer failure.

3.3 Operating Characteristics of Transformer Operation Monitoring Since the loss of active and reactive power increases when a transformer fails, the active and reactive power losses of the transformer are selected as the operational characteristics for transformer operation detection in this project[16], and the calculation of the operational characteristics is achieved using the sum of the three-phase active injection and the sum of the reactive injection of each generator. Next, the Gaussian mixture model parameter sets of the adjacent two-year transformer active and reactive losses are obtained separately using the EM algorithm. Then, the JS scatter is used to calculate the Gaussian mixed model distances of transformer active and reactive losses for two years separately using the JS scatter that takes values between 0 and 1. This value can be regarded as indicating the transformer fault probability. Finally, the fault probbilities calculated by the two operating characteristics

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are combined convexly, and the average value of the fault probabilities calculated by the two operating characteristics is selected in this paper.

4 Typical Cases and Calculation Results Using the above arithmetic ideas, a substation is used as an example, and based on the above modeling process for the application of Gaussian mixture model in substation equipment fault warning, a fault probability model is established for this substation, and the Gaussian mixture probability densities of active and reactive losses for two transformers in 2020 and 2021 are derived, as shown in Fig. 4-figure supplement 7, and then the Gaussian mixture probability densities are converted into the transformer fault probabilities (Figs. 5, 6 and 7). From Fig. 4-figure supplement 7, it can be seen that the active losses of both transformers are slightly higher in 2021 than in 2020. After calculation, the weighted JS scatter value of the operating characteristic probability density function of transformer 1 for two years is 0.0027, which translates into a fault probability of 0.27%; the weighted JS scatter value of the operating characteristic probability density function of transformer 2 for two years is 0.003, which translates into a fault probability of 0.3%. The method is more comprehensive and accurate in estimating the probability of failure, considering that the operating conditions of two transformers are often highly correlated when modeling the probability distribution.

Fig. 4 Gaussian mixed probability density curve of active power loss of transformer 1 in 2020 and 2021

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Fig. 5 Gaussian mixed probability density curve of transformer 1 reactive power loss in 2020 and 2021

Fig. 6 Gaussian mixed probability density curve of active power loss of transformer 2 in 2020 and 2021

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Fig. 7 Gaussian mixed probability density curve of transformer 2 reactive power loss in 2020 and 2021

5 Conclusion In this paper, based on the fault warning problem, the correlation between multiple substation operation states is considered and a Gaussian mixed model is selected to study the probability of substation equipment failure. Firstly, Gaussian mixed model with high precision random variable characteristics is introduced, and active and reactive power losses are selected as operational characteristics for transformer operation detection, then a substation equipment fault warning application model is established based on Gaussian mixed model, and the distance between Gaussian mixed models of transformer operational characteristics between two years is calculated using scatter and transformed into probabilistic information for characterizing the fault probability of transformers. Finally, the results of fault probability can be obtained by model operation through examples, and the GMM algorithm can be used to compare the transformer historical operation data with the current operation data to derive the current operation status, and view the historical records of condition detection and historical operation data, which has significant development potential and application value.

References 1. Bo P, Chi Z, Hua Z et al (2020) Exploration and Application of digital Twin Substation in digital intelligence transformation of power grid enterprises. Power Energy. 41(05):558–560+590 2. Zhihua L (2017) Analysis of the problems existing in the operation and maintenance work of smart substation and the countermeasures. Shandong Indus. Technol. 09:155–156 3. Chenxu W, Fei T, Dichen L et al (2022) Probabilistic-interval power flow calculation and sensitivity analysis based on a bilayer agent model. Electrotechn. J. 37(05):1181–1193

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4. Gu, Y., Su, L., Zhong, Y.: On-line fault early warning method of wind power gear box based on interval division. Elect. Power Sci. Eng. 30(08), 1–5+11 (2014) 5. Research on intelligent fault diagnosis and test verification and evaluation method of Liu Lei equipment. Zhengzhou University (2017) 6. Ming C, Guo X, Xu Y (2010) LS-SVM based on Cauchy distribution, motor fault diagnosis. Coal Mine Mach. 31(10):238–241 7. Yan L, Li W, Zhou L et al (2021) Research on the high proportion of anomaly data detection method in photovoltaic power generation output based on Gaussian hybrid model. Electr Measur Instrum 58(09):14–21 8. Jiao Z, Shao X, Zheng X et al (2021) Fault diagnosis of porcelain pillar insulator based on vibration signal spectral Gaussian hybrid model. Electr Technol 22(06):36–42 9. Li X, Han B, Li G et al (2020) Two-stage probabilistic state estimation for AC-DC distribution networks considering non-Gaussian coupling uncertainty. Electrotechn J 35(23):4949–4960 10. Guo Y, Jia H, Song Y, et al. (2002) Parameter correction method of electromagnetic transient model based on GMM-PSO hybrid algorithm. Power Grid Technol. 1–8 11. Nan Y (2017) Expectation maximization clustering algorithm based on a Gaussian mixture model. Statist. Decis. Making. 04:87–89 12. Zhao P, Li Z, Xu G et al (2020) Study on capacitor resonance based on KL divergence. Electr Technol 21(03):27–30 13. Wei W, Li W, He M et al (2018) Uncertain probabilistic constraint optimization method based on JS-divergence. J Jilin Normal Univ (Nat Sci Edn) 39(02):54–58 14. Wu T (2022) Application of a Gaussian mixture model based on the EM algorithm in the Iris dataset. Netw Secur Technol Appl 04:47–49 15. Yang W, Zhang P, Chen Y et al (2019) Vibration signal noise reduction method based on a quantum Gaussian hybrid model. Vibr Impact 38(11):235–241 16. Research on on-line detection technology of distribution transformer based on loss power comparison. Shandong University of Technology (2021)

Sensorless Control for Contra-Rotating Permanent Magnet Synchronous Machine Based on MRAS Shukuan Zhang, Fachen Wang, Yuling Liu, and Huacai Lu

Abstract Contra-rotating permanent magnet synchronous machine (CR-PMSM) has two rotors rotating opposite each other during operation, which has high energy utilization efficiency and is not easy to generate rolling torque. Therefore, CR-PMSM has significant application potential in the field of underwater vehicle and aerial vehicle. In general, CR-PMSM requires two position sensors to complete the vector control of the motor. However, CR-PMSM is restricted by the operating environment in contra-rotating propeller, and in order to reduce the failure rate and cost, it is not suitable to use position sensor. Therefore, this paper studies the sensorless control technology of CR-PMSM. This paper adopts a sensorless control method for CRPMSM based on the model reference adaptive system (MRAS). This method can effectively improve the accuracy of CR-PMSM rotor position estimation and the dynamic and static characteristics of CR-PMSM, which is conducive to the stable operation of the motor. Finally, the stator current, torque and speed of CR-PMSM are simulated and analyzed under the condition of no-load starting and balanced load variation. The results show that the control system can well meet the requirements of CR-PMSM balance load changes, and has the advantages of short response time and high estimation accuracy. Keywords CR-PMSM · sensorless control · MRAS · balance load

S. Zhang (B) · F. Wang · Y. Liu College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China e-mail: [email protected] S. Zhang · H. Lu Key Laboratory of Electric Drive and Control of Anhui Province, Anhui Polytechnic University, Wuhu 241000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_8

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1 Introduction The counter-rotating propeller is composed of two blades that can rotate in opposite directions. When the two blades rotate, they will generate two vortices that can cancel each other, thus reducing unnecessary energy loss of the system. Compared with the single-blade propeller, the counter-rotating propeller can improve its own energy utilization rate. Meanwhile, the anti-rotating propeller generally does not have roll torque, and its propulsive efficiency and safety are higher than that of ordinary single propeller. The contra-rotating propeller system driven by CR-PMSM has the advantages of high transmission efficiency, good quiet type and small size. Therefore, CR-PMSM with two rotors to drive the counter-rotating propeller has obvious advantages. It can be used to drive underwater vehicles and aerial vehicles. For the control system of double rotor motor, rotor position information is indispensable. In general, the acquisition of rotor position information needs to install position sensors for each rotor, but this will greatly increase the volume of the motor system, and followed by the increase of instability, not only improve the cost, but also reduce the reliability of the whole system, and the use of sensorless control scheme can be a good solution to the above problems. In this paper, the sensorless control of CR-PMSM is studied in theory and simulation. In [1, 2], so as to improve the performance of four quadrant transducer (4QT) and the control effect of the motor, a prototype with the best winding structure and pole pair combination was built by optimizing the magnetic field distribution of the motor on the basis of 4QT. In [3], modeling was carried out for CR-PMSM, and the characteristics of back potential and torque generation under unbalanced load at different angle positions between two rotors were studied, and a prototype CR-PMSM was made for experimental verification. To ensure the synchronous rotation of the two rotors, the current control modes include: master control, master–slave control, cross coupling control, virtual spindle control, etc. In [4, 5], the master–slave control method was adopted to switch the master–slave rotors by comparing the position angles, so as to realize the closed-loop control of the rotors with large loads. In [6, 7], a vector optimization control strategy based on load torque compensation was proposed for direct torque control of two rotors. Experimental results show that this strategy can improve the robustness of the rotor under large load mutation. In [8], a sensorless control system using back electromotive force sliding mode observer and main correction fuzzy logic controller is proposed. This control system can track dynamic velocity profile under large load torque conditions, with minimum rise time and response time without overdrive. In [9], a rotor position estimation algorithm based on back potential algorithm was proposed. However, as only the experimental results at constant speed are considered, the error of the estimated rotor position is large. In [10], two extended state observers are utilized to estimate the αβ- axis back electro-kinetic forces, and design the corresponding speed estimation compensation scheme for the quadraturephase-locked loop. The experimental results show that the scheme can reduce the transient speed and position errors in the case of wide speed acceleration and deceleration. In [11], proposes a parallel observer for separate

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observation of the back electromotive force, rotor Angle and rotor velocity, and gives the corresponding compensation method to improve the observation accuracy and transient performance of the sensorless control strategy. In [12], adopted backstepping control and extended state observer to carry out sensorless control, estimated load torque, and provided the implementation steps of backstepping controller. In this paper, the electrical model and mechanical model of CR-PMSM are first established, and the reference model and the adjustable model based on the model reference adaptive system (MRAS) are also established to realize the estimation of the rotation speed and position of CR-PMSM. Then, based on the established mathematical model, the simulation model of the sensorless control system of CRPMSM was established. Finally, the stator current, torque and speed of the motor were simulated and analyzed under the condition of no-load starting and load change, and the research results were verified.

2 Mathematical Model of CR-PMSM and MRAS System 2.1 Mathematical Model of CR-PMSM The mathematical model of CR-PMSM was obtained by referring to the traditional mathematical model of permanent magnet synchronous motor (PMSM) and combining the relationship between the two rotors. Due to the complexity of CRPMSM system and the coupling between its parameters, the mathematical model is established based on d-q coordinate system for the convenience of analysis [13, 14]. (1) Voltage equation. Rotor1: ⎡ ⎢ ⎣

⎤ di d1 − ω1 Li q1 ⎥ dt ⎦ di q1 + ω1 Li d1 + ω1 ψ f 1 = Rs i q1 + L dt

(1)

⎤ di d2 − ω2 Li q2 ⎥ dt ⎦ di q2 + ω2 Li d2 + ω2 ψ f 2 = Rs i q2 + L dt

(2)

u d1 = Rs i d1 + L u q1

Rotor2: ⎡ ⎢ ⎣

u d2 = Rs i d2 + L u q2

In the above equations, ud1 , ud2 are the voltage of two rotors; id1 , id2 are the direct axis current of both rotors; iq1 , iq2 are the alternating axis current of the two rotors. (2) Motion equation Rotor1:

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⎤ J1 dω1 − T = T e1 L1 ⎥ ⎢ p dt ⎥ ⎢ ⎦ ⎣ dθ1 ω1 = dt

(3)

⎤ J2 dω2 − T = T e2 L2 ⎥ ⎢ p dt ⎥ ⎢ ⎦ ⎣ dθ2 ω2 = dt

(4)

⎡

Rotor2: ⎡

T e1 , T e2 are the electromagnetic torque of two rotors; T L1 , T L2 are the load torque of the two rotors; J 1 , J 2 are the moment of inertia of the two rotors; ω1 , ω2 are the speed of two rotors; θ 1 , θ 2 are the position angle of the two rotors. (3) Torque equation Rotor1: Te1 = pψ f 1 i q1

(5)

Te2 = pψ f 2 i q2

(6)

Rotor2:

ψ 1 , ψ 2 are the permanent magnet flux of the two rotors. CR-PMSM can be regarded as two traditional PMSM connected in series, so the parameters between the two rotors can be described as follows:  

i d1 = i d2 = i d i q2 = i q2 = i q



u d = u d1 + u d2

(7)

u q = u q1 + u q2

(8)

When two rotors rotate synchronously: ω1 = ω2 θ1 = θ2

(9)

By combining the above voltage equations, the voltage equation of CR-PMSM can be obtained as follows:

Sensorless Control for Contra-Rotating Permanent Magnet …



 id −ω1 L q1 − ω2 L q2 Rs = ω1 L q1 + ω2 L q2 Rs uq iq

   0 id L d1 + L d2 0 + + p 0 L q1 + L q2 ω1 ψ f 1 + ω2 ψ f 2 iq ud



113



(10)

L d1 , L d2 are the straight axis inductance of the two rotors; L q1 , L q2 are the inductance of the intersection axis of the two rotors.

2.2 Motor Speed and Position Estimation Model Based on MRAS Design of Reference Model and Adjustable Model In the rotor synchronous rotation coordinate system, the CR-PMSM voltage Eq. (10) obtained in the previous section is sorted out, and the following equation can be obtained [13]: ⎡ di ⎤ d

⎢ dt ⎥ ⎣ ⎦= di q dt



− LRdS −ω LL qd

L ω L qd − LRqS

⎤ ⎡u d  ⎥ ⎢ Ld id ⎥ +⎢ ⎣ u ψ q f ⎦ iq −ω Lq Lq

(11)

In the above equation, RS is the stator resistance; ud , uq , id and iq are the quadrature axial components of stator voltage and stator current of d q axis, respectively. In order to facilitate analysis, the following four variables u, u, i and i are introduced: ⎡

i d∗ = i d +

ψf Ld

⎤

⎢ ⎥ ⎢ ∗ ⎥ ⎢ i = iq ⎥ ⎢ q ⎥ ⎢ ⎥ ψf ⎥ ⎢ ∗ ⎢ ud = ud + RS ⎥ ⎣ Ld ⎦ u q∗ = u q

(12)

By rewriting the reference model Eq. (12), the following formula can be obtained: ⎡

⎤ ⎡ di d∗   Lq RS ⎢ dt ⎥ ⎢ i d∗ ⎢ ∗ ⎥ = − LLd ω LRd +⎢ ⎣ di q ⎦ ⎣ −ω L qd − L qS i q∗ dt

u ∗d ⎤ Ld ⎥ ⎥ u q∗ ⎦ Lq

(13)

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Convert the alternating direct axis current component and electrical angular velocity in Eq. (13) into the estimated corresponding quantity, and obtain the adjustable model as follows: ⎡ ˆ∗ ⎤ ⎡ u∗ ⎤ d id d   ∗ L R q S ⎢ dt ⎥ ⎢ Ld ⎥ iˆd − ω ˆ L L d d ⎢ ⎥ ⎥ +⎢ ⎣ d iˆ∗ ⎦ = −ωˆ L d − R S ⎣ u q∗ ⎦ iˆq∗ Lq Lq q Lq dt

(14)

The following equation can be obtained by integrating:  ∗ iˆd iˆq∗

⎧ ⎡ u ∗ ⎤⎫ d ⎪ ⎪  ⎪ ⎪ ⎬ ⎨ − R S ωˆ L q ˆd∗ ⎢ i L d⎥ Ld Ld ⎥ ⎢ = + ∗ Ld RS ⎣ u q ⎦⎪dt ⎪ −ωˆ L q − L q iˆq∗ ⎪ ⎪ ⎭ ⎩ Lq

(15)

From the above equation, the alternating direct axis current of the motor can be estimated when the motor starts according to the parameters and known quantities of the motor such as RS , L d , L q , ud , uq . Speed and Position Estimation In this adaptive, the motor body is taken as the reference model, and the adjustable model is built in Eq. (14). According to Eq. (15), the estimated current iˆd and iˆq of the direct alternating axis of the motor are taken as the output yˆ of the adjustable model, and the output of the reference model is y. The adaptive is designed to adjust them, so that the estimated current iˆd and iˆq quickly converge to the actual value. Define a state error e and subtract Eq. (14) from Eq. (13) to obtain the following error state equation [8]: de = dt



L

− LRdS ω L qd −ω LL qd − LRqS



ed eq





L

0 L qd + (ω − ω) ˆ − LL qd 0

 ∗ iˆd iˆq∗

(16)

Among, ed = i d∗ − iˆd∗ , eq = i q∗ − iˆq∗ . The above formula can be abbreviated as: de = Ae − E iˆ∗ (ω − ω) ˆ dt

(17)

Let W = E iˆ∗ (ωˆ − ω), output equation v = Ce According to Eq. (16), the state space of the forward channel can be described as: ⎤ de = Ae − W ⎦ ⎣ dt v = Ce ⎡

(18)

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To ensure the stability of the system, it is necessary to analyze and design the nonlinear feedback path and matrix C reasonably. In the design of the adaptive of MRAS system, the proportional integral form, namely PI form, is generally used. Meanwhile, the adaptive of speed identification is as follows: t ωˆ = F1 (v, t, τ )dτ + F2 (v, t) + ω(0) ˆ (19) 0

In the above equation, ω(0) ˆ is the initial value of the estimated angular velocity. The motor rotor speed is estimated as follows: 

ki ωˆ = k p + s

  

 L Lq ψf  Lq ˆ ˆ Ld d ˆ iq − iq + − iq id − − id iq id iq Ld Lq Lq Lq Ld

(20)

The following motor rotor position estimation equation is obtained by integrating the speed equation, as shown in the following equation: θˆ =

ωdt ˆ + θ0

(21)

In the above equation, θ 0 is the initial position angle.

3 Establishment of CR-PMSM Control Model Based on MRAS System 3.1 Modeling of CR-PMSM The structure and principle of CR-PMSM and PMSM are basically the same, so there is no significant difference between the simulation model of CR-PMSM and that of PMSM. Combined with the mathematical model of CR-PMSM discussed above, it is easier to establish the ontology simulation model of CR-PMSM. The ontology model can be divided into two parts: electrical model and mechanical model. The electrical simulation model of CR-PMSM can be obtained according to the voltage equation of Eqs. (1) and (2), as shown in Fig. 1. According to the motor motion equation in Eqs. (3) and (4), the mechanical models of the two rotors of CR-PMSM can be established respectively, as shown in Fig. 2. By integrating the above electrical model with the mechanical model, the ontology simulation model of CR-PMSM can be obtained, as shown in Fig. 3.

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Fig. 1 d and q axis current diagram.

Fig. 2 Mechanical equation construction of rotor

Fig. 3 Schematic diagram of motor simulation model

3.2 Establishment of Sensorless Control System Based on MRAS The simulation module of the MRAS adjustable model is built according to Eq. (14), as shown in Fig. 4.

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Fig. 4 MRAS adjustable model simulation module

MRAS adaptive simulation module, as shown in Fig. 5. The MRAS speed and angular position estimation module is formed by combining the above adjustable model with the simulation module of the adaptive, as shown in Fig. 6.

Fig. 5 MRAS adaptive simulation module

Fig. 6 MRAS speed and rotor position estimation module

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Fig. 7 Master–slave control simulation module

As can be seen from Fig. 6, the variables entered by the input ports 1 and 2 are the input quantities of the adjustable model, and the ud and uq of the adjustable model are calculated to obtain the id and iq , which are entered into the adaptive, and compared with the id and iq of the reference model entered by the 3 and 4 ports to obtain the error, and then calculated according to Eq. (16). Finally output the estimated speed and rotor angular position.

3.3 The Master–Slave Control Module is Built The master–slave control module is required during the dual-rotor control of CRPMSM. When the system detects that the load of rotor 1 increases instantaneously, after comparator comparison, the system will assign the electromagnetic torque of rotor 2 with small load as Te·cosθ, where θ is the absolute value of the difference between the instantaneous angle θ 1 and θ 2 of the two rotors. Meanwhile, the rotor with large load is selected to form the main motor. In this way, the two rotors under the action of different electromagnetic torque, the load is large acceleration, the load is small relative deceleration, can slowly let the two rotors tend to be stable. In this way, two rotors can be controlled with a set of closed-loop control. Which rotor has a large load will be included in the closed-loop control, and the same electromagnetic torque will be obtained when the load of two rotors is the same. Its control block diagram is shown in Fig. 7.

4 Simulation and Result Analysis 4.1 Simulation Results and Analysis of No-Load Starting Given a speed of 500 r/min, both rotors of CR-PMSM start with no load. At 0.15 s, the load of both rotors changes from 0 to 1 N·m at the same time. The speed, torque and current waveform of CR-PMSM when the load balance changes are shown in Fig. 8.

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According to Fig. 8(a), under the sensorless control mode based on MRAS, CRPMSM has a good low speed and dynamic response speed of the balanced load of the dual rotors, the rotational speed can be stable near the given value, the fluctuation value is within an acceptable range, and the synchronous operation of the dual rotors is in good condition. Figure 8(b) shows that the torque fluctuates significantly after the load is applied, but soon levels off. As can be seen from Fig. 8(c), the stator threephase current also fluctuates after the load is applied, and then becomes stable within a short time, but the current waveform still has more burrs. From the results, although the dual-rotor load balancing effect at low speed is not perfect, it is acceptable.

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4.2 Simulation Results and Analysis Under Load Variation Given a speed of 500 r/min, both rotors of CR-PMSM start with no load. At 0.3 s, the load of both rotors changes from 1 N·m to 3 N·m at the same time. The speed, torque and current waveform of CR-PMSM when the load balance changes are shown in Fig. 9. According to Fig. 9(a), the two rotors of the motor run smoothly after reaching stability at 0.1 s, and the speed fluctuates at 500 r/min. After the load changes abruptly at 0.3 s, the speed fluctuates significantly, and then quickly stabilizes at 500 r/min and can run smoothly. According to Fig. 9(b), the torque of the two rotors stops fluctuating substantially after 0.1 s and stabilizes around 1N·m. At 0.3 s, the load changes abruptly and the torque fluctuates significantly and stabilizes quickly around 3N·m. According to Fig. 9(c), the stator current waveform is not smooth and accompanied by a lot of noise from 0.1 s stability to 0.3 s load sudden change. After 0.3 s load 20 15

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sudden change, the current waveform fluctuates significantly, and then the amplitude increases significantly and tends to be stable.

4.3 Comparison of Sensorless Control Effect Based on MRAS When the motor starts with no load and the initial speed is set at 500 r/min, the load of the two rotors suddenly changes to 3 N·m at 0.1 s. The waveform of the difference between the estimated speed of the two rotors and the actual speed and the waveform t of the difference between the actual position and the estimated position of the two rotors in CR-PMSM under such working environment are shown in Fig. 10–11. Fig. 10 Waveform of the difference between the estimated position and the actual position of the rotor

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It can be seen from Fig. 11 that during the motor starting process, there is a significant error between the estimated speed value obtained by the MRAS control system and the actual speed value. After some 0.1 s, the control system reaches a relatively stable state, and the estimated speed can well follow the actual speed value. As can be seen from Fig. 11, after the motor has been started for about 70 ms, the estimated rotor position obtained by the MRAS control system is relatively stable with the actual rotor position, with a slight Angle difference, but it can follow the rotor position well. This section firstly simulates the balance load of CR-PMSM two rotors unchanged and the balance load changes, and mainly analyzes the speed, torque, stator current and other data of the motor under different conditions, and gives a simple analysis of the load operation. Then the validation of the sensorless control of CR-PMSM based on MRAS is simulated, and the estimation of the rotor position and speed of the motor is analyzed and compared with the actual value. The results show that CR-PMSM can meet the demand of balanced load under the control of sensorless system, but when the given speed is too large, the motor speed cannot follow well. Meanwhile, the sensorless control system based on MRAS can well estimate the rotor position of the motor, but it will be accompanied by a small negligible deviation.

5 Conclusion In this paper, the position sensorless control of CR-PMSM is studied theoretically and analyzed by simulation. The rotor position and speed of the motor are estimated by using the model reference adaptive control method. The position sensorless control system of CR-PMSM based on MRAS is established and the simulation model is established. The simulation analysis is carried out on the condition that the balanced load of both rotors of CR-PMSM changes. The results show that the control system can cope with the balanced load changes well, and has a short response time and estimation accuracy, improve the driving performance and reliability of CR-PMSM, has better dynamic and static characteristics, the method can be applied in engineering has a certain practicability. Acknowledgements This work was supported in part by the Key Laboratory of Electric Drive and Control of Anhui Province, Anhui Polytechnic University under Grant DQKJ202202 and in part by “the Fundamental Research Funds for the Central Universities”, under Grant 3132023114.

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References 1. Bai J, Liu J, Zheng P, Tong C (2019) Design and analysis of a magnetic-field modulated brushless double-rotor machine—Part I: pole pair combination of stator, PM rotor and magnetic blocks. IEEE Trans Indus Electron 66(4):2540–2549. https://doi.org/10.1109/TIE.2018.284 2739 2. Bai J, Liu J, Zheng P, Tong C (2019) Design and analysis of a magnetic-field modulated brushless double-rotor machine—Part II: winding configuration. IEEE Trans Indus Electron 66(4):2550–2560. https://doi.org/10.1109/TIE.2018.2842736 3. Zhong Y, Huang S, Luo D, Wu X (2018) Characteristics of an axial-flux permanent magnet synchronous machine with contra-rotating rotors under unbalanced load condition from 3-D finite element analysis. CES Trans Elect Mach Syst 2(2):220–225. https://doi.org/10.30941/ CESTEMS.2018.00027 4. Zhong Y, Huang S, Luo D, He R (2017) Speed synchronism of permanent magnet synchronous motor with dual contra-rotating rotors under load variation. IET Power Electron 10(12):1479– 1486. https://doi.org/10.1049/iet-pel.2016.0894 5. Cheng S, Luo D, Huang S, Chen Z, Huang K (2015) Control strategy for permanent magnet synchronous motor with contra-rotating rotors under unbalanced loads condition. IET Elect Power Appl 9(1):71–79. https://doi.org/10.1049/iet-epa.2014.0130 6. Luo D, He R, Huang SS, (2019) Optimized vector control strategy for contrarotating permanent magnet synchronous motor under serious unbalanced load adopting torque compensation. In: International Conference on Electrical Machines and Systems, IEEE, Harbin, China 7. Lou DR, Sun X, He SRZ (2019) Dual-rotor direct torque control strategy of disk-type contrarotating permanent magnet synchronous motor under heave unbalanced load torque. Trans China Electrotechn Soc. 34(22):4678–4686 ˘ M (2019) Adaptive sensorless control of PMSM using back-EMF 8. Nicola M, Nicola CI, DuT¸ A sliding mode observer and fuzzy logic. In: Electric Vehicles International Conference, IEEE, Bucharest, Romania 9. Wang Tong et al (2019) An EMF observer for PMSM sensorless drives adaptive to stator resistance and rotor flux linkage. IEEE J Emerg Sel Top Power Electron 7(3):1899–1913. https://doi.org/10.1109/JESTPE.2018.2865862 10. Jiang F et al (2021) Robustness improvement of model-based sensorless SPMSM drivers based on an adaptive extended state observer and an enhanced quadrature PLL. IEEE Trans Power Electron 36(4):4802–4814. https://doi.org/10.1109/TPEL.2020.3019533 11. Liu T, Fang J, Xi J (2020) Sensorless finite control set model predictive control with rotating restart strategy for PMSM drive system. In: International Power Electronics and Motion Control Conference, IEEE, Nanjing, China 12. NICOLA M, NICOLA CI (2020) Sensorless control of PMSM using backstepping control and ESO-type observer. In: International Conference on Electronics, Computers and Artificial Intelligence, IEEE, Bucharest, Romania 13. Wang DW, Li CJ, Wu X (2014) Model predictive current control scheme for permanent magnet synchronous motors. Trans China Electrotechn Soc 29(1):73–79 14. Zhang QS, Huang SD, Zhong YC (2018) Model predictive control of anti-rotary permanent magnet synchronous motor. Elect Mach Control Appl. 45(8):33–38

Hybrid Power System and Battery Technology

Research on Hybrid Cooling Circuit Control in Hybrid Vehicles Junchao Jing, Yiqiang Liu, Weishan Huang, Qi Li, and Zhentao Wang

Abstract The hybrid cooling circuit strategy of the P2.5 hybrid system is based on the battery cooling control, the motor, inverter cooling control and the evaporator cooling control. The specific contents are as below: 1) The compressor control. The requested compressor speed is based on the feedforward and PID control. The requested compressor speed is dependent on the battery cooling request and the evaporator cooling request. 2) The water pump control. The cooling circuit includes the motor, the inverter, OBC and the water pump. The requested flow is the arbitration of the request of the inverter, the motor OBC and DCDC. 3) The fan control. The requested air flow is the arbitration of the request of the motor, the battery and PWM is defined. The vehicle results are conducted and show that the cooling strategy can meet the cooling request of the battery and the motor to avoid the overtemperature risk. Keywords Cooling Circuit Control · Compressor Control · Fan Control · P2.5 Hybrid System

1 Introduction The hybrid systems and pure electric systems are two good options of the global energy delimma and environmental pollution issues [1–4]. However, battery technology is still immature and many charging facilities are needed, the hybrid systems have been drawn great attention. Thermal management system, as one of the core systems of pure electric vehicle, its main function is to make the components of the vehicle work in the normal range, and ensure that the passengers in the car in a comfortable temperature environment [5–9]. Generally speaking, the battery thermal management system can control cooling when the power battery temperature is high J. Jing (B) · Y. Liu · W. Huang · Q. Li · Z. Wang Ningbo Geely Royal Engine Components Company Limited, Ningbo, Zhejiang, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_9

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and temperature rise when the power battery temperature is low. The motor cooling loop will discharge a large amount of heat generated by the driving motor to the external environment through the radiator. The cooling control strategy designed in this paper is as follows: when the vehicle is in a high temperature external environment, the normal refrigeration effect is achieved, the normal work of batteries, motors and other on-board equipment are protected, and the comfortable temperature environment in the cabin is provided. The designed method can not only realize the complete refrigeration function, but also has higher economic benefits and market value. Considering all the control strategies above, Geely has developed a cooling management strategy based on the battery cooling control, inverter cooling control and the evaporator cooling control in P2.5 system control system.

2 P2.5 Hybrid System The P2.5 hybrid system which is shown in Fig. 1. The main parameters of vehicle are shown as Table 1. The cooling management is divided into several subsystems: 1) The compressor control. The requested compressor speed is based on the feedforward and PID control. The requested compressor speed is dependent on the battery cooling request and the evaporator cooling request 2) The water pump control. The cooling circuit includes the motor, the inverter, OBC and the water pump. The requested flow is the arbitration of the request of the inverter, the motor OBC and DCDC.

Fig. 1 P2.5 hybrid system overall layout

Research on Hybrid Cooling Circuit Control in Hybrid Vehicles Table 1 The main parameters of the vehicles

Components

Parameters

Engine

3 cylin-ders,1.5 T, 95 kw/5500 rpm

Gearbox

7 DCT

Battery

6.9 Ah

Motor

60 kw

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3) The fan control. The requested air flow is the arbitration of the request of the motor, the battery and PWM is defined.

3 The Hybrid Cooling Circuit Control 3.1 Water Temperature Prediction The cooling circuit overall layout is shown in Fig. 2. The cooling circuit includes the motor, the inverter, the OBC and the water pump. The water temperature sensor in the electric drive loop is arranged at the water outlet of the radiator. There is no water inlet temperature sensor. Therefore, the water temperature at the inlet needs to be estimated when calculating the air volume requirements of the radiator and the water requirements of each sub-component of the electric drive loop. Water temperature prediction is based on the heat transfer formula, the heat of each sub-component of the loop is converted into the contribution to the loop temperature rise, and the inlet water temperature is obtained by adding the temperature of radiator water in the previous calculation cycle. Fig. 2 The cooling circuit overall layout

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3.2 Cooling Flow Request The basic task is to control the water pump to ensure that the necessary cooling flow is reached. The main input is based on the cooling need from the motor, inverter and DCDC. The cooling need is also dependent on the driving mode and two possible modes defined as drive mode and after run mode. Actual after run cycle is determined when after run mode is activated. The flow request in the after run mode is dependent on the hybrid water temp, the motor winding temperature and inverter temperature. The max duration of the after run cycle mode is 6 min once it has been initiated. The maximum cooling flow is used and after limitation and filtering it is converted to an electrical duty cycle to control the water pump. Furthermore, an estimation of the actual cooling flow is calculated the information is sent on CAN to the motor controller.

4 The Compressor Control 4.1 The Request Speed Control There are two working conditions of compressor speed requirement calculation during passenger cooling: the first case is the passenger cooling alone and the second case is the passenger cooling and battery cooling simultaneously: The feedforward of speed request can be obtained by referring to the table of evaporator temperature, air volume calculated by CCM, the ambient temperature and throttle cycle ratio. The feedforward speed control calibration shall be divided into 6 ambient temperature ranges which includes the areas below 10 °C, 10 °C −20 °C, 20 °C −30 °C, 30 °C −40 °C, 40 °C −50 °C, and above 50 °C. The PID control is performed according to the difference between the evaporator target temperature and the evaporator actual temperature calculated by CCM. When the battery cooling is request, the speed request of battery cooling is based on the cooling request of the passengers and the ambient temperature. Based on battery coolant temp, different compressor control feedforward contribution for active battery cooling can be used. Three breakpoints in coolant temp (Lo - Mid - Hi) gives the possibility to add some non-linearity if needed.

4.2 The Request Power Control The accuracy of compressor demand power calibration affects idle charging torque compensation (which greatly affects fuel consumption). Especially in the case of high temperature, when passenger cooling and battery cooling are triggered at the same time, the compressor demand power is looked up according to the demand

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speed and air conditioning pressure, and then the calibrate offset power is increased according to whether there is battery cooling.

4.3 The Speed Limit Control The maximum speed is based on the NVH requirement and is limited by ambient temperature. If the running time is less than 200 s, the compressor initial operation limits the maximum speed which is 800 rpm. The compressor speed when the battery is charged; The maximum rotational speed of the compressor is limited because the system temperature exceeds the upper limit. The minimum speed is to prevent over temperature risk in the entire cooling system.

4.4 The Evaporator Valve and Chiller Valve Control The evaporator valve and the chiller valve are shown in Fig. 3. The evaporator valve is used to meet the passenger cooling request. The enable conditions are as below: a. there is no fault that prevents the fan from working. b. the engine running time exceeds the calibration limit

Fig. 3 The evaporator valve and chiller valve overall layout

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c. the air mass flow through the air conditioner is greater than the limit (0.1); d. the evaporator air temperature is not below −1 °C; e. the high side of the refrigerant pressure is not too high If the air conditioner has been enabled and the compressor is working, but the cool function is not triggered. The evaporator valve stuck is considered. If the fault persists for over 60 s, a fault is detected. The evaporator valve is requested to open and shut down every 1 s in the first 6 s after a fault is confirmed. The fault needs to be reconfirmed after 60 s. If the fault is confirmed in a cycle over 5 times, the DFC will be shown and sent to the driver. The chiller valve is used to meet the battery cooling request. The chiller valve enable conditions are as below: a. b. c. d. e. f.

the high voltage battery cooling requests is received. there is no fault that prevents the fan from working. the battery temperature is higher than the threshold. The high side of the refrigerant pressure is not too high. The cooling water flow of the high voltage battery is not lower than the threshold. The cooling water temperature of the high voltage battery is not lower than threshold. g. The chiller valve is not forced to close. When the delta between the actual evaporator temperature and the required temperature is greater than a certain limit due to opening the chiller valve, the chiller valve will be forced to close until the delta is less than the limit again. The goal is to prevent the actual evaporator temperature from rising too much due to cooling the high-pressure battery cooling water.

5 The Air Flow Control The thermal management airflow model controls the engine cooling fan, grill and spoiler shutters. The airflow require is for the cooling of combustion engine and the AC system. The model is used to determine how much airflow the vehicle is providing through the cooling pack depending on vehicle speed, fan speed and shutter position.

5.1 Air Flow Control The main purpose of air flow manager is to convert the airflow requests to a shutter and fan request, filter the shutter and fan request and select the correct shutter and fan request depending on vehicle state. If the vehicle is a traditional vehicle, the airflow is only needed when engine is running. If the vehicle is HEV or PHEV, the AC airflow request is allowed even when combustion engine is off to allow pre-conditioning and

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cooling of high voltage battery. The explicit spoiler airflow request allows the request through the charge air cooler and from electric drivetrain. The after run condition is the situation that fan is running at low speed once the after run cycle has been completed. In this case the rear engine air temperature may increase due to heat re-circulation. The fan shall be re-triggered to cool the air and to protect rear engine bay components. If the coolant temp sensor is faulty and the engine temperature is unknown, hence a safe after run request can be triggered depending on ambient temperature.

5.2 The Shutter and Fan Control The shutters should be opened in the following situations: 1) The shutters are opened at high speed to improve vehicle stability. The shutters are opened if there is high engine temp and low vehicle speed and depending on ambient temperature, open shutters to reduce engine bay area temperatures. 2) If there is a sensor fault on the coolant temp, shutters and fan can be triggered to avoid overheating and high engine bay temperatures. If there is a shutter fault, the fan can be triggered to compensate for the reduced airflow caused by a shutter that may have failed in closed position. 3) At high ambient temperature and low vehicle speed, the fan is requested to run. In winter conditions, the shutter is requested to be set a minimum default position to avoid freezing. The shutters are opened if there is high load and high engine temp to reduce engine bay temperatures.

6 Test Result 6.1 The Hybrid Cooling Circuit Test Figure 4 is the thermal CC46 cycle test, during the whole test, the water coolant temperature of the cooling circuit is below 65 °C. The motor temperature is blow 100 °C and the inverter temperature is below 80 °C. The requested flow is 0–11 L/ min and the water pump speed which is dependent on the requested flow is 0–255.

6.2 The Compressor Control Test Figure 5 is the evaporator and the chiller valve cooling test. At 25 min 53 s, the requested evaporator temperature is 3 ° and the actual evaporator temperature is 6.9 °, the request compressor speed is 6000 rpm. The battery coolant temperature is

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Fig. 4 The hybrid cooling circuit test

above 33 ° and the chiller valve is request to enable. The requested battery coolant temperature is 15 °. The requested battery compressor cooling speed is 2626 rpm. Therefore, the total compressor cooling speed is 8626 rpm. The requested cooling power which is based on the speed and evaporator is 3780 w and the actual power is 2607 w. At 34 min 5 2 s, the battery coolant temperature is decreased to 15 ° and the battery chiller valve is requested to disable. The requested compressor speed is changed from 8500 to 6000 rpm. The requested compressor power is changed from 4500 to 3600 w.

6.3 The Air Flow Control Test Figure 6 is the air flow control test. At 18 min 58 s, the requested air flow is changed from 52 kg/s to 87 kg/s. The requested fan pulse wave modulation is changed from 52 to 90.

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Fig. 5 The compressor control test

Fig. 6 Air flow control test

References 1. Qu X, Wang Q, Yu YB (2014) Power demand analysis and performance estimation for activecombination energy storage system used in hybrid electric vehicles. IEEE Trans Veh Technol 63(7):3128–3136

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2. Hannan MA, Azidin FA, Mohamed A (2014) Hybrid electric vehicles and their challenges: a review. Renew Sustain Energy Rev 2014(29):135–150 3. Liu B, Li L, Wang X et al (2018) Hybrid electric vehicle downshifting strategy based on stochastic dynamic programming during regenerative braking process. IEEE Trans Veh Technol 67(6):4716–4727 4. Zheng C, Li W, Liang Q (2018) An energy management strategy of hybrid energy storage systems for electric vehicle applications. IEEE Trans Sustain Energy 9(4):1880–1888. https:// doi.org/10.1109/TSTE.2018.2818259 5. Taha MS, Abdeltawab HH, Mohamed YAI (2018) An online energy management system for a grid-connected hybrid energy source. IEEE J Emerg Sel Top Power Electron 6(4):2015–2030. https://doi.org/10.1109/JESTPE.2018.2828803 6. Wang Y, Li J, Tao Q et al (2019) Thermal management system modeling and simulation of a full-powered fuel cell vehicle. J Energy Res Technol 142(6):1–40 7. Chen Q, Zhang G, Zhang X et al (2021) Thermal management of polymer electrolyte membrane fuel cells: a review of cooling methods, material properties, and durability. Appl Energy 286(1):116496 8. Esfahanian V et al (2013) Design and simulation of air cooled battery thermal management system using thermoelectric for a hybrid electric bus. In: Proceedings of the FISITA 2012 World Automotive Congress. LNEE, vol 191. Springer, Berlin. https://doi.org/10.1007/978-3642-33777-2_37 9. Tian X, He R, Xu Y (2018) Design of an energy management strategy for a parallel hybrid electric bus based on an IDP-ANFIS scheme. IEEE Access 6:23806–23819. https://doi.org/10. 1109/ACCESS.2018.2829701

Optimized Power Sharing Models for HyForce: A Hydrogen-Powered Harbor Craft Nirmal Vineeth Menon

and Siew Hwa Chan

Abstract Marine decarbonization is a multi-faceted issue that requires a global concerted action plan. With 50,000 merchant ships transporting 90% of the world’s trade, the marine sector is responsible for 3% of all global emissions. Anticipating the projected growth of commerce and reliance in the marine industry to fulfil these obligations to result in these emissions peaking at 17% if left unchecked, decarbonization of this key sector is crucial to support and meet the targets set by 196 countries in their nationally determined contributions in accordance with the Paris Agreement. This paper dives into the role Hydrogen can play as an actor in maritime decarbonization specifically on the added value proposition that can be leveraged from optimized power-sharing models. This research identified the number of towing jobs a harbor craft can perform between bunkering intervals as the cost function for optimization. A theoretical model framework was developed to provide an overview and govern the power-sharing strategies. The inputs to this model were complemented with statistical data to accurately represent the performance of the powertrain. A 15% increase was observed because of this research indicating a significant improvement in the on-hire availability of the tugboat. Keywords Liquefied Hydrogen · Marine applications · Hydrogen value chain · Hydrogen fuel cells · Power sharing optimization

N. V. Menon Sembcorp Marine Ltd., Nanyang Technological University, Singapore, Singapore S. H. Chan (B) School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_10

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1 Introduction Climate change is a visible, existential issue of this century. The potential devastation to the livelihood and economic havoc it would cause has rallied countries to come together to evaluate the emissions they produce. The historic COP 21, held in December 2015 in Paris saw 196 signatories to a landmark multilateral climate change accord. This agreement reiterates the signatories’ commitment to communicate their plans to reduce anthropogenic Greenhouse Gas (GHG) emissions. To ensure that the targets and plans are kept abreast to the latest scientific knowledge, these nationally determined contributions are re-submitted every five years. These contributions reflect the progressive ambitions of each country to play their part in their pursuit to combat climate change. In partnership with this goal, the International Maritime Organization (IMO) adopted ambitious GHG reduction targets of their own to be achieved incrementally by 2050. The baseline of these targets are pegged against the total emissions produced in 2008 to better reflect the peak emissions observed during that year have consequently motivated the maritime industry to decarbonize their operations. It is clear that if left unchecked and if the industry continues to operate with the current business-as-usual model, its trajectory is projected to be inadequate to meet the 50% reduction in absolute GHG emissions goal by 2050 [1]. The fourth IMO GHG study 2020, estimated that shipping accounts for 2.89% of the total global CO2 emissions or 1,056 million tonnes of CO2 equivalent in 2018, approximately 90% of 2008 emissions [2]. To meet these targets, the maritime industry is evaluating the use of alternative fuels in tandem with the ongoing energy transition happening globally. It is evident that such a transition will take a huge amount of investment and time, which has prompted the IMO to consider an incremental approach in reducing emissions. This incremental approach entails the need for a near-term goal of reducing carbon intensity which is a measure of carbon dioxide emissions per ton of cargo transported per nautical mile (i.e., emissions with respect to distance the cargo moves). IMO envisages a near-term goal of reducing the carbon intensity by 40% within 2030 to serve as a necessary checkpoint to validate the systematic, methodical plans of the industry towards a 70% reduction by 2050. A collective effort by the maritime ecosystem is instrumental in achieving this goal. One pathway identified is the optimum use of resources to extract maximum value. Power optimization via load sharing, therefore, brings about the advantage to create a significant dent in reducing the about of fuel utilized therefore directly reducing the emissions produced during operations. A further benefit of load sharing is the ability to increase the interval between each bunkering activity. This reduces the downtime of the harbor craft essentially allowing it to be on hire longer. The implication of this is paramount specifically for a fuel like hydrogen which requires detailed and often unconventional bunkering requirements. Furthermore, hydrogen bunkering would typically be carried out in a specifically designated location, unlike the bunkering of traditional fuels like Marine Gas Oil or diesel. This time-consuming process would have a significant impact on the operational readiness of the harbor craft and its profit-making capability. The

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attractiveness of using hydrogen as a choice of fuel is very dependent on the ability to generate revenue comparable to other alternative fuels. This paper discusses the methodology of power-sharing between hydrogen fuel cells or engines and batteries in a hybrid powertrain. A comparison of the results of the optimized power-sharing model against a baseline case scenario is also shared.

2 Hydrogen Overview 2.1 Hydrogen as a Marine Fuel Traditionally, the use of heavy fuel oil (HFO) has been the prevailing choice of fuel for most shipowners. The cost of HFO has historically been affordable and works well in the efficient 2-stroke engines that have been developed for marine use. There have been certain emission control areas that impose specific restrictions on the amount of sulphur the fuel can contain. Compliance to these prevailing regulations are mandatory and enforced diligently. Currently, The International Convention for the Prevention of Pollution from Ships (MARPOL) has mandated the use of lowsulphur fuel with a cap of 0.5% since 2020 and a more stringent requirement of 0.1% in special emission control areas. When considering hydrogen as a fuel, we immediately notice the value proposition, the absence of any carbon atoms; its intrinsic ability to meet and further exceed the requirements of the emissions reduction targets of IMO. To further quantify its true potential as a potential future fuel, a detailed analysis compared against traditional marine fuels is required. Firstly, there are several different storage technologies for hydrogen and its derivatives [3]. These technologies have been developed to aid the industrial use of hydrogen with its demand steadily increasing over the past decade. Industrial use of hydrogen includes the petrochemical refining (i.e., cracking of longer chain hydrocarbons), the production of Ammonia (typically to be used to make fertilizers) and direct reduced iron (i.e., removing of oxygen from iron oxides in its solid state). The growing demand for hydrogen has been met with compressed storage capabilities as the preferred choice due to its simplicity and affordability. Compression, whereby hydrogen is stored at pressures ranging from 300 to 700 bar, provides a promising solution for the maritime industry. These containment systems are classified into four different types, each providing a different design consideration from the next with a common objective to ensure that the hydrogen is stored safely and reliably. Amongst the four types, Type III and Type IV containment systems are non-load bearing (i.e., self-supporting) are ideal for marine applications as it circumvents adding additional stresses to the ship’s structure [4, 5]. To further enhance the storage capacity and provide a longer operational endurance, a 3.6 wt% improvement in gravimetric storage capacity is observed when increasing the storage pressure of hydrogen from 300 to 700 bar [6]. Hydrogen can also be stored at cryogenic temperatures (-253 °C) in its liquid form.

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Handling products at such low cryogenic temperatures might seem daunting for many, the marine industry is no stranger to this. The industry has had the opportunity to gain valuable experiences handling liquified natural gas (−162 °C) for several decades, both as a cargo and as a fuel. These experiences gained would aid to flatten the learning curve and hence, the marine industry is poised to adopt the use of Liquified hydrogen (LH2 ) at a much quicker rate. The incentive for considering LH2 is the significant increase in density (71 kg/m3 ) which directly correlates to an increase in volumetric energy-carrying capacity (8.5 MJ/L). However, liquefaction is not without its drawbacks. The most prominent, is the energy penalty which constitutes a third in the overall energy shrink (28.8–36 MJ/kg) [7]. This is well illustrated in which depicts the equivalent fuel tank size required for different alternative fuels based on the energy content of 1000 m3 of marine gas oil. The results given in Fig. 1 above is anticipated but the reality and its corresponding limitations needs to be understood. The four-to-seven-fold increase in fuel tank size undoubtedly impinges on the cargo-carrying capacity of the vessel. To ship owners and charterers, this limitation would be costly and potentially challenging logistically as more vessels are required to transport the ever-increasing amount of cargo. One potential way to manage this challenge is to limit the size of the fuel carrying capacity and consider use cases where frequent refueling is possible. A possibility is nearshore applications [8]. Some examples include passenger launch boats, harbor crafts, ferries, bunkering vessels, and offshore support vessels. Figure 2 shows a tugboat, an important asset for port and shipyard operations installed with an LH2 tank at the stern. The volumetric capacity of 50m3 is designed to ensure that refueling is required only after 8 days of operations.

Fig. 1 Fuel tank size equivalent for various alternative fuels

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Fig. 2 HyForce with LH2 fuel storage

3 Hydrogen Utilization in Marine Applications The end use of hydrogen can be via the conversion of its potential energy to either electrical or mechanical energy through the use of a fuel cell or internal combustion engine respectively (Table 1).

3.1 Fuel Cells Regardless of the technology listed above, the working principle of a fuel cell remains the same. Broadly, a single cell in a fuel cell stack is made up of several distinct components. The membrane-electrode assembly consists of two electrodes separated by an electrolyte. This electrolyte is typically the key differentiator between fuel cell technologies listed above. A single cell has a relatively small potential difference, approximately 0.7 V. Therefore, they are arranged in series to produce the desired power required. The two electrochemical half-reactions for a commonly used Proton Exchange Fuel Cell (PEMFC) are shown below. At the anode: 2H2 → 4H+ + 4e−

(1)

O2 + 4e− + 4H+ → 2H2 O

(2)

At the cathode:

Fuel cells have the capability to replace all the power generating equipment required on board vessels. This includes producing power required by the main propulsion powertrain and powering hotel and auxiliary loads on board the vessel.

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Table 1 Fuel cells suitable for marine applications Type of Fuel Cell

Description

PEM

Proton Exchange Membrane Fuel Cells (PEMFC) have been the preferred choice of technology specifically for marine applications. They also have a considerable footprint in land-based applications. The maturity of the technology and popularity has enabled PEMFC manufacturers to leverage economies of scale resulting in competitive pricing. The low operational temperature limits additional safeguards and safety concerns. The use of pure hydrogen (99.99%) results in water as the only emission To address limitations and sensitivity towards fuel impurities for PEMFC, a high-temperature (HT – PEM) version of the fuel cells is under development [9]

Solid Oxide

Solid Oxide Fuel Cell (SOFC) has a dominant presence in land-based applications and is currently being developed as a promising alternative to PEMFC specifically for use in marine applications. This is a result of their highly efficient stack which is also moderately sized. Typically, the size of the fuel cells has been a key determinant for the selection of fuel cell for use in newbuild or retrofit projects. To achieve their high stack efficiency, heat recovery is typically maximized resulting in an overall efficiency of up to 85%. Apart from a select few demonstration projects, the use of SOFCs in the marine industry is relatively novel and hence there is currently insufficient track record to validate its viability. Another promising versatile characteristic of SOFCs is its ability to accept the use of different fuels such as LNG, methanol, and ammonia to broaden the range of use cases. The projected acceptance of using SOFCs in the future would indicate a more competitive price point in the future

Regardless of which capacity the fuel cell is utilized, the fuel cell is supported by several balance of plant equipment to ensure the safe and reliable delivery of power under normal operating conditions. One such example is the supporting battery storage system that is designed to handle intermittent and transient loads. This further supplements any response delays of the fuel cells to meet the delta in power demand. The power management system on the ship automates the channelling of excess power produced by the fuel cell to charge the battery (i.e., power take-off) or discharge the battery (i.e., power take-in) to peak shave high power demands.

3.2 Internal Combustion Engines The use of Internal Combustion Engines (ICE) requires no introduction. Since the 2nd industrial revolution, they have been a highly utilized energy conversion equipment in the automotive, locomotive and marine industries. This enormous wealth of knowledge has always contributed to the ever-prevailing cutting-edge design, construction, operation and troubleshooting of these engines. Therefore, undoubtedly, the combustion properties of hydrogen can be studied in detail to determine the best-fit approach to facilitate the introduction of an engine capable of operating solely on hydrogen [10]. Engine manufacturers have also been mindful to progressively increase the

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percentage of hydrogen in the fuel blend to balance the availability of hydrogen, added storage requirements and eventual size of the engine. This approach allows the manufacturers and key stakeholders to assess its suitability for implementation as well as to design the necessary safeguards that need to be in place. The environmental benefits of using hydrogen are clear. With the ambitious goals of IMO clearly indicating to the industry that change away from a business-as-usual scenario is required. These factors have been a compelling motivator for engine manufacturers to widen their product solutions in anticipation of market demand. As highlighted previously, the use of hydrogen will eliminate GHG emissions such as carbon dioxide with zero sulphur emissions, ICE are notorious for their formation of nitrogen oxide (NOx ), an undesirable GHG resultant from the nitrogen, oxygen, and the presence of heat. To mitigate this, there are several exhaust gas post-treatment devices capable of reducing these emissions to an acceptable level are included in the overall design of the engine [11]. The intrinsic combustion characteristics of hydrogen would ultimately govern the design of the ICE. Such characteristics include minimum ignition energy, autoignition temperature, and flammability limits. These values provide the designers with the fundamental considerations that need to be addressed and managed well. An example of such consideration is the stoichiometric air-fuel ratio. For a hydrogen ICE, this ratio is 34:1 or higher which would allow the engine to have a lean burn, (i.e., excess air) Otto cycle. The benefits of such an operating point is the reduction of harmful NOx emissions and considerably manage any pre-ignition caused by its high auto-ignition temperature and low ignition energy. However, this does not come without drawbacks. This design would consequently result in the need for a large combustion cylinder resulting in a bigger footprint. Figure 3 above provides an illustrative representation of the powertrain setup of a ship powered by an ICE. This configuration allows for both compressed hydrogen cylinders or LH2 to be used. The eventual design would be based on the necessary endurance for the use case mission requirements.

4 Power Sharing Models Tugboats, unlike several other marine applications such as ferries or ocean-going vessels, are designed to provide reliable and stable power throughout a dynamic range of operational scenarios. Their primary objective often entails the safe transit (i.e., towing) of larger marine vessels in a near-to-shore setting. The marine vessels under towage typically berth alongside a shore infrastructure for embarkation/ disembarkation (passenger ferries) or the loading and unloading of cargo (containerships/bulk carriers). This specific operation requires the tugboat to perform a series of pushing or pulling operations (i.e., bollard pull) under the direction of the harbor pilot for the safe mooring of the vessel. Understandably, due to the proximity to shore, the associated risks to human life and property damage are higher in such a scenario as compared to the voyages in the open ocean. Hence, it is imperative for tugboats to

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Fig. 3 Internal combustion engine powertrain

be able to spontaneously meet the dynamic power demand. To ensure its’ reliability, extensive work on the simulation and validation of test scale models are required to be performed to ensure the maneuverability meets design and classification society standards [12, 13]. Traditionally, tugboats meet this power demand through diesel-driven ICE which relies on the torque produced by shaft power from the rotating engines to turn the propellors [14]. The powertrain on HyForce challenges this status quo and is designed to meet the power requirements through a combined power-sharing strategy of fuel cells/ICE and batteries [15]. The proposed powertrain configuration is shown in Fig. 4 below where ESS is the energy storage system of the batteries. The methodology used to design the power-sharing models is derived from the load requirements of the tugboat at different operating scenarios. A reasonable assumption of the operational data to be used as inputs for the power-sharing model is the estimated and anticipated time spent at each load profile to best depict the overall power requirement of the tug during a typical operational cycle (i.e., 1 day). This assumption is assured through empirical data obtained through close collaboration with Jurong Marine Services, a tugboat operator based in Singapore with a fleet of 12 diesel-driven tugs. Data collected from these tugs include the time spent at the different pre-identified operational profile, namely idle, towing and bollard pull. The required power, fuel consumption, efficiency and time spent is given in Table 2 below. Both powertrains are capable of operating in 3 different distinct power modes. The first mode is either fuel cells or an internal combustion engine the second is via battery power and the last mode is a combination of the first two (i.e., hybrid). Opportunities to optimize the fuel cells, the internal combustion engine and, batteries are limited as these equipment are supplied with published data on their performance as part of the scope of supply to comply with the power design requirements.

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Fig. 4 HyForce powertrain block diagram

Table 2 Hydrogen fuel consumption at different operating profiles Operation Profile

Power (kW)

PEMFC Fuel Usage (g/s)

PEMFC efficiency (%)

Hydrogen ICE Fuel Usage (g/s)

Time Spent (hh:mm:ss)

Idling

400

6.3

53.44

9.52

07:12:00

Transit

800

12.5

53.87

19.05

01:48:00

Standby

600

9

56.11

14.28

01:12:00

25% bollard pull

700

10

58.92

16.66

00:46:00

50% bollard pull

1086

17

53.77

25.85

00:32:00

75% bollard pull

1428

23

52.26

33.99

00:15:00

100% bollard pull

1940

35

46.65

46.17

00:15:00

The first two, power from fuel cells/ICE and battery power are individually limited in optimization as these standalone pieces of equipment are sized and designed under the purview of the manufacturer. However, when integrated, opportunities arise to develop a robust power-sharing philosophy that fulfils the end objective of the desired cost function. Cost functions are variables and examples include (i) reduced fuel consumption (ii) improved battery life by reducing charge cycles (iii) minimized excess power generation (iv) optimal reserve power allowable in the battery pack. Ideally, a cost function’s desired end outcome is identified in each optimized

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Fig. 5 Theoretical model framework of the power-sharing strategy

power-sharing strategy and downstream measures are implemented correspondingly. Specific to this study, the cost function identified was to reduce fuel consumption and consequently prolong the time intervals between each bunkering operation to maximize the operational time of the tugboat. To curate an accurate and viable representation of the power-sharing strategy, the topological hierarchy of the power schematic needs to be developed. The primary objective is to ensure that the nodes and their corresponding connections within the overall framework are aligned to (i) the design capabilities of the individual components (i.e., Fuel cells/Hotel load/Battery consumption) (ii) the cause and effect of the various nodes (i.e., Yes/No) (iii) the ability to produce the desired results at the end state (Hydrogen fuel consumption/battery state of charge). Figure 5 below illustrates the theoretical framework developed to govern the power-sharing strategy. The robustness of the theoretical model framework is verified against a series of potential operating profiles given in Table 2 to ensure completeness. Through these series of tests, the specific operating conditions in which the (i) battery would be providing 100% of the power (ii) fuel cells would be providing excess power in a Power Take Off (PTO) mode to charge the battery and (iii) the battery would top-up the remaining power in a Power Take In (PTI) mode were identified. The theoretical model thus provides an overview of the pockets of opportunity and the basis for power optimization to be explored. A summary of the power sources at various operating profiles is illustrated in Table 3 below. With the interfaces between the individual components in the powertrain mapped out using the theoretical model, the overall operability of the envisaged powertrain can be simulated. The simulation provides clarity on the constraints and limitations surrounding the baseline operating scenario which can be analyzed graphically. It provides the benchmark against which future optimization strategies can be compared to. To build the powertrain accurately in a virtual environment, statistical data on the individual components are gathered through the manufacturer’s datasheets and literature sources that provided empirical data [16]. The data were digitized and analyzed in Matlab. The accuracy of the digitized data was verified through a vigorous

Optimized Power Sharing Models for HyForce: A Hydrogen-Powered … Table 3 Power source inputs are different operating profiles

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Operating Profile Fuel Cells Electric Motor Battery Idle

Off

Off

On (PTI)

Transit 6 knots

Off

On

On (PTI)

Transit 8 knots

Off

On

On (PTI)

Transit 10 knots

On

On

On (PTI/PTO)

Transit 12 knots

On

On

On (PTI/PTO)

Bollard Pull (25% to 75%)

On

On

On (PTI/PTO)

Bollard Pull (100%)

On

On

On (PTI)

series of tests. Figure 6 below depicts the results of the simulation runs performed to validate the robustness of the statistical model. The ESS is designed to attain a full charge after two hours to allow a quick turnaround in its usage. The ability of the battery to meet this key performance criterion is imperative as the continuous usage of the battery will provide opportunities to maximize the earlier mentioned cost functions specifically to reduce hydrogen fuel consumption. It is observed that the ESS exhibits an asymptotic-like charging rate as it approaches full charge. This is to be expected as the charging rate approaches zero gradually from a peak of 700 kW. As the tug completes one job and proceeds on to the next, she will essentially perform four jobs in a 12-h shift according to the time spent at each operating profile given in Table 2. The cyclic nature of this transition is well observed in Fig. 6 above. The baseline operating scenario incorporates a hard limit on the lowest state of charge for the battery at 30%. This is to ensure sufficient reserve power in the event of an emergency and/or loss of power from the hydrogen fuel cell/ICE.

Fig. 6 (a) Battery charging rate to max capacity of 452 kWh (b) Battery state of charge during baseline operating scenario

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Fig. 7 Power sharing between e-motor (batteries) and H2 ICE

Figure 7 attempts to illustrate the power-sharing model when the tugboat is travP elling at 10 knots and 12 knots transit. The symbol, α, is the ratio of power PEngine T otal

whereas the symbol, β is the ratio of time TTe−motor . The power is shared using the T otal hydrogen engine and (1-α) of the remaining power comes from the batteries whereas at 12 knots transit, time spent on the engine to deliver the required power is (1- β) in a power take-off mode and the remaining power is supplied by the batteries (β). As the daily operational requirement of the tugboat differs due to the dynamic nature of its deployment, a parameter sweep is performed by running several iterations of this model. Besides the two operating profiles shown above in Fig. 7 above, changes include varying the power source inputs in which the battery can either be operating in PTI or PTO modes as indicated in Table 3 above. This provides an in-depth analysis of the capabilities of the design capacity of the batteries and its corresponding effect on the cost function which remains unchanged. The desired measurable is the number of towing jobs completed per full tank of LH2 . This measured variable would denote the operational performance of the tugboat with a higher value indicating more on-hire availability. The result of the parameter sweep is seen in Fig. 8 below. To orientate Fig. 8 above, the y-axis is α which is the ratio of power produced by the H2 ICE and the x-axis is β which is the ratio of time in which the power is delivered by the e-motor (batteries). The total for both will equate to 1 whereby the remaining power is either produced by e-motor (1-α) or H2 ICE (1-β). The baseline scenario results in the ability of the tug to perform 81 towing operations. This scenario is when 100% of the required power is delivered by the H2 ICE engine at 8 knots and 12 knots (i.e., the batteries were never used) Under this scenario, the hybrid mode is not activated, and the effect is the quick depletion of LH2 . Under varying hybrid conditions, our results point towards an optimized number of towing jobs at 93. This corresponds to a 15% increase potentially resulting in a significant improvement to the overall commercial viability of using hydrogen as a source of fuel to fulfill the decarbonization agenda of IMO. However, this improvement in the number of towing jobs completed was achieved by infringing

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Fig. 8 Power sharing optimization results

Fig. 9 Integrated framework with proxy model

periodically on the 30% state of charge limit on the batteries. This was occurrence was observed during the 100% bollard pull operation where the batteries are in a PTI mode (i.e., discharging). The state of charge recovers above this limit relatively quickly before the start of the next job. The implications of this infringement are minimal unless in an unlikely scenario whereby the tug loses power at the same moment where the state of charge is below 30%. The severity of this is dependent on the location of the tug when this occurs. Should the tug be relatively near to shore, the remaining power available by the batteries would be sufficient to safely bring the tug back to shore for diagnostics. Optimized power-sharing provides the unique opportunity to further enhance the value proposition of alternative fuels. Hydrogen is inherently disadvantaged with

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a low energy density and high energy penalty because of liquefaction. With the optimized use of hydrogen, tugboat owners will be further incentivized to consider switching to alternative fuels to ensure compliance with any regulatory requirements.

5 Conclusion and Future Work This paper took a systemic approach to arrive at an optimized power-sharing model for HyForce. A theoretical model framework for the powertrain was first developed to provide an overview of the potential power-sharing strategies. Statistical data were digitized to create a virtual digital environment as the platform to implement the power-sharing strategies. The results are promising indicating a 15% increase in the total number of towing jobs in between bunkering intervals which was the cost function used to govern the optimization potential. The power-sharing model provides a solid foundation for future work. Moving forward, a proxy model of HyForce can be incorporated to include environmental considerations such as wind speed, wave height and current, hull resistance, seawater temperature, and salinity to fully encapsulate the factors that might influence the performance of HyForce. This will also provide an indication as to whether the current powertrain and design capacity of 2 MW is sufficient even in the worst-case operating scenarios as well as the ability to quantify the operational windows in which HyForce would be able to perform remarkably well. Figure 9 below provides an insight into how the proxy model can be incorporated to provide a holistic overview of the operation of HyForce.

References 1. DNV, Energy transition outlook 2020 - A global and regional forecast to 2050 (2020) 2. Faber, JH, et al (2020) Fourth IMO GHG study 3. Silvestri L et al (2022) Power-to-hydrogen pathway in the transport sector: How to assure the economic sustainability of solar powered refueling stations. Energy Convers Manage 252:115067 4. de Miguel N et al (2015) Compressed hydrogen tanks for on-board application: thermal behaviour during cycling. Int J Hydrogen Energy 40(19):6449–6458 5. Zhao L et al (2019) Thermodynamic analysis of the emptying process of compressed hydrogen tanks. Int J Hydrogen Energy 44(7):3993–4005 6. Van Hoecke L et al (2021) Challenges in the use of hydrogen for maritime applications. Energy Environ Sci 14(2):815–843 7. Yin L, Ju Y (2020) Review on the design and optimization of hydrogen liquefaction processes. Frontiers Energy 14(3):530–544 8. Zhao L, Brouwer J (2015) Dynamic operation and feasibility study of a self-sustainable hydrogen fueling station using renewable energy sources. Int J Hydrogen Energy 40(10):3822– 3837

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9. Authayanun S et al (2013) Comparison of high-temperature and low-temperature polymer electrolyte membrane fuel cell systems with glycerol reforming process for stationary applications. Appl Energy 109:192–201 10. White CM, Steeper RR, Lutz AE (2006) The hydrogen-fueled internal combustion engine: a technical review. Int J Hydrogen Energy 31(10):1292–1305 11. Zareei J, Rohani A, Wan Mahmood WMF (2018) Simulation of a hydrogen/natural gas engine and modelling of engine operating parameters. Int J Hydrogen Energy 43(25):11639–11651 12. Piaggio B et al (2022) Z-Drive escort tug manoeuvrability model and simulation, part ii: a full-scale validation. Ocean Eng 259:111881 13. Piaggio B et al (2019) Z-drive escort tug manoeuvrability model and simulation. Ocean Eng 191:106461 14. Chen ZS, Lam JSL (2022) Life cycle assessment of diesel and hydrogen power systems in tugboats. Transp Res Part D: Transp Environ 103:103192 15. Menon NV, Chan SH (2022) Technoeconomic and environmental assessment of HyForce, a hydrogen-fuelled harbour tug. Int J Hydrogen Energy 47(10):6924–6935 16. Chen W et al (2022) DC-distributed power system modeling and hardware-in-the-loop (HIL) evaluation of fuel cell-powered marine vessel. IEEE J Emerg Sel Top Ind Electron 3(3):797–808

Battery Management System Using Relay Contactor by Arduino Controller for Lithium-Ion Battery Thitiwut Sathapornbumrungpao, Donwiwat Moonjud, Natthapon Donjaroennon, Uthen Leetond, Suphatchakan Nuchkum, and Thanatsorn Chaisirithungnaklang

Abstract Currently, almost every lithium-ion used in many electronic products has new capabilities. When the battery’s capacity is too large or insufficient, the battery’s performance will be damaged. A battery management system (BMS) is currently order from China and other countries. This is expensive, thousands of, and cannot be modified. This research aims to design and develop an NMC18650 lithium-ion battery used in the battery management system (BMS) 3 cells of 12 Vdc can provide the highest circuit of 2000 mAh as a microcontroller to program according to the configuration of researchers. In this study, the discharge test’s effect was 0.5A. The results show that the critical point decreases at 2.8 Vdc This battery management system will lower the discharge voltage of any battery by 3 Vdc To prevent danger from occurring in the battery. Keywords Battery Management System · Lithium-ion batteries · Arduino Controller · Relay Contactor

1 Introduction Lithium-ion batteries (LIBs) are widely used. There is a strong power supply. Moreover, stable all the time and can be recharged into a new charge (SoC). Long service life. It is environmentally friendly and can store more energy than other types of batteries in both mass (gravimetric energy density) and volumetric energy density (volumetric energy density). Light weight makes it convenient, as shown in Fig. 1, so it has many applications with electrical appliances. That need to be charged, such as batteries of mobile phones, digital cameras, electric bicycles, laptops, and medical T. Sathapornbumrungpao · D. Moonjud · N. Donjaroennon · U. Leetond · S. Nuchkum · T. Chaisirithungnaklang (B) School of Mechatronic Engineering, Institute of Engineering Suranaree University of Technology Nakhon Ratchasima, Suranari, Thailand e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_11

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Fig. 1 The ability to store electrical energy from different types of batteries [2]

equipment, including the application of LIBs to electric vehicles (EV) [1] to power vehicles such as the Tesla electric car, the Honda e: HEVs, for example, by LIBs are based on electrochemical principles when charged, lithium ions move out of the anode structure through the semipermeable membrane into the cathode, forming a compound of lithium and carbon. Furthermore, at the same time, electrons will move from the positive to the cathode through the external circuit. Furthermore, the reaction occurs in the opposite direction at the discharge time. The process by which lithium ions are inserted into the anode or cathode material is called lithium intercalation. Therefore, the voltage imbalance between batteries may be caused by various factors, such as the change of use environment, lifetime, and voltage imbalance between cells. MATLAB has many functions for calculating engineering tasks. This paper discusses the System Identification Toolbox function and Signal Constraints, which is one of the tools used to determine parameters for designing a control system. The greatly improved efficiency of lithium batteries is designed to allow devices to work continuously for hours or days on a single charge. to be able to do this There is a high level of electrical energy inside the battery. If packaged incorrectly or damaged during transportation Lithium batteries may short-circuit. This results in abnormally high heat and combustion. Therefore, a battery management system (BMS) is used in conjunction with a small control system. (Microcontroller) to control the system’s operation and can program to configure various conditions (Condition), which can be applied in various applications, as well as program commands to control the Input / Output pins. In this research, we choose Arduino UNO R3, which can receive both Analog and Digital [3] and can be commanded to control various devices connected to the circuit with Arduino. It is also cheap and easy to buy.

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Caring for lithium-ion batteries starts with storing them. or equipment using this type of battery to be within the temperature and proper humidity Avoid charging under high temperatures. (Because it will cause the battery to deteriorate faster), do not use it until the battery is completely depleted. or very close Should keep charging the battery often. Because frequent charging does not affect the deterioration of the battery, use a standard charger. There is a stable and stable power supply to the battery. Do not charge the car frequently. because the lights in the car are not stable. Currently, lithium-ion batteries have been developed to increase efficiency continuously. In addition, it can also be used to store electricity from wind turbines and solar cells, which can be said to be an innovation that truly revolutionizes the way of life of modern humans.

2 Literature Review 2.1 Principle of Lithium-Ion Battery Lithium-ion batteries operate based on the electrochemical principle. The electrical energy that we charge causes a chemical reaction inside the battery. Chemical reaction forces the lithium ions to flow out of the structure of the cathode material. Then flows through the electrolyte, then through the separator, and intercalate is in the structure of the anode material. This reaction causes the cathode materials such as LiMO2, LiM2O4, and LiMPO4 and the anode materials such as C, Sn, and Si to become unstable. While in use is discharge, chemical reactions in the battery can occur as Spontaneous reactions. In other words, lithium ions flow out of the structure of the cathode material. They are, moreover, inserted into the anode terminal. It flows out of the anode material structure and into the original cathode material structure, re-stabilizing the system and allowing electrons to pass through the electric circuit, where electrons flow through the current metal collector and output electrical energy. Whenever all the lithium ions flow back to their original state, the reaction will either end or run out of charcoal. Which battery is used, it must be charged again, which continues until the battery is depleted and the life of that battery type is reached. In general, each type of battery has different discharge characteristics. As can be seen, lithium-ion batteries have a higher voltage than other types of batteries. It depends on how to look at the green line as shown in Fig. 2 with voltage. The maximum power is about 4.2Vdc. Then when the load is connected to the battery, there will be a slight decrease in voltage and gradually decreases according to the duration of the current consumption of the load that is used to a particular value; it will cut off. The work point is called the cutoff point about 3Vdc. In this research, a lithium-ion battery type NMC was selected. The material used for the cathode is nickel manganese cobalt, and the anode is silicon because it has a long life, provides more voltage than other types of batteries, and is lightweight. In addition, be easy to

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Fig. 2 Discharge characteristics of Li-ion, lead acid, Ni-Zn, NiCd, NiMH and Zn-MnO2 cells [4].

carry and suitable for experiments or research, which can be used as a prototype in the future and can increase the voltage even more. This type of battery works based on electrochemical principles. the charged energy will go into a chemical reaction inside the battery That causes the lithium-ion to flow out. by flowing through the electrolyte After that, it passes through a separator and is inserted into the structure of the anode material. As a result of the reaction, the cathode material and the anode are in an unstable state [5, 6]. And while in use or discharging Battery chemistry can occur spontaneously. And will make the system stable again. And whenever the lithium-ion flows back to its original state That means the battery is gone.

2.2 Lithium-Ion Battery Components Lithium-ion batteries have four main components [7], as shown in Fig. 3. 1) The electrode consists of the cathode and the anode. 2) The separator prevents the cathode from coming into contact with the anode terminal until a short circuit occurs. Fig. 3 Lithium-ion battery components [2]

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3) electrolyte (Electrolyte) is a solution of lithium salts, which acts as a conductor that allows ions to flow but does not allow electrons to pass through. Therefore, it is a good ionic conductor. But it is a bad electronic conductor. 4) Current collector is the part of the conductor metal that allows electrons to flow through the external circuit and lead to the use of electrical energy such as copper (Cu), aluminum (Al), etc.

2.3 Analog Read for Arduino For the Arduino board, the Analog output channel has a resolution (Resolution) at 10 bits, which means 210 = 1024 level. It can accept Analog to Digital Converter (ADC) input voltage. The maximum value is VCC or 5Vdc ADC can be calculated from Eq. 1. ADC =

Ui × 1024 U0

(1)

where Ui is analog voltage input and U0 is Ground voltage input. The Arduino board will accept both voltage values and process them through Comparator, as shown in Fig. 4, and output them as Digital data enter the computer.

Fig. 4 Block diagram of analog to digital conversion [8]

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3 Methodology 3.1 Equipment The experiment used all equipment following as. 1) 2) 3) 4) 5)

Lithium-ion batteries type NMC18650 Arduino UNO R3 Board. Magnetic Relay 7 pcs. Load spec 500 mAh. Imax B6 80W digital charger.

3.2 Prepare Equipment 1) The NMC18650 lithium-ion battery was assembled by first connecting two batteries in parallel [9-10] and then using spot welding to connect the nickel to the positive and negative terminals of the LIBs, as shown in Fig. 5, to obtain three cells to increase the current. 2) Serialize all three battery cells to increase the battery voltage to 12Vdc 3) Charge this battery pack and balance each cell’s voltage with the Imax B6 80W digital charger. Then measure the voltage on the battery pack with a digital multimeter, and the total voltage is 12Vdc. All three cells’ voltage is 4Vdc. 4) Connect the experimental circuit as shown in Fig. 8. 5) Discharge of the battery by using a digital charger Imax B6 80W to find the current that the battery dissipates equally to 2000 mAh 6) Install all seven magnetic relays and connect the circuit to connect. With Arduino board by using a breadboard to help connect the electricity to flow fully, as shown in Fig. 6. 7) Connect a lithium-ion battery to supply power. And then connect a 0.5 A load. Fig. 5 The battery pack has a capacity of 12Vdc

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Fig. 6 The experiment circuit

3.3 Experimental Design The experiment tries to determine the critical voltage of lithium-ion batteries. First, connecting a 0.5 A lamp device to the battery pack discharges [11] its electricity to the point where one of the battery cells has a dramatic voltage drop. Then, all the recorded voltage values plot to show the theoretical trend, as shown in Fig. 7. From Fig. 9, found that the second cell battery has a critical voltage of 2.8Vdc. Battery usage Therefore set the cut-off point of the discharge circuit at 3Vdc.

Fig. 7 Test the battery to discharge to determine the critical voltage

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Fig. 8 BMS Program Operation Diagram

Fig. 9 BMS Program Operation Diagram

3.4 The Battery Management System Test The researchers are Testing the battery management system by defining various conditions, as shown in Fig. 8, to stop discharging to the load. Connecting a lithiumion battery to a 1600 mAh load, the voltage measured from the battery’s three cells is logged every minute.

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4 Experimental Result The voltage critical point from connecting the 0.5A load and letting the battery. discharge from Fig. 9, the voltage of each cell from 4Vdc will gradually decrease. The second battery has a voltage drop faster than the other cells, indicating that the second cell has lower battery health than the others. After the voltage is 3Vdc, the second cell battery has a lower voltage drop. Rapidly, while cells 1 and 3 tended towards the same direction. The battery’s total voltage is the yellow line with a starting voltage of 12.0Vdc. When discharged, the voltage drops to a critical point of 10.7Vdc. In testing, it found that the battery management system can cut the discharge from the battery to load according to user-defined conditions to prevent damage to the battery.

5 Conclusion This research aims to design and develop the NMC 18,650 lithium-ion battery used in a 3-cell 12Vdc Battery Management System (BMS) capable of providing a maximum capacity of 2000 mAh. The experiment used a microcontroller for the user to set the voltage rating to stop the battery discharge. The results of the discharge test to a 0.5A load showed that this battery pack has a critical point of 2.8Vdc, so this battery management system will not discharge the load at any cell voltage below 3Vdc to prevent harm to the battery. Furthermore, the battery management system (BMS) can modify various program configurations and be researched and applied to batteries with higher voltages, such as 24Vdc, 48Vdc, 72Vdc, Etc, making them applicable to a wide range of electrical equipment. Acknowledgements This work was supported by the Suranaree University of Technology.

References 1. Na JK, Na KS, Lee HJ, Ko YS, Won CY (2014) Power conversion sys-tem control method for hybrid ESS. In: 2014 IEEE Conference and Expo Trans-portation Electrification Asia-Pacific (ITEC Asia-Pacific), pp 1–5 2. Tarascon J-M, Armand M (2001) Issues and challenges facing recharge-able lithium batteries, vol 414, November 2001, pp 2–5 3. Fransiska RW, Septia EMP, Vessabhu WK, Frans W, Abednego W (2013) Electrical power measurement using arduino uno microcontroller and LabVIEW. In: 2013 3rd International Conference on Instrumentation Com-munications Information Technology and Biomedical Engineering (ICICI-BME) Bandung, pp 226–229 4. Mahammad AH, MD.Murshadul H, Yushaizad Y, Pin J (2018) Special section on advance energy storage technologies and their application state-of-the-art and energy management system of lithium-ion batteries in electric vehicle. 6:19362–19378 (2018)

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5. Wen J, Jiang J (2008) Battery management system for the charge mode of quickly exchanging battery package. In: 2008 IEEE Vehicle Power and Propulsion Conference, Harbin, China, pp 1–4. https://doi.org/10.1109/VPPC.2008.4677489 6. Wey C -L, Jui P-C (2013) A unitized charging and discharging smart battery managemenst system. In: 2013 International Conference on Connected Vehicles and Expo (ICCVE), Las Vegas, NV, USA, pp 903–909 (2013). https://doi.org/10.1109/ICCVE.2013.6799924 7. Dahn J, Ehrlich G (2011) Lithium-ion batteries. In: Reddy T, Linden D (eds), Linden’s Handbook of Batteries, 4th ed., McGraw-Hill, pp 26.1–26.79 8. Daniela S, Slavi L, Katya A (2019) In: Proc.X National Conference with International Participation Electronica 2019, Hardware and Software for Learning Ana-log-to-Digital Converters in Engineering Education, Sofia, Bulgaria 9. Chen Y, Zhu Y, Sha C, Zhang X (2022) Design of hardware-in-the-loop test system for new energy vehicle battery management system. In: 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, pp 1183–1187. https://doi.org/10.1109/IMCEC55388.2022.10019828 10. Devi B, Kumar VS (2022) Lithium-ion battery management system: a review. In: 2022 International Conference on Power, Energy, Control and Trans-mission Systems (ICPECTS), Chennai, India, pp 1–6. https://doi.org/10.1109/ICPECTS56089.2022.10047494 11. Lawder MT et al (2014) Battery energy storage system (BESS) and battery management system (BMS) for grid-scale applications. Proc IEEE 102(6):1014–1030. https://doi.org/10. 1109/JPROC.2014.2317451

Electric Power Construction and Power Market Analysis

Analysis of the Impact of Unit Output and Quotation on Locational Marginal Price of Unit Nodes Dong Liu, Fan Li, Ke Sun, Hanqing Liang, Kexin Zhang, and Yong Xing

Abstract The node marginal electricity price in the spot market is uncertain and volatile, so it is important to strengthen analysis of the spot market’s electricity price law and identify the major factors influencing locational marginal price of unit nodes. This will help market participants understand market laws in-depth and enable them to make better market decisions. In this paper, a new node electricity price decomposition method is introduced to quantitatively analyze and study the influencing factors of locational marginal price of unit nodes. Firstly, the formula of the relationship between locational marginal price and the shadow prices of unit operating constraints and system reserve constraints is deduced considering the system reserve. Secondly, the influence of unit’s upper and lower power constraints on locational marginal price, system power price and congestion price are analyzed. An analysis tool for the formation and influencing factors of the locational marginal price is provided, and it is helpful for market participants to make a referential assessment of the impact of their own unit’s physical parameters on earnings. Finally, based on a 5-node case study, the influence of unit output and quotation on the locational marginal price and system energy price are analyzed in different case studies. Keywords Locational Marginal Price · Unit Output and Quotation · Electricity Price Impact Factors Analysis

1 Introduction Currently, many studies have focused on renewable energy sources, HVDC transmission projects [1] and coupling of multiple energy sources [2] because of the energy crisis and environmental concerns. To alleviate the energy crisis and improve the D. Liu · F. Li (B) · K. Sun · H. Liang · K. Zhang State Grid Economic and Technological Research Institute Co. Ltd., Beijing 102209, China e-mail: [email protected] Y. Xing The School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_12

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efficiency of resource allocation, the construction of the spot market has also begun to accelerate in an all-round way. The spot market based on the locational marginal price (LMP), where the LMP is determined by the marginal cost of supplying electricity to customers at each node, can scientifically measure the value of energy and promote full competition in the market. In a perfectly competitive market environment, marginal cost pricing maximizes social welfare while achieving individual benefits and reflects system blockage [3, 4]. To improve the efficiency of energy use, some scholars have also contributed to the control methods of doubly-fed wind turbines [5] and the energy scheduling of shipboard integrated power systems [6]. However, the market is the most efficient means of allocating resources, the locational marginal price has a positive contribution to system construction and has been adopted by several pilot units [7, 8]. Strengthening the analysis of the electricity price law in the spot market, analyzing the main factors affecting the marginal electricity price of the nodes, and analyzing the causes and laws of abnormal prices are important links in establishing a fair, transparent, orderly and stable electricity market, and also help market members to have an in-depth understanding of market law and better make market decisions. The calculation of node marginal electricity price has been relatively mature in theoretical research and engineering practice, and the representation of node marginal electricity price in most literature is composed of three components: system power price, blocking price, and network loss price [9–11]. Therefore, it can be analyzed that the marginal electricity price of nodes is affected by system balance constraints, section blocking constraints, and network losses. In fact, the shadow price of the system balance constraint is not only affected by the system balance constraint, but also by the maximum and minimum output constraint of the unit, the system backup constraint and other constraints. The above components do not provide enough information to express how these constraints affect the system power price, and a new node electricity price decomposition method needs to be introduced to quantitatively analyze and study the influencing factors of the locational marginal price of unit nodes. Domestic and foreign researchers have carried out some relevant research on other influencing factors of locational marginal price. In [12], the issue of describing how data quality effects real-time locational marginal pricing (LMP) is taken into consideration. Under different bad data models, the worst-case impact of bad data on real-time LMP is analyzed. In [13], The impact of privileged renewable power generation on German spot market prices is analyzed. In [14], based on the actual data of the PJM (Pennsylvania-New Jersey-Maryland) market in the United States, the statistical properties of electricity prices were verified by the K-S (KolmogorovSmirnove) test. In [15], under the framework of an optimal power flow market clearance, an expression is given to calculate the sensitivity of regional marginal prices to electricity demand. In addition, sensitivity to other parameters can also be obtained. Finally, through examples and case studies, the derived expressions are illustrated. In [16], based on the actual operation data of Zhejiang electricity market, the spot electricity price in the electricity market was statistically analyzed. Some abnormal fluctuations in electricity prices in the electricity market are explained, including

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the reasons for negative electricity prices and zero electricity prices. Literature [17] introduces a simplified risk price component into the locational marginal price. The literature [18] qualitatively analyzes the effects of system load changes, abnormal equipment status, unit climbing and other limitations of the operating characteristics of the machine itself and network blockage, as well as the influence of human exercise of market power or mutual collusion, system reliability and failure. However, the above literature lacks quantitative analysis of the impact of unit operation constraints on electricity prices. Therefore, this paper comprehensively considers the problems existing in the above research, the formula of the relationship between locational marginal price and the shadow prices of unit operating constraints and system reserve constraints is deduced considering the system reserve. The influence of unit’s upper and lower power constraints on locational marginal price, system power price and congestion price is analyzed. The research results of this paper can provide an analysis method for the analysis of the influencing factors of LMP. It is helpful for market members to make reference and evaluation of the impact of their own unit physical parameters on revenue.

2 Single-Time Clearing Optimization Model for Spot Market 2.1 Objective Function min F = min{

I 

[Ci ( pi )]}

(1)

i=1

In the formula (1), F represents the power generation cost based on the quotation, including the operation cost and the start-up cost. pi is the active power of unit i. Ci ( pi ) is the operating cost of unit i. I is the number of system units.

2.2 System Operational Constraints 1) The upper and lower constraints of the power of the unit λ1 : pi ≤ Pi

λ11 : pi ≥ Pi

(2)

In the formula (2), λ1 and λ11 are the shadow prices of the corresponding constraints, respectively.

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2) Cross-sectional flow constraints λ2 : pl ≤ Pl

λ21 : pl ≥ Pl

(3)

In the formula (3), Pl is the cross-sectional flow. Pl and Pl are the upper and lower limits of the cross-sectional flow, respectively. λ2 and λ21 are the shadow prices of the corresponding constraints, respectively. 3) System power balance constraints λ3 :

I  i=1

pi =

J 

dj

(4)

j=1

In the formula (4), d j is the system load. λ3 is the shadow price of the system balance constraint. To simplify the derivation process, this article does not consider the system network loss. 4) system reserve constraints λ4 :

I  i=1

(Pi − pi ) ≥ R

λ41 :

I 

( pi − Pi ) ≥ R

(5)

i=1

In the formula (5), R is the system’s up-regulated reserve requirement, R is the system’s downgrade reserve requirement. λ4 and λ41 are the shadow prices of the corresponding constraints, respectively. Pi and Pi are the upper and lower limits of the unit output power, respectively.

3 Calculation Method of Locational Marginal Price of Unit Nodes The locational marginal price of unit nodes is the minimum production cost incurred to supply the next power at a given location, considering all transmission constraints, and using all available generation resources. It is a measure of the value of energy at a node at this moment in the current system operating state. According to the aforementioned optimization model for single time period clearing in the spot market, construct extended Lagrange function to solve this optimization model.

Analysis of the Impact of Unit Output and Quotation on Locational …

L=

I 

[Ci ( pi )] +

i

+

I 

+

λ1 × ( pi − Pi ) +

I 

i

λ2 × ( pl − Pl ) +

i I 

I 

λ11 × (Pi − pi )

i I 

λ21 × (Pl − pl )

i

λ3 ×(

i

J 

d j − pi ) − λ4 × [

j=1

−λ41 × [

I 

169

I 

(6) (Pi − pi ) − R]

i

( pi − Pi ) − R]

i

Based on the KKT condition, the bias derivatives are found for the decision variables pi and pl , as shown in formula (7)-(8). ∂L = Ci + λ1 − λ11 + λ3 + λ4 − λ41 = 0 ∂ pi

(7)

∂L = λ2 − λ21 + λ3 = 0 ∂ pl

(8)

According to the definition of locational marginal price, the locational marginal price of node n can be divided into two parts without considering the network loss: the system electricity price, and the congestion price. For unit i located at node n, both ∂L and ∂∂pLl are equal power transfer factors for that node to the cross section l. ∂ pi From formula (7), it can be seen that the LMP of unit nodes = the quotation of the unit + (the shadow price of the upper limit constraint of the power limit of the unit − the shadow price of the lower limit constraint of the unit) + (the shadow price of the standby constraint on the system − the shadow price of the standby constraint under the system) = the price of the system power − the blocking price. When in uncongested period, −λ3 is the electrical energy price of the system, as shown in formula (9). −λ3 = Ci + λ1 − λ11 + λ4 − λ41

(9)

From formula (9), it can be seen that the locational marginal price of the generator unit node is related to the unit offer, the shadow price of the unit upper and lower output constraints, and the shadow price of the system reserve constraint.

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4 Case Study Based on the PJM 5-bus model, this paper analyzes the impact of unit operation constraints on the system electricity price and the LMP. Figure 1 shows the simplified model of the 5-bus system, where unit 1 is the reference node. The physical parameter information for the unit and transmission line is shown in Tables 1 and 2. Unit5 Unit3

Unit1

Unit2 Unit4

Fig. 1 PJM 5 bus model

Table 1 Physical parameters of units

Table 2 Physical parameters of line

Unit

Maximum output power (MW)

Minimum output power (MW)

Unit offer (yuan/MWh)

Unit1

520

104

188

Unit2

110

22

85

Unit3

600

120

58

Unit4

100

20

91

Unit5

200

40

253

Line

Maximum transmission capacity (MW)

Line-AB

500

Line-BC

400

Line-CD

500

Line-AD

400

Line-AE

400

Line-ED

230

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4.1 Effect Analysis of system congestion on Locational Marginal Price of Unit Nodes (1) In uncongested period, the system load is 852 MW. The calculation results of unit output and LMP are shown in Table 3. It can be seen from the above calculation results, in an uncongested period, the LMP of unit nodes is the system electric energy price, that is, the system electric energy price = the quotation of the marginal unit + the blocking price. At this time, the blocking price is 0, and the system electric energy price is equal to the quotation of the marginal unit. The marginal electricity price of the system is 85 yuan/MWh. (2) In congestion period, the system load is still 852 MW, the line ED transmission power is limited from 230 to 180 MW. The calculation results of unit output and LMP are shown in Table 4. From the above calculation results, it can be seen that when the system is in uncongested period, regardless of network loss, the marginal electricity price of the system is equal to the regional marginal electricity price. In congestion period, the blocking price of the transmission line is 27 yuan/MWh due to the blockage of unit 3, and the marginal electricity price of the node of unit 3 is 58yuan/MWh, while the other four units are not blocked, and the clearing price is 85 yuan/MWh. Table 3 The calculation results of unit output and LMP in uncongested period Unit

Unit output (MW)

Unit1

104

marginal unit √

Unit offer (yuan/MWh)

λ1

λ11

LMP

188

0

103

85

Unit2

88

85

0

0

85

Unit3

600

58

27

0

85

Unit4

20

91

0

6

85

Unit5

40

253

0

168

85

Table 4 The calculation results of unit output and LMP in congestion period Unit offer (yuan/MWh)

λ1

λ11

LMP

188

0

103

85

85

0

0

85

58

0

0

58

20

91

0

6

85

40

253

0

168

85

Unit

Unit output (MW)

Unit1

104

Unit2

108

Unit3

580

Unit4 Unit5

marginal unit √

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4.2 Effect Analysis of Units Output on Locational Marginal Price of Unit Nodes (1) The min-output case In uncongested period, the system load is 852 MW. From the base case, Unit 1 is quoted at 188yuan/MWh and the lower limits of power constraint shadow price is 103yuan/MWh, and the difference between the two determines the LMP. In order to compare and verify the effect of unit’s lower limit of units output constraint on LMP, the minimum technical output of unit 1 was modified from 104 to 300 MW, while other parameters of the case remained unchanged. This case was called the min-output case, and the clearing calculation was carried out again, as shown in Tables 5 and 6. In the min-output case, the output results of each unit are changed, resulting in the marginal unit changing from unit 2 to unit 3, the minimum technical output of reference node 1 increasing, the power lower bound constraint shadow price changing from 103 yuan/MWh to 130 yuan/MWh, and the system electric energy price chancing to 58 yuan/MWh. (2) The max-output case From the base case, Unit 3 is quoted at 58 yuan/MWh and the upper limits of power constraint shadow price is 27 yuan/MWh, and the sum between the two determines the LMP. Table 5 Units output compare between base case and min-output case in uncongested period (Min-output case) Unit

Base case

Min-output case

Unit output (MW)

marginal unit

Unit output (MW)

Unit1

104

Unit2

88

Unit3

600

470

Unit4

20

20

Unit5

40

40

marginal unit

300

√

22

√

Table 6 LMP results of calculation in uncongested period (Min-output case) Unit

Base case

Min-output case

λ1

λ11

LMP

λ1

λ11

LMP

Unit1

0

103

85

0

130

58

Unit2

0

0

85

0

27

58

Unit3

27

0

85

0

0

58

Unit4

0

6

85

0

33

58

Unit5

0

168

85

0

195

58

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Table 7 Units output compare between base case and min-output case in uncongested period (Max-output case) Unit

Base case

Max-output case

Unit output (MW)

marginal unit

Unit output (MW)

Unit1

104

Unit2

88

302

Unit3

600

300

Unit4

20

100

Unit5

40

40

√

marginal unit √

110

Table 8 LMP results of calculation in uncongested period (Max-output case) Unit

Base case

Max-output case

λ1

λ11

LMP

λ1

λ11

LMP

Unit1

0

103

85

0

0

188

Unit2

0

0

85

103

0

188

Unit3

27

0

85

130

0

188

Unit4

0

6

85

91

0

188

Unit5

0

168

85

0

65

188

In order to compare and verify the effect of the upper limit of units output constraint on LMP, the maximum technical output of unit 3 was modified from 600 to 300 MW, while other parameters of the case remained unchanged. This case was called the max-output case, and the clearing calculation was carried out again, as shown in Tables 7 and 8. In the max-output case, the output results of each unit are changed, resulting in the marginal unit changing from unit 2 to unit 1, the minimum technical output of reference node 3 decreasing, the power upper bound constraint shadow price changing from 27 yuan/MWh to 130 yuan/MWh. At the same time, the lower power limit constraint shadow price of Unit 2 and Unit 4 becomes 0 yuan/MWh, and the power upper limit constraint shadow price increases to 103 yuan/MWh and 91 yuan/ MWh, respectively. The price of system electricity becomes 188 yuan/MWh.

4.3 Effect Analysis of Units Offer on Locational Marginal Price of Unit Nodes (1) In uncongested period The offer for unit 3 was increased from 58 yuan/MWh to 87 yuan/MWh, and the results were recalculated to obtain the following Tables 9 and 10.

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Table 9 Units output compare between base case and increase unit 3 quotation case in uncongested period Unit

Base case

Increase unit 3 quotation case

Unit output (MW)

marginal unit

Unit output (MW)

Unit1

104

Unit2

88

Unit3

600

578

Unit4

20

20

Unit5

40

40

marginal unit

104

√

110

√

Table 10 LMP results of calculation in uncongested period Unit

Base case

Increase unit 3 quotation case

λ1

λ11

LMP

λ1

λ11

LMP

Unit1

0

103

85

0

101

87

Unit2

0

0

85

2

0

87

Unit3

27

0

85

0

0

87

Unit4

0

6

85

0

4

87

Unit5

0

168

85

0

166

87

From the results in Tables 9 and 10, it can be seen that after increasing the unit 3 offer, the marginal unit changes from unit 2 to unit 3, the output of unit 2 increases and the output of unit 3 decreases. The system LMP becomes 87 yuan/MWh. (1) In congested period Based on the above case of uncongested period (increasing unit 3 offer), the line ED transmission power is limited from 230 to 180 MW, and the result is the same as the case of uncongested period (increasing unit 3 offer). The modification of unit 3 offer causes the reduction of unit 3 output and the increase of unit 2 output, which leads to the disappearance of the blocking case to the line ED, and the congestion price at each node of the system becomes 0 yuan/MWh. At this time, the locational marginal price is 87 yuan/MWh.

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5 Conclusion Based on the relationship between the locational marginal price of the unit and the unit operating constraint and the system reserve constraint, as well as the analysis of the impact of the unit operating constraint on the system electric energy price, the following conclusions can be drawn. (1) The locational marginal price of the generating unit is positively related to the shadow price of the maximum technical output constraint of the unit and negatively related to the shadow price of the minimum technical output constraint of the unit. (2) Increasing or decreasing the output of generating units can cause the change of output of each unit, which can relieve the line blockage; the change of the offer generating units can cause the change of output of each unit, which can relieve the line blockage. Using the LMP decomposition formula derived in this paper, we are able to finely locate the impact of each operating constraint on the system electric energy price and LMP, and obtain the contribution of each operating constraint shadow piece. The research results of this paper provide an effective method to analyze the components and influences of LMP and their components. At the same time, it can help generationside market members make decisions on declarations of maximum and minimum generation capacity in the spot market, as well as on unit technology innovation. Acknowledgements This work is supported by Science and Technology Project of State Grid Corporation of China (Study on a study of the renewable energy carrying capacity of receiving-end power grid considering the multi spatial-temporal distribution characteristics of renewable energy, 5100-202256018A-1-1-ZN).

References 1. Qin BY, Liu WS, Li HY, Ding T, Ma K, Liu TQ (2022) Impact of system inherent characteristics on initial-stage short-circuit current of MMC-based MTDC transmission systems. IEEE Trans Power Syst 37(5):3913–3922 2. Li HY, Qin BY, Jiang Y, Zhao YH, Shi W (2022) Data-driven optimal scheduling for underground space based integrated hydrogen energy system. IET Renew Power Gener 16(12):2521–2531 3. He YH, Zhou M, Wu ZY, Long SY, Xu J (2018) Study on operation mechanism of foreign representative balancing markets and its enlightenment for China. Power Syst. Technol. 42(11):3520–3528 4. Chen D, Zhong HW, Xia Q (2017) Coordinated planning of generation-transmissionconsumption based on total cost price. Power Syst. Technol.. 41(9):2816–2822 5. Qin BY, Sun HY, Ma J, Li W, Ding T, Wang ZJ, Albert YZ (2019) Robust H ∞ control of doubly fed wind generator via state-dependent Riccati equation technique. IEEE Trans Power Syst 34(3):2390–2400

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Research on Bayesian Game Strategy of Multi-agent Demand Response in Industrial Parks Based on Incomplete Information Xiao Hu, Huifeng Wang, Houhe Chen, Yong Sun, Jiarui Wang, Xiangdong Meng, and Baoju Li

Abstract With the promotion and development of China’s electricity market reform process, how to capture the maximum profits of all responding entities at the level of industrial parks to guide users to actively participate in demand response has become a new problem to be solved. Based on the situation that the market information disclosure is limited and users and aggregators freely conclude contracts in the response process, this paper proposes an optimal game strategy from the perspective of aggregators and a revenue distribution method for users. Considering the interests of users in the industrial park, a multi-agent and two-tier game structure is established with incomplete information Bayesian game as the upper layer and cooperative game as the lower layer; Secondly, in order to maximize their own interests, an algorithm combined with improved counterfactual regret minimization idea and heuristic algorithm is used to solve the game equilibrium to achieve a stable cooperative relationship in the park. Finally, a numerical example is used to verify that multiple comparison models are set up for the declaration strategy and income distribution in different scenarios. The results show that the strategy obtained under the proposed method can improve user income, and the reasonable selection of the benefit distribution mechanism can play a catalytic role in improving the enthusiasm of users to respond to their needs. Keywords Multi-agent demand response · Bayesian game · Electricity market · Industrial park · Incomplete information

X. Hu · H. Wang · H. Chen (B) School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China e-mail: [email protected] Y. Sun · B. Li State Grid JILIN Electric Power Company Limited, Changchun 130022, China J. Wang · X. Meng State Grid JILIN Electric Power Research Institute, Changchun 130021, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_13

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1 Introduction Demand Response (DR), as an important interactive resource under the smart grid framework, plays a positive role in maintaining system stability, reducing grid company investment, promoting new energy consumption, etc. As the degree of user side multi energy load coupling is deepening, the potential of adjustable resources and user energy preference are closely related to the efficient operation of the system, by the accurate modeling of user participation in demand response revenue, the selection of benefit distribution and the analysis of game process are of great significance to continue to promote the electricity market reform. Benefiting from the process of electricity marketization, a complete electricity financial system and a high degree of marketization freedom, game theory has been used for a long time in demand response research in many countries. The literature [1] shows the application prospects of different types of games in different fields of the electricity market and the literature [2] discusses the impact of demand response on reactive power support and grid performance improvement based on non-cooperative game theory, but the economic considerations are vague. The literature [3] takes the economic perspective as the main starting point, and demonstrates that all parties involved in the response under its model can benefit from it by formulating corresponding strategies and subsidy prices for the demand of e-commerce sellers; The literature [4] continues to combine economic factors, and for risk averse energy, considering the uncertainty of market price, proposes a bi-level double-stage model to explore the bidding strategy of power retailers and energy pricing problems; The literature [5] made improvements in the model from the perspective of the power generation side and selected the master-slave game with bargaining game to jointly form a bi-level model, demonstrating that some changes to the mechanism can further improve the economic benefits of all players in the game, but ignored the important position of the power side as the main body of demand response. From the perspective of families, literature [6, 7] uses multi period differential game to establish a model, uses 0–1 mixed linear programming to calculate Nash equilibrium, and gives a game strategy for individual homeowners and power sup-pliers to participate in demand response; The literature [8] designed point-topoint energy transaction between households from the perspective of users and discussed the dynamic pricing strategy under the transaction in combination with non-cooperative game theory. The literature [9–13] focuses on industrial users, chooses master-slave game to describe the relationship between aggregators and users then discusses the benefits of interaction between them under this framework. However, without combining the current situation and mechanism of the domestic power market enough, resulting in the combination of game relationship description and reality can be improved. In general, the existing research on the positive response of users to decisionmaking is not comprehensive. For example, in terms of target group selection, most literatures only focus on the behavior of a single user, while ignoring the bounded

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rationality of multi-users behavior. In terms of user energy consumption decisionmaking, existing research usually quantifies the user’s energy consumption valuation by considering such factors as the user’s purchase cost when using energy, income participating in demand response, subsidies and market consumption surplus, and establishes a user energy consumption decision-making model, the description of the impact of demand response behavior on users is still rough [14, 15]. In this paper, Bayes game theory is introduced to study the game behavior of users’ participation in demand side response. On the basis of distinguishing different volume users according to capacity, a bi-level model is established, the interaction between levels is constructed and the benefit distribution mechanism between aggregators and users is designed; Finally, through the case, the user’s cooperation strategy tendency and the declaration strategy of price & capacity for aggregators to participate in the demand response under different benefit distribution mechanisms of aggregators are obtained. It is analyzed that the demand response in the park has an important role and significance in the user side resource regulation, and to some extent, it can provide some reference and basis for the decision-making of various stakeholders and the formulation of demand response policies.

2 Analysis on Demand Response Subjects of Industrial Parks 2.1 Demand Response Interaction Structure Load aggregator, power dispatching center and power trading center are the main roles in the demand response process. Load aggregator (LA) is responsible for aggregating various power user demand response resources, participating in market demand response, and providing services for two types of users with annual power consumption above 5 GWh (wholesale users) and below 5 GWh (retail users); The Power Dispatching Center is responsible for organizing the security inspection, transaction clearing, evaluation, construction, operation and maintenance of the registration of market entities; The Power Trading Center is responsible for the registration and declaration, contract management, information disclosure, issuance and settlement of demand response market entities [16]. This interaction can be described from the information layer and the physical layer, as shown in Fig. 1 and Fig. 2. Information layer interaction refers to the interaction status of information flow of all subjects participating in demand response in the whole system. As shown in Fig. 1, there are several load aggregators of different sizes in the park. Each load aggregator signs a service contract with users in the park who are interested in participating in demand response. Within the validity of the contract, the aggregators participate in demand response on behalf of others. It should be noted that wholesale users can bypass load aggregators and directly participate in load response.

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Fig. 1 Information layer interaction structure for demand response in industrial parks

Fig. 2 Physical layer interaction structure for demand response in industrial parks

Since the aggregator itself does not provide any load, it is only a collection of loads of all users it represents, so there is only one type of node physically, namely, power users, including wholesale users and retail users. On the whole, the dispatching center and the trading center can be regarded as two forms of expression of the power supply company in the physical layer and the information layer in the process of participating in demand response management. The former pays more attention to the response execution behavior itself in the demand response process, while the latter pays more attention to the incentive and positive guiding role of users in the market at the economic level.

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Fig. 3 Demand response invitation process

2.2 Demand Response Transaction Mechanism Referring to the existing policies, the demand response adopts the invitation system. Set the invitation to be released in the morning 2 days before the response execution day. After the demand is released, each load aggregator and wholesale user will determine the declared capacity and declared price respectively. The market declaration will be completed within 12 h after the invitation is released. Then, during the market clearing process, the invitation will be called in the order of the declared price from low to high. When the quotation is the same, the invitation will be called in the order of the deadline and the latest declaration time until the response capacity demand is met, that means the clearing capacity will win the bid according to the total quantity of marginal virtual power plant, and the response will be implemented the next day. After the implementation of the response and before the appeal of the results, the response evaluation and settlement will also be made, as shown in Fig. 3.

3 Game Structure of Demand Response in Park 3.1 Benefit Boundary and Bi-level Model Due to the declaration process and market clearing in Fig. 3, there are conflicts of interest between different load aggregators. The mode of aggregators, wholesale users and retail users participating in demand response is shown in Fig. 4. Because users have the right to independently choose the aggregators to sign up, aggregators should defeat other aggregators as much as possible in the game process to seek more user resources and maintain the signed users in their own names through a reasonable benefit distribution mechanism to maximize their own interests. The overall game structure is shown in Fig. 5. Since all participants in the upper level of the game directly obtain income from the trading center, and the total amount of income obtained depends on the settlement price of the response and the settlement price is strongly related to the strategic decisions of each participant (the response declaration capacity and the declaration price), so in the process of the response, the strategic decisions of any participant will affect the income of other participants,

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Fig. 4 Participate in the general state of the demand response

This is a typical multi-players non-cooperative game [17]. Also, because only the bid winning capacity and bid winning unit price are disclosed in the actual clearing results, other information of competitors is missing for the game players and cannot give a clear guidance to their declaration strategies, so the game is an incomplete information game. When the sub game equilibrium of the game changes, the red interest boundary in Fig. 4 will be pushed to the new sub game equilibrium. As a result, a multi-player Bayes game pattern has been formed at the upper level. In the lower level of the game from the perspective of substructure, because wholesale users themselves are not allowed to organize other users to participate in demand response, retail users must ally with aggregators if they want to obtain the benefits of demand response. Because the aggregator itself is not a user in the park, it cannot provide any response capacity itself. In essence, it is an alliance agent composed of many users, which provides agreement binding force for the alliance by signing contracts with users, redistributes the profits obtained, and then distributes them to its users; This means that in the lower level of the game, each load aggregator has formed its own cooperative game with transferable payment [18]. Therefore, a number of cooperative game patterns have been formed at the lower level. So far, a two-level game model with Bayes game as the upper layer and cooperative game with transferable payment as the lower-level has been constructed.

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Fig. 5 Bi-level game structure of demand response in the park

3.2 User Dissatisfaction and Interaction Between Levels in Model The dissatisfaction of users determines that the lower-level model forms a cooperative game alliance with transferable payment. As shown in Table 1, the number 1 indicates that users in the corresponding row form an alliance with load aggregators in the corresponding column, otherwise use number 0 as replacement. Once the alliance is formed, changes are not allowed until next month. When the initial lower-level contract relationship table is given, the responsive capacity of each aggregator will be determined. For users, the game strategy is shown as the change of cooperation relationship, and the responsive capacity will directly affect the returns of aggregators in the upper game. After Bayes game, the upper income table generated is shown in Table 2. In the table, r represents the user’s income and R represents the aggregator’s income. In the lower-level alliance relationship table, any user can only sign a contract with one aggregator this month, which means forming an alliance with all other users under the name of the aggregator, and then starting the ith iteration process.

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Table 1 Lower level contract relationship LA 1

LA 2

LA 3

LA 4

…

LA n

User 1

0

1

0

0

…

0

User 2

0

0

1

0

…

0

User 3

0

0

0

0

…

0

User 4

0

0

0

0

…

1

…

…

…

…

…

…

…

User m

0

0

1

0

…

0

Table 2 Matrix of upper level LA 1

LA 2

LA 3

LA 4

…

LA n

User 1

0

(r1, R2)

0

0

…

0

User 2

0

0

(r2, R3)

0

…

0

User 3

0

0

0

0

…

0

User 4

0

0

0

0

…

(r4, Rn)

…

…

…

…

…

…

…

User m

0

0

(rm, R3)

0

…

0

In the process of iteration i, during the upper-level game, the upper limit of the declared response capacity is subject to the current alliance relationship table, and the upper limit of the response capacity will affect the declaration strategy of the aggregator, further affect its income, and change the elements in Table 2, which forms the influence of the lower-level game on the upper-level game. After the end of the response process, the aggregator directly affects the income of each user in its alliance by redistributing the income obtained. When users are dissatisfied with the income, they will change their alliance object when the contract expires, switch to other aggregators and change the cooperation object, which will lead to changes in the elements in the lower-level alliance relationship table, forming the impact of the upper-level game on the lower-level game. Through this mutual influence and constant iteration, the bi-level game establishes an internal correlation through this mechanism, as shown in Fig. 6. Finally, after experiencing multiple sub game equilibria, a stable alliance relationship can be achieved. In this relationship, any participant who leaves the alliance cannot unilaterally obtain higher returns than before. This state is a Nash equilibrium point of the game. For any aggregator, the declaration capacity and declaration price to be adopted at this time are the best response of all its action strategies, also known as the optimal strategy. Since the user may not achieve the expected value of the profit after redistribution to a load aggregator, the user may change to another cooperative aggregator next month in order to obtain more revenue, specifically, when the dissatisfaction with

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Fig. 6 Iterative relationship between layers

the current object has accumulated to a certain extent, the user will change the corresponding elements in the lower-level contract relationship table, taking the dissatisfaction function as the probability of the user making the change. Dissatisfaction function z k is shown in formula (1): ) ]) ( ∑ ( [ r (i) ak , ak < 1, bk > 0 z k = ReLU bk 1 − ∑ Er (i )

(1)

∑ where ak and bk are dissatisfaction function parameters, r (i ) is used to adjust the influence weight of dissatisfaction; Represents the total response revenue actually ∑ Er (i) represents the response revenue obtained by user i in this month, and expected by user i in this month (the arithmetic mean of the revenue of five other users with the lowest absolute value of a user’s own response capacity); The function Fig. 7 Changes in dissatisfaction caused by different expected earnings

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ReLU(·) is used as the activation function to zero the non-positive part. For example, when user i actually gets 30,000 yuan in this month, ak = 0.8 and bk = 1, the change of dissatisfaction caused by different expected income is shown in Fig. 7. When the game reaches the equilibrium point and no external conditions change, all users will not actively change their cooperation strategies in the next month.

4 Model of Aggregator 4.1 Aggregator Revenue Function The revenue of aggregator RLA adopts the method of daily clearing and monthly settlement, and its revenue includes three parts, the response revenue of day ahead invitation demand RRI , the response revenue of interruptible load transaction RIL and the retail shared electricity revenue RRS , as shown in Formula (2): RLA = RRI + RIL + RRS

(2)

As shown in Formula (3), the response revenue of demand for day ahead invitation is mainly composed of response cost RRIDR and assessment cost RRIA . These are mainly built around effective response capacity and invalid response capacity respectively. See the following and appendix in [16] for part meanings of some parameters in this section. ∑ ∑ RRI = RRIDR − RRIA = Q E CRI + (3) (Q I · max(M1 CRI , P3 )) In general, positive returns are obtained according to the effective response capacity, and penalties are obtained according to the invalid response capacity, which is called the assessment cost as shown in Formula (4): max

{∑

} ∑ } } min Q r,RI , C1 ≤ Q R,RI ≤ max Q r,RI Q WB,RI ≤ Q R,RI

(4)

The response income of interruptible load transaction is composed of two parts: standby cost RSP and call cost RTR , as shown in Formula (5):

RTR

RIL = RSP + RTR ∑ CIL · L 1 · Q SP RSP = (1 − u) (∑ ) ∑ =u Q E CIL + (Q I · max(M2 CIL , P6 ))

(5)

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In the process of a single response, if an aggregator is called in the same time, the standby expense part will not get any benefits, which means that the benefits of the two parts cannot exist at the same time. Therefore, the call status function u is used, so that u = 1 means called, and u = 0 means not called, as shown in Formula (6): max

{∑

} ∑ } } max Q r,IL min Q r,IL , C1 ≤ Q R,IL ≤ Q WB,IL ≤ Q R,IL

(6)

The invalid response capacity Q I and effective response capacity Q E in the above categories are calculated according to Formula (7): Q I = max{R1 Q WB − Q F , 0} QE = ]−1 }[ { F F − R1 , 0 QQWB − R1 N1 Q F + max QQWB ]−1 }[ { F F − R2 , 0 QQWB − R2 QF+ max QQWB { ]−1 }[ F F max QQWB − R3 , 0 QQWB − R3 R3 Q F

(7)

Q F = Q BL − Q R where, Q F is the actual response capacity, Q B L is the settlement baseline load, and Q R is the measured load.The retail electricity sharing fee is determined by the ratio of the monthly electricity user sharing fee in the demand area to the monthly actual electricity consumption of the regional electricity users, as shown in Formula (8): RRS =

∑

RZU

/∑

Q RU

(8)

where RZU represents the cost of users share. It is unrealistic to increase the cost of electricity per kilowatt hour sharing infinitely, the upper limit C T of electricity per kilowatt hour sharing is set on this basis. When RZU > C T , no more demand response transaction in the current month and will adjust the response revenue in equal proportion according to the conversion factor K shown in Eq. (9): / K = RZU CT

(9)

In the previous calculation of the actual response capacity, the settlement baseline load Q B L is required, which is the power load that responds to resources when demand response and orderly power use are not implemented. More details about Q B L refer to the appendix of the citation [16]. The mathematical expression of the game and the specific proof of the existence of equilibrium are shown in Appendix A.

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4.2 Algorithm for the Equilibrium In the aspect of obtaining the equilibrium solution, this paper uses an algorithm combined with improved counterfactual regret minimization [20, 21] idea and heuristic algorithm, which randomly takes one out of the action space as the action strategy, determines the virtual regret value by relying on clearing information and actual income, and rewards and punishes the previously generated strategies in this round by the steepest descent direction of the virtual regret gradient, The specific weight of reward and punishment is determined according to the Euclid space distance between the generation strategy and the clearing information. When the minimum average global regret value tends to 0, the average strategy obtained is considered to converge to the Nash equilibrium strategy approximately through the 2ε-equilibrium theorem [22]. The algorithm flow is shown in Fig. 8. This algorithm continuously self-learning by rewarding or punishing the strategy with regret value, so as to guide the strategy generation tendency to approach the direction of smaller average overall regret at a faster speed and ensure the model convergence and solution efficiency. Fig. 8 Algorithm flow chart for the game equilibrium

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5 Case This case adopts the data of an industrial park in Guangdong of China in 2020 and 2021. Parameters of the formula in Sect. 3 refer to the appendix of the citation [16], where 28 aggregators have made statistics on the actual average income of 5 aggregators randomly selected from a total of 1585 demand responses in the park in 2020 and 2021. Table 3 gives the expected income that the five aggregators can achieve if they adopt the strategies given in this paper and compares them. In addition, the average revenue growth per response and the average revenue growth per response of all 28 aggregators are shown in Fig. 9. For most aggregators, if the strategies proposed in this paper are adopted, their aver-age revenue will increase compared with that before the strategies are adopted, while the revenue of a few aggregators will decrease to some extent. In terms of the change trend of the strategy curve, take aggregator 1 as an example, and its declaration strategy curve is shown in Fig. 10. Each demand scenario contains the historical data of all previous scenarios. It can be seen from Fig. 10 that the declared price strategy of the aggregator began to stabilize near the 300th invitation response scenario, and finally fluctuated slightly at 800 yuan/MWh after experiencing all 1585 invitation response scenarios; Its declaration capacity strategy began to stabilize near the 380th invitation response scenario. After experiencing all 1585 invitation response scenarios, it finally fluctuated around 3.5 MWh, but with a large range. This is because the dissatisfaction mechanism proposed above, as a probability of changing the partnership, will lead to the fluctuation of declaration strategy to a certain extent. According to the statistics of multiple learning results, along with the increase of the number of training sets, its strategy volatility will gradually become smooth to a certain extent. For the benefit distribution mechanism of the lower-level game, in order to facilitate comparative analysis, four treatment scenarios are carried out: 1) In order to eliminate the uncertainty impact on the results caused by incomplete rationality of users in the lower level game, assume that users are completely rational, that is, probabilistic dissatisfaction z k in Eq. (1) is simplified and ' replaced by deterministic dissatisfaction z k in Eq. (10), and probabilistic dissatisfaction z k is only used as an evaluation indicator here: z k'

{ =

∑ ∑ 1, Er (i ) − r (i ) > 0 0, other s

(10)

2) The upper limit of response capacity provided by User i every month is constant, that is, it does not change with the month. 3) The total payment allocated to users by aggregators is fixed as a percentage of their total revenue. Here sets 75%. 4) Dissatisfaction cooling: users will not return to an aggregator within two months after leaving the name of the aggregator.

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Table 3 Actual income of some aggregators and expected income after adopting the strategies in this paper Aggregator serial number In Fact

1

9

18

22

25

Average bid winning capacity of 1506–1585 responses (MWh)

1.35

1.53

1.39

1.34

1.67

Average declared price strategy of 1506–1585 responses (¥/ MWh)

1926.28

1751.27

1824.29

1859.53

1765.05

Average return of 1506–1585 responses (¥)

2602.44

2682.39

2518.64

2497.18

2954.72

Strategic The optimal Expectations declaration capacity strategy obtained by this algorithm (MWh)

3.35

3.41

3.38

3.51

3.64

The best declared price strategy obtained by this algorithm (¥/ MWh)

796.34

824.13

772.78

805.20

854.61

Average expected 2667.74 return of strategy obtained by this algorithm (¥)

2810.18

2612.00

2826.25

3110.78

127.79

93.36

329.07

156.06

Average revenue growth per response (¥) Average increase in revenue per response

65.3 2.51%

4.76%

3.71%

13.18%

5.28%

On this premise, in order to facilitate the analysis of the benefit distribution mechanism, a new case is set here. The 5 aggregators in Table 3 are selected as all the optional aggregators in the park. Each aggregator is assigned a different benefit distribution mechanism. That means all 137 users in the park can only choose one of the five aggregators to cooperate each month. To facilitate comparison, the number of initial cooperative users under these five types of allocation mechanisms should be consistent as far as possible, as shown in Table 4: Figure 11 and Fig. 12 respectively show the change trend of the number of users and the change trend of the average dissatisfaction of users under the five different allocation methods. The analysis of five income distribution mechanisms is as follows:

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Fig. 9 Average revenue per response growth and average revenue per response growth across all aggregators

Fig. 10 Curve of declaration strategy of aggregator 1 under 1585 demand response scenarios

Equal distribution (LA1): Due to the difference in the volume of users in the initial cooperation, it will always seriously damage the interests of some users, maintaining a high level of dissatisfaction and this change has an upward trend which leading to continuous loss of customers until the number of users drops to a sufficiently low level, benefits are also correspondingly reduced with low response capacity ceiling. When aggregators cannot provide enough response capacity, it is not allowed to

192 Table 4 Selected aggregators and their corresponding income distribution mechanism

Fig. 11 The change trend of the number of users under the aggregator name under different allocation methods

Fig. 12 Change trend of average user dissatisfaction of aggregators under different distribution methods

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Aggregator serial number

Income distribution mechanism adopted

Initial cooperative user quantity setting

1

Equal distribution

28

9

Distribution by capacity proportion

28

18

Distribution by Shapley value

27

22

Distribution by nucleolus

27

25

Distribution by MDP 27 indicators

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participate in the upper-level game, and will no longer obtain any benefits, and finally the aggregators adopting this allocation method will be delisted. Distribution by capacity proportion (LA9): Compared with the equal division, this will reduce the overall dissatisfaction of users to a certain extent. In general, it can be considered that in a sufficiently long-time scale, the mechanical allocation based on the capacity proportion will still lose all users until the aggregator is delisted. Distribution by Shapley value (LA18): The distribution method based on Shapley value is actually based on the marginal contribution of participants. As an egalitarian distribution method, it is relatively fair for users. In the figure, the average level of dissatisfaction under this distribution mode is relatively low among all five modes. On the whole, although the user composition may not be stable, the upper limit of the response capacity that can be provided and the benefits that are strongly related to it must be relatively stable, which can be considered as a relatively fair distribution method. Distribution by nucleolus (LA22): The most obvious feature of distribution by nucleolus is that the average dissatisfaction of users can be maintained at a low level, but there is still a slow downward trend. Although the number of users is growing at a high level, it is still dominated by small capacity users, and the trend of its total capacity ceiling is unclear. It means although the idea of minimizing the maximum surplus is more consistent with the decision-making philosophy of most users, with the formation of monopoly alliances under other distribution methods, its stability will inevitably decline slowly and there will still be delisting risks. In theory, if other aggregators fail to form an alliance with sufficient volume for a long time, it can still be considered as excellent and can maintain a slow growth of scale under appropriate conditions. Distribution by MDP indicators (LA25): In this allocation mode, the average level of user dissatisfaction is high. Since this method focuses on the ratio of the loss caused to others by quitting the alliance to the loss caused to themselves, it means that users with large volumes can usually obtain lower dissatisfaction, that is, users with higher capacity tend to choose this one. To sum up, in this case, equal distribution and distribution by capacity proportion will lose users at different speeds, resulting in the collapse of the alliance, while the others can maintain a relatively stable operating condition for a relatively long time and each of them has its own advantage; Among them, distribution by nucleolus performs better in terms of user dissatisfaction, while distribution by MDP indicators can play a dominant role when certain conditions are met, and the Shapley one can alleviate user churn to some extent while maintaining a low level of average user dissatisfaction, which is a more balanced alternative allocation method.

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6 Conclusion In this paper, from the perspective of different interests, the feasibility and effectiveness of the proposed method are verified by an example by proposing the relationship of interests that all demand response participants in the park participate in the demand response process and building a game framework, and the following conclusions are drawn: 1) Through theoretical analysis and derivation, it is proved that the Bayes game model proposed has an equilibrium solution, an algorithm combined with improved counterfactual regret minimization idea and heuristic algorithm is applied to solve it. The results show that the algorithm has a good convergence effect and can guide the generation of game strategies when part of the information is not disclosed. 2) The simulation results of the cases show that this paper can improve the revenue of aggregators by the game framework to describe the relationship between aggregators and users in the process of participating in demand response. It also maintains the revenue of users and attract users who have not participated in demand response. 3) From the perspective of aggregators, their income distribution system will be delisted in a relatively short time if they choose equal distribution and distribution by capacity proportion. In different external conditions such as user capacity variance and upper limit of declared response capacity, other three distribution methods have their different advantages, that still needs to be further calculated in combination with the specific conditions under the specific environment to ensure that the selected income distribution method can work. Acknowledgements This work was supported by the Optimization Planning of Multi-form Hybrid Energy Storage System for Clean Replacement of Energy Supply Main Body in Jilin Province (Grant No. 20220508009RC), Science and Technology Development Plan Project of Jilin Province, China (Grant No. 52234221000D), and the Research on Smoke Analysis Theory and Modeling of Electricity/Gas/Hydrogen/heat/Wind/Light Coupling Integrated Energy System (BSJXM-2021101).

Appendix A For the non-cooperative relationship between aggregators, the Bayes game model can be described as follows using quintuple ⎡ = ⟨N , S, Θ, p, u⟩: ∏ ( p) ⎡ = , i ∈ N + i∈N

(11)

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where, Ni (represents all aggregators in the game; Si represents the strategy of aggrep) gator i, Si aic , ai represents the combined strategy∏of aggregator i on the two action Θi is the type space formed by sets of declared capacity and declared price; Θ = i∈N

the type combination of all aggregators; p is the inference of the conditional probability distribution p(θ−i |θi ) of other types of aggregators by aggregator i when it knows its own type is θi ; u i means that aggregator i uses strategy si when it knows that its own type is θi , and speculates on the benefits obtained by other aggregators when they adopt s−i . In terms of proving the existence of equilibrium solution, Nash gave the existence theorem of mixed strategy Nash equilibrium on the basis of Kakutani theorem and Berge theorem, that is, for strategic game G = {N ; S1 , · · · , Sn , u 1 , · · · , u n }, if strategy set Si is a non empty compact subset of Euclid space, and payment function u i is continuous with strategy combination s, then the game has mixed strategy Nash equilibrium [19]. For the game model in this paper, since the strategy set comes from the two-dimensional strategy space composed of two unrelated actions (the declared price and the declared capacity), and both of them are continuous in their respective dimensions, the two-dimensional strategy space formed by them is also continuous, so the strategy set is obviously a non empty compact subset of the Euclid space; For the return function of Eq. (4), since its three parts RRI , RIL and RRS are continuous, the sum is also continuous, meeting all the conditions of the theorem, so the existence of the equilibrium solution of the game is proved.

References 1. Cheng L, Yu T (2019) Game-theoretic approaches applied to transactions in the open and ever-growing electricity markets from the perspective of power demand response: an overview. IEEE Access 7:25727–25762 2. Ghorbanian M, Dolatabadi SH, Siano P (2021) Game theory-based energy-management method considering autonomous demand response and distributed generation interactions in smart distribution systems. IEEE Syst J 15(1):905–914 3. Kunjian GUO, Ciwei GAO, Guoying LIN et al (2020) Optimization strategy of incentive based demand response for electricity retailer in spot market environment. Autom Electric Power Syst 44(15):28–35 4. Wei W, Liu F, Mei S (2015) Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty. IEEE Trans Smart Grid 6(3):1364–1374 5. Bruninx K, Pandži´c H, Le Cadre H et al (2020) On the interaction between aggregators, electricity markets and residential demand response providers. IEEE Trans Power Syst 35(2):840–853 6. Belhaiza S, Baroudi U (2015) A game theoretic model for smart grids demand management. IEEE Trans Smart Grid 6(3):1386–1393 7. Belhaiza S, Baroudi U, Elhallaoui I (2020) A game theoretic model for the multiperiodic smart grid demand response problem. IEEE Syst J 14(1):1147–1158 8. Zhang M, Eliassen F, Taherkordi A et al (2022) Demand-response games for peer-to-peer energy trading with the hyperledger blockchain. IEEE Trans Syst Man Cybern Syst 52(1):19–31 9. Nekouei E, Alpcan T, Chattopadhyay D (2015) Game-theoretic frameworks for demand response in electricity markets. IEEE Trans Smart Grid 6(2):748–758

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Research on Investment Decision of Power Transformation Digital Demonstration Project Based on B-S Option Pricing Model Yang Sun, Xiaodong Xie, Teng Feng, Jianya Pan, and Liu Han

Abstract The digital demonstration project of power transformation is an inevitable trend to improve the quality of power supply and the consumption of renewable energy, and to realize the development of low carbonization and high efficiency of power system. However, due to the large investment and the uncertainty of the comprehensive benefits of the digital demonstration project, the traditional economic evaluation methods cannot effectively support it. This paper proposes a power investment decision model based on B-S option pricing, which combines real options with multi-project portfolio investment. To evaluate the project with flexible option correctly, avoid underestimating the project value, and provide decision support for power grid enterprises to quickly analyze the comprehensive benefits of power grid investment. Keywords Substation digital demonstration project · Project investment decision-making · B-S model · Real option

Y. Sun Equipment Department, State Grid Electric Power Co. Ltd., Beijing, China X. Xie Equipment Department, State Grid Electric Power Co. Ltd., Beijing, China T. Feng (B) China State Grid Corp State Power Economic Research Institute, Beijing, China e-mail: [email protected] J. Pan State Grid Jiangsu Electric Power Co. Ltd., Nanjing, China L. Han State Power Economic Research Institute China State Grid Corp, Beijing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_14

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1 Introduction With the continuous deepening of the new round of power system reform, the profit space of power grid enterprises is greatly compressed, and the investment decision of power grid is also faced with greater risks, therefore, to construct a set of scientific and reasonable smart grid investment evaluation method is of great importance for the development of enterprises Sect 5. The investment methods of smart grid project mainly include comprehensive evaluation method and investment value measurement method. But both have their imperfect aspects.[1]. No matter what kind of project is involved, users have to pay a certain cost, which needs to be weighed with the benefits obtained after participating in the project, so as to decide whether to participate in the project or upgrade the intelligent instrument. [2] However, the uncertainty in the future market brings great obstacles for users to evaluate the value of the project. The proposal of real option can make up for the deficiencies of net present value in project evaluation, and reflect the uncertainty and risk.[3]. And it is widely used in electrical industry, especially in emerging industries and projects. Some scholars use Black–Scholes option pricing method to construct the value evaluation model of the corresponding project of electric load reduction.[1]. Some study on economic evaluation for grid investment based on real option B-S method which is closely related to this topic.[4]. But there is no precedent for the application of demonstration projects with long cycle, large investment and wide impact. In the aspect of power project investment decision, when there are too many uncertain factors, some scholars use the method of stochastic optimization to avoid the risks brought by it.[5]. Some scholars apply real option method based on Monte Carlo simulation to evaluate the uncertain situation.[6]. Taking Chilean electricity market as an example, some scholars applied the concept of stochastic domination to investment decisionmaking and used long-term planning model to obtain the probability distribution of net present value of investment, while considering investors’ risk preference and the overall distribution of investment income.[7]. For multi-project decision applications, this paper proposes a power investment decision model based on B-S option pricing, then combines real options with multiproject portfolio investment into a multi-project optimization model, which can accurately measure the value of investment uncertainty and investment flexibility. Then a concrete example is given to show its rationality and demonstrate it has strong applicability to projects with long investment cycle, large investment cost and large investment risk.

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2 Evaluation Process of Power Distribution Internet 2.1 Applicability of Option Value for Smart Grid Projects Power grid enterprises usually adopt the traditional decision-making method or the real option decision-making method when selecting the project alternative database. However, traditional decision-making methods such as net present value method and payback period method have certain time certainty and value visibility, and do not take into account the flexible value and uncertainty factors of the project. The real option method can better deal with the project investment decision problem under the uncertain condition. When calculating the project value, the real option method not only includes the time value of cash flow, which is calculated by general financial evaluation, but also fully considers the value brought by the flexible value of investment and the reduction of information uncertainty. By adding a different Angle to measure the project value and considering the project value from a more comprehensive perspective, the calculation method will be more scientific and reasonable. Real options method that the option value is derived from the uncertainty, and the relationship has positive correlation with uncertainty. As a result, some investment projects seem unacceptable under the traditional investment decision method, while the real option method considers them worthy of investment. It can magnify the value of investment projects, evaluate the investment value of projects from a more comprehensive perspective, especially put forward the uncertainty of investment projects and decision-making flexibility also has value. This part of the value of option comes from the fact that it has mastered more and richer appraisal information over time as well as its flexible response to the uncertainties of the objective world. The real option property of smart grid investment is reflected in uncertainty, irreversibility and investment flexibility. The uncertainty is reflected in the long investment cycle of the project and diversified investment risks. And the long-time span investment cycle leads to the accuracy of investment income forecast also brings great influence. The irreversibility is reflected in the specificity of the power grid, which cannot be modified into other facilities except the production and service contents of the power grid enterprises after investment. Flexibility is reflected in the small competition among power grid enterprises compared with other industrial markets, and they have the right to choose the investment timing of power grid projects.so it is suitable to adopt the real option method, and can improve they scientific and precision of investment. The types of real options included in smart grid projects can be roughly divided into growth investment options, expansion investment options, phased investment options and delayed investment options. The four types of call options are all called options. Originally, they are financial options. Investors buy options because they have a positive attitude towards the future development of the stock price and believe that the stock will rise in the future and the investment can make profits.

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2.2 System Dynamics Regression Model B-S Option Pricing Model Theory Besides B-S model, real option pricing models also include binary tree model, Trinomial tree model, Geske model, stochastic differential equation method and Monte Carlo simulation method [8]. Stochastic differential equation method is based on a series of assumptions to build a bounded stochastic differential equation, through mathematical calculation and derivation to get the value of the option. Monte Carlo simulation method is to simulate the possible price change path of the underlying asset before the option expiration date by using random numbers after setting the probability distribution of uncertain parameters when the underlying asset price, execution price and execution time are uncertain. Compared with other models, B-S model assumes strict conditions, rigorous reasoning process, and has the advantages of fewer parameters and simple calculation, which can weaken the error caused by excessive parameters. The Black Scholes model is used to calculate European call options. Its pricing method is to assume no arbitrage, use partial differential equation and boundary conditions to describe the value of the option, express the value of the option as a function, and construct a portfolio composed of underlying asset stocks and risk-free assets. Stock prices follow the geometric Brownian motion, which is as follows: dS = μSdt + σ Sdz

(1)

where S is the price of the underlying asset; μ is the drift rate of the underlying asset price; σ is the price volatility of the underlying asset; dz is the increment of the √ Wiener process, and dz = ε dt, ε satisfies the standard normal distribution. (2)

s.t.

Economic Subsystem Model In general, financial evaluation, the actual meaning of net present value is to compare the estimated net income with the interest paid at the discount rate. If the net income is not enough to pay the interest, that is, the net present value is less than zero, it means that the project will not only fail to make a profit but also lose money. If the net income can just pay the interest, that is, the net present value is zero, it means that the investment in this project does not gain or lose. It can be evaluated according to other investment factors. If there is still surplus of net income after the payment of

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interest, that is, the net present value is greater than zero, it means that the remaining income is the economic benefit of investment that can be obtained by the enterprise, and the project can be invested. However, the calculation of net present value ignores the uncertain value of the project, which will underestimate the actual value of the project. Judging whether the project is worth investment by whether the net present value is greater than or equal to zero will make some projects with investment value actually missed and abandoned. Therefore, the first step of smart grid project investment decision is to use real option method and B-S model. This paper will only discuss call options and assume that the options generated by smart grid project investment decisions are European style options, also assume that project net present value conforms to lognormal distribution and does not require risk compensation. The B-S model is used to calculate the real option value of smart grid project investment. Then use B-S model to calculate the real option value of smart grid project investment. The formula is as follows: Ci = S0 N(d1 ) - Xe - rT N(d2 )   ln(S0 /X) + r + σ 2 /2 T d1 = √ σ T   ln(S0 /X) + r + σ 2 /2 T where d1 = √ σ T √ d2 = d1 −σ T

(3)

In the above, Ci is the value of the investment option of the ith investment project, S0 s the current value of the underlying assets, namely the net present value of the power transformation digital demonstration project. N(·) is cumulative normal distribution function. X is the option strike price, also means the initial total investment of the digital demonstration project of power transformation. r is the risk-free interest rate. T is the validity period of the option. σ is the volatility of the underlying asset. The net present value of the project is NPV =

n  (CI - CO)t (1 + i) - t

(4)

t=0

According to the real option method for single project test, the main steps included are to calculate the real option (formula (1) (2)), calculate the net present value of the project, add the first two to get the expanded net present value of the power transformation digital demonstration project (ENPVi), namely the total value of the investment project, screen out the infeasible projects and eliminate them. When the extended net present value is greater than zero, the project is feasible. On the contrary, the project is not feasible and should be eliminated. At the same time, the investment

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decision is very important for the evaluation of the self-healing ability of the digital demonstration project. The evaluation index of self-healing ability can be self-healing speed. When the self-healing speed is greater than 3 min, it means that the power grid is not self-healing. As a demonstration project of power transformation digitalization, it does not meet the characteristics of safe self-healing and should not be invested [7] When making the optimal decision of multi-project portfolio, it is necessary to strive for the maximum investment economic benefit under the condition of limited construction funds, and at the same time, it is necessary to consider the influence of project reliability and social benefit. Integrating various elements, establish a multi-project optimization model: First, determine decision variables. Let xi (i = 1,2… n) is the decision variable indicating whether to invest or not, n is the number of projects. If xi = 0, it means no investment in the project; If xi = 1, it means investment in the project. Then determine the objective function. The vestment objective is to maximize the utility of investment. Suppose there are n projects to be invested, the total project value of project I is ENPVi, and the total budget expenditure is F. xi (i = 1,2… n) is the decision variable indicating whether to invest or not, n is the number of projects. If xi = 0, it means no investment in the project; If xi = 1, it means investment in the project. The decision variables are zero–one variable, the model is as follows:  n  n  i = 1 xi ci + c n maxZ = xi ENPVi + −1 Cimax - P(x) i = 1 ci + c i=1 i=1 ⎧ n  ⎪ ⎪ xi Ii ≤ F ⎪ ⎪ ⎪ i=1 ⎪ ⎧  ⎪ xc +c ⎪ ⎪ ⎨ α( xi ci + c - θ L), i Li >θ ⎪ ⎪ ⎨ P(x)= xi ci + c ] 0, L ∈ [γ  ,θ  ⎩  s.t. xi ci + c c c , 1.8 ⎨ ⎪ 11000 6 ⎪ ⎨ xi ci +2200 i=1 P(x) = ∈ [1.6, 1.8]  11000 ⎪ s.t.  0, ⎪ 6 ⎪ ⎪  ⎪ ⎪ ⎪ ⎪ 0.2 1.6 × 11000 − xi ci − 2200 , ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ i=1 ⎪ ⎪ 6 ⎪ ⎪ ⎩ ⎪ i=1 xi ci +2200 ⎪ < 1.6 ⎪ 11000 ⎪ ⎪ ⎪ ⎪ x2 − x6 = 0 ⎪ ⎪ ⎪ ⎪ xi = {0, 1} ⎪ ⎪ ⎩ i = 1, 2 . . . , n By solving the above equation, it can be concluded that when × 1 = 1, × 2 = 1, × 3 = 0, X4 = 0, X5 = 1 and X6 = 1, the model has the optimal solution. That is, when the first, second, fifth and sixth project portfolios are invested, the investment return is the largest, and the maximum total project return is 79.3978 million yuan.

4 Conclusion Based on the characteristics of uncertainty, irreversibility and investment flexibility of the digital demonstration project of power transformation, this paper uses the Black Scholes option pricing model to establish a mathematical programming model by comprehensively considering the economy, reliability and social costs of smart grid projects, then carry out the uncertainty economic analysis of the digital demonstration project of power transformation. The evaluation results can objectively reflect the real value of investment, provide a reference investment decision method for power grid enterprises, and enrich the research on investment decision method of substation digital demonstration project. In addition to highlighting the risk characteristics of options, this model can also avoid undervaluing the value of projects: because the inconsistency between the investment subject and the beneficiary subject of environmental benefits weakens the enthusiasm of the investment subject. However, environmental benefits will eventually be reflected in economic benefits through long-term transformation, so a more comprehensive measurement can be made based on.[8]. However, the simple B-S model does not take into account the technical benefits of the project construction and the different characteristics of the needs and investment of the power grid development in different regions, and its dynamics is relatively narrow, without forming a sustainable benefit evaluation system. [9] n the basis of this paper, other indicators can be combined for flexible quantification and a more complete scheme can be constructed.

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References 1. Xingyou, Y., Chao, J.: Study on economic evaluation for grid in investment based on real option method. Int. J. Hydroelectr. Energy 28(10),145–147 (2020) 2. Xing, Q., Fu, J., Wen, Q.: Real option method and its application in power system. Automat. Electr. Power Syst. 4(22), 11–18 (2005) 3. Ye, B., et al.: Real option based investment decision-making for incremental distribution network. Autom. Electr. Power Syst. 42(21), 178–184 (2018) 4. Spyros, G., et al.: Long-term expansion planning of the transmission network in India under multi-dimensional uncertainty. Energies 14(22), 7813–7813 (2021) 5. Blanco, G., Waniek, D., Olsina, F., Garcés, F., Rehtanz, C.: Flexible investment decisions in the European interconnected transmission system. Electr. Power Syst. Res. 81(4) (2014) 6. Ramanathan B, Varadan S.: Analysis of transmission investments using real options. In: Power Systems Conference and Exposition, Atlanta, USA, 2006. RG. Liu,KJ.Zhao, CMSA.SERC (2018) 7. Prina, J.: Investment decision making in a deregulated electric industry using stochastic dominance. In: Proceedings of the 2000 IEEE Engineering Management Society, Albuquerque, NM, USA, IEEE, 2000, pp. 546–551 8. Cox John, C., Ross Stephen A., Mark, R.: Option pricing: a simplified approach. J. Financ. Econ. 7(3), 229–263 (1979) 9. Kong, X.Y., Rao, J.T., Cui, K., Sun, F.Y., Yang. J.C.: Investment decision method for regional distribution network planning considering distributed power supply access. China Power 3(04), 41–48 (2020) 10. Cheng, L., Hu, W., Hu, W.: Power grid project investment decision making method based on dynamic iterative ranking. Power Syst. Clean Energy 30(12), 84–90 (2014) 11. Xie, X.: Innovation and application of investment decision method in power grid enterprises. Financ. Account. 24, 36–37 (2003)

Grid Investment Performance Portfolio Forecasting Model Based on PLS-VIP-GA-ELM Yizheng Li, Dong Peng, Lang Zhao, Cong Liu, and Yawei Xue

Abstract This paper conducts data mining on the basic data of Hunan power grid investment performance indicators and 170 influencing factors from 2010 to 2020, compares and determines the ga-elm algorithm through a variety of machine learning prediction methods, processes the data of various influencing factors by using PLSVIP and GA-ELM combined machine learning algorithm, and obtains the prediction results of power grid investment performance in 2020. By analyzing the final predicted values of all performance indicators, the overall prediction effect is good and the prediction accuracy is high. Keywords machine learning · variable screening · investment performance · prediction

1 Introduction Grid investment performance is the efficiency and outcome of the investment behaviour of the managers of grid investment enterprises, and is related to the quality of future project construction and profitability of the grid enterprise, so prediction of grid investment performance results is becoming a pre-investment concern for managers. There are various methods for predicting grid investment performance, and now with the development of machine learning research field, the use of machine learning algorithms to build grid investment performance prediction models is a common and efficient method for predicting grid investment performance. Based on the functional and formal similarities of the algorithms, this forecasting method Y. Li (B) · D. Peng · L. Zhao · Y. Xue State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China e-mail: [email protected] C. Liu Economic and Technological Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha 410004, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_15

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provides higher prediction accuracy and more accurate prediction trends through the analysis of predicted values compared to actual values. Machine learning is a major tool for data analysis and mining in the era of big data. Time-series data prediction has always been a key and difficult area in data mining. Zhao Tingting et al. [1] take stock time-series data prediction as an example and provide a detailed introduction to time-series data prediction methods, focusing on non-linear prediction methods and discussing their future development trends. In the field of price forecasting, machine learning algorithms have a wide range of applications. Shen Hong et al. [2] used a deep learning long short-term memory (LSTM) neural network model for long- and short-term forecasting of six non-ferrous metal futures prices respectively. Liu Ling et al. [3] constructed a combined kernel function support vector regression forecasting model based on differential evolutionary optimization algorithm to predict international crude oil prices. Deng Jiali et al. [4] constructed a new nonlinear function based on Tukey M estimation and built a new MTICA-AEO-SVR stock price forecasting model, and proved the high accuracy of the model through empirical analysis. Machine learning algorithms also have a large number of applications in power load forecasting problems. Xu Jianping et al. [5] proposed a hybrid model based on machine learning, starting from the changing characteristics of electric load, and used a multi-objective pollination optimization algorithm to optimize the extreme learning machine. Wang Shihan, Zhou France [6] proposed a support vector machine (SVM)-based electricity load forecasting model based on existing electricity load forecasting methods to improve the accuracy of electricity load forecasting. Zhao Yang et al. [7] further proposed a short-term power load forecasting method based on a time-convolutional deep learning network based on the classical machine learning algorithm, and the experimental results showed that the time-convolutional network could obtain higher forecasting accuracy. Pang Chuanjun et al. [8] proposed a load forecasting method based on long and short-term memory units (LSTM), and the comparison results showed that the proposed method could effectively improve the accuracy of load forecasting. Xu Qing et al. [9] proposed a short-term power load forecasting method based on Ridge regression estimation for RBF neural networks, which provided a new idea for power load forecasting. The data-driven ultra-short-term wind power prediction is one of the key foundations for large-scale wind power grid-connected operation. Yang, Mao, and Zhang, Robin [10] summarized the prediction ideas of offline data-driven/deep learning algorithms and online applications according to the prediction process, gave evaluation tools for wind farm data screening, and summarized the latest research progress of deep learning algorithms. Currently, in the existing field of machine learning, there is less research on investment performance prediction. The use of machine learning algorithms has greatly improved the prediction accuracy of the prediction model, reduced the fitting error, and solved many prediction problems that could not be effectively predicted. Introducing machine learning algorithms into power grid investment performance prediction can give play to its outstanding advantages, obtain more accurate power grid investment performance prediction values, analyze the development trend of

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power grid investment performance, and have a good guiding role in future power grid investment planning.

2 Methodology 2.1 Variable Filtering Method PLS-VIP is an effective variable selection method that can accurately measure the importance of the influence of explanatory variables on the target variable by calculating the VIP value of the variable, and thus has good real-time, robustness and generalisation capabilities in achieving dimensionality reduction of high-dimensional variables (PCA method), typical correlation analysis between two sets of variables, and improving the predictive accuracy and stability of the model. PLS method integrates principal component analysis and multiple linear regression, and its basic principle can be expressed by two relationships: X = T PT + E

(1)

Y = U QT + F

(2)

X and Y are independent and dependent variables, T and U are score matrices for X and Y , P and Q are load matrices, E and F are residual matrices for X and Y fitted using PLS. Generally, T /= U , when two matrices are used to determine the factors, the factors of X and Y are: U = Bt + e

(3)

B is the regression coefficient of the latent variable, and e is the corresponding residual in PLS regression. After PLS decomposes the data, a new position data space is constructed using VIP indicators, thereby achieving the goal of screening variables. The VIP indicator measure xi is used to explain the importance of Y . For the ith independent variable, its expression is defined as: / V I Pi =

k

m ∑ h=1

Rd(Y :th )wh2 i ∑m h=1 Rd(Y :th )

(4)

The number of independent variables is k; wh i is the weight of the independent variable on the principal component; th is the h column component of matrix T ; Rd(Y : th ) is the ability to interpret Y for the h th principal component.

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Fig. 1 PLS-VIP method variable screening flowchart

Standardized matrix X

Determining the number of latent variables through explanatory testing and cross testing

PLS regression modeling Calculate the VIP value of each variable

Remove the argument with the lowest VIP value N

Calculate whether R ^ 2 meets the accuracy requirements Y

Finish

Based on the PLS-VIP algorithm, the process of screening and fitting independent variables is shown in the Fig. 1.

2.2 Combined Forecast Model Basic Principles of Extreme Learning Machines Extreme learning machine (ELM) is a single implicit layer feedforward neural network learning algorithm, which randomly sets the input layer weight matrix and threshold value before training and keeps it constant during the training process to improve the prediction accuracy by optimising the number of neuron nodes in the implicit layer. It has the characteristics of simple structure, less computational parameters, fast training speed and good generalization performance. In contrast to traditional feedforward neural networks, which often fall into local optimum solutions due to step size setting problems when using the gradient descent method, extreme learning machines remove the complex structure and numerous computational processes, and obtain higher accuracy and faster speed in processing simple models. Assuming there are N training samples (xi , yi ), input data xi = [xi1 , xi2 , · · · xim ]T , the expected output is yi = [xi1 , xi2 , · · · xim ]T that is, the input layer has m neuron nodes, and the output layer has n neuron nodes. Let the hidden layer have l node, ωi j represents the weight values of input layer node i and output layer node j, bl represents the threshold value of hidden layer node l, both of which

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are randomly generated. If taken βi j is the connection weight between the hidden layer i and the output layer j, and g(·) is the activation function. If these two are adjustable objects, the actual output y ' can be expressed as: y 'j =

l ∑

βi · g(ωi · xi + bi ), j = 1, 2, · · · , n

(5)

i=1

If the model is represented by a matrix, take the known part, and let H be the hidden layer output matrix, and T be the pole learning machine output matrix, β Is the weight matrix between the hidden layer and the output layer, then the above formula can be expressed as: H ·β = T

(6)

⎤ g(ω1 x1 b1 ) · · · g(ωl x1 bl ) ⎥ ⎢ .. .. .. H =⎣ ⎦ . . . g(ω1 x N b1 ) · · · g(ωl x N bl ) ⎡ T⎤ β1 ⎢ .. ⎥ β=⎣ . ⎦

(8)

⎤ y1T ⎢ ⎥ T = ⎣ ... ⎦

(9)

⎡

⎡

(7)

β NT

y NT Given the activation function g(·), H and T are both known quantities. Using the Moore–Penrose generalized inverse to solve the least squares generalized inverse of the equation, it is obtained that: β∗ = H + · T

(10)

Introduction to the Model Since the weight matrix and threshold values of the ELM algorithm are generated randomly, it is impossible to guarantee that the ideal prediction accuracy can be obtained for each generated random value brought into the operation, and there are problems of poor stability and complex network structure. In addition, different activation functions can have a large impact on the prediction results of the algorithm. Therefore, in order to enhance the accuracy and stability of the ELM model output, it is also important to select the appropriate number of nodes and activation functions for the hidden layer. In this paper, the weight matrix and threshold values are optimised using a genetic algorithm.

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The Genetic algorithm (GA) is a heuristic intelligent search algorithm, which searches for optimal solutions by simulating the process of gene selection, crosspairing and trait inheritance in natural organisms, incorporating the natural evolutionary idea of survival of the fittest and elimination of the unfit, with the characteristics of good robustness and strong global search capability. the GA-ELM algorithm integrates the advantages of both, mapping the weight matrix ω and the threshold b bias to the genes of individuals in the population, the fitness corresponds to the training error of ELM, and with the activation function and the number of nodes in the hidden layer set, the fitness of each chromosome is calculated and the optimal chromosome is selected.

3 Empirical Analysis 3.1 Grid Investment Performance Indicators Measuring grid investment performance requires a selection of indicators that reflect the actual level of grid investment performance in a number of ways. In this paper, a total of 25 performance indicators have been selected from six dimensions to analyze and assess grid investment performance. Taking safety and quality, service quality, green and low-carbon, and technological innovation as prerequisites, nine indicators such as N-1 passing rate, load capacity ratio and customer service satisfaction are set as the basis and guarantee for enhancing efficiency and effectiveness. Taking operational efficiency and management effectiveness as the main focus, 16 indicators are set. Firstly, operational efficiency focuses on the efficiency of people, finance and materials, setting 8 indicators such as unit investment in electricity sales and investment to capital ratio; secondly, management effectiveness focuses on operating income, cost control and profitability, setting 8 indicators such as the increase in supply and sales index. Now take the profit margin, unit power grid asset sales revenue, and unit investment increased sales of electricity as examples to specifically analyze the impact of these three indicators on power grid investment performance. (1) Profit margin. The profit margin used as a performance indicator for power grid investment here includes the profit margin of power grid operating revenue and the return on net assets. It is a business benefit indicator in power grid investment, reflecting the profitability of power grid investment. The profit margin indicator is influenced by the unit price of electricity, the amount of electricity sold, and the fixed and variable costs of producing unit electricity. (2) Revenue from electricity sales per unit of grid assets. It refers to the income obtained from the sale of electricity produced through unit grid assets. It is a business benefit indicator in grid investment, reflecting the operating income of grid investment. The revenue indicator for power sales per unit grid asset is

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affected by the total investment in the grid, the price per unit of power, the cost per unit of power production, and the variable cost. (3) Increased sales of electricity per unit investment. It refers to the increment of electricity sales per unit amount of power grid investment. It is an operational efficiency indicator in power grid investment, reflecting the financial efficiency of power grid investment. The index of increased sales of electricity per unit investment is affected by the total investment in the power grid, the estimated annual sales of electricity, the sales of electricity in the previous year, and the estimated annual investment in power grid business.

3.2 Data Source and Processing The complete data used in this paper are 25 performance indicators and 170 influencing factors for power grid investment in Hunan Province from 2010 to 2020. Some of the impact factors are missing data for individual years and are interpolated to fill in the data, while individual impact factor data cannot meet the screening requirements before variable screening is carried out and are discarded.

3.3 Variable Filter Results Using the set of operational efficiency performance indicators for 2010–2020 with 170 influencing factor values as input data, the results identified a total of 161 key influencing factors with operational efficiency VIP values greater than 0.8. Some of the operational efficiency key influencing factors are shown in Table 1. This paper selects the set of management effectiveness and operational efficiency performance indicators as the research object, and based on the PLS-VIP method, the Table 1 VIP values for selected operational efficiency key influences

Influencing factor number

VIP value

6

1.53355

121

1.41189

45

1.40609

7

1.3947

62

1.30378

126

1.2955

60

1.2725

49

1.26181

138

1.24751

164

1.24406

216 Table 2 VIP values for selected key influences on business effectiveness

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Influencing factor number

VIP value

54

1.32378

126

1.29828

96

1.29624

44

1.23362

82

1.22238

45

1.2172

52

1.20775

105

1.20635

72

1.18934

106

1.17492

set of management effectiveness performance indicators from 2010–2020 with 170 influencing factor values as input data, the results identify the key influencing factors with management effectiveness VIP values greater than 0.8, a total of 154. Some of the key influencing factors for management effectiveness are shown in Table 2. Based on the PLS-VIP method, using the set of business performance indicators and 170 influencing factor values from 2010 to 2020 as input data, it is determined that the impact of photovoltaic power generation on business performance indicators in business benefits is relatively large to explain: (1) Due to the short time span of data, the accuracy of intelligent algorithms may be reduced to some extent, resulting in increased uncertainty in the regression correlation coefficient. (2) Photovoltaic power generation was zero from 2010 to 2013, with a rapid growth trend in subsequent years, while debt levels also showed a downward trend. (3) Although Hunan Power Grid mainly engages in hydropower, the hydropower VIP value is 0.94926, and the photovoltaic VIP value is 1.14733, both of which are greater than 0.8. These are important influencing factors and will be uniformly used as input for machine learning. (4) In subsequent studies, we used a VIP greater than 1 as a risk factor for failing to meet performance indicators, while the significant fluctuations in photovoltaic power generation and the stability of hydroelectric power generation also confirmed that the risk factor affecting the debt level was photovoltaic power generation, not hydroelectric power generation.

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Fig. 2 GA-ELM machine learning fit curve for each performance indicator of grid investment and business effectiveness

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3.4 GA-ELM Based Learning Fit of Performance Indicator Values Fitting Result After screening each performance indicator in turn for influencing factors using the PLS-VIP variable screening method, the resulting influencing factors and corresponding performance indicators were used as inputs to the GA-ELM model to learn to fit the performance indicator values from 2010–2019. Taking the set of business effectiveness indicators as an example, the results of the performance indicator values after model fitting are shown in Fig. 2. Analysis of the Accuracy of the Fitted Results Overall, for the 16 performance indicators for which complete data is available for the operational efficiency and business effectiveness indicator sets, the combined PLSVIP + GA-ELM algorithm performs very well in terms of both trend and accuracy of fit to the data (Table 3). Table 3 Fitting accuracy based on PLS-VIP + GE-ELM machine learning Performance Indicator

Fitting accuracy MSE

Operational efficiency

S1

0.0009

0.9902

S2

0.0009

0.9998

S3

0.0002

0.9778

S4

business effectiveness

R^2

10.06

0.9858

S5

0.0625

0.9971

S6

0.0017

0.9964

S7

0.0013

0.9980

S8

0.7856

0.9982

S9

0.1781

0.9981

S10

0.1275

0.9968

S11

0.0045

0.9984

S12

0.000007

0.9922

S13

0.0128

0.9994

S14

0.0001

0.9994

S15

0.0044

0.9982

S16

0.0772

0.9960

Note: The closer MAPE (mean absolute percentage error) is to 0, the better the model; the closer R2 (goodness of fit, correlation coefficient) is to 1, the better the fit.

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Table 4 Comparison of statistical actual values of performance indicators with machine learning predictions Classification

Performance Indicator

Safety and quality

S1

72.87

S2

1.86

Service Quality

S1

98.5

99.1

S2

99.92

99.95

S1

45.26

50.35

S2

18.85

19.07

S3

96.7

96

Green and low carbon

2021 Statistical Actual

Machine learning predictions for 2021 70.17 1.8564

Science and Technology Innovation

S1

2.5

2.5

S2

35.8

39.5

Operational efficiency

S1

0.23

0.61

S2

73.59

73.75

S3

Business effectiveness

1.264

1.251

S4

167.25

172.38

S5

41.68

46.05

S6

7.76

8.84

S7

11.68

11.76

S8

285.58

277.75

S1

83.5

83.9

S2

98.5

99.5

S3

5.89

6.23

S4

0.194

0.1997

S5

5.33

4.89

S6

−9.8

S7

12.65

−12.4 10.88

S8

68.98

68.09

4 Conclusion Based on the fitted results, machine learning predictions were made for 25 performance indicators for 2021 and compared with actual statistical values, the results of which are shown in Table 4. The forecasted values of all the performance indicators show a good overall performance, but there are some errors when comparing the forecasted values of S8 in the operational efficiency category and S6 and S7 in the business effectiveness category. This is due to (1) the existence of internal and external risks.(2) the volatility of the performance indicators given the short forecast time horizon (10 years).

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In future research, with richer historical data collection and further analysis and mining of influencing factors of investment performance indicators, the prediction model proposed in this study can achieve better prediction results.

References 1. Zhao T, Han YJ, Yang MN, et al (2021) A review of research on machine learning-based prediction methods for time-series data. J Tianjin Univ Sci Technol 2. Shen H, Li X, Pan Q (2021) Research on non-ferrous metal futures market forecasting based on deep learning long and short-term memory neural networks. J Nanjing Univ Technol 45(03) 3. Liu L, Wang JJ, Li JP (2022) A study on international crude oil price forecasting by integrating news influence decay. Syst Eng Theory Pract 4. Deng J, Zhao F, Wang X (2022) MTICA-AEO-SVR stock price prediction model. Comput Eng Appl 58(08) 5. Xu JP, Hu JH, Sun WZ (2021) Application of machine learning based hybrid model in power load forecasting. Hebei Electric Power Technol 40(01) 6. Wang S, Zhou F (2020) Support vector machine based load forecasting for power grids. Mod Inf Technol 4(24) 7. Zhao Y, Wang H, Kang L et al (2022) Short-term power load forecasting based on timeconvolutional networks. J Electr Eng Technol 37(05):1242–1251 8. Pang C, Zhang B, Yu J (2021) Short-term power load forecasting based on LSTM recurrent neural network. Power Eng Technol 40(01) 9. Xu Q, Zhou C, Zhao SS (2019) Research on short-term power load forecasting method based on machine learning. Electr Meas Instrum 56(23) 10. Yang M, Zhang LB (2019) A review of data-driven ultra-short-term wind power forecasting. Power Syst Prot Control 47(13)

Research on Early Warning of Cost Deviation of Electricity Transmission and Transformation Engineering Based on MCS-SVM Junqiang Sha, Yanhui Lu, Rui Xia, Huiting Dong, and Linpeng Nie

Abstract In this paper, we first collected the historical data of relevant transmission and transformation projects, processed these raw data in absolute value, calculated the deviation ratio, listed the voltage level and construction difficulty after introducing the influencing factors, and used the K-means algorithm to classify all the deviations of budget and settlement into four categories. Then, the raw data after processing is fed into the SVM algorithm for learning. To further analyse the influence of the four influencing factors on the final deviation settlement of the project, the range of values for the four influencing factors in the original data is examined to determine the distribution pattern and then 2,000 sets of prediction data are generated randomly by the MCS method. The prediction sample set is then input into the trained SVM algorithm to determine the deviation settlement level of the prediction sample set, and the results are analysed to determine the range of values for the four influencing elements corresponding to various warning levels. For the four deviation situations, particular countermeasures are also recommended. Finally, project samples are used to develop the application. The prediction model in this paper can increase the precision of cost prediction, and the analysis of the correlation between the preliminary cost deviation ratio, construction difficulty, voltage level, and final deviation level can provide valuable reference information for transmission and transformation project managers and decision-makers. Keywords Transmission and transformation engineering · Construction cost · Support vector machine · Monte Carlo simulation · Early warning model

J. Sha · Y. Lu (B) · H. Dong · L. Nie State Grid Jiangsu Electric Power Co. Ltd., Construction Branch, Nanjing, China e-mail: [email protected] R. Xia State Grid Jiangsu Electric Power Co. Ltd., Nanjing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_16

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1 Introduction The traditional cost management of power transmission and substation projects is facing the following difficulties as a result of economic and social development, the energy decarbonization transformation caused the significant investment decisionmaking needs of power transmission and substation projects, as well as the precise investment management requirements of power grid enterprises. (1) There are many transmission and substation engineering construction projects, a sizable investment scale, and a rising cost variance. (2) Cost overruns are frequent and project cost management for transmission and substations has many flaws. (3) New techniques and concepts for cost management have been made possible by the advancement of computer technology. In the context of lean and standardized management of power grid projects, it is necessary to pay more attention to the study of cost deviation early warning, combine computer technology for deviation prediction, improve the prediction accuracy, and finally improve the level of engineering cost control are indeed important in the context of lean and standardized management of power grid projects. There is a wide range of domestic and international research on cost-influencing factors, including studies from the perspectives of project construction analysis and cost practitioner analysis. These studies used a variety of analytic techniques, most of which included computer technology, the majority of which involved computer technology. And the following are some specific research findings: The literature [1] examined the influencing variables that affect cost deviations and analyzed the cost deviations caused by frequent errors in quantity calculation, common errors in engineering quota setting, errors in adjusting unit prices of labor, materials, and machinery, improper engineering costing, and improper use of costing software. The main cost analysis method was employed in the literature [2] to determine the primary contributing causes of cost variance. According to the literature [3], schedule and cost target deviations were using the earned value method, and additional research was done to identify the underlying causes of significant impacts on actual schedule and cost. Multiple regression was utilized in the literature [4] to pinpoint the cost-influencing elements, including scale, construction standards, construction technology, and standards. According to the literature [5], the relationship between cost deviation and time depends on the type of project; for example, road construction projects have a negative relationship between cost deviation and project duration, but building construction projects have a positive relationship between the two. Through the use of a questionnaire, the literature [6] investigated the frequency and severity of the causes of cost variations in Colombian building projects. The literature [7] employed a survey-descriptive study methodology, secondary analysis of empirical data, structured interviews, in-depth interviews, and triangulation to identify the most important variables influencing cost variances in wastewater projects in Bulgaria (access to financing at the design stage, lowest procurement bids, inadequate pricing methods, and bureaucracy). To determine the causes of cost discrepancies in transportation projects, the literature [8] examined a dataset of 1091 central, regional,

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and local transportation projects created by the Portuguese government between 1980 and 2012. In terms of cost management, there is a wealth of research on cost management in China, starting from all aspects and using a variety of investigation and research methods to put forward many constructive ideas on cost management, the following are some specific research results: The study of Zhang Jin [9] aimed at constructing a pre-engineering project cost management system, aiming at improving the lean management of power grids. Firstly, it analysed the factors affecting the pre-engineering construction cost management, then established a comprehensive assessment index system and constructed a fuzzy comprehensive evaluation model based on the entropy weight method, and finally carried out the verification of actual project cases and gave the suggested measures related to pre-engineering management. The study of Yuan Yu [10] summarised the results of the application of BIM technology in the cost management of engineering projects, and also analysed the shortcomings in the application and pointed out the areas that needed improvement. Theoretically, it investigates the importance of BIM technology, the theory of engineering cost management, cost control mechanism and the basic theory of earned value. It analyses the engineering cost management problems arising from traditional management methods and analyses how to effectively solve these problems and improve cost management performance through the application of BIM technology. Finally, the application of BIM in specific cases at different stages of GLD projects is discussed, and the effectiveness of BIM in improving cost management is tested by comparing the application of BIM with traditional cost management. Li Xinmin’s [11] research is aimed at the analysis of cost deviation and management effectiveness of power grid projects, firstly pointing out the three main bodies of engineering construction, i.e. design units, construction units and construction units, then establishing a model for assessing the effectiveness of cost management based on these three types of problems, using a comprehensive multi-level fuzzy assessment method to analyse specific projects, and finally, on the basis of the analysis, proposing a cost management optimization strategies, providing a framework for improving the efficiency and effectiveness of engineering investments and simplifying control. This paper presents an innovative application of the theoretical method in the prediction of cost deviation of transmission and transformation projects, which is to develop a cost deviation analysis and prediction model based on the collection and collation of a large number of cost data of transmission and transformation projects and the introduction of clustering theory and intelligent machine learning algorithms.

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Fig. 1 Cost deviation warning model design concept

2 Analysis of the Cost Influencing Factors of Power Transmission and Transformation Projects 2.1 Design Concept Firstly, the model is tested using the historical data that has been gathered after creating a support vector machine classification prediction model. If the achieved prediction accuracy falls short of the necessary standards, the model is changed until the prediction accuracy reaches 85% or more, allowing for the acquisition of the trained support vector machine model for deviation prediction. The probability distribution of the influencing factors is identified in order to analyse the impact of each influencing factor on the final cost deviation. Next, Monte Carlo simulation is used to generate prediction samples at random, which are then input into the trained support vector machine model for prediction. After receiving the prediction results, analysis is then carried out to determine the correspondence between the range of values of each influencing factor (Fig. 1).

2.2 Method Description Given the one-time and procedural nature of engineering projects, there is a phenomenon of project cost conduction from the early stage to the later stage, and

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the deviation of cost in the conduction process may result in the “butterfly effect” of the project cost. As a result, the development of a cost deviation prediction model is important for large-scale, complex construction projects. The principle of the support vector machine algorithm is as follows: Assume that the training sample set is {(xi , yi ), i = 1, 2, . . . , n, xi ∈ Rn , yi ∈ R}

(1)

The basic principle of a support vector machine is to find a nonlinear mapping of φ(x), project x into a high-dimensional feature space F by φ(x), and perform a linear regression in that high-dimensional feature space F using the following estimation function f (x). f (x) = [ω · φ(x)] + b, φ : Rm → F, ω ∈ F

(2)

In (1), ω is the weight vector; b is the bias. Its function approximation problem is equivalent to the following function minimum. Rr eg [ f ] = Remp [ f ] + λω2 =

s 

C(ei ) + λω2

(3)

i=1

In (3), Rreg [f] is the expected risk; Remp [f] is the empirical risk; λ is a constant. By constructing a loss function and using the idea of structural risk minimization, the support vector machine determines a regression function that minimizes the objective function according to statistical theory, where the objective function and constraints are shown below:   n    ξi∗ + ξi min 21 ω2 + C i=1 ⎧ (4) ⎨ yi − ω · φ(x) − b ≤ ε + ξi∗ (ω, φ(x)) + b − yi ≤ ε + ξi ⎩ ξi∗ , ξi ≥ 0 In (4), C is the weight parameter used to balance the model complexity term and the training error term; ξ∗i , ξi is the relaxation factor; and ε is the insensitive loss function. The problem can be solved using the LaGrange pairwise method as well as the KKT condition. Solving the pairwise problem above yields the support vector machine regression function: f (x) =

n

  ai − ai∗ K (X i , X ) + b i=1

(5)

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The LaGrange multiplier in (5) is called the kernel function, and the most  2   1 commonly used Gaussian kernel function K xi , x j = ex p − 2σ 2 xi − x j is generally chosen. Therefore, the parameters ε, C, and σ have to be determined when using support vector machines, which are generally chosen empirically and are highly subjective. The model takes the deviation ratio of estimate to budget, the deviation ratio of estimate to budget, construction difficulty and voltage level as influencing factors, and the deviation warning level of budget to settlement as the result, and uses the support vector machine for classification learning, takes part of the collected historical data of transmission and substation project cost as the training set, relies on the neural network optimized by genetic algorithm to learn and simulate the potential relationship between the data, and obtains the support vector machine nonlinear mapping to test its function of cost deviation prediction. The remaining part of the data is used as a test set to test the prediction effect of the support vector machine, and if the prediction accuracy does not meet the requirements, the model is modified until the modified support vector machine model meets the prediction accuracy requirements, so as to ensure the feasibility of the method for predicting deviations. Then, finding out the range of values and probabilities of the cost deviation ratio of estimate to budget, estimate to budget, construction difficulty and voltage level, two thousand sets of data are randomly generated and input into the trained support vector machine for prediction, and the prediction results are tabulated in terms of influencing factors. The model introduces machine learning theory and uses bionic algorithms to optimise the neural network, making further improvements to the generalisation and learning capabilities of the model. The performance of the improved model is significantly optimised in terms of training time and prediction accuracy, reducing the negative impact of the initial random weights and thresholds of the model on network learning, and effectively adapting to the multi-dimensional and redundant environment of cost deviation prediction for complex large-scale construction projects The improved model can effectively adapt to the multidimensional and redundant environment of complex large construction project cost prediction.

2.3 Empirical Analysis Firstly, processing of historical data collected on the cost of power transmission and substation projects is carried out. Considering the applicability of early warning theory applied to practical operations, the following discussion is made in relation to the practical problems that may be encountered in construction projects: (1) There are differences in the size of transmission and substation engineering construction projects, and projects of different sizes are affected differently by the cost of cost deviation.

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(2) There are positive and negative deviations in the cost deviation of transmission and substation projects. (3) The terrain and topography also affect the cost of power transmission and transformation projects, mainly in that the terrain and topography will affect the choice of construction routes and the difficulty of construction. If the construction site has rugged terrain and more high mountains, the construction of the project will be difficult and require more man-days, and the technical level and work proficiency of the construction personnel will be more demanding. (4) The voltage level affects the cost of transmission and substation projects. Different voltage levels have different construction difficulties, which in turn affects the difficulty of cost management, leading to a possible increase in deviation. Based on the above analysis, the following methods are used for processing cost deviation data: (1) Introduction of cost deviation ratios. The use of cost deviation ratios eliminates the problem of different project sizes having different sensitivities to deviations, and the processing of deviations enables historical data on cost deviations of engineering construction projects to be used for early warning analysis. (2) Absolute value processing. Considering that both positive and negative deviations are deviations between actual and planned values, negative deviations are treated as absolute values for the purpose of analysis. (3) Introduce a list of terrain and landscape influencing factors. The terrain type is divided into mountainous, hilly and flat areas, and for the purpose of analysis, the construction difficulty is input into the support vector machine as an influencing factor: Construction difficulty = a * mountainous area + b * hilly area + c * plain area a, b and c are the construction difficulty coefficients for mountains, hills and plains respectively, and are set to 5, 3 and 1 (4) Add voltage level as an influencing factor. The raw cost data collected were processed according to the above method and the characteristics of the data obtained were as follow in Table 1. The following Table 2 of alarm status classification is produced by using the k-means algorithm to divide the budget and settlement deviation ratios into four categories: “no alarm, light alarm, medium alarm, and heavy alarm,” which correspond to the numbers 1, 2, 3, and 4, respectively. The classification graphic obtained after running is shown in Fig. 2 below. Finally, the scale of the terrain along the route is processed and introduced into the table as construction difficulty, resulting in Table 3 for this data feature. Following data processing, these data were loaded into the support vector machine for intelligent learning, and a portion was randomly selected as the training set, and the remainder was used as the test set. Considering that the outcomes of each run of the support vector machine algorithm could differ, the algorithm was run multiple

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Table 1 Raw Data Characteristics Topographic scale along the route,% Design

Voltage level (kV)

Estimate to budgetary estimate

Budgetary estimate to budget

Mountain

Hill

Flatland

Budget to closing

Maximum value

500

0.0979

0.0498

100

49

100

0.049

Minimum value

35

0.0012

0.0008

0

0

0

0.0011

Average value

261.75

0.0515

0.0246

29.8

28.1

42.8

0.0236

Standard deviation

160.0771

0.0258

0.014

36.9

16.4

31.5

0.0126

Median value

330

0.0532

0.0234

0

31.5

53.5

0.0242

Table 2 Classification of Alarm Conditions Alarm Condition

No alarm

Light alarm

Medium alarm

Heavy alarm

Threshold

0–0.9%

0.9%–1.9%

1.9%–3.4%

3.4%–5.0%

Fig. 2 K-means classification result graphic

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Table 3 Post-disaggregated data characteristics Design

Voltage level (kV)

Estimate to budgetary estimate

Budgetary estimate to budget

Construction difficulty

Budget to closing

Maximum value

500

0.0979

0.0498

5

4

Minimum value

35

0.0012

0.0008

1

1

Average value

261.75

0.0515

0.0246

2.7262

2.48

Standard deviation

160.0771

0.0258

0.014

11.3321

1.204

0.0532

0.0234

1.93

3

Median value 330

times in order to determine the prediction accuracy, and the variation of the prediction accuracy was obtained in the range of 85%–95%, demonstrating the viability of using the support vector machine for prediction. The results of the prediction accuracy of 95% are displayed in Fig. 3. The prediction results are then achieved by applying the support vector machine algorithm that has learned the original data, after which 2000 sets of data are randomly generated as the sample set to be predicted using the Monte Carlo approach. As can be seen from Table 4, the range of values corresponding to each influencing factor and the warning level overlap in different degrees. For instance, both main and secondary warnings may apply when the deviation ratio of estimate to budgetary estimate is within the range of 0–0.045. Because of this, in a practical application, the first determination of deviation level is mostly based on the trained support vector machine prediction model, and the values of the estimate to budgetary estimate Fig. 3 Predicted results graph c

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Table 4 Correspondence between the Numerical Range of Influencing Factors and Alarm Conditions Deviation ratio\Classification of alarm conditions

No alarm

Light alarm

Medium alarm

Heavy alarm

Estimate to budgetary estimate

0–0.045

0–0.065

0.030–0.078

0.065–0.100

Budgetary estimates to 0–0.025 budge

0.015–0.047

0–0.025

0.025–0.050

Construction level

1.0–3.5

1.25–3.5

1.4–2.0

3.5–5.0

Voltage level, kV

35

220

330

330

110

500

deviation ratio, budgetary estimates to budget deviation ratio, construction difficulty, and voltage level are then verified against Table 4 The deviation level within the budget is then determined by comparing the voltage level, construction complexity, and deviation ratio of estimate to budget in Table 4, so as to obtain the final deviation level judgment result.

3 Suggested Measures 3.1 Basic Principles of Deviation Response (1) Comprehensive principle The principle of comprehensiveness refers to the control of the whole process, all personnel and all aspects. The whole process control is to control the project cost from the pre-decision stage, the middle implementation stage and the later operation and maintenance stage of the project, covering the whole life cycle of the project. All-personnel control is to control the project costs by involving all participating subjects, departments and personnel related to the project. Allaspect control is to control the content, subject, standard and effect of the project. (2) Optimal principle Due to the limited resources of the project, the cost control should seek the optimal solution under a variety of constraints to maximize the benefits of the project. (3) Cost-effectiveness principle The cost-benefit principle means that the benefits of the project are higher than the cost of the investment, therefore, the costs and benefits of the project must be monitored in real time and analysed in stages. (4) Dynamic control principle

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As the engineering project is a one-off, cost control after the completion of the project is not very meaningful. During the construction of the project, the cost of the project must be monitored dynamically. Through the method of interlocking nature, changes in project costs can be effectively prevented and controlled in a timely manner, thus achieving dynamic management. (5) Target management principle The principle of target management refers to the target-oriented management of deviations in the project and the dynamic adjustment of the reasonableness and applicability of the target in the course of project implementation.

3.2 Specific Deviation Warning Response Advice (1) Heavy warning: i.e. major risks, key risk areas are to be identified in a timely manner, with detailed analysis and summarisation of previous or current risk events affecting project cost deviations, and analysis of the main business processes where costing deviations occur, including progress reports, procurement details, risk points, etc.; identify the main causes that will result in final losses or cost errors at each stage of development. Within the scope of the target alert, the responsibilities of the relevant departments are clarified, appropriate countermeasures are selected, corresponding responses are developed and implemented, and deviations are corrected. (2) Medium alert: Continuous collection and monitoring of various indicators, issuance of early warning monitoring reports based on indicators that exceed the alert line; drawing up of special contingency plans, deviation remediation procedures; on-site inspection supervision; (3) Light alert: Establish numbered management of costed deviations, analyse the causes of deviations and whether they will persist; implement the relevant departments and establish dedicated management for costed deviations; (4) No warning: alerting the department to costing deviations.

4 Conclusion This paper proposes the MCS-SVM model, which focuses on the prediction of final cost deviations and analyses the relationship between influencing factors and warning levels, and proposes suggested measures to deal with alerts. The main research contents and conclusions of this paper are as follows: (1) K-means clustering algorithm was used to grade the cost deviation of transmission and substation project budget and settlement. Using the K-means clustering algorithm, the final deviation score was divided into four categories, corresponding to four warning levels, i.e. no warning, light warning, medium warning and heavy warning, so as to obtain the cost deviation training samples.

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(2) Designed the MCS-SVM model for cost deviation prediction and analysis. After designing the SVM prediction model, the original data was input to test its prediction accuracy, and the model was modified until the prediction accuracy was stabilised at 85% or above. obtained the prediction results, and the analysis of the prediction results identified the corresponding relationship between the values of the influencing factors and the warning levels. (3) Analyse the principles and specific measures for police response. Based on the principles of comprehensiveness, optimality, cost-effectiveness, dynamic control and optimal management, specific responses to the four types of police situations are proposed.

References 1. Li H (2017) What is the reason causing the construction cost of the error. Archit Knowl 37(02):40–41 2. Lu Y, Gen P, Qiao S (2021) Analysis of cost deviation and countermeasures of power transmission and transformation projects. China Power Enterpr Manag 33:82–83 3. Xu W, Feng W, Zhang D (2016) Analysis of deviation from the target of cost control in the whole process of power transmission and transformation project construction. China Power Enterpr Manag (12):40–48 4. Chen J, Hou K, Gao X (2016) Research on cost reasonability evaluation method of power transmission and transformation projects. Southern Power Syst Technol 10(08):95–101 5. Belay AM, Torp O (2017) Do longer projects have larger cost deviation than shorter construction projects? Procedia Eng 196:262–269 6. Sánchez O, Castañeda K, Herrera RF, Pellicer E, Almanza L, Cadavid R (2021) Cost deviation causes in Colombian construction projects: a frequency and severity analysis. In: 2021 congreso internacional de innovación y tendencias en ingeniería (CONIITI), pp 1–6 7. Teneva A, Nikolova-Alexieva V, Tuntova A (2018) Maintaining steady cost projection on wastewater engineering projects in Bulgaria. In: 2018 international conference on high technology for sustainable development (HiTech), pp 1–4 8. Catalão FP, Cruz CO, Sarmento JM (2019) The determinants of cost deviations and overruns in transport projects, an endogenous models approach. Transp Policy 74:224–238 9. Zhang, J (2019) Research on the evaluation system of pre-construction cost management of power grid construction project of BJ Company. Xi’an University of Science and Technology 10. Yuan Y (2020) Research on the application of BIM technology in engineering cost management. Qingdao University 11. Li X (2021) Research on the analysis of deviation and evaluation of management effectiveness of cost of power grid projects. J North China Electric Power Univ (Soc Sci Ed) (01):56–65

Power Demand Side Management and Electricity Consumption Behaviours

Exploring Employees’ Electricity-Saving Intentions in the Workplace Based on the Extended NAM: A Case Study in Rwanda Yaxin Wu, Umwere Virginie, Di Bao, Iradukunda Aline Banashenge, and Yi Wang

Abstract This article investigated the impact of personal norm, habit, and organizational electricity-saving climate on employees’ electricity-saving intentions using an extended norm activation model. This study collected the data from employees in Rwanda through online questionnaires. The results suggest that personal norm, organizational electricity-saving climate, and habit all significantly and positively influence electricity-saving intention. Moreover, organizational electricity-saving climate and habit positively and significantly impact electricity-saving intention. When the organizational electricity-saving climate of saving electricity is strong, the effect of personal norm on intention is weakened, while the habit does not moderate the effect. Furthermore, awareness of consequence positively impacts ascription of responsibility, influencing personal norm positively. Based on the findings, this paper contributed to electricity-saving literature, discussed the recommendations for policymakers, and offered directions for future research. Keywords Electricity-saving · NAM · Habit · Organizational Electricity-saving Climate

1 Introduction Rwanda’s economic reform has led to rapid economic growth in the past decade. According to World Bank, Rwanda’s GDP growth rate, at 9.4% in 2019, is the highest worldwide. Rapid economic growth causes a surge in demand for energy, especially electricity. However, like many least developed countries in Africa, Rwanda has Y. Wu · U. Virginie · I. A. Banashenge · Y. Wang (B) North China Electric Power University, Beijing, China e-mail: [email protected] D. Bao Victoria University of Wellington, Wellington, New Zealand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_17

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experienced a power shortage because of insufficient electricity production capacity and the high cost of electricity. In the electricity shortage scenario, saving electricity and reducing electricity waste can mitigate electricity shortage issues in Rwanda. According to the statistical report of the Ministry of Infrastructure of Rwanda, electricity consumption is mainly divided into household and industrial sectors, with each part accounting for nearly half. Compared with the industry sector, household users utilize energy principally for basic living requirements [1], and household energy consumption increased by just 1.7 per cent from 2003 to 2018 [2]. In contrast, Rwanda’s service sector is booming, accounting for more than half of total GDP, and has become a primary source of electricity consumption in the industrial sector. Employees play an essential role in local electricity consumption, and it is more efficient to promote electricity-saving from employees than from households in Rwanda. Understand electricity-saving intentions of employees is of great importance in Rwanda. The literature review shows relatively limited literature on energy-saving behavioural intention in the least developed areas such as sub-Saharan Africa. However, this literature about energy saving in sub-Saharan Africa only focused on the household sector [3]. Considering accessible electricity is insufficient, along with the fast growth in electricity consumption in Rwanda, investigating electricitysaving behavioural intention and its influencing factors could be beneficial. In addition, previous research about electricity-saving behavioural intentions in workplaces in other countries provides inspiration and references for this study. Among the behavioural models, the TRA model and TPB model are chosen for research when self-interest motivation is activated [4]. However, when the behaviour is triggered by altruism motivation instead, NAM is mainly utilized to study prosocial behavior [5]. As discussed above, employees do not benefit from saving electricity at home, so employees’ electricity-saving behaviour in the companies can be regarded as a kind of prosocial behaviour. Consequently, this paper adopts NAM as the fundamental theoretical model. Based on previous research, organizational electricity saving climate and habit are added into the NAM model as independent variables to form an extended Norm Activation Model, improving the explanatory ability of the model. This paper mainly studies the electricity-saving intention of office workers in Rwanda through the extended Norm Activation Model. This study fills the research gap on employees’ willingness to save electricity in Rwanda and similar poor areas. It provides helpful information and recommendations for energy policymaking in these areas. Effective energy policy can promote economic development, improve the quality of people’s lives, and reduce the energy cost of enterprises. In addition, the extended Norm Activation Model in this paper could be used as a reference and template for future relevant research in other areas.

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2 Literature Review and Hypotheses Development 2.1 Research Model: Extended Norm Activation Model NAM is an effective research model for analyzing human altruistic behaviour. Schwartz [6] argues that altruism is influenced by a sense of moral obligation to behave according to norms held by the individual. Specifically, NAM mainly entails sacrificing an one’s interests for the benefit of others [6], including pro-environmental behaviour, helping behaviour, food waste reduction behaviour, and payment for public goods [7]. As mentioned above, employees are not required to pay for the electricity they use in the workplace, so their electricity-saving behavior is prosocial. Therefore, NAM is appropriate to study the electricity-saving behavior of employees in Rwanda. For previous studies, NAM was selected for investigating energy-saving (mainly electricity-saving) behavior [8]. NAM is made up of three primary components to predict individual altruistic behaviour: awareness of consequences (AC), ascription of responsibility (AR), and personal norm (PN) [5]. Firstly, AC means whether an individual could realize the negative effect he/she makes on others when he/she does not engage in prosocial behavior [5]. Secondly, AR refers to the sense of responsibility for the adverse results when he/she does not behave prosocially [5]. Finally, PN is a moral obligation of fulfilling or refusing to engage in a certain activity [9]. PN is the core variable among all three dimensions. De Groot and Steg [5] state that NAM has two ways to explain: mediation and moderation. Some researchers compared the two interpretation approaches to NAM and concluded that the mediation model is more suitable for analyzing prosocial intentions [5]. The reason is that people’s awareness of outcomes develops responsibility; the individual’s responsibility could affect personal norms; and then personal norms would determine whether he/she performs prosocially or not (AC – AR – PN – Prosocial Behavior) [5, 10]. Kim et al. [11] emphasize that the extended NAM can significantly increase the explanatory degree of prosocial behavior after introducing some relevant variables compared with the original NAM. Based on previous research about energy-saving intentions and behaviour, this study integrated habit (HAB) and organizational electricity-saving climate (OESC) into the original NAM separately to analyze employees’ electricity-saving intentions in Rwanda.

2.2 Awareness of Consequence (AC) Awareness of consequence (AC) refers to the possibility of a person to be conscious of the result of his/her certain activities or omissions [12]. In this research, the consequence of not adopting electricity-saving measures in workplaces should be raising

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electricity costs for employers, wasting necessary electricity for economic development, and even threatening the local environment. When employees realize the adverse effect on their employers, other colleges, and local society, they probably intend to save electricity in the workplace. Stern et al. [13] point out that people’s awareness of consequences relates to their value orientation. In terms of utilitarianism, previous research illustrates that financial incentives are not significantly effective at promoting power-saving behaviour in the workplace [14]. Moreover, most research about electricity-saving intentions in offices focuses on the benefits to the environment [15], the reduction of the companies’ electricity cost [16], and the benefits to the company [17].

2.3 Ascription of Responsibility (AR) Ascription of responsibility (AR) is a person’s sense of responsibility for the adverse consequences caused by not performing a particular action [5]. For this paper, the responsibility is regarded as the common sense of responsibility for the negative consequences discussed in Part 2.2 caused by an individual’s failure to electricitysaving in the workplace. Therefore, individuals who realize the consequence caused by refusing to save electricity when working are supposed to gain the ascription of responsibility. In contrast, employees who do not possess the awareness of the negative consequence are unlikely to acquire the responsibility for not saving electricity. Importantly, AR is essential to electricity-saving behaviour in the workplace, especially in shared offices [18]. The reason is that employees themselves do not benefit directly from saving electricity, and it is difficult to divide responsibility for electricity consumption in shared space. Therefore, employees without AR always regard saving electricity as the organization’s duty instead of their own [16, 19] and are not supposed to change their energy consumption [18]. De Groot and Steg [5] approved the significant, positive, and direct relationship between AC and AR. Previous research about prosocial behaviour demonstrates the positive impact of AC on the AR [15, 20–22]. Moreover, most researchers agree to utilize the individual’s perceived responsibility for electricity usage, reducing electricity consumption, and running out of power when he/she is working [8]. Hypothesis 1: In the workplace, the awareness of the consequence of not saving electricity (AC) positively affects the ascription of responsibility (AR).

2.4 Personal Norm (PN) Personal norm is the moral obligations that arise from people’s decision to behave or not engage in a particular behaviour. Actually, personal norm is the core aspect of NAM [23] and the closest variable of prosocial behaviour within the normative

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activation framework. This paper regards PN as the moral obligation to save electricity in the workplace. Obviously, employees who feel responsible for the negative results caused by not saving electricity are more likely to cultivate personal electricity norms within the workplace than those who do not. Previous research illustrates that individual who thinks he/she possesses the responsibility to behave prosocially has higher personal norms than those who do not. In other words, AR has a significant positive effect on PN [5]. Further research about prosocial behaviour, especially energy-saving behaviour, also approves the inference above [8]. Hypothesis 2: In the workplace, the ascription of responsibility for saving electricity (AR) positively affects the personal norm (PN).

Moreover, the personal norm is a fundamental motivation of prosocial behaviour because personal norm refers to the behaviour that people want themselves to do. Prosocial behaviour often occurs when people feel a moral obligation to do it. Therefore, individuals’ prosocial behaviour depends on the personal norm which can drive them to act altruistically. The reason is that when an individual forms a personal norm, he/she will feel pride when obeying the norms and guilty when violating the norms [24]. In addition, individuals’ anticipation of good feelings can lead them to think they should engage in a particular behaviour, while that horrible feeling can cause an opposite consequence. Most researchers that pay attention to the intention of prosocial behaviour agree with the statement above [15]. As mentioned above, saving electricity in the company has little benefit to the employees themselves. Therefore, it can be inducted that employees with higher personal norms will feel guilty for wasting power in the workplace, making them intend to save electricity when working. Hypothesis 3: In the workplace, the personal norm about saving electricity (PN) positively affects electricity-saving intention.

2.5 Habit (HAB) Habit is a relatively fixed way of thinking, intention, and behaving from previous repetitive experiences [25]. Researchers agree that habit plays an essential role in generating intentions for certain behaviours [26]. The existing research states that habits are formed when behaviours are repeated over time and can activate the intention of certain future behavior [27]. Therefore, some scholars have introduced the habit factor into prosocial behaviour research. These research results prove that prosocial behaviour can operate in habitual ways. Gardner et al. [28] suggested that habit could influence intention, impacting behaviour. In conclusion, this paper assumes that employees’ electricity-saving habit can influence their electricity-saving intention. Hypothesis 4: In the workplace, electricity-saving habit (HAB) positively affects the electricity-saving intention.

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As mentioned in 2.4, personal norm is a particular moral obligation. Moreover, moral obligation such as virtue is not an innate trait but acquired through repeated good deeds. Previous research about the intentions of transportationchoosing behavior [29] and electricity-saving behavior [30] suggests the impact of habit on personal norm. Employees with electricity-saving habit at work are more likely to conserve power and, hence, more easily establish personal norms about electricity-saving than those who do not. Based on the discussion above, hypothesis 5 is conducted as follows: Hypothesis 5: In the workplace, electricity-saving habit (HAB) positively affect the personal norm of electricity-saving (PN).

In summary, habit could influence electricity-saving intention directly and indirectly through the personal norm, while personal norm could affect electricity-saving intention directly too. Therefore, there should be some interaction or competition between the effects. For example, if an individual has strong habit, his/her intention is more likely to be shaped by habit without consciousness. In addition, personal norm come into play during decision-making. Consequently, when the effect of habit is strong, the impact of personal norm on electricity-saving intention is weak [31]. Trafimow [32] indicated that habit could moderate the effect of the norm on intention. Hypothesis 6: In the workplace, electricity-saving habit (HAB) negatively moderate the impact of personal norm (PN) on electricity-saving intention.

2.6 Organizational Electricity Saving Climate (OESC) Organizational climate (also named corporation climate) is the common practices, beliefs, opinions, and value systems followed by the members of a particular organization [33]. In organizations, organization climate exists in the form of expectations about the behaviour of individuals within an organization [34]. In a sense, organizational electricity saving climate can be regarded as a social norm or subjective norm in a particular situation, a meaningful and effective way to predict human behavior [35]. Previous management research, especially human resource management, states that employee attitude, intentions and behaviour can be influenced and even shaped by organizational electricity saving climate [36]. In addition, researchers introduce organizational electricity saving climate to analyze intentions of prosocial behaviour, especially energy-saving, and the results approve the statement above. In other words, if an organization has cultivated the climate, the members are more likely to save electricity. The reason is that they feel a sense of belonging when meeting the expectation of the organization and feel horrible when not. Therefore, it is necessary to explore the effect of the current organization’s electricity-saving climate on employees’ intention to save electricity. Hypothesis 7: In the workplace, organizational electricity-saving climate (OESC) positively affects electricity-saving intention.

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Besides members’ intentions of prosocial behaviour, members’ personal norm is also influenced by organizational electricity saving climate [37]. The reason is that members are willing to alter their personal norms to be consistent with the organization’s expectations to reduce the negative emotions generated by violating the organizational electricity saving climate. In fact, organizational electricity saving climate is the most used type of moral reasoning when predicting members’ personal norms [38]. Researchers argue that organizational electricity saving climate influences individual intentions through personal norms, especially prosocial behaviour related to morality. Related research indicates that PN mediates the influence of organizational electricity saving climate on individual intentions [15]. Therefore, it can be deduced that organizational electricity saving climate could trigger members’ personal norms of electricity-saving, and members are more likely to believe electricity-saving is correct and indispensable. Hypothesis 8: In the workplace, organizational electricity-saving climate (OESC) positively affects personal norm (PN).

In addition to the mediator effect, previous research results manifest that members’ PN also moderates the relationship between organizational electricity saving climate and individual intention [15]. In other words, extrinsic motivation (OESC) weakens the influence of intrinsic motivation (PN) on altruistic intentions [15]. If OESC is high, members will behave altruistically to meet the organization’s expectations, weakening the effect of personal norms on behavioural intentions. In contrast, the effect of PN on intentions to engage in a certain behaviour is strengthened if there is a high organizational electricity saving climate. Hypothesis 9: In the workplace, organizational electricity-saving climate (OESC) negatively moderates the impact of the personal norm (PN) on electricity-saving intention.

3 Research Methods 3.1 Data Collection and Sample A questionnaire was used to collect data for this study. With the help of some Rwanda students, the online questionnaire was snowballed from two Rwandan students to more than 640 actual employees in Rwanda. These employees were from energy production and supply industry, manufacturing and other high carbon emission industries. To ensure the authenticity and validity of the collected data, we used an online questionnaire system named Sojump to monitor the address of the answer, the time of response, and the identical answers. In addition, each valid questionnaire provider can receive a particular financial reward. To obtain valuable data, we conducted a second follow-up for the questionnaires that were obviously filled in randomly and missing data. 582 questionnaires in all were submitted. Following the deletion of the questionnaires with the missing data and identical responses to all questions, 514 valid

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questionnaires were left for further research. In the participants, male accounted for 58.0%, female accounted for 42.0%. Participants aged 25 to 34 accounted for 47.9% of the total. They have a high level of education, with 58.1% having a bachelor’s degree or higher. Moreover, 59.9% of the participants s were single.

3.2 Reliability and Validity SPSS 24.0 and AMOS 23.0 were used to analyze the reliability and validity of the survey data.To evaluate reliability, Cronbach’s Alpha (α) and composite reliability (CR) of each variable were calculated. Table 1 reveals that the constructs’ CR values ranged from 0.753 to 0.906 and their Cronbach’s α values from 0.749 to 0.902.All of the data exceeded the recommended value of 0.7. Therefore, the results prove that the survey data have good reliability [39]. As shown in Table 1, the standardized factor loadings of all items were greater than 0.6 and the SMC values were more than 0.4, demonstrating the ideality of standardized path coefficient. The convergent validity test requires that the average variance extracted (AVE) be greater than 0.5 [40]. As shown in Table 1, the smallest Table 1 Results of measurement model evaluation Construct

Items

Std

SMC

AVE

CR

Cronbach’s α

Awareness of Consequence

AC1

0.785

0.616

0.594

0.814

0.812

AC2

0.820

0.672

AC3

0.703

0.494

AR1

0.754

0.569

0.536

0.776

0.774

AR2

0.750

0.563

AR3

0.691

0.477

INT1

0.750

0.563

0.755

0.902

0.897

INT2

0.963

0.927

INT3

0.881

0.776

OESC1

0.865

0.748

0.764

0.906

0.902

OESC2

0.968

0.937

OESC3

0.779

0.607

PN1

0.638

0.407

0.506

0.753

0.749

PN2

0.804

0.646

PN3

0.682

0.465

HAB1

0.737

0.543

0.586

0.808

0.804

HAB2

0.853

0.728

HAB3

0.698

0.487

Ascription of Responsibility

Intention

Organizational Electricity Saving Climate

Personal Norm

Habit

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Table 2 Results of discriminative validity analysis M

SD

AC

AC

5.130

0.870

0.771

AR

INT

PN

HAB

AR

5.436

0.783

0.702

0.732

INT

4.977

1.015

0.426

0.349

0.869

PN

4.899

0.982

0.664

0.532

0.475

0.711

HAB

4.228

1.154

0.366

0.208

0.387

0.348

0.766

OESC

5.416

1.071

0.420

0.321

0.492

0.443

0.204

OESC

0.874

Note: Values in bold are the square roots of AVE and other values are the correlations between constructs

value of AVE was 0.506, which was greater than 0.5. Therefore, the model has sufficient convergence validity. By contrasting the square root of AVE and the correlation between constructs, we evaluated the discriminant validity of the model. Table 2 demonstrated that the square root of AVE of each construct was larger than its correlations with other constructs, indicating that the discriminative validity is supported [40].

3.3 Common Method Bias The results showed that six factors with eigenvalues greater than 1 were extracted without rotation factor analysis, which explained 70.557% of the variance. Among them, the variance explained by the first factor was 35.228%, which did not exceed 50% of the judgment standard, indicating that the overall variance was not affected by one single factor, and there was no serious common method bias.

4 Results 4.1 Research Hypotheses Analysis All the hypotheses were supported. Awareness of consequence positively and significantly affected ascription of responsibility (β = 0.728, p < 0.001), confirming hypothesis 1. In addition, ascription of responsibility influenced personal norm positively and significantly (β = 0.453, p < 0.001), so hypothesis 2 is supported. Moreover, electricity-saving intention was positively and significantly affected by personal norm (β = 0.251, p < 0.001), organizational electricity-saving climate (β = 0.335, p < 0.001), and habit (β = 0.233, p < 0.001), supporting hypothesis 3, hypothesis 7, and hypothesis 4 respectively. Furthermore, organizational electricity-saving climate

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Table 3 Results of the mediation tests Constructs

Path coefficient

Independent variable (X)

Mediator (M)

Dependent variable (Y)

X→Y

HAB

PN

INT

0.377*** 0.489***

OESC

X→M

X+M→Y

Mediation existence

X→Y

M→Y

0.340***

0.252**

0.383***

Partial

0.436**

0.352***

0.315***

Partial

Note: *** p < 0.001.HAB: habit; OESC: organizational electricity-saving climate; PN: personal norm; INT: intention

showed a positive and significant effect on personal norm (β = 0.257, p < 0.001), and habit exerted a positive and significant impact on personal norm (β = 0.201, p < 0.001), confirming hypothesis 8 and hypothesis 5 respectively.

4.2 Examining the Mediating Effects Based on research by Baron and Kenny [41], we took three steps to test the mediating role of PN between HAB and INT. According to the Table 3, PN partially mediate the impact of HAB on INT. The Sobel test result showed that PN had a significant mediating effect (Z = 4.441 > 1.96, p < 0.05). It can be observed from the Bootstrap analysis results that bootstrap 95% confidence intervals for PN did not contain zero (0.065 ~ 0.164), indicating that PN had a significant mediating effect. According to the Table 3, PN partially mediate the impact of OESC on INT. The Sobel test result showed that PN had a significant mediating effect (Z = 4.564 > 1.96, p < 0.05). It can be observed from the Bootstrap analysis results that bootstrap 95% confidence intervals for PN did not contain zero (0.060 ~ 0.183), indicating that PN had a significant mediating effect.

4.3 Examining the Moderating Effects The study used the latent variable moderating effect model to analyze the moderating effect of HAB in PN and INT, and uses the unconstrained model without using the mean structure. According to Table 4, the main effect of HAB on INT (β = 0.250, p < 0.001) and the main effect of PN on INT (β = 0.382, p < 0.001) were significant. However, the moderating effect of HAB on the relationship between PN and INT was not significant (β = 0.051, p > 0.1). As for the moderating effect of OESC in PN and INT, according to Table 4, the main effect of OESC on INT (β = 0.323, p < 0.001) and the main effect of PN on

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Table 4 Results of moderating effect analysis Path

Unstd.

Std.

S.E.

C.R.

P

HAB

→

INT

0.209

0.250

0.043

4.834

***

PN

→

INT

0.397

0.382

0.060

6.644

***

PN × HAB

→

INT

0.054

0.051

0.056

0.960

0.337

OESC

→

INT

0.290

0.323

0.047

6.190

***

PN

→

INT

0.329

0.316

0.058

5.650

***

PN × OESC

→

INT

−0.059

−0.053

0.029

−2.025

0.043

Note: Unstd.: non-standardized path coefficient, Std.: standardized path coefficient, S.E.: standard Error, C.R.: critical ratio

INT (β = 0.316, p < 0.001) were significant. And the moderating effect of OESC on the relationship between PN and INT was significant (β = -0.053, p < 0.05).

5 Discussion 5.1 Main Findings Promoting electricity saving is an increasingly essential issue for governments and companies. This study explored the antecedents influencing employees’ electricitysaving intentions at work. In this study, habit and organizational electricity-saving climate were integrated into the norm activation model. The results in Part 4 illustrated that personal norm, organizational electricity-saving climate, and habit present a similar and significant influence on electricity-saving intention. In addition, the personal norm is influenced by organizational electricity-saving climate and habit. And ascription of responsibility is the most significant factor affecting personal norm. Furthermore, the demographic variables do not significantly influence electricitysaving intentions. As for the moderate effect, the impact of personal norm on electricity-saving intention is moderated by organizational electricity-saving climate but not by habit. All the findings suggest that, in the workplace, people who realize the terrible outcome of wasting electricity will gain a sense of responsibility to save power, activating more electricity-saving intentions than others who do not. The findings in Rwanda are consistent with previous research in more economically developed regions [8, 15, 17, 42, 43]. As for habit, people accustomed to saving electricity are more likely to maintain the behaviour in the workplace. The findings are similar to studies in China and Australia [15, 31]. Furthermore, employees in companies with a high overall electricity-saving climate are less likely to waste electricity, as in previous research [15, 43, 44]. However, the moderate effect results differ

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from previous research in developed countries [15, 43]. Organizational electricitysaving climate negatively moderates the effect of personal norm on electricity-saving intention, and habit does not moderate the effect of personal norm on electricitysaving intention.

5.2 Theoretical Implications Firstly, this paper fills in the research gap on employees’ electricity-saving behavioural intention in the least developed countries like Rwanda. Previous studies on employees’ saving energy at work focused on developed countries such as the United States and European countries [43, 45]. Studies about electricity-saving in the least developed areas are rare, primarily the reflections in the workplace [8]. However, electricity plays an essential role in economic development, and electricity prices are too high for households in the least developed counties. In addition, people’s electricity-consumption intentions at home differ from that in the workplace. Moreover, different cultures, beliefs, classes, and the economy will influence the factors related to prosocial behavior [46], so the research models and findings in developed areas are not applicable in the least developed countries. Therefore, this paper refined the theory and provided an extended norm activation model for employees’ electricity-saving research in the least developed countries, filling this research gap preliminarily. Secondly, this research is a synthesis and development of major past studies about the electricity-saving behavioural intention in the workplace. Past papers mainly were based on traditional NAM [42] or MOA model [16], and a few articles considered organizational electricity saving climate or habit alone [8]. Previous studies did not consider combining the two factors in the activation process. Hence, this research introduced organizational electricity-saving climate and habit to establish a refined extended-NAM. Based on the refined model, we concluded that habit and organizational electricity saving climate positively affect personal norm and could also influence intentions directly. In addition, organizational electricity saving climate negatively weakened the influence of personal norm on intentions. In conclusion, the findings improved the understanding of the process by which electricity-saving behavioral intention is generated, contributing to the electricity-saving research area.

5.3 Policy Implication Based on the findings above, we offer recommendations to the policymakers in Rwanda and similar least developed countries. Firstly, the government should provide a variety of publicity and education activities to encourage people to save electricity. As mentioned in Part 5.1, personal norm

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plays a vital part in electricity-saving intentions. In addition, awareness of consequences could lead to ascription of responsibility, and then cause personal norm about electricity consumption. At the organizational level, managers should organize related activities to make employees realize the terrible outcome of wasting electricity, such as lack of electricity, increased cost, and local ecological destruction. Furthermore, the propagation should include something specific enough to let employees acknowledge the effect of their behaviour on the companies and others. Secondly, organization leaders should cultivate an organizational electricitysaving climate. As discussed above, the organizational electricity saving climate could significantly and positively impact personal norm and intentions. Moreover, previous research illustrated that organizational electricity saving climate is influenced by regulations, leadership, training, and communication [46–48]. Therefore, comprehensive and reasonable training and communication should be developed from top to bottom when formulating guiding policies for electricity saving. Most importantly, managers and policymakers must lead by example to foster an organizational electricity saving climate. Thirdly, the government should utilize education and incentives to generate people’s electricity-saving habit. The findings presented that people with more vigorous electricity-saving habits are more likely to save power in the workplace. Habit could be created through education [49] and incentives. Moreover, environmental control can perpetuate habit. Therefore, the government should educate citizens from primary schools to cultivate habit from children, and people who save electricity should be rewarded. When people receive incentives, they will likely repeat energy-saving behaviour and form the habit of saving electricity. If the organizational electricity saving climate is created, the habit would last for a long time and consistently affect intentions, behavior and personal norm.

References 1. Martey E, Etwire PM, Adusah-Poku F, Akoto I (2022) Off-farm work, cooking energy choice and time poverty in Ghana: An empirical analysis. Energy Policy 163:112853 2. RENA. (2021). Rwanda Energy Profile. United Arab Emirates. https://www.irena.org/IRENAD ocuments/Statistical_Profiles/Africa/Rwanda_Africa_RE_SP.pdf 3. Agyarko KA, Opoku R, Van Buskirk R (2020) Removing barriers and promoting demand-side energy efficiency in households in Sub-Saharan Africa: a case study in Ghana. Energy Policy 137:111149 4. Goodwin N, Harris JM, Nelson JA, Rajkarnikar PJ, Roach B, Torras M (2018) Microeconomics in Context. Routledge, Milton Park 5. De Groot JI, Steg L (2009) Morality and prosocial behavior: the role of awareness, responsibility, and norms in the norm activation model. J Soc Psychol 149(4):425–449 6. Schwartz SH (1977) Normative influences on altruism. In: Berkowitz L (ed) Advances in Experimental Social Psychology, vol 10. Academic Press, pp 221–279. https://doi.org/10.1016/ S0065-2601(08)60358-5 7. Kim W, Che C, Jeong C (2022) Food waste reduction from customers’ plates: applying the norm activation model in South Korean context. Land 11(1):109

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8. Wang S, Wang J, Ru X, Li J, Zhao D (2019) Understanding employee’s electricity conservation behavior in workplace: do normative, emotional and habitual factors matter? J Clean Prod 215:1070–1077 9. Schwartz SH, Howard JA (1981) A normative decision making model of altruism 10. Onwezen MC, Antonides G, Bartels J (2013) The norm activation model: an exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. J Econ Psychol 39:141–153 11. Kim YG, Woo E, Nam J (2018) Sharing economy perspective on an integrative framework of the NAM and TPB. Int J Hosp Manag 72:109–117 12. Schwartz SH (1968) Awareness of consequences and the influence of moral norms on interpersonal behavior. Sociometry 31(4):355–369 13. Stern PC, Dietz T, Abel T, Guagnano GA, Kalof L (1999) A value-belief-norm theory of support for social movements: the case of environmentalism. Hum Ecol Rev 6(2):81–97 14. Handgraaf MJJ, Lidth V, de Jeude MA, Appelt KC (2013) Public praise vs. private pay: effects of rewards on energy conservation in the workplace. Ecol Econ 86:86–92 15. Zhang Y, Wang Z, Zhou G (2013) Antecedents of employee electricity saving behavior in organizations: an empirical study based on norm activation model. Energy Policy 62:1120–1127 16. Li D, Xu X, Chen C-F, Menassa C (2019) Understanding energy-saving behaviors in the american workplace: a unified theory of motivation, opportunity, and ability. Energy Res Soc Sci 51:198–209 17. Leygue C, Ferguson E, Spence A (2017) Saving energy in the workplace: why, and for whom? J Environ Psychol 53:50–62 18. Tverskoi D, Xu X, Nelson H, Menassa C, Gavrilets S, Chen C-F (2021) Energy saving at work: Understanding the roles of normative values and perceived benefits and costs in single-person and shared offices in the United States. Energy Res Soc Sci 79:102173 19. Xu X, Maki A, Chen C-F, Dong B, Day JK (2017) Investigating willingness to save energy and communication about energy use in the American workplace with the attitude-behavior-context model. Energy Res Soc Sci 32:13–22 20. Guagnano GA (2001) Altruism and market-like behavior: an analysis of willingness to pay for recycled paper products. Popul Environ 22(4):425–438 21. Vaske JJ, Jacobs MH, Espinosa TK (2015) Carbon footprint mitigation on vacation: a norm activation model. J Outdoor Recreat Tour 11:80–86 22. Wang B, Wang X, Guo D, Zhang B, Wang Z (2018) Analysis of factors influencing residents’ habitual energy-saving behaviour based on NAM and TPB models: egoism or altruism? Energy Policy 116:68–77 23. Gao L, Wang S, Li J, Li H (2017) Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resour Conserv Recycl 127:107–113 24. Vandenbergh MP (2004) Order without social norms: how personal norm activation can protect the environment. Nw. UL Rev. 99:1101 25. Andrews BR (1903) Habit. Am J Psychol 14(2):121–149 26. Gardner B (2015) A review and analysis of the use of ‘habit’ in understanding, predicting and influencing health-related behaviour. Health Psychol Rev 9(3):277–295 27. Ouellette JA, Wood W (1998) Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychol Bull 124(1):54 28. Gardner B, Lally P, Rebar AL (2020) Does habit weaken the relationship between intention and behaviour? Revisiting the habit-intention interaction hypothesis. Soc Personal Psychol Compass 14(8):e12553. https://doi.org/10.1111/spc3.12553 29. Setiawan R, Santosa W, Sjafruddin A (2015) Effect of habit and car access on student behavior using cars for traveling to campus. Procedia Engineering 125:571–578 30. Shi D, Wang L, Wang Z (2019) What affects individual energy conservation behavior: personal habits, external conditions or values? An empirical study based on a survey of college students. Energy Policy 128:150–161

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31. Webb D, Soutar GN, Gagné M, Mazzarol T, Boeing A (2022) Saving energy at home: exploring the role of behavior regulation and habit. Int J Consum Stud 46(2):621–635 32. Trafimow D (2000) Habit as both a direct cause of intention to use a condom and as a moderator of the attitude-intention and subjective norm-intention relations. Psychol Health 15(3):383–393 33. Glisson C, James LR (2002) The cross-level effects of culture and climate in human service teams. J Organ Behav 23(6):767–794 34. Janz BD, Colquitt JA, Noe RA (1997) Knowledge worker team effectiveness: the role of autonomy, interdependence, team development, and contextual support variables. Person Psychol 50(4):877–904. https://doi.org/10.1111/j.1744-6570.1997.tb01486.x 35. McDonald RI, Crandall CS (2015) Social norms and social influence. Curr Opin Behav Sci 3:147–151 36. Kuenzi M, Schminke M (2009) Assembling Fragments into a lens: a review, critique, and proposed research agenda for the organizational work climate literature. J Manag 35(3):634– 717 37. Bock G-W, Zmud RW, Kim Y-G, Lee J-N (2005) Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Q 29(1):87–111 38. Arnaud A (2010) Conceptualizing and measuring ethical work climate: development and validation of the ethical climate index. Bus Soc 49(2):345–358 39. Hair JF (2009) Multivariate data analysis 40. Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50 41. Baron RM, Kenny DA (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51(6):1173 42. Chen C-F, Knight K (2014) Energy at work: social psychological factors affecting energy conservation intentions within Chinese electric power companies. Energy Res Soc Sci 4:23–31 43. Zhang Y, Wang Z, Zhou G (2014) Determinants of employee electricity saving: the role of social benefits, personal benefits and organizational electricity saving climate. J Clean Prod 66:280–287 44. Tang Z, Warkentin M, Wu L (2019) Understanding employees’ energy saving behavior from the perspective of stimulus-organism-responses. Resour Conserv Recycl 140:216–223 45. Wang X, e., Li, W., Song, J., Duan, H., Fang, K., & Diao, W. (2020) Urban consumers’ willingness to pay for higher-level energy-saving appliances: focusing on a less developed region. Resour Conserv Recycl 157:104760 46. Gifford R, Nilsson A (2014) Personal and social factors that influence pro-environmental concern and behaviour: a review. Int J Psychol 49(3):141–157 47. Choudhury G (2012) The dynamics of organizational climate: an exploration. Manag Insight 7(2) 48. Finney C, Stergiopoulos E, Hensel J, Bonato S, Dewa CS (2013) Organizational stressors associated with job stress and burnout in correctional officers: a systematic review. BMC Public Health 13(1):82 49. Pérez-Ferra M, Quijano-López R, García-Martínez I (2020) Impact of educational habits on the learning of 3–6 year old children from the perspective of early childhood education teachers. Sustainability 12(11):4388

Research on Clustering Method of Deferrable Load Lishi Du, Chang Liu, Liang Yue, and Long Yu

Abstract Due to the large amount of new energy accessed in power system, the capacity on generation side is decreased. More importance has been attached on the demand side adjustment to maintain the grid balance. The proposed first-time clustering method categorized the electric users into six groups, the two-time clustering method categorized electric characteristics into four types. According to the commonly used indictors, the comprehensive clustering method provide a more reliable result. Keywords New power load management · Load aggregation load side management · Electrical characteristics

1 Introduction With the development of China’s economy, the power load is growing continuously, in the meantime, under the background of Low-Carbon development, more clean energy is accessed to the grid [1–3]. Therefore, the contradiction between power supply and demand is prominent, the importance of power load management is highlighted [4– 6]. The electricity demand is commonly characterized as “double peaks” in winter and summer, the difference between peak and valley is expanding, and the difficulty of power supply is increasing. It is necessary to pay more attention to demand side management to guide demand side resources matching supply side independently, realize intelligent interaction between power supply and demand, and enhance the system’s ability to accept new energy development [7–9]. Demand response is a very important technical means to adjust the demand side in the power system. Making full use of demand response resources can help improve the economic and environmental benefits of the power system. L. Du · C. Liu (B) · L. Yue · L. Yu Beijing Fibrlink Communication CO., LTD., Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_18

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In order to fully mobilize demand side resources to participate in power demand side management business, and at the same time, make decision-makers better understand the current load energy consumption [10–14]. Based on the status of new power load management system accessing data, this paper proposes a data quality management method, and briefly classifies the types of power users in a province according to the actual user data.

2 Clustering Analysis Method for Electric Power Users 2.1 The First-Time Clustering Considering Electrical Characteristics of Loads The efficiency and scalability of K-means algorithm make it the preferred algorithm for processing power load data. Based on the advantages of this algorithm in time and accuracy, this paper selects K-means clustering algorithm to carry out a cluster analysis of user load characteristics. The algorithm first selects the initial cluster center, and the input layer is the number of clusters K and the user load dataset containing N objects, which are determined by the CH index (CHI) of the selected user load dataset and the number of user samples respectively; Secondly, we need to classify all user sample points, and constantly adjust the cluster center by calculating the average value of each cluster; Finally, the user groups with consistent load characteristics have the maximum similarity, and the user groups with inconsistent load characteristics have the minimum similarity. When the cluster center is no longer changed, the power consumption K modes of users is output.

2.2 Quadratic Clustering Considering Adjustable Potential of Loads In order to further master the comprehensive information of adjustable resources, this paper proposes a secondary clustering considering the user’s adjustable potential. The data set of secondary clustering is the power big data considering the factors that affect the user’s electricity use behavior. Because of the high dimension of the data set, the clustering algorithm takes a long time to analyze, so the principal component analysis (PCA) is used to reduce the dimension of the data set. Assume that there are n power users in total, and the influencing factor data of each user’s power consumption behavior has p indicators, X1 , X2 ,…, Xp is the set of initially set random variables, and the matrix X of the original data is

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⎡

x11 ⎢ x21 X=⎢ ⎣ C xn1

x12 x22 C xn2

B B E B

⎤ x1 p x2 p ⎥ ⎥ = (X1 X2 B X P ) C ⎦ xnp

253

(1)

wherein, the array in the p index of matrix X is linearly combined to obtain: ⎧ F1 = a11 x1 + a12 x2 + · · · a1 p x p ⎪ ⎪ ⎪ ⎨ F2 = a21 x1 + a22 x2 + · · · a2 p x p .. ⎪ ⎪ . ⎪ ⎩ F p = a p1 x1 + a p2 x2 + · · · a pp x p

(2)

2 2 where: ai p is the coefficient and meets ai1 + ai2 + · · · + ai2p = 1, i = 1, 2, · · · , p; x1 , x2 , · · · , x p is the corresponding value of each indicator; F1 , F2 , · · · , F p is a new irrelevant variable. The dimension reduced principal components are used as the input layer of the secondary clustering. Based on the dimension reduction results of PCA, selforganizing competitive neural network (SOM) is used to cluster the adjustable potential of users twice. This algorithm belongs to a typical unsupervised learning algorithm, which automatically classifies the submitted input patterns through the training link. Because there is no clear classification and clustering standard for the power consumption behavior of a large number of users in the existing research, this algorithm can be well applied to the characteristics of clustering work.

3 Example Analysis of Data Experiment Based on the proposed comprehensive clustering method of load characteristics and adjustable potential for massive data of large-scale adjustable load resources, the feature optimization process is introduced to verify its generalization ability, and the comprehensive clustering result fully considering user load characteristics and adjustable potential is obtained.

3.1 Clustering Results Analysis Based on Load Characteristics By the load data first time clustering, different types of users with the same load characteristics are categorized as shown in the Fig. 1 below, and the typical power consumption behavior characteristics of various types of users are summarized in Table 1.

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Fig. 1 The first clustering results of load resource behavior pattern Table 1 Typical electricity consumption behavior characteristics of various users Category Behavior pattern 1

The user’s electricity load is stable and high throughout the entire day

2

The user maintains a certain degree of power load throughout the day, while the outgoing line of power load rises significantly during the day, and there is no obvious consumption valley

3

The users have obvious power consumption peaks in the morning and afternoon, and the load level at the peak is high

4

The difference between peak and valley of user pow-er load is large, and there is a power peak at night, and the power peak is obvious

5

The users use electricity intensively from 9 p.m. to 5 a.m., and the degree of electricity load is average

6

The user’s load level is high throughout the day, with obvious peaks near noon and evening, and power consumption troughs in the afternoon and early morning

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Table 2 Evaluation value and index weight of adjustable resource load characteristics Characteristic index

Peak hour power consumption

Valley coefficient

Daily peak Daily valley load rate difference

Average daily load

Daily maximum load

Weight

0.06

0.14

0.37

0.18

0.21

0.04

Category 1

0.42

0.35

0.04

0.84

0.16

0.19

Category 2

0.47

0.29

0.19

0.45

0.26

0.42

Category 3

0.32

0.16

0.42

0.54

0.29

0.53

Category 4

0.45

0.37

0.13

0.71

0.17

0.24

Category 5

0.43

0.32

0.18

0.60

0.17

0.36

Category 6

0.48

0.68

0.44

0.66

0.58

0.77

Combined with the evaluation value of extracted load characteristic and the weight of each typical user load characteristic index, the power consumption behavior of resource cluster after clustering is further analyzed, and is shown in Table 2. The analysis of load cluster behavior characteristics for mining the adjustable potential of demand response projects is as follows:The daily load rate of category 3 and 6 users is relatively high, but considering that they may be industrial and commercial loads, reasonable response strategies should be formulated in combination with user satisfaction; The second Category users have high daily load rate and large electricity consumption in the flat section, which can be used as a representative of residents’ demand response. For such users, a higher peak time electricity price is formulated to further guide them to implement peak load cutting and valley filling, and promote the optimal allocation of power resources; The daily load rate of Category 1, 4 and 5 are high, and the daily power consumption of Category 1 and 4 users is relatively average, so they can cooperate with Category 5 users to make scheduling arrangements to fill the low load valley.

3.2 Clustering Results Analysis Based on Response Characteristics The two-time clustering is carried out for the same type of load characteristic user groups, considering the following performance indicators: ➀ load regulation coefficient, ➁ load regulation depth, ➂ load stability, ➃ load fluctuation entropy, ➄ load time shift coefficient. The selected data with good quality are used, and the pattern classification performance of self-organizing competitive neural network is used to obtain the following secondary clustering results considering the user’s adjustable potential (Fig. 2). Take users under the “double peak” load characteristic behavior mode as an example and analyzed as follows:

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Fig. 2 The second clustering results of load characteristics and adjustable potential of all users

In Fig. 3, the vertical axis represents the response completion, and the horizontal axis represents the response volume. It can be seen that Cluster 0 and Cluster 2 have good adjustable potential. It can be seen that the users of cluster 0 and cluster 1 are close to the cluster center and the distance between classes is uniform, and the corresponding users’ power consumption behavior characteristics are relatively fixed, which has a relatively stable and adjustable potential; The adjustable potential of clusters 2 and 3 is relatively random. Fig. 3 The second cluster analysis results of load resource response characteristics (Category 3)

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Figure 4 shows the clustering results table more clearly and draws the probability density function of different user clusters, which can intuitively compare the potential response potential value of different adjustable load clusters under different indicators, as shown in Table 3 The results of the example show that: 1) The influence of user load characteristics and adjustable potential on user power consumption behavior is fully considered through the multidimensional analysis of influencing factors of user power consumption behavior oriented to power big data. 2) The double-layer clustering model based on the reverse adjustment principle is adopted to improve the poor clustering results obtained by the primary clustering based on K-means clustering algorithm and the secondary clustering based on self-organizing competitive neural network, improve the clustering quality and accuracy, and fully consider the ability of users to adjust the potential to improve the clustering results. 3) The proposed comprehensive classification method has strong generalization ability under different user basic information coverage of multiple scenarios.

3.3 Test Results of Algorithm Classification Performance In order to quantitatively test the effectiveness of the proposed clustering algorithm, three indicators of DBI/SI/WSS (Within Sum of Squares) are adopted. Test Results of Load Characteristic. K-means clustering algorithm and BP neural network classification algorithm are the two commonly used algorithms applying to cluster the electric loads. In that case, this paper compares the clustering results between K-means clustering algorithm, BP neural network and paper used algorithm in indicators of DBI and SI, the results are shown in Fig. 5. The noise content in Fig. 5 is represented for the degree of data deficiency. The higher noise content is, the higher the degree of data loss. It is indicated from the figure that the DBI index under the three clustering methods all shows an upward trend when the noise content grows. According to DBI index, the smaller the value is, the higher the clustering effectiveness is. In the same noise content, the DBI value of proposed method is the lowest one. The lower SI index presents a better stability. Instead of growing, the SI index value shows a different changing mothed from DBI, it gradually decreases with the growth of noise content. Among these three methods, the proposed one shows a better stability. Based on the classification results of load characteristics, K-means clustering and BP neural network classification algorithm (refer to other relevant documents) are selected for load pattern clustering, and the results are compared from two aspects of DBI and SI, as shown in the figure. With the increase of noise content (that is, the degree of data deficiency increases), the DBI index under the three clustering methods shows an upward trend, and the SI index shows a downward trend. Compared with

258 Fig. 4 Probability Density Functions of Various Indexes of Different User Clusters

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Table 3 Potential response value of different user clusters Cluster

Number of resources

Estimated adjustable potential

Average response

Average response rate

Load reduction rate

Response fluctuation rate

0

12

3770

0.105

0.310

0.459

0.032

1

14

1380

0.025

0.050

0.118

0.028

2

12

6290

0.172

0.671

0.849

0.056

3

5

16,130

0.597

0.171

0.294

0.363

Fig. 5 Comparison of classification results considering adjustable load resource behavior patterns

the K-means clustering and BP neural network classification methods, the proposed algorithm has a more consistent change range of each clustering performance index with the increase of the degree of data deficiency, and the clustering results show better stability. Two-Time Clustering Results. KNN clustering algorithm, BP neural network classification algorithm, SOM algorithm and self-organizing center K-means algorithm are conducted for clustering results comparison in terms of WSS/SI/DBI. The results are shown in Table 4. The WSS and DB index have the lowest value under the proposed method, and the corresponding value of SI index is closer to 1, which

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Table 4 Clustering quality comparison between different clustering mehtods Clustering quality evaluation index

WSS

SI

DBI

KNN

0.1598

0.8582

1.2037

BP neural network

0.1473

0.8643

1.1978

SOM algorithm

0.1452

0.8878

0.1834

Paper proposed

0.1044

0.9339

0.9550

reflects that the quadratic comprehensive clustering method has better clustering quality than other commonly applied clustering algorithms. The double-layer aggregation of two-time clustering improves the poor clustering results of primary clustering based on K-means algorithm and the clustering based on self-organizing competitive neural network.

4 Conclusion In the context of actively building a new power system, it is difficult to meet the growing demand for grid balance only by regulating generator units to participate in grid balance regulation. Therefore, it is crucial that controllable electric users with similar electrical characteristics are clustered to participant in demand side management. Based on the actual electric user’s information, the main electric users of one city can be clustered into six different groups. The proposed two-time classification method has strong generalization ability under different user basic information coverage of multiple scenarios. The analysis above shows that it improves the clustering quality and accuracy, and it offered a more reliable and stable results, even under data missing circumstances. The research will be conducted to calculate the adjustable potential of clustered group to provide a rigorous reference for decision makers.

References 1. Zhu Z, Zhang J, Yanglong (2021) Research on modeling of power load demand side response capability evaluation. Modern Inf Technol 5:142–144 2. Cao M, Li G, Feng Z, Guo Y, Luo Y, Li J (2020) Design and research of software data platform based on power system big data governance. Comput Technol Autom 39:135–139 3. Wang C, Liang Z, Li Q, Hong B, Huang B, Jiang L (2021) Key technologies and prospects of demand-side resource utilization for power systems dominated by renewable energy. Autom Electr Power Syst 45:37–48 4. Jost T (2022) Combinatorial optimization for electric vehicles management. J Energy Power Eng 5 5. Yang M, Sun Y, Sun ZJ et al (2014) Design and development of large-scale data management system of wind farm. J Northeast Dianli Univ

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6. Wang Y, Long Y, Song G (2022) Review and suggestion on domestic standard system of new power load management system. Power Demand Side Manag 24:2–7 7. Xu Z, Ruan W, Xiao C, Zhou Y, Xu C, Zhu L (2022) A new power load management system supporting flexible regulation of load-side resources. Power Demand Side Manag 24:8–14 8. Jiang A, Wei H, Deng J, et al (2020) Cloud-edge cooperative model and closed-loop control strategy for the price response of large-scale air conditioners considering data packet dropouts. IEEE Trans Smart Grid (99):1–1 9. Pandey A et al (2020) Effect of two step GaN buffer on the structural and electrical characteristics in AlGaN/GaN heterostructure. Vacuum 178:109442 10. Sichuan Sunlight Intelligent Electric Equipment Co. Ltd.; Patent Issued for Exothermic Welding Apparatus And Exothermic Welding Method (USPTO 10,758,997) (2020). Chemic Chem 11. Energy; Universita politecnica delle marche researchers report on findings in energy (Modeling of Failure Probability for Reliability and Component Reuse of Electric and Electronic Equipment) (2020). Energy Weekly News 12. Ning J, Wu J, Jiang C, Zhang Z, Zhang Y, Xu R (2022) Optimal control strategy of peak and frequency regulation for adjustable loads considering operation characteristics of resources 15:11–19 13. Wang J, Fang K, Yang Y et al (2018) A game theory based interaction strategy between residential users and an electric company 13(1):11–19 14. Zheng M, Sun J, Meinrenken Christoph J et al (2019) Pathways toward enhanced technoeconomic performance of flow battery systems in energy system applications 16(2)

Clean Energy Technology, Low-Carbon Transformation and Energy Consumption Analysis

Energy-Related CO2 Emissions and Urbanization in Peri-Urban, Pathum Thani Province, Thailand Pawinee Iamtrakul , Sararad Chayphong , I.-Soon Raungratanaamporn , and Nuwong Chollacoop

Abstract More than half of the world’s population now lives in urban areas, with several migrations from rural to urban due to the attractiveness of urban activities. On the other hand, the urban environment has been continuously degrading, particularly the increasing CO2 emissions due to such development based on changes in land uses and urban activities. This study aims to determine trends and challenges between energy-related CO2 emissions and suburbanization. Pathum Thani province was selected as a case study due to the peri-urban characteristic of proximity to Bangkok. The study gathered data from the open-source database and analyzed it by using statistical analysis and a Geographic Information System (GIS). The results showed that urban expansion could be considered urban sprawling due to the scattering of low to moderate density all over the site. This transition of suburbanization affected land use changes and local lifestyle, mainly resulting in CO2 emissions. The finding revealed that the increasing amount of CO2 emissions in recent years incorporates urbanization, which has been aggravated by several factors, including building density, transportation network, and the number of vehicle owners. Therefore, it is necessary to cautiously establish a proper plan to mitigate those adverse effects by promoting low-carbon and implementing resource-efficient policy based which can minimize the consequences of material consumption in the peri-urban area. Keywords Built environment · Climate change · Greenhouse gas · Land use change · Suburbanization P. Iamtrakul (B) · S. Chayphong Center of Excellence in Urban Mobility Research and Innovation, Faculty of Architecture and Planning, Thammasat University, Khlong Luang, Pathum Thani, Thailand e-mail: [email protected] I.-S. Raungratanaamporn School of Transportation Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand N. Chollacoop Low Carbon Energy Research Group, National Energy Technology Center (ENTEC), Khlong Luang, Pathum Thani, Thailand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_19

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1 Introduction In 2022, the degree of urbanization worldwide was at 57%, which most of the population living in urban areas [1]. In high-income countries, it is more than 80%, and in middle-income countries, between 50 to 80% of the world population. However, most low- to lower-middle-income countries still live in rural areas [2, 3]. This phenomenon has shifted overall population growth over several decades; the world’s population living in cities is expected to rise more than doubling its current size by 2050 [4]. Mass migration has occurred from rural to urban areas, resulting in escalating land use demand, creating urban activities, and infrastructure improvements in the city, where buildings serve people and support daily activities. The consequence of this situation has led to an increase in energy consumption, which can result in negative living quality, especially environmental impacts (i.e., air pollution). It is widely known that approximately 75% of global CO2 emissions are produced in urban areas, and urbanization, intense economic development, and industrial activities also affect CO2 emissions [5]. The study of Arvin et al. (2015) pointed out that the movements of populations into cities promote the growth of the transportation sector and are associated with a larger carbon footprint [6]. This phenomenon affects climate change and environmental concerns, which have become one of the most urgent global environmental problems and increasingly dominate future scenarios. Urban areas or cities are responsible for most of the world’s sources of CO2 emissions [7]. The Climate Risk Index is evidence of the CO2 emissions situation, which showed countries most affected by climate change are located in the ASEAN region; Thailand was ranked 13th [8, 9]. Thailand’s urban population (% of the total population) will be approximately 52% in 2021 [3]. Thailand has faced the problems of urbanization like many cities, especially Bangkok and other countries’ capital cities. Thailand has continued to expand urbanization, estimated to account for 73% of the population living in urban areas by 2050. However, it was found that its’ development is concentrated in the central provinces with more prosperous not only in Bangkok but also expanded to the vicinity areas [10]. Indeed, from the urban expansion analysis of Bangkok and its vicinities, it was found that the trend of changes in population and activity in urban areas has continued to increase in intensity [11]. This phenomenon spreads to the surrounding provinces (its vicinities), especially Pathum Thani province, which is one of the areas to support Bangkok as a living and employment, as well as serve several urban activities spreading out from Bangkok. It is located as an area with potential in terms of a large concentration of multifunction activities such as agricultural, commercial, industry, and higher education institutions [12]. Therefore, the phenomenon of the urbanization process from numerous studies and statistical analyses indicated the relationship with an increase in CO2 emissions. Thus, this study aims to consider the urbanization process in Pathum Thani Province by focusing on the peri-urban of the country’s capital. With high growth potential to be considered,

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the relationship between urbanization and the impact of CO2 emissions can be represented as a case study to suggest appropriate ways to mitigate existing problems or may be more likely to become more severe in the future.

2 Literature Review Climate change has become one of the most urgent global environmental problems, driven by increasing atmospheric greenhouse gases, most notably CO2 emissions [13]. Several factors contribute to CO2 emissions, with urbanization being one of the factors that many studies are trying to understand and investigate. Urbanization is an active moderation of population and the social and economic capability from rural to urban areas. Urbanization has resulted from increased energy demand and subsequent environmental concerns [14]. The research about the correlation between the environment related to CO2 emissions and urbanization usually addresses. Several studies considered a range of factors related to CO2 emissions for the study, for example, economic growth [15, 16], industrialization [17, 18], renewable and nonrenewable energy [19–21], etc. For the issue of urbanization, for example, Gierałtowska et al. (2022) [22] estimated the link between urbanization, renewable energy, and CO2 emissions, and the results pointed to find that renewable energy has a negative effect on GHG, while urbanization increases CO2 emissions. Wang et al. (2012) [23] showed that urbanization level was the main driving factor for CO2 emissions in Beijing. Furthermore, the study of Bechet and Othman (2017) [24] indicated a positive association between emissions and urbanization at the early level of urbanization, however a negative one was found at the higher level. By Martínez-Zarzoso and Mariotti (2011) [25], the results also revealed different patterns for categorization of countries, the elasticity of emission-urbanization is favorable for low and negative for prominent levels, and some group is not statistically significant. Moreover, urbanization also contributes to the distinct levels of CO2 emission based on the types of local activities. The urbanization process in some areas can result in a significant increase or decrease in CO2 emissions or insignificant reductions in CO2 emissions. For example, the study of He et al. (2017) [14] showed that CO2 emissions (on average) decrease as the urbanization level grows. In addition, Chen et al. (2019) [26] study pointed to urbanization playing a different role in the eastern and middle western regions of CO2 emissions. A number of previous studies demonstrated that the effects of urbanization provide different results on CO2 emissions, some studies on increased urbanization resulted in reduced CO2 emissions, and some studies have opposite by increasing CO2 emissions. The difference in impact is related to energy efficiency and urban management within the city, such as easing the enforcement of environmental regulations, encouraging the usage of public transport instead of private vehicles, promoting using renewable energy, and promoting innovative activities, etc.

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3 Methodology The study area, Pathum Thani province, is represented as a case study since it is considered an area of peri-urban which supports the rapid urbanization development from Bangkok as depicted in Fig. 1. In the study of Iamtrakul, Padon and Klaylee (2022) [11] pointed out the pattern of urban distribution. They confirmed the dispersion of urban growth; as a result, the level of dispersion of the spatial expansion of different provinces presented Pathum Thani province has a relatively high sprawling rate. The study area tends to grow with the direction of urbanization spreading from urban areas to surrounding areas where agricultural areas are diminished and replaced

Fig. 1 Framework of study

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by urban areas. This study applied annual data from Pathum Thani province’s opensource database 2010–2020 [27–29]. Data collection can be divided into three categories which comprises of; (1) energy-relate to CO2 emission (CO2 emission per capita (ton/person), energy demand per capita (ton/person) (initial energy), energy consumption per capita (ton/person/year) (final chain of energy consumption)), (2) urbanization data (population, building, transportation network), and (3) economic growth (GPP per capita, the registered vehicle). All factors are divided into two parts. Firstly, it can be considered from the changing trend’s statistical value during 2010–2020 (14 years). The second part is to consider the physical characteristic of study area data by importing the data into the geographic information system (GIS), which defines the display format through grid cells of 250*250 square meters (total of 6,302 grids). This process is to consider the comparison of spatial changes and visualization by map. GIS has been widely used, which provides an opportunity for visual representation and spatial analysis [30, 31].

4 Result of Study 4.1 Trend of Influencing Factors on CO2 Emission During Years 2010–2020 Consideration about energy-related of country, it was found that energy demand and energy consumption tendency to increase steadily. Until 2019, it seems to decline due to the impact of the Covid-19 epidemic which affects the chain of business to be continuously affected. As a result, energy demand declined, consequently the domestic economy slowed down. CO2 emission was found to increase from 2010 (3.45 tons/person) until 2018 (3.97 tons/person), and then decreased until 2020 (3.75 tons/person) (see Fig. 2). The direction of reducing CO2 emissions is consistent with the reduced energy demand and energy consumption due to the impact of the Covid19 epidemic [32]. The factors that were considered to meet this study’s objectives were divided into three groups: energy-related, urbanization data, and economic growth in Pathum Thani Province. Such factors can be used to consider the situation of a phenomenon and the correspondence of urbanization situation with emissions. Data in Table 1 shows a trend of each factor from the year 2010–2020, showing the number and annual rate of change. It was found that the total population and population density has been increasing growth rate but increasing at a lower rate than the city compared annually over the past years. When considering GPP per capita, it was revealed that until 2010, GPP per capita had decreased in 2011–2012, after that, GPP per capita tended to slightly increase in the range of between 200,000 – 240,000 million baht. The situation presents a relatively stable direction and is still lower than before the year 2012.

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Fig. 2 Trend of CO2 emissions of the country during 2010–2020

Table 1 Influencing factors of socioeconomic profiles Items Population (person) Number of houses (number) Density of population (person per area) Number of registered vehicles (number) GPP per capita (million baht) CO2 emissions (ton/person)

2010 985,643.00 457,458 .00 645.90 5,030.00 400,648.00 3.45

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

0.03

0.05

0.07

0.09

0.11

0.13

0.15

0.16

0.18

0.19

0.03

0.07

0.11

0.14

0.21

0.24

0.28

0.32

0.37

0.40

0.03

0.05

0.07

0.09

0.11

0.13

0.15

0.16

0.18

0.19

-0.16

0.19

0.20

0.09

0.22

0.38

0.31

-0.05

-0.04

-0.07

-0.15

-0.45

-0.43

-0.44

-0.41

-0.41

-0.36

-0.42

-0.38

-0.40

0.01

0.08

0.08

0.12

0.12

0.14

0.13

0.15

0.12

0.09

When considering the number of registered vehicles, it was found that during 2012– 2017. There was a trend of new car registrations increasing; however, in the year 2018, there was a downward trend. Furthermore, considering the number of migrations, the number of in-migrations is greater than the number of out-migrations each year, indicating an increase in traveling into the city [31, 32]. When considering the CO2 emission in Pathum Thani province, it was found that the average CO2 emission in 2010 was 725.67; after that, the CO2 emission tended to increase [33]. From Table 2, population, several houses, and population density positively correlate with CO2 emission. That is to say, the influencing factors are related in the same direction as the relationship (positive). While GPP per capita negatively correlates with CO2 emission, it refers to having a relationship in opposite directions (one variable is high, and another variable will have a low value).

4.2 CO2 Emissions in Spatial Perspective It considers the trends in the physical transformation from urbanization and road network data between 2010 and 2017 (physical data limitations updated to 2017). Data in 2010 and 2017 for comparison of changes in physical characteristics of buildings and transportation network (see Fig. 3), the number of buildings representing the activity density is expressed through the density of buildings concentrated in

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Table 2 Correlation analysis Variables

Population Number Density of (person) of houses population (number) (person per area)

Population (person)

1

Number of houses (number)

0.995**

1

Density of population (person per area)

1.000**

0.995**

1

Number of registered vehicles (number)

0.038

−0.009

0.038

1

GPP per capita −0.618* (million baht)

−0.547

−0.618*

−0.433

1

CO2 emissions 0.951** (ton per person)

0.923**

0.950**

0.274

−0.741**

Number of registered vehicles (number)

GPP per capita (million baht)

CO2 emissions (ton per person)

1

Remark: *Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level.

the provincial city center area. It is represented the area with extensive commercial activities such as shopping malls and transit points of transportation that provides easy access by private cars throughout the highway networks to the surrounding provinces at the local and regional level.

(a) Building characteristic (number of buildings per grid)

(b) Transportation network (point of intersection per grid)

Mean

Std. D.

Min

Max

Mean

Std. D.

Min

Max

108.39

174.29

0

1,819

10.57

17.68

0

198

Fig. 3 Spatial perspective of building and transportation network

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(a) CO2 emission in 2010 (Ton/person/grid)

(b) CO2 emission in 2017 (Ton/person/grid)

Mean

Std. D.

Min

Max

Mean

Std. D.

Min

Max

725.67

909.88

0

7,940.16

1,053.85

1,384.58

0

12,883.10

Fig. 4 CO2 emissions in spatial perspective in 2010 and 2017

In 2017, it showed that the city had increased its activity density and expanded its settlements into the surrounding areas. As for the road network, it is also presented increasing its coverage with the rapid development throughout the city. From Fig. 4, it was found that CO2 emission in 2010 was approximately 725.67 (mean) while in 2017, it has increased to 1,053.85 (mean). According to the data, emissions have tended to increase in recent years with the direction of spatial expansion by reflecting the direction of spreading from urban centers to agricultural areas corresponding to the urban sprawling and road network development. When considering the R-square value of the linear graph of the relationship between several buildings and road intersections and CO2 emission per grid, it was found that R-square is in the range of 0.30–0.40. The relationship is considered a moderate correlation (see Fig. 5). The data indicates that the situation is likely to increase, in line with the annual considerations mentioned in the previous section. CO2 emission tends to increase yearly except for 2019. The data shows that the transition of Pathum Thani province begins from an agricultural-based economy to an industry and service-based economy.

Fig. 5 Relative of CO2 emissions in spatial perspective (year 2017)

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This urban transformation process brings the labor force in rural areas transferred to urban areas, which leads to increase in terms of population, activity density, traffic volumes, economic activities, and infrastructure development [34]. It has resulted in an incredible increase in energy demand, furthermore urbanization is significant on energy intensity in the long and short runs [35, 36]. However, even if the energy demand is likely to increase, the city must establish efficient energy management significantly to lessen energy consumption, including reducing activities that may cause emissions in the city to sustain the emission reduction of the city in the long term. Importantly, the cities or urban areas must initiate urban policy related to reducing CO2 emissions [37, 38] which can be in terms of circular economy strategies [39, 40], use of renewable energy and resilient urban development, promoting public transportation, etc. for mitigation of CO2 emissions [41, 42].

5 Conclusions This study aims to study the trend, challenge, and correlations between energyrelated CO2 emissions and urbanization by focusing on the peri-urban case of Pathum Thani province, Thailand. Pathum Thani Province is one of the areas affected by the urban expansion of the capital city, Bangkok which is considered one of the cities in Thailand with a high urbanization rate due to a large number of activities in the multifunction of the industrial estate and prominent educational institutions, resulting in an attractive area for the population. The impact of emissions is another issue that several studies have investigated since emissions are a crucial issue in relevance to the severity of the climate change situation. The study was conducted by employing data from the open-source database and analyzed based on statistical analysis and geospatial analysis. For CO2 emissions in country level, the statistics presented that CO2 emissions were found to increase from 2010 (3.45 tons/person) until 2018 (3.97 tons/person). Then, it decreased until 2020 (3.75 tons/person) which this trend is consistent with the reduction of energy demand and energy consumption due to the impact of the Covid-19 epidemic. For spatial perspective, the R-square value of the linear graph of the relationship is considered a moderate correlation (the range of 0.30–0.40). The transition of urban development has influenced the transformation of suburbanization and local lifestyles. Each area has specific characteristics of land use patterns contributing to the diversity of economic, environmental, and urban activities. Thus, compared with other studies, those suggest that urbanization contributes a significant effect on CO2 emissions which presented the similarity of finding from the relationship between building density and highway networks on CO2 emissions. This is due to emissions associated with energy management and efficiency. The city must present its development plan with the appropriate measures by promoting renewable energy, low-carbon cities, and public transport to reduce the number of personal vehicles. However, there are some limitations on the retrieved data in this study which are necessary to consider other variable policies related to reducing emissions

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for further studies. Findings from the study contribute to the policy maker identifying the scenarios that suggest cities’ plans must cope with future CO2 emissions threats. Thus, sustainable urbanization must be a transformative process that promotes an integrated approach that values economic, social, and environmental dimensions to meet people’s needs. Acknowledgements This project is funded by National Research Council of Thailand (NRCT) under project entitled “Integrating Health and Well-being in Sustainable Urban Development”, Contract No. N42A650244 and partially supported by Center of Excellence in Urban Mobility Research and Innovation, Faculty of Architecture and Planning, Thammasat University, Pathum Thani, Thailand.

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Value Creation of Flue Gas for Hydrogen and Power Production Using RSOFC System Woranee Mungkalasiri and Jitti Mungkalasiri

Abstract Flue gas is an exhaust gas generated through the combustion process of fossil fuels which consisted of CO2 . For low-carbon society, flue gas treatment or utilization should be considered. In this work, value creation of flue gas producing hydrogen and electrical power using reversible solid oxide fuel cell (RSOFC) was proposed. RSOFC is the integration of SOEC and SOFC operations. The SOEC operation requires electrical power to produce hydrogen and the SOFC operation consumes hydrogen to produce electrical power. The RSOFC was studied via process simulation by ASPEN Plus program. The objective was to evaluate the optimal operating conditions of RSOFC which can produced maximum net power. The results revealed that the RSOFC system with condition of minimum SOEC power consumption should be operated as it achieved electrical power benefit. The optimal operating conditions for RSOFC were 1000°C of SOEC temperature, 1 bar of SOEC pressure, 1000°C of SOFC temperature and 3 bar of SOFC pressure. The net power production of RSOFC was 0.88 kW/m2 . Keywords Flue Gas · Process Simulation · Reversible Solid Oxide Fuel Cell

1 Introduction Dramatic economic growth has been the principal driver for large amounts of fossil fuel consumption. Worldwide energy demand is growing rapidly in recent years. The increased demand is mainly met by reserves of fossil fuel that emit both greenhouse gases (GHGs) and other pollutants. Flue gas is an exhaust gas generated through the W. Mungkalasiri (B) Chemical Engineering Department, Engineering Faculty, Thammasat School of Engineering, Thammasat University, Khlong Luang, Pathumthani, Thailand e-mail: [email protected] J. Mungkalasiri Technology and Informatics Institute for Sustainability, National Science and Technology Development Agency, Khlong Luang, Pathumthani, Thailand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_20

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combustion processes of fossil fuels. Flue gas is usually composed of CO2 , water vapor, nitrogen, and oxygen. For CO2 emission control, flue gas treatment is an interested topic. CO2 conversion and utilization are gaining significant attention not only because CO2 has an impact on global climate change, but CO2 also provides an important carbon source for potential fuels and chemicals [1]. Hydrogen is considered as a revolutionary clean energy carrier. Hydrogen is expected to meet our demands for energy supply security and low-carbon society. Hydrogen can be produced using fossil fuels or renewable energy sources. Among the various hydrogen production technologies, co-electrolysis process become an efficient way of producing hydrogen from electricity. A co-electrolysis of CO2 and H2 O process can produce synthesis gas or syngas that consist of H2 and CO. Especially, high temperature solid oxide electrolysis cells (SOECs) are electrochemical device which convert electrical energy and thermal energy into chemical energy. SOECs are advanced energy storage and conversion device with high conversion and high energy efficiency. Compared with low temperature electrolysis cells as alkaline and proton exchange membrane electrolysis cells, SOECs possess unique advantages. The high temperature operation of SOECs improve the reaction kinetics and significantly increases the efficiency. Moreover, SOECs can directly electrolyze pure or humidified CO2 , which not only reducing industrial emission of CO2 , but also generating H2 and CO mixture for further synthesis of hydrocarbons [2]. In recent years, co-electrolysis technology using SOECs has been developed rapidly. Many research studies the SOECs’ performance, operating conditions and cell materials. Many literatures’ works reveal that SOECs showed great promise for efficient and economic production of syngas via co-electrolysis using renewable electricity form solar, wind, hydro or geothermal power source [2, 3]. Moreover, since hydrogen production via SOEC requires electrical energy, the source of required electrical energy should be considered because of operation cost, process efficiency and emissions resulting from electricity generation. Hydrogen production from SOEC can result in zero greenhouse gas emission, if electrical energy produced by fuel cell as solid oxide fuel cell (SOFC) is used to provide the electrical energy required for the operation of SOEC. The integrated system of SOFC and SOEC is known as reversible solid oxide fuel cell (RSOFC). Reversible solid oxide fuel cell (RSOFC) is able to both generate and store energy in a single device under dual-mode operation conditions of fuel cell and electrolysis. RSOFC is favorable for intermittent energy supply and storage in application of renewable energies [4]. Therefore, the waste-to-energy process of flue gas to produced electrical energy using an integrated system of co-electrolysis and fuel cell called RSOFC was studied in this work. Flue gas from combined cycle power plant was used as feedstock. SOEC optimal operating conditions were evaluated. The co-electrolysis (SOEC) process was simulated via ASPEN Plus program. Due to co-electrolysis requires electrical energy, thus optimal operating conditions of power generation system using a solid oxide fuel cell (SOFC) was also studied via process simulation. The objective of this work was to evaluate the optimal operating conditions of RSOFC for maximun net power production.

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2 Methodology 2.1 Solid Oxide Electrolysis Cell (SOEC) Electrolysis is considered as a promising pathway for the production of sustainable hydrogen. In this work, co-electrolysis of CO2 and H2 O was studied where flue gas from coal power plant was used as reactant which consisted of 18% CO2 , 35% H2 O and 47% N2 with a flowrate of 200 l/hr [5]. The Aspen simulation block diagram of SOEC was shown in Fig. 1. Flue gas stream was fed to heat exchanger and first reactor (LRWGS) then flow to heat exchanger and second reactor (ELECT) which steam electrolysis and CO2 electrolysis reactions occurred. Next, the oxygen in outlet gas was separated. Then, the outlet gas was fed into third reactor (HRWGS) which reverse water gas shift reaction occurs and syngas was produced. SOEC reactions Steam electrolysis H2 O → H2 + 1/2O2

(1)

CO2 electrolysis CO2 → CO + 1/2O2

(2)

Reverse water gas shift CO2 + H2 ↔ CO + H2 O

(3)

Electrical energy is required for SOEC operation. The mathematical equations called electrochemical models are used to calculated SOEC electrical power consumption at any operating condition. In this studied, the electrochemical model and input parameters were referenced by [6, 7]. The influences of important operating variables as SOEC temperature and SOEC pressure were studied and the operation ranges were shown in Table 1.

Fig. 1 SOEC simulation block diagram using ASPEN Plus program

Table 1 SOEC operation rages [6–11]

Variables

Operation ranges

SOEC temperature (o C)

550–1000

SOEC pressure (bar)

1–30

280

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2.2 Solid Oxide Fuel Cell (SOFC) In SOFC operation, hydrogen produced by SOEC was consumed in an electrochemical reactions to produce electrical power. The reactions occurring within SOFC were Hydrogen oxidation reaction H2 + O2− ↔ H2 O + 2e−

(4)

Oxygen reduction reaction 1/2O2 + 2e− ↔ O2−

(5)

Overall reaction H2 + 1/2O2 ↔ H2 O

(6)

The simulation of the SOFC consisted of ANODE and CATHODE as shown in Fig. 2. Syngas obtained from SOEC, was heated by the heat exchanger and fed into ANODE where it reacted with oxygen. Oxygen was separated from AIR stream by CATHODE and the product gases were EXHAUST stream. To determine electrical power produced from SOFC, the electrochemical model and input parameters of SOFC were reference by [12–14]. The influences of important operating variables as SOFC temperature and SOFC pressure were studied and the operation ranges were shown in Table 2. Fig. 2 SOFC simulation block diagram using ASPEN Plus program

Table 2 SOFC operation rages [12–14]

Variables SOFC temperature

Operation ranges (o C)

SOFC pressure (bar)

700–1000 1–5

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Fig. 3 RSOFC diagram

2.3 Reversible Solid Oxide Fuel Cell (RSOFC) In this study, a reversible solid oxide fuel (RSOFC) or integrated system of SOEC with SOFC was studied. RSOFC operated efficiently in both electrolysis cell and fuel cell modes. In the electrolysis cell mode, the RSOFC operated as SOEC producing hydrogen when coupled with an energy source. In the fuel cell mode, the RSOFC operated as SOFC, generating electrical power by electrochemical combination of hydrogen fuel with oxygen. The RSOFC possesses all the desired characteristics to serve as a green, flexible and efficient energy system. The diagram of RSOFC was shown in Fig. 3.

3 Results and Discussion 3.1 Optimal Operating Conditions of SOEC Effect of Temperature and Pressure on Hydrogen Production The influences of temperature and pressure on hydrogen production from SOEC process were studied via process simulation by Aspen Plus program. The SOEC operation ranges were 550–1000°C of temperature and 1–30 bar of pressure where the limitations of temperature and pressure are based on cathode and anode materials of SOEC. The results of hydrogen flowrate were shown in Fig. 4 and Table 3. The simulation results of SOEC temperature effect showed that increasing temperature, hydrogen production rate was increased and then decreased at high temperature. Because reverse water gas shift reaction that hydrogen reacts with carbon dioxide to produce steam occurs at high temperature operation. In addition, the SOEC pressure effect showed that for SOEC temperature operation of 550–900°C, increasing pressure the hydrogen production rate was decreased. However, at high temperature operation (1000°C) increasing SOEC pressure the hydrogen product was not significantly decreased. Thus, maximum hydrogen production rate was 0.403 kmol/hr achieved at temperature of 600°C and pressure of 1 bar.

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Fig. 4 Effect of SOEC temperature and pressure on hydrogen production

Table 3 Effect of SOEC temperature and pressure on hydrogen production Pressure (bar)

Hydrogen production flowrate (kmol/hr) Temperature (o C) 550

600

700

800

900

1000

1

0.370

0.403

0.398

0.384

0.372

0.363

5

0.243

0.311

0.385

0.383

0.372

0.363

10

0.191

0.254

0.358

0.379

0.372

0.363

15

0.164

0.221

0.332

0.374

0.371

0.363

20

0.146

0.199

0.311

0.368

0.370

0.362

25

0.133

0.183

0.293

0.361

0.368

0.362

30

0.123

0.170

0.278

0.354

0.367

0.362

Effect of Temperature and Pressure on SOEC Power Consumption In general, SOEC operation consumes a large amount of power (electrical power). Therefore, the influences of operating conditions on SOEC power consumption also studied in this work. The SOEC power consumption was calculated via Electrochemical models and input parameters were referenced by [6, 7]. For stable operation, the current density of SOEC should be specified in 3000– 6000 A/m2 ranges [6]. From literature reviews, the effect of current density on operating potential (cell voltage) and power consumption was shown that both the cell voltage and power consumption increased with increasing current density [6]. As a result, lower current density of 3000 A/m2 was favorable as the SOEC operated at maximum efficiency and the power consumption was lowered largely. The results as showed in Fig. 5 and Table 4 revealed that increasing temperature the power consumption of SOEC was decreased. Because activation overpotentials of SOEC decrease when temperature increase that resulted in the cell voltage of

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Fig. 5 Effect of SOEC temperature and pressure on SOEC power consumption

Table 4 Effect of SOEC temperature and pressure on SOEC power consumption Pressure (bar)

Power consumption of SOEC (kW/m2 ) Temperature (o C) 550

600

700

800

900

1000

1

8.37

6.27

4.63

4.02

3.79

3.69

5

8.36

6.29

4.67

4.08

3.85

3.74

10

8.36

6.29

4.7

4.11

3.89

3.79

15

8.36

6.3

4.71

4.14

3.92

3.82

20

8.36

6.3

4.72

4.15

3.94

3.84

25

8.36

6.3

4.73

4.17

3.95

3.86

30

8.36

6.3

4.73

4.18

3.97

3.88

SOEC decrease. Then, with low cell voltage, SOEC power consumption decreased. Moreover, increasing SOEC pressure, power consumption increased as well. Because increase in pressure leads to increase in partial pressures of hydrogen, oxygen and steam; therefore, cell voltage and power consumption increased. These results were shown that the optimal operating conditions of SOEC minimized power consumption were 1000°C and 1 bar where SOEC power consumption was 3.69 kW/m2 .

3.2 Optimal Operating Conditions of SOFC The valuable product of SOEC is hydrogen and hydrogen is considered to be chemical or renewable energy. Fuel cell is a green power generation where hydrogen is used as a main reactant. Therefore, fuel cell as solid oxide fuel cell (SOFC) was also studied.

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Hydrogen produced by flue gas using SOEC was fed into SOFC for electrical power production. The influences of SOFC temperature and pressure on power production were evaluated. The SOFC operating condition ranges were 700–1000°C of temperature and 1–5 bar of pressure. The SOFC current density was specified at 5000 A/m2 for steady power production [14]. Effect of Temperature and Pressure on SOFC Power Production The simulation results as showed in Fig. 6 and Table 5 revealed that the increase of SOFC temperature affected decreasing fuel cell resistance resulted in increasing power production. However, SOFC temperature should not be operated at high temperature (higher than 1000°C) because of unstable oxidation and reduction reactions in anode and cathode [15]. Moreover, increasing SOFC pressure resulted in higher power production due to the increasing of partial pressure of H2 and O2 and reducing the loss of cell voltage caused by activation overpotentials, ohmic loss and concentration overpotentials. However, although increasing the pressure provided the higher power production, the operation is more complex, short fuel cell lifetime and there will be higher operating expenses.

Fig. 6 Effect of SOFC temperature and pressure on SOFC power production

Table 5 Effect of SOFC temperature and pressure on SOFC power production Pressure (bar)

Power production of SOFC (kW/m2 ) Temperature (o C) 700

750

800

850

900

950

1000

1

2.56

3.12

3.62

3.99

4.20

4.28

4.28

3

2.81

3.39

3.92

4.31

4.54

4.64

4.67

5

2.90

3.48

4.01

4.41

4.65

4.76

4.79

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From these results, the optimal operating conditions of SOFC that maximized power production were 1000°C and 5 bar. However, under high pressure of SOFC operation, it is not only more complex operation but also not significantly increase power compared with SOFC operation of 1000°C and 3 bar. Therefore, the suitable operating conditions were 1000°C and 3 bar, and the SOFC power generation was 4.67 kW/m2 .

3.3 Optimal Operating Conditions of RSOFC The RSOFC is an integrated system that produce hydrogen as well as convert hydrogen to electricity, which is the individual mechanism of the solid oxide electrolysis cell (SOEC) and solid oxide fuel cell (SOFC). To evaluate the RSOFC performance, SOEC power consumption and SOFC power production were compared. In case of maximum hydrogen production condition of SOEC (SOEC temperature of 600°C and pressure of 1 bar) integrated with SOFC, the results of RSOFC system revealed that SOEC operation mode consumed more power than electrical power production by SOFC even though SOFC pressure was increased as shown in Table 6. Moreover, in case of minimum SOEC power consumption condition (SOEC temperature of 1000°C and pressure of 1 bar) integrated with SOFC, hydrogen produced by SOEC was less than the operation at 600°C, so electrical power production by SOFC was lower. However, the net power of RSOFC as in Table 7 shown that, SOFC operation mode can produced more electrical power than electrical power consumption by SOEC. Increasing SOFC pressure, the net power production from RSOFC system was increased. Therefore, the RSOFC system with condition of minimum SOEC power consumption should be operated as it achieved electrical power benefit.

Table 6 RSOFC system operated with maximum SOEC hydrogen production condition SOEC power consumption (kW/m2 )

SOFC power production (kW/m2 )

Net power of RSOFC (kW/m2 )

SOFC (T = 1000°C, P = 1 bar)

4.28

−2.44

SOFC (T = 1000°C, P = 3 bar)

4.67

−1.60

SOFC (T = 1000°C, P = 5 bar)

4.79

−1.48

SOEC (for max H2 ) (T 6.27 = 600°C, P = 1 bar)

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Table 7 RSOFC system operated with minimum SOEC power consumption condition SOEC power consumption (kW/m2 )

SOFC power production (kW/m2 )

Net power of RSOFC (kW/m2 )

SOFC (T = 1000°C, P = 1 bar)

4.19

0.50

SOFC (T = 1000°C, P = 3 bar)

4.57

0.88

SOFC (T = 1000°C, P = 5 bar)

4.68

0.99

SOEC (for min power consumption) (T = 1000°C, P = 1 bar)

3.69

4 Conclusion In this work, the waste-to-energy process of flue gas to produce electrical power using a reversible solid oxide fuel cell or RSOFC was evaluated by ASPEN Plus simulation. RSOFC is the integration of SOEC and SOFC operations. The SOEC operation requires electrical power to produce hydrogen and the SOFC operation consumes hydrogen to produce electrical power. Therefore, the objective of this work was to evaluate the optimal operating conditions for both SOEC and SOFC (RSOFC) which can produce maximum net power. The results revealed that the optimal operating conditions of SOEC provided the maximum hydrogen production were 600°C and 1 bar which produced 0.403 kmol of H2 /hr. However, the optimal operating conditions of SOEC required the minimum power consumption were 1000°C and 1 bar which lower hydrogen production rate (0.363 kmol of H2 /hr) than the SOEC operation at 600°C and 1 bar. In addition, for SOFC operation the results revealed that the optimal operating conditions were 1000°C and 5 bar where the maximum SOFC power generation was 4.79 kW/m2 . Moreover, the RSOFC performance was evaluated in term of net power production. The results showed that the RSOFC operated with minimum SOEC power consumption conditions provide net power benefit. Where increasing SOFC pressure, higher the net power of RSOFC was produced. However, although increasing SOFC pressure provided more electrical power, the high-pressure operation will be more complex and short fuel cell life time. From these results, it can be concluded that the suitable operating conditions for RSOFC were 1000°C of SOEC temperature, 1 bar of SOEC pressure, 1000°C of SOFC temperature and 3 bar of SOFC pressure. The net power production of RSOFC was 0.88 kW/m2 . Furthermore, SOEC operation consumes high electrical power, thus using renewable energy source as solar energy or wind energy for SOEC is able to increase the performance of RSOFC system. Moreover, the RSOFC system should be considered in energy efficiency aspect.

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Acknowledgements This work was supported by Engineering Faculty, Thammasat School of Engineering, Thammasat University.

References 1. Yishan Z, Juan C, Shujing Z, Helen L, Tracy B (2018) High-temperature electrolysis of simulated flue gas in solid oxide electrolysis cells. Electrochim Acta 280:206–2015 2. Chengqiao X, Junkang S, Anqi W, Jun Y, Xiaopeng Q, Wanbing G, Jianxin W, Subhash S (2022) Electrochemical performance and durability of flat-tube solid oxide electrolysis cells for H2O/CO2 co-electrolysis. Int J Hydrogen Energy 47:10166–10174 3. Cui C, Wang Y, Tong Y, Wang S, Chen C, Zhan Z (2021) Syngas production through CH4assisted co-electrolysis of H2O and CO2 in La0.8Sr0.2Cr0.5Fe0.5O3-δ-Zr0.84Y0.16O2-δ electrode-supported solid oxide electrolysis cells. Int J Hydrogen Energy 46:20305–20312 4. Chao Y, Chen S, He M, Zhouhang W, Yu W, Jiatang W, Jiapei Z, Fu W, Weiqiang Y, Jinliang Y (2019) Dynamic modelling and performance analysis of reversible solid oxide fuel cell with syngas. Int J Hydrogen Energy 44:6192–6211 5. Jean-Marc A, Chakib B (2009) CO2 capture from power stations running with natural gas (NGCC) and pulverized coal (PC): Assessment of a new chemical solvent based on aqueous solutions of N-MethylDiEthanolAmine + TriEthylene TetrAmine. Energy Procedia 1:909–916 6. Redissi Y, Bouallou C (2013) Valorization of carbon dioxide by co-electrolysis of CO2/H2O at high temperature for syngas production. Energy Procedia 37:6667–6678 7. Meng N (2012) An electrochemical model for syngas production by co-electrolysis of H2O and CO2. J Power Source 202:209–216 8. Pozzo M, Lanzini A, Santarelli M (2015) Enhanced biomass-to-liquid (BTL) conversion process through high temperature co-electrolysis in a solid oxide electrolysis cell (SOEC). Fuel 145:39–49 9. Parigi D, Giglio E, Soto A, Santarelli M (2019) Power-to-fuels through carbon dioxide reutilization and high temperature electrolysis: a technical and economical comparison between synthetic methanol and methane. J Clean Prod 226:679–691 10. Rivera-Tinoco R, Farran M, Bouallou C, Auprêtre F, Valentin S, Millet P, Ngameni R (2016) Investigation of power-to-methanol processes coupling electrolytic hydrogen production and catalytic CO2 reduction. Int J Hydrogen Energy 41:4546–4559 11. Li W, Wang H, Shi Y, Cai N (2013) Performance and methane production characteristics of H2O-CO2 co-electrolysis in solid oxide electrolysis cells. Int J Hydrogen Energy 38:11104– 11109 12. Chatrattanawet N, Saebea D, Authayanun S, Arpornwichanop A, Patcharavorachot Y (2018) Performance and environmental study of a biogas-fuelled solid oxide fuel cell with different reforming approaches. Energy 146:131–140 13. Tu B, Wen H, Yin Y, Zhang F, Su X, Cui D, Cheng M (2020) Thermodynamic analysis and experimental study of electrode reactions and open circuit voltages for methane-fuelled SOFC. Int J Hydrogen Energy 45:34069–34079 14. Issara C, Kanokporn S, Tanya P, Apinan S (2010) Performance assessment of SOFC systems integrated with bio-ethanol production and purification processes. Eng J 14(1):1–14 15. Tarancón A (2009) Strategies for lowering solid oxide fuel cells operating temperature. Energies 2:1130–1150

Simulation on Technology Comparison for CO2 Enhanced Oil Recovery in the Gulf of Thailand Pariwat Wongsriraksa, Truong Sinh Le, and Kreangkrai Maneeintr

Abstract Fossil fuels are widely used all over the world and it generates carbon dioxide (CO2 ) which is the one of the main sources for climate change and global warming. Carbon capture, utilization, and storage (CCUS) is the practical technology used for enhanced oil recovery and stored CO2 in the geological reservoir. In Thailand, there are some potential sites of the geological reservoir especially in the Gulf of Thailand. However, the high temperatures gradient in the gulf of Thailand causes the higher minimum miscibility pressure (MMP). The study on this issue in Thailand is rarely available. This study concentrates on the simulation of CO2 enhanced oil recovery (CO2 EOR). The fracture pressure is estimated to prevent the reservoir fracture whilst injecting CO2 . The CO2 EOR technologies including Huff and Puff then CO2 flooding, CO2 flooding and water alternating gas (WAG) within 20 years are performed in 3D-heterogeneous models representing the real information from the Pattani basin in the Gulf of Thailand. The results present that WAG method can produce oil with the highest recovery factor. Also, the amount of CO2 stored from each technology has been study. The results of the study can contribute to the CCUS project in the Gulf of Thailand in the future. Keywords CO2 enhanced oil recovery · simulation · water-alternating gas

1 Introduction Since the eighteenth century, the human activities have raised the amount of atmospheric carbon dioxide (CO2 ) from 365 parts per million (ppm) in 2002 to over 400 ppm nowadays [1]. CO2 is from the burning of coal, oil, and natural gas creating the climate change and global warming. Carbon capture utilization and storage (CCUS) is the suitable technology that can achieve CO2 emission and play a key role P. Wongsriraksa · T. S. Le · K. Maneeintr (B) Carbon Capture, Storage and Utilization Research Group, Department of Mining and Petroleum Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_21

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in CO2 mitigation. In 2016, Thailand emits CO2 around 271,040,160 tons (0.76% world share) [2] and has onshore and offshore potential basins that can be considered as one of the potential sites to study and to perform CCUS for reducing CO2 emission and using it for enhanced oil recovery (CO2 EOR) which is a highly efficient tool for reducing CO2 emissions. The total CO2 storage of onshore and offshore basin in Thailand are 165.12 Mt and 2,120 Mt, respectively [3]. WAG simulation study for enhanced oil recovery in Fang area at northern part of Thailand has 57% for oil recovery which is good result for oil recovery [4]. Consequently, the main potential of the offshore geological EOR could be in the oil and gas fields in the Gulf of Thailand due to its basin size, basin property and crude property. However, the research on this topic is limited. The technologies used for CO2 injection will be Huff and Puff [5], CO2 flooding and water alternating gas (WAG) [7, 8]. Huff-n-Puff process is the process of the cyclic process for oil recovery. The CO2 is injected into the reservoir through the well and the injected CO2 is soaked inside the reservoir whilst the well is shut in. Finally, the well is opened to produce again for some period and the cyclic will be start over again. Yoosook et al., [5] evaluated the performance of CO2 Huff-nPuff process in depleting heterogeneous reservoir by using simulation and results presented that oil can be produced more with higher utilization of CO2 for storage. Furthermore, Parker et al., [6] used CO2 flooding to inject CO2 for oil production as well as for carbon storage and Han et al., [7] and Bhatia et al., [8] applied water alternating gas (WAG) by using water to increase more oil production with conventional CO2 injection. Therefore, this study is focusing on the study of oilfield development for CO2 enhanced oil recovery in the Gulf of Thailand by using 3D simulation model with the actual geological data in the Pattani basin. The technologies used for CO2 injection are Huff and Puff, CO2 flooding and water alternating gas (WAG) into oil reservoir as well as the technology comparison for oil production has been studied. This study can contribute as a fundamental knowledge for CO2 EOR in an offshore area of Thailand in the future.

2 Simulation 2.1 Method The method of this study applies the ECLIPSE software to set up and simulate the 3D heterogeneous model of CO2 enhanced oil recovery for comparing CO2 EOR technologies, CO2 Huff and Puff, CO2 flooding and water alternating with CO2 within 20 year periods of injection. Also, the possibility of CO2 storage in the studied formation in the Pattani basin in the Gulf of Thailand has been studied.

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In this study, Pattani basin is selected because this area provides the properties favorable for this technique such as light oil with high percentage of intermediate crude oil (C5 to C12 ), depth, temperature [6]. However, the higher temperature gradient in the Gulf of Thailand cause the higher minimum miscible pressure (MMP). The effective CO2 EOR would be limited by pressure build up and amount of CO2 for injection. Moreover, while CO2 is injected, the CO2 moves up to the top of the formation and performs a gas driving. The higher flow of CO2 gas can displace more oil from the reservoir and reduce the residual oil saturation. This can be an important oil displacement mechanism for CO2 flooding. In addition, the MMP is an important parameter for screening the reservoir to perform the simulation for the highest recovery [9, 10]. For this case, the Pattani basin has reservoir pressure which is lesser than estimated MMP, although some oilfields in the Pattani basin have high ranking in CO2 EOR [11]. The MMP can be calculated from Eq. (1). MMPCO2 = 0.15TR − 30.92

(1)

where TR is reservoir temperature in Kelvin [11].

2.2 Reservoir Geological Data The actual fundamental data in the reservoir for creating the 3D-heterogeneous reservoir model is obtained from the government agency of Thailand as presented in Table 1. The buildup of pressure is an important condition from the injection of CO2 .The fracture pressure is calculated to determine the working pressure that must not exceed the calculated fracture pressure [12]. Moreover, the working pressure will be limited at 90% of fracture pressure. The fracture is calculated from Eaton’s equation as shown in Eq. (2).  Fracture Pressure = (OBP − P)

γ 1−γ

 +P

(2)

where OBP is the overburden pressure, a pressure from weight of overlying formations which included rock layers and water column. P is the pore pressure and γ is the Poisson’s ratio. The characteristics of the reservoir of this study is 2-phase reservoir which contains oil and water with the strong water drive. The reservoir thickness in the Gulf of Thailand is normally thin. The 3D-heterogeneous reservoir model is set up with its initial parameters as shown in Fig. 1a and 1b for permeability and porosity distributions, respectively. The model is developed by using the distribution of porosity and permeability and Lorenz coefficient between 0 and 1. If the model is homogeneous reservoir, the

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Table 1 Reservoir geological data Parameters

Value

Parameters

Depth (ft)

5,891–5,949

Initial Reservoir Pressure (psi) 3,023

Value

Reservoir Thickness (ft) 58

Initial Temperature (°F)

257

Porosity (%)

Oil gravity (°API)

39

Horizontal permeability 66– 467 (mD)

Vertical permeability (mD)

6.6–46.7

Calculated Fracture Pressure(psi)

Calculated MMP (MPa)

28.8025

Calculated MMP (psi)

4,177

21–29

4,414

Working pressure (90%) 3,973 (psi) Grid dimension (X, Y, Z)

46 × 46 × 30

Rock Type

Sandstone

Distance from well to well (ft)

1,640

Distance from well in x, y dimension (ft)

1160, 1160

Fig. 1 3-D heterogeneous reservoir models a) permeability distribution b) porosity distribution

Lorenz coefficient number will be 0. An increasing number of the Lorenz coefficient, the model is more heterogeneous.

3 Results and Discussion The process of these CO2 EOR technologies starts with the natural drive for 50 days with its initial parameters. Due to an increase in high water production, the use of CO2 in the miscible condition for each technology will be followed and the enhanced oil recovery in the formation in the Gulf of Thailand will be compared for 20-year period.

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3.1 Technology Comparison This study uses 3 CO2 EOR technologies to perform the simulation including (1) Huff and Puff CO2 injection [13] with the cyclic production with CO2 for 3 sequences and then CO2 flooding until the period of 20 years, (2) CO2 flooding for period of 20 years, (3) water alternating gas with CO2 (WAG) [7, 14] for period of 20 years. Figure 2, 3 and 4 present the original oil in place and oil remaining, the total oil production and the recovery factor (RF) within the range of 20 years, respectively. From the result, WAG provides the highest oil production around 200,000 STB, CO2 flooding produces oil around 149,000 STB and Huff and Puff method produces slightly lower. WAG can produce more oil for 35.14% compare to Huff and Puff technology. In term of the recovery factor, WAG has the highest RF at 0.7 or 70%. CO2 flooding has RF at 0.59 or 59% and Huff and Puff has RF at 0.58 or 58%. The calculation of recovery factor is presented in Eq. (3). RF =

(OIPat initial − OIPat present ) OIPat initial

(3)

For Huff-n-Puff process, from Fig. 2, the original oil in place reduction is lesser than CO2 flooding and WAG because the well is shut-in for soaking period and it takes about 90 days for non-production. And at the beginning of Huff-n-Puff curve is fluctuated because of the soaking process. For Fig. 3, the cumulative oil production and Fig. 4, the recovery factor has less value than CO2 flooding and WAG due to soaking period for 90 days of non-production. Also, the 2D cross-section of CO2 movement between the injection well (from the left side) and the production well (from the right side) for each time step (0, 5 10 and 20 year periods) are shown in Fig. 5, 6 and 7 for Huff and Puff, CO2 flooding and for WAG, respectively. From the simulation results, when CO2 is injected into the reservoir, CO2 will help to sweep the remaining oil as a gas drive and push the lighter oil components to the production well. Therefore, the remaining oil will be lower as

Fig. 2 Results of Original oil in place and oil remaining for 3 technologies within 20 years

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Fig. 3 Results of cumulative oil production for 3 technologies within 20 years

Fig. 4 Results of oil recovery factor for 3 technologies

shown in the later year. However, free CO2 tends to be dispersed both vertically and horizontally. The mobility of CO2 EOR can be a cause of CO2 breakthrough because free CO2 flows through the reservoir faster than oil. If more CO2 injected, it will allow free CO2 to move up to the production well. Moreover, CO2 will displace and push the remaining oil to deeper zone due to the gravity force as shown in Fig. 5, 6 and 7 for Huff and Puff, CO2 flooding and WAG, respectively. There are 2 disadvantages of using CO2 for EOR from this study, (1) the early breakthrough of CO2 from the injection well to the production well and (2) the remaining oil is displaced by injected CO2 then it moves down to the lower part of the reservoir due to CO2 displacement. Besides, CO2 has low volumetric sweep efficiency than water. Therefore, water is applied to enhance these processes, (1)

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Fig. 5 2-D cross-section of CO2 movement between injection and production wells with Huff and Puff technology

Fig. 6 2-D cross-section of CO2 movement between injection and production wells with CO2 flooding technology

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Fig. 7 2-D cross-section of CO2 movement between injection and production wells with WAG technology

reducing the penetration of CO2 front for prolonging early breakthrough situation, (2) displacing the remaining oil by moving under the remaining oil for pushing the remaining oil close to perforating zone of production well due to the high density of water and (3) increasing the volumetric sweep oil efficiency. The moving of remaining oil shows in Fig. 7. From the figure, with WAG process, the remaining oil is still in the higher level and water is supported at the lower level. Consequently, oil can be produced more with higher recovery factor. Figure 8 displays the gas-oil ratio of 3 technologies and the breakthrough time of WAG is the slowest one. Although, the breakthrough of CO2 of Huff and Puff is the last because the well is shut down of 3 steps of soaking periods which is 3 months each. Consequently, the time should not be counted. For oil reservoir, the mostly production of the oil reservoir is liquid (oil or water) so less gas production is expected while producing the oil from the reservoir. The GOR is gas-oil ratio which is collect from production data. The higher the GOR, the higher of gas production. From Fig. 8, for Huff-n-Puff technology, the GOR is very high at the beginning of oil production with 3 spike cycles is observed. Consequently, in the production period after CO2 injection and soaking period, high GOR or high amount of gas is produced, it means CO2 is produced back to the surface with less oil production. The CO2 can dissolve less in oil due to high MMP and it is liberated around the well bore. According to the literature [8, 15], CO2 flooding has low sweep efficiency. Water can be applied to improve the sweep efficiency; thus, making WAG provides the

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Fig. 8 Gas – oil ratio and breakthrough time of CO2

highest performance compared to others. It can support the process of WAG for CO2 EOR.

3.2 Potential of CO2 Utilization and Storage Based on an increasing oil production from CO2 EOR technologies, the simulation study also measures the amount of injected CO2 in the reservoir as well as the amount of CO2 produced with oil up to the surface. Furthermore, some CO2 is stored underground [16, 17]. The total amount of CO2 injections for each technology is shown in Fig. 9. The result presents that CO2 flooding requires the highest amount of CO2 injecting for 3,640 MMscf, 3,589 MMscf for Huff and Puff and 2,000 MMscf CO2 for WAG. For CO2 storage, the possibilities of CO2 can be stored in the reservoir as shown in Fig. 10. The method of Huff and Puff can store CO2 with the highest amount around 164 MMscf equivalent to 4.57% of total rate of injection. Also, CO2 flooding has little lower amount of CO2 stored around 158 MMscf (4.34% of total rate of injection) and WAG has CO2 storage around 64 MMscf (3.20% of total rate of injection). From the result, in terms of economics, WAG uses less CO2 which can save the cost of CO2 . It means that in view of CO2 supply, WAG is the lowest operating cost compared to the others. It can lead to project feasibility for CO2 EOR. However, after the production terminated, CO2 storage can be applied for permanent CO2 storage area in the future.

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Fig. 9 Amount of CO2 injection from 3 technologies

Fig. 10 Amount of stored CO2 from 3 technologies

4 Conclusions This simulation study is concentrating on the Pattani basin, the oilfield area in the Gulf of Thailand. The properties of oil field production area in the Gulf of Thailand are thinner in terms of thickness than the other places as well as the higher of temperature gradient in the Gulf of Thailand which can cause the higher MMP. Although, this area has the properties such as crude oil composition, depth which are suitable for

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CO2 miscible displacement. Due to the calculated MMP, this study and the other reservoirs that have higher temperatures are partially miscible CO2 flooding. Although, this study is partially miscible CO2 flooding, the candidate reservoir in this basin is selected and set up a 3D-heterogeneous reservoir model. The CO2 EOR technologies for 20-year period of injection are the Huff and Puff, CO2 flooding and water alternating CO2 (WAG). The simulation results report that WAG can produce oil with the highest volume and has the highest recovery factor (RF) with the least remaining oil in reservoir. Moreover, WAG used the less amount of CO2 injection compared to other methods. Therefore, WAG is suitable for oil production in the reservoir from the Pattani basin in the Gulf of Thailand. It can be applied for further study both oil production and CO2 storage and potential site selection in other area in the Gulf of Thailand in the future. Acknowledgements The authors would like to acknowledge the Department of Mineral Fuels (DMF) and the Malaysia-Thailand Joint Authority (MTJA) for financial support of this project.

References 1. NASA Homepage. https://climate.nasa.gov/vital-signs/carbon-dioxide/. Accessed 1 Feb 2023 2. Worldometers Homepage. https://www.worldometers.info/co2-emissions/co2-emissions-bycountry/. Accessed 25 Jan 2023 3. Choomkong A, Sirikunpitak S, Darnsawasdi R, Yordkayhun S (2017) A study of CO2 emission sources and sinks in Thailand. Energy Procedia 138:452–457 4. Yoosook H, Maneeintr K (2018) CO2 geological storage coupled with water alternating gas for enhanced oil recovery. Chem Eng Trans 63:217–222 5. Yoosook H, Maneeintr K, Boonpramote T (2017) CO2 utilization for enhance oil recovery with Huff-N-puff process in depleting heterogeneous reservoir. Energy Procedia 141:184–188 6. Parker EM, Meyer JP, Meadows SR (2009) Carbon dioxide enhanced oil recovery injection operations technologies. Energy Procedia 1:3141–3148 7. Han L, Gu Y (2015) Miscible CO2 water-alternating-gas (CO2 -WAG) injection in a tight oil formation. SPE, SPE-175108-MS 8. Bhatia J, Srivastava JP, Sharma A., Sangwai JS (2014) Production performance of water alternate gas injection techniques for enhanced oil recovery: effect of WAG ration, number of WAG cycles and type of injection gas. Int J Oil Gas Coal Technol 7(2):132–151 9. Li D, Xie S, Li X, Zhang Y, Zhang H, Yuan S (2021) Determination of minimum miscibility pressure of CO2 – oil system: a molecular dynamics study. Molecules 26:4983 10. Feng H, Haidong H, Yangqing W et al. (2016) Assessment of miscibility effect for CO2 flooding eor in a low permeability reservoir. J Petrol Sci145:328–335 11. Zhang K, Bokka HK, Lau HC (2022) Decarbonizing the energy and industry sectors in Thailand by carbon capture and storage. J Petrol Sci 145:109979 12. Kananithikorn N, Songsaeng T (2021) Pre-drilled ECD design by using fracture pressure Model in Satun-Funan Fields, Pattani Basin, Gulf of Thailand. IPCT, IPTC-21368-MS 13. Zuloaga P, Yu W, Miao J, Sepehrnoori K (2017) Performance evaluation of CO2 huff-n-puff and continuous CO2 injection in tight oil reservoirs. Energy 134:181–192 14. Verma MK (2015) Fundamental of carbon dioxide-enhanced oil recovery (CO2 EOR) – a supporting document of the assessment methodology for hydrocarbon recovery using CO2 EOR associated with carbon sequestration. USGS Report, 2015-1071

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15. Xie J, Hu X, Liang H, Li Z, Wang R, Cai W, Nassabeh SMMN (2020) Experimental investigation of permeability heterogeneity impact on the miscible alternative injection of formation brine-carbon dioxide. Energy Rep 6:2897–2902 16. Arnaut M, Vulin D, Lanberg VGJG, Jukic L (2021) Simulation analysis of CO2 EOR process and feasibility of CO2 storage during EOR. Energies 14:1154 17. Ampomah W, McPherson B, Balch R, Grigg R, Cather M (2022) Forecasting CO2 sequestration with enhanced oil recovery. Energies 15:5930

Simulation and Optimization of High Heating Value for Rice Husk Biomass in Torrefaction Process Somboon Sukpancharoen, Rachaya Sirimongkol, Sujira Khojitmate, Nopporn Rattanachoung, Nitikorn Junhuathon, and Natacha Phetyim

Abstract The present investigation aimed to enhance the high heating value (HHV) of rice husk (RH) via a steady-state model of the torrefaction process. This model was developed utilizing the software ASPEN Plus v12.1 and was validated via comparison with previously published literature. The present study implemented the utilization of Response Surface Methodology (RSM) in conjunction with the utilization of Design Expert v13.0 software to establish the optimal process conditions for torrefaction. The parameters that were taken into consideration during this analysis included temperature, time, blend ratio, and flow rate ratio of biomass to the gas carrier, as well as the optimal HHV. The Box-Behnken Design (BBD) technique was implemented in order to perform an in-depth analysis of the torrefaction process. The results revealed that the optimal conditions for torrefaction were a blend ratio of 0.498 for N2 :CO2 , a feed flow rate ratio of biomass to the gas carrier of 0.50, a temperature of 256.174 O C, and a time of 1800s. These conditions yielded an HHV of 26.152 MJ/kg. These

S. Sukpancharoen Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand R. Sirimongkol ndependent Researcher, Bangkok, Thailand S. Khojitmate Department of Textile Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand N. Rattanachoung Department of Physical and Material Sciences, Faculty of Liberal Arts and Science, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand N. Junhuathon Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology, Pathum Thani 12110, Thailand N. Phetyim (B) Department of Chemical and Materials Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_22

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findings suggest that the HHV of biomass from RH can be enhanced by up to 56.33% by implementing the optimized torrefaction process conditions. Keywords Torrefied Biomass · Design of Experiment · Bioenergy · ThermoChemical Process · Process Simulation

1 Introduction Biomass, as a substantial renewable energy source, is well-suited to meeting the world’s energy demands. In fact, it currently accounts for approximately 40% of the world’s primary energy and could be a reliable and sustainable energy source [1, 2]. Being one of the oldest forms of energy, it is extensively utilized in the agricultural regions of countries. Furthermore, it is a cost-effective and easily accessible energy source [3]. Biomass, the organic matter of plants and animals, can be converted into energy. A wide range of biomass sources, including plant matter and waste, can be utilized as energy sources. With the increasing concern for the environment, Biomass is becoming an increasingly important energy source, as it produces fewer greenhouse gases than traditional fossil fuels while providing an equivalent amount of energy. Consequently, Thailand is heavily reliant on exports from its agricultural sector. The country has a diverse range of agricultural by-products, such as rice husks (RH), bamboo, bagasse, corncobs, coconut shells, and grass, which are utilized as energy sources [4]. Thailand’s growth as one of the top exporters of agricultural and food goods worldwide is directly attributable to the country’s agriculture sector. Furthermore, Thailand is the second-largest exporter and the sixth-largest producer of rice globally. Additionally, Thailand’s contribution to the global rice supply is significant, as it supplies 4.2% of the global rice supply [5]. The agricultural sector is a pivotal component of Thailand’s economy, placing a significant emphasis on rice production. Thailand is a major player in the global rice industry, generating around 38 million metric tons of rice in 2014 [4]. The rice milling process generates a considerable amount of RH as a byproduct, which accounts for roughly 20% of the overall weight of harvested rice. In the past, this waste material was left untreated, posing a challenge for waste management and contributing to methane emissions [6]. Nevertheless, through torrefaction, RH can be converted into electricity, releasing a substantial amount of heat energy, approximately 15.90984 MJ/kg [7]. This process has the potential to address the waste management and emissions problems associated with RH, making it a valuable solution for sustainable waste management. Thermochemical pretreatment also referred to as torrefaction, is a widely recognized method for altering biomass’s physical and chemical properties [8]. The torrefaction process is a mild thermal pretreatment technique that involves heating the material to temperatures between 200 and 300 degrees Celsius in an oxygen-deficient atmosphere for enough time about 0.5 to 2 h [9]. The resulting solid, produced from

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woody biomass, is known as torrefied biomass [10]. This process can be described as a form of “mild pyrolysis” due to the elimination of smoke-causing components and the production of a solid result [11]. Additionally, the procedure is highly effective as well, demonstrating an impressive retention rate between 80 and 90% of the original energy content and around 70% of the original weight. Scientific consensus holds that the process of biomass torrefaction enhances the energy density and grindability of the material, thereby decreasing waste, transportation expenses, and power requirements during the milling stage. This is supported by various studies [12]. As such, there has been a notable increase in research on biomass torrefaction in recent years. The utilization of computer simulation has been employed in the modeling of the torrefaction process of RH. However, there is a limited availability of simulation models for biomass torrefaction in commonly used process modeling software such as ASPEN Plus v12.1. To address this issue, a method for RH modeling using a virtual reality environment torrefaction has been developed. The torrefaction process was simulated and modeled using the ASPEN Plus v12.1 software package, which is commonly used in the field of chemical engineering for modeling various processes including reactors and equipment operation such as crushers, reactors, separators, and heaters [13, 14]. In order to enhance the efficiency of generating biochar from biomass through the optimization of torrefaction process parameters, statistical modeling techniques have been proposed. Utilization of the Box-Behnken design (BBD), a method of experimental design, has been suggested as a means of minimizing the overall number of experiments required and subsequently reducing associated research costs. The alternative approach of utilizing one component at a time or full factorial design is considered less practical and economical due to the increased number of required experiments [15–17]. A technique referred to as the Response Surface Methodology (RSM) has been proposed, which employs statistical and mathematical techniques to identify optimal conditions for a given process. RSM is utilized to analyze the interactions between parameters and improve the precision of experiments. It employs appropriate experimental methods to gather data to maximize the response. RSM is also used to establish the link between response values and independent factors using polynomial modeling [18–20]. This study aims to simulate the torrefaction process of waste-RH using the integrated process model software ASPEN Plus v12.1, to predict the high heating value (HHV) and conduct a comprehensive analysis of the final product. It is imperative that the model closely mimics the behavior and response of a real system, thus, validation through experimental studies is deemed necessary. The experimental design was implemented using the BBD method via Design Expert v13.0 to determine the optimum values that result in the highest HHV under various conditions, including the temperature and duration of the torrefaction process, the mole ratio of nitrogen and carbon dioxide gases, and the feed flow rate between the biomass and gas carrier.

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Grind and mesh

Fig. 1 RH waste biomass

2 Materials and Method 2.1 Materials This study obtained RH waste as a feedstock from Pathum Thani, Thailand. The RH biomass samples were prepared by undergoing crushing and screening processes to achieve a particle size of 0.6 mm., as depicted in Fig. 1.

2.2 Aspen Plus Model Development The present study utilizes a comprehensive solution to address process engineering challenges as outlined [21]. The chosen biomass sample for this examination is RH. The operational blocks of the simulation model for the process under examination are presented in Table 1 along with a brief description of each block. To accurately reproduce a chemical process using ASPEN Plus v12.1, a lot of work was put into the design and internal operations of the blocks [22]. The impact of torrefaction temperature was evaluated in the range of 220 to 340 degrees Celsius, with residence time ranging from 1800 to 5400 s. The biomass to gas flow rate ratio and gas carrier blend ratio of N2 :CO2 gas were varied at 0.2, 0.4, 0.6 and 0.5, 1, and 1.5, respectively, to produce biomass from the torrefaction process. The simulation process was conducted using ASPEN Plus v12.1, as illustrated in Fig. 2, which can be divided into seven main parts. These include a grinder (GRINDER) for reducing the particle size of biomass, a screener (MESH) for separating the particle size of biomass, biomass decomposition (RYIELD) for decomposing cellulose, hemicellulose, and lignin, and a continuous stirred tank reactor (RCSTR) for effective mixing to model the functional variables necessary to produce bio-oil, gas, char, and radical intermediates from biochemical components. The solid products (CHAR) and liquid products (OIL) are formed from condensable gases, most of which are organic water and free particles, while the gas products (GAS) consist of hydrogen, carbon monoxide, hydrocarbons, and carbon dioxide [23].

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Table 1 Model utilizes ASPEN Plus v12.1 operation blocks Block diagram Description GRINDER

Utilized to reduce biomass particle size

MESH

To ensure the size of feedstock is satisfied and the particle size was 0.6 mm

RYIELD1

Decomposing biomass into cellulose, hemicellulose, and lignin is a reaction

RCSTR

The first of the torrefaction step, produces bio-oil, gas, charcoal, and radical intermediates from biochemical components

RYIELD2

Used to correct the torrefaction product by a linear regression model development

Fig. 2 The simulation’s process flow diagram (PFD)

2.3 Product Characterization The HHV of the raw material, which is measured in units of Mega Joules per kilogram (MJ/kg), was determined for RH, as shown in Eq. (1) [24]. This calculation was performed using the equation based on the material’s elemental composition. HHV = 3.55C2 − 232C − 2230H + 51.2CxH + 131N + 20600

(1)

where C, H, N represents the carbon, hydrogen, and nitrogen contents (wt%, d.b.), respectively.

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2.4 Modeling and Numerical Optimization of the Torrefaction Process The RSM-BBD methodology has undergone a process of optimized and evaluation. The Design Expert v13.0 software (developed by Stat-Ease, located in Minneapolis, United States) was employed to design the experiment and conduct statistical analysis on the collected data. The independent variables utilized in the BBD and their coded levels, are outlined in Table 2. In addition, the mathematical representation of the polynomial statistical model is presented in Eq. (2) [16]. Y = β0 +



βi X i +



βii X i2 +



βi j X i X j

(2)

Let Y denote the simulated output, and let X i and X j represent individual independent variables. The regression constants for the intercept, linear, square, and interaction impacts are denoted by β0 , βi , βii , and βi j , respectively.

3 Results and Discussion The physical and chemical properties of the raw material, RH, prior to torrefaction were analyzed through proximate and ultimate analysis. The results of the proximate analysis, as presented in Table 3, indicate that the percentages of volatile matter, moisture, and fixed carbon are 71.40, 15.00, and 13.60%, respectively. The results of the ultimate analysis, also presented in Table 3, reveal that the percentages of ash, carbon (C), oxygen (O), hydrogen (H), nitrogen (N), chlorine (Cl), and sulfur (S) are 16.53, 42.02, 35.29, 5.60, 0.37, 0.15, and 0.04%, respectively. Tables 4 present the comparative validation results of the three types of biomass: RH, corncob, and wheat straw, as reported by Tumpa et al. [25], by comparing the HHV and ultimate element. As measured in this study, the HHV of RH, corncob, and wheat straw are 15.60, 21.19, and 20.18, respectively. Tumpa et al. [25] reported values of 17.40, 20.10, and 19.10, respectively. The error values between the two sets of measurements are 1.80, 1.09, and 1.08, respectively. The ultimate elements of the three types of biomass were also analyzed and compared to the values reported Table 2 Experimental independent variables and their coded levels for the BBD Independent variable

Factor

Levels -1

0

+1

Temperature (o C)

X1

220

280

340

Time (s)

X2

1800

3600

5400

blend ratio of carrier gas N2 : CO2

X3

0.2

0.4

0.6

Feed flow rate ratio of biomass:gas carrier

X4

0.5

1

1.5

Simulation and Optimization of High Heating Value for Rice Husk … Table 3 Physical–chemical properties of raw RH

307

Proximate analysis (wt.%, db) Volatile

71.40

Moisture

15.00

Fixed carbon

13.60

Ash

16.53

Ultimate analysis (wt.%, db) C

42.02

O

35.29

H

5.60

N

0.37

Cl

0.15

S

0.04

HHV (MJ/kg)

16.728

by Tumpa et al. [25], with a relatively small error value, indicating that the results are acceptable. This suggests that the model used in the torrefaction process is valid and appropriate. Table 4 The comparing results of HHV product and ultimate element product of torrefaction process Materials

RH

HHV (MJ/kg)

Ultimate element product (%)

This work

Tumpa et al. [22]

Error

Element

This work

Tumpa et al. [22]

15.60

17.40

1.80

Carbon (C)

38.16

42.60

Hydrogen (H) Oxygen (O) Nitrogen (N) Corncob

21.19

20.10

1.09

20.18

19.10

1.08

5.00

34.86

35.90

0.30

0.30

53.17

47.80

5.70

6.30

Oxygen (O)

31.52

40.20

Nitrogen (N)

0.64

0.40

50.84

48.40

Carbon (C) Hydrogen (H)

Wheat straw

4.90

Carbon (C) Hydrogen (H)

5.51

5.20

Oxygen (O)

36.85

45.00

Nitrogen (N)

1.12

0.90

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3.1 Response Surface Analysis The performance of the BBD models was evaluated using R2 values derived from modeling and optimization of the torrefaction process [26]. Table 5 compares the actual and predicted values of the HHV for various combinations of four factors: temperature, time, blend ratio of gas carrier N2 : CO2 , and feed flow rate of biomass: gas carrier. For example, at a temperature of 280 °C for 3600 s, with gas carrier blend ratio N2 : CO2 ratio of 0.4 and a biomass: gas carrier ratio of 1, the actual HHV value obtained from ASPEN Plus v12.1 was 23.09 MJ/kg. When calculated using Eq. (3) for the quadratic model, the predicted HHV value was 23.10 MJ/kg. The residual value comparing the actual and predicted values was found to be 0.01, and other values were less than |1|. This indicates that the model is suitable for use in the BBD model. Based on the analysis of the data, it can be inferred that the HHV for the torrefaction process of RH can be precisely modeled using a quadratic function, as represented by Eq. (3). in terms of the code variable. HHV = −75.51622 + 0.729212(X1 ) − 0.000543(X2 ) + 5.10405(X3 ) − 8.07226(X4 )− 2.48999x10−7 (X1 )(X2 ) + 0.015163(X1 )(X3 ) − 0.009336(X1 )(X4 ) + 0.000256(X2 )(X3 ) +0.000076(X2 )(X4 ) + 9.13634(X3 )(X4 ) − 0.001278(X1 )2 + 3.28876x10−8 (X2 )2 14.51726(X3 )2 + 1.38003(X4 )2

(3) The results of the statistical analysis of parameters derived from the analysis of variance (ANOVA) for the torrefaction model of RH biomass are presented in Table 6. The p-value and F-value for the developed model of HHV were < 0.0001 and 31.28, respectively, indicating that the developed model is statistically significant [27]. Additionally, for the model development of factors X1 , X3 , and X4 , the p-values and F-values were 0.0193, 6.99, < 0.0001, 53.7, and < 0.0001, 88.4, respectively. On the basis of these outcomes, it may be inferred that the model is appropriate for usage in the BBD model. In order to identify the optimal conditions within the range of the process parameters, the effect of all four variables on the HHV of the torrefaction process of RH biomass was evaluated. Table 7 presents the optimal process conditions determined by the responses using the desirability function technique [28]. The HHV of 26.152 MJ/kg was obtained at a reaction temperature of 286.174 °C, a reaction time of 1800s, gas carrier blend ratio of N2 :CO2 at 0.498428, and a feed flow rate of biomass: gas carrier at 0.5.

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Table 5 Experimental design with independent variables and value responses Run

Temperature (°C)

Time (sec)

Blend ratio of carrier gas N2 : CO2 (mol:mol)

Biomass:gas carrier (kg:hr/ g:hr)

HHV (MJ/kg) Actual

Predicted

1

220

5400

0.4

1

17.85

17.71

0.14

2

280

3600

0.4

1

23.09

23.10

0.01

3

280

1800

0.2

1

22.04

21.51

0.53

4

220

3600

0.2

1

16.79

15.98

0.81

5

280

3600

0.4

1

23.09

23.10

0.01

6

340

3600

0.4

0.5

21.29

21.68

-0.39

7

280

5400

0.2

1

20.39

20.62

-0.23

8

340

1800

0.4

1

18.83

19.55

-0.72

9

280

3600

0.4

1

23.09

23.10

0.01

10

280

3600

0.2

1.5

16.91

18.39

-1.48

11

220

1800

0.4

1

17.55

18.37

-0.81

12

340

3600

0.4

1.5

17.69

17.12

0.57

13

340

3600

0.2

1

17.42

16.74

0.69

14

280

5400

0.4

1.5

21.46

21.26

0.20

15

280

3600

0.4

1

23.09

23.10

0.01

16

280

3600

0.6

0.5

26.41

25.50

0.90

17

280

3600

0.6

1.5

23.09

23.33

-0.24

18

280

3600

0.2

0.5

23.88

24.21

-0.34

19

280

5400

0.4

0.5

25.14

25.12

0.02

20

340

3600

0.6

1

19.82

20.22

-0.39

21

280

5400

0.6

1

23.53

23.92

-0.39

22

220

3600

0.4

0.5

19.58

20.00

-0.42

23

280

1800

0.4

0.5

26.19

25.97

0.22

24

340

5400

0.4

1

19.02

18.78

0.24

25

280

1800

0.6

1

24.82

24.44

0.38

26

280

1800

0.4

1.5

22.24

21.84

0.41

27

280

3600

0.4

1

23.09

23.10

0.01

28

220

3600

0.6

1

18.46

18.73

-0.27

29

220

3600

0.4

1.5

17.09

16.56

0.53

Residual Value

3.2 HHV’s Response to Process Conditions In order to examine the combined effect of temperature and residence time on the HHV of RH biomass during the torrefaction process, Fig. 3 presents a threedimensional (3D) graphical representation generated using RSM. As noted by Singh et al. [29], it is not possible to demonstrate the impact of all variables on the response

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Table 6 ANOVA for Quadratic model of torrefaction process Source

Sum of Squares

df

Mean Square

Model

237.8

14

16.99

X1

3.8

1

3.8

X2

1.53

1

1.53

X3

29.15

1

29.15

53.7

X4

48

1

48

88.4

X1 . X2

0.0029

1

0.0029

0.0053

X1 . X3

0.1324

1

0.1324

0.2439

0.6291

insignificant

X1 . X4

0.3138

1

0.3138

0.5779

0.4597

insignificant

X2 . X3

0.034

1

0.034

0.0625

0.8062

insignificant

X2 . X4

0.0185

1

0.0185

0.034

0.8563

insignificant

X3 . X4

3.34

1

3.34

6.15

0.0265

significant

(X1 )2

137.3

1

137.3

< 0.0001

significant

(X2 )2

0.0736

1

0.0736

0.1356

0.7182

insignificant

(X3

)2

F-value

p-value

Remarks

< 0.0001

significant

6.99

0.0193

significant

2.82

0.1154

insignificant

< 0.0001

significant

< 0.0001

significant

0.9428

insignificant

31.28

252.87

2.19

1

2.19

4.03

0.0645

insignificant

(X4 )2

0.7721

1

0.7721

1.42

0.2529

insignificant

Residual

7.6

14

0.543

Lack of Fit

7.6

10

0.7601

Pure Error

0

4

0

Cor Total

245.4

28

Table 7 The results of the torrefaction process’s optimization formulation Variables Temperature

(o C)

Target

Experimental range

Optimal value

In range

220–340

286.174

Time (s)

In range

1800–5400

1800

Blend ratio of carrier gas N2 : CO2

In range

0.2–0.6

0.498428

Feed flow rate ratio of biomass:gas carrier

In range

0.5–1.5

0.5

HHV (MJ/kg)

Maximize

16.7903–26.4059

26.152

simultaneously when there are three independent variables. Therefore, the effect of two factors was examined while holding the other variable constant. The Fig. 3 clearly illustrates that temperature is the most significant factor impacting the response of the process [26]. It has been observed that the torrefaction temperature range of 270– 290 °C can result in elevated HHV and higher energy density of torrefied RH biomass. This temperature range can lead to increased removal of moisture and volatile matter from the biomass, which contributes to the enhanced HHV of the resulting torrefied

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Fig. 3 The 3D response surface plots of the relationship between temperature and time

RH biomass. This RH biomass with a favourable HHV was also considered optimal in the subsequent performance analysis [30]. However, it is important to note that the optimal torrefaction temperature range for achieving the highest HHV of biomass can vary depending on several factors such as the type of biomass, initial moisture content, and torrefaction conditions. Therefore, determining the optimal temperature and time conditions to maximize HHV requires careful consideration of these factors to achieve the desired properties of the torrefied biomass.

4 Conclusions A simulation model was developed utilizing ASPEN Plus v12.1 software to investigate the torrefaction process of RH waste, which has a HHV of 16.728 MJ/kg. Through the examination of various parameters such as temperature, duration, blend ratio of gas carrier, and the ratio of biomass to gas carrier flow rate, the optimal conditions for the torrefaction of RH were determined. The simulation results revealed that the process’s duration had a relatively insignificant impact on the HHV compared to the reaction temperature and the feed rate ratio of biomass to gas carrier. Subsequently, statistical models were formulated and an equation fitting was accomplished. Furthermore, the ideal conditions for the HHV were determined to be a reaction temperature of 286.174 °C, a duration of 1800s, a blend ratio of carrier gas N2 : CO2 0.498, and a feed flow rate ratio of 0.50 for biomass to the gas carrier. Consequently, the HHV of the char produced after torrefaction was found to be approximately 26.152 MJ/kg, indicating a significant increase of 56.34% as compared to the HHV of the biomass before torrefaction.

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Acknowledgements This research and innovation activity is funded by National Research Council of Thailand (NRCT), in fiscal year 2022 (contact no. /13/2565). I would like to express my sincere gratitude to Khon Kaen University and Rajamangala University of Technology Thanyaburi for graciously allowing me access to essential research facilities during the course of this study. Furthermore, I would like to take this opportunity to extend my heartfelt appreciation to Mr. Phatiya Boontor for his invaluable guidance and assistance during the simulation process.

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19. Sukpancharoen S, Hansirisawat P, Srinophakun TR (2022) Implementation of response surface to optimum biodiesel power plant derived from empty fruit bunch. J Energy Res Technol 144(1):012101 20. Yatim LN, Ayu PKS, Suprapto S (2022) The application of silica gel synthesized from chemical bottle waste for zinc (II) adsorption using Response Surface Methodology (RSM). Heliyon 8(12):11997 21. Ahmed A, Prakash P, Hamish RM, Tareq A, Gordon M (2021) Pyrolysis study of different fruit wastes using an aspen plus model. Front Sustain Food Syst. 5:604001 22. Hasan MM, Rasul MG, Jahirul MI, Khan MMK (2022) Modeling and process simulation of waste macadamia nutshell pyrolysis using Aspen Plus software. Energy Rep 8:429–437 23. Glushkov DO, Nyashina GS, Anand R, Strizhak PA (2021) Composition of gas produced from the direct combustion and pyrolysis of biomass. Process Saf Environ Prot 156:43–56 24. Friedl A, Padouvas E, Rotter H, Varmuza KK (2005) Prediction of heating values of biomass fuel from elemental composition. Anal Chim Acta 544(1–2):191–198 25. Tumpa RS, Sonil N, Ajay KD, Venkatesh M (2021) A review of torrefaction technology for upgrading lignocellulosic biomass to solid biofuels. BioEnergy 14:645–669 26. Ozonoh M, Oboirien BO, Daramola MO (2020) Optimization of process variables during torrefaction of coal/biomass/waste tyre blends: application of artificial neural network & response surface methodology. Biomass Bioenerg 143:105808 27. Satyansh S, Jyoti PC, Monoj KM (2019) Optimization of process parameters for torrefaction of Acacia nilotica using response surface methodology and characteristics of torrefied biomass as upgraded fuel. Energy 186:115865 28. Ugochukwu MI, Maxwell O, Nnanna-Jnr MO, Michael OD (2021) Effect and optimization of process conditions during solvolysis and torrefaction of pine sawdust using the desirability function and genetic algorithm. ACS Omega 6(31):20112–20129 29. Rishikesh KS, Jyoti PC, Arnab S (2020) Optimizing the torrefaction of pigeon pea stalk (cajanus cajan) using response surface methodology (RSM) and characterization of solid, liquid and gaseous products. Renew Energy 155:677–690 30. Yajun W, Ling Q, Tianle Z, Xuanmin Y, Kang K (2019) Optimization of carbonization process for the production of solid biofuel from corn stalk using response surface methodology. BioEnergy Res 12:184–196

Implementation of Hybrid Energy Sources with Grid Interaction for Modern Net-Zero Energy Buildings Supanida Kaewwong, Panida Thararak, Peerapol Jirapong, Sirawit Hariwon, Sekthaphong Chaisuwan, and Churat Thararux

Abstract A net-zero energy building (NZEB) concept is a critical integrated solution to manage energy consumption in buildings and support worldwide carbon neutrality policies. However, restrictions on connecting the NZEB to the power grid of some countries complicate the NZEB design in balancing electrical power from multiple sources, including electricity grids, renewable energy sources, and energy storage systems. This paper presents a new NZEB design methodology for renovating a building implemented with the installation of building-applied photovoltaic (BAPV), building-integrated photovoltaic (BIPV), and battery energy storage system (BESS), considering the interaction between the building’s electrical system and the grid. The installed capacities of the PV and BESS are determined according to the energy balance criteria based on load consumption, PV excess energy, battery stored energy, and grid purchased energy. A building information modeling (BIM) concept implemented with the Autodesk Revit software is used to validate the hybrid energy source installation for the NZEB. In addition, the building’s annual net energy is analyzed using the PVsyst software to determine the balance of electricity generation and consumption. The simulation results from a case study of a university building show that the proposed NZEB design approach provides sufficient PV and BESS installed capacities to effectively renovate the existing building into a new NZEB. The proposed NZEB design concept makes it possible to reduce building annual electricity costs, encourage the use of renewable energy, and support carbon neutrality policies. Keywords Net-Zero Energy Building · Hybrid Energy Sources · Grid Interaction · Photovoltaic System · Battery Energy Storage System S. Kaewwong · P. Thararak (B) · P. Jirapong · S. Hariwon · S. Chaisuwan Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand e-mail: [email protected] S. Kaewwong · S. Hariwon · S. Chaisuwan Graduate School, Chiang Mai University, Chiang Mai 50200, Thailand C. Thararux School of Renewable Energy, Maejo University, Chiang Mai 50290, Thailand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_23

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1 Introduction A net-zero energy building (NZEB) concept is an efficient solution to accommodate the increase in electric power consumption, promote the use of renewable energy, increase electric efficiency, and support carbon neutrality policies [1, 2]. The concept is to improve the energy efficiency of both buildings and electricity grids by installing renewable energy sources (RESs) to balance the building’s electricity demand, thereby reducing grid energy consumption and energy loss in power distribution systems [3]. Most RESs used for the NZEB are photovoltaic (PV), wind turbines (WT), and hydropower plants [4, 5]. Therefore, an essential first step in converting an existing building to NZEB is determining the design configuration and size of the RES, which includes an energy storage system used for backup power and reducing power fluctuations from RES. However, the application of this concept to dense urban buildings has yet to be successful due to the difficulty of design and the limited RES installation space. Therefore, developing an efficient NZEB design method for urban buildings presents a challenge for balancing the load consumption and power generation by RES as well as the energy stored in the energy storage system. In addition, the NZEB design must consider the interaction between building power generations and electricity grids as it affects the design of the system size. Most of the NZEB building designs proposed in literature used RESs from PV, WT, hydropower plant, and hydrogen to generate electricity for the buildings and meet net-zero daily energy targets [4–6]. However, these RES may not be suitable for typical urban buildings with limited installation space and inadequate renewable energy resources. Therefore, installing a PV system is the most suitable option for NZEB in urban areas [7]. A comparative study of PV installations between fixed and tracking systems in [8] demonstrated that both could bring a building’s annual net energy to zero. However, installing rooftop PV on a building still requires quite a lot of space on the roof to generate enough electricity to meet the building’s electricity consumption. In addition, the power produced by PV fluctuates depending on the climate and the amount of solar radiation, which affects the reliability and disruption of the building’s electrical system. Therefore, studies in [9] and [10] proposed the application of battery energy storage systems (BESS) to back up energy from PV and use the stored energy when the PV system was unable to generate electricity. Buildings equipped with RES in conjunction with BESS can deliver uninterrupted power, increasing the flexibility of the electrical system and reducing dependence on the grid. However, designing RES and BESS for urban buildings with limited space still needs to consider the interaction between the building’s electrical system and the power grid to increase the stability of the building’s electrical system. Grid interaction of NZEB can be divided into stand-alone and grid-connected configurations. Studies in [11] and [12] showed that the design and installation of PV combined with BESS for stand-alone NZEB was not suitable for urban building applications due to the large size and cost of BESS installation. On the other hand, studies in [13] and [14] indicated that installing PV with BESS for grid-connected NZEB could exchange energy between the building’s electrical system and the grid.

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The exchange of energy in the form of grid interactions allows for a reduction of the installed size of the BESS and reduces the cost of building investments to NZEB. However, the grid codes for connecting a building’s electrical system to the grid in some countries do not allow the building to return power to the utility grid, which complicates the design and installation of the PV combined with BESS for NZEB. According to relevant literature reviews, there are no studies on developing NZEB by installing PV together with BESS for urban buildings, considering the limitations of such grid interactions. Therefore, a new NZEB design method considering grid interaction to increase the efficiency and effectiveness of NZEB is necessary to be studied and developed. This paper presents a new NZEB design methodology with the implementation of hybrid energy sources considering grid interaction for renovating existing urban buildings into NZEB. The combined energy sources used for the NZEB consist of building-applied photovoltaic (BAPV), building-integrated photovoltaic (BIPV), and BESS. Multiple grid interaction schemes are considered in conjunction with the NZEB design approach to determine the optimal sizing of PV and BESS and increase the flexibility of the electrical power source for buildings. The proposed design approach can determine the suitably installed capacities of hybrid energy sources for NZEB in urban areas, reducing building annual electricity costs, encouraging renewable energy use, and supporting carbon neutrality policies.

2 Net-Zero Energy Building Renovating an existing conventional building into NZEB requires installing hybrid energy sources to generate additional electrical power. In addition, considering the grid interaction scheme during the NZEB design process enables optimum sizing of the hybrid energy sources and greatly reduces grid power consumption.

2.1 Conventional and Net-Zero Energy Buildings Conventional buildings have electrical power demand for the loads installed. Most of these buildings receive electricity from utility grids to supply the loads within the buildings, as shown in Fig. 1. NZEBs differ from conventional buildings by installing self-generating power sources inside the buildings to supply power to the load achieving net-zero energy and reducing reliance on the power grid. The grid interaction scheme of an NZEB can be categorized into stand-alone and grid-connected configurations, as shown in Fig. 2. Most rural stand-alone NZEBs are equipped with renewable energy sources to supply the loads without being connected to the grid. Sometimes, a generator may

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Fig. 1 Conventional buildings with a grid-connected configuration

Load

Electricity Grid

NZEB Self-generation Renewable Storage energy source

NZEB Self-generation Renewable Storage energy source

(a)

Load

(b)

Load

Fig. 2 NZEB with a stand-alone and b grid-connected configurations

be installed and combined with battery storage to store energy and power the load when needed. On the other hand, an NZEB with a grid-connected configuration receives a single energy source from the utility grid in response to the building’s energy consumption. Exchangeable energy transfer between NZEB and the grid allows for the reduced size of installed RES and BESS, increasing the reliability of the building’s electrical system and simplifying the building energy balance [15]. The comparison of conventional building and NZEB configurations considering grid interaction is shown in Table 1. Table 1 Comparison of conventional building and NZEB configurations Configurations Stand-alone

Grid-connected

Conventional building

NZEB

Conventional building

NZEB

Energy sources

• Electric generator • RES

• RES • Storage

• Utility grid

• RES • Storage • Grid

Advantages

• Suitable for remote area

• Suitable for remote • Convenient area installation • Smaller system than that of the existing building • Clean energy

Disadvantages • Unstable • Not suitable for power source large buildings

• Purchased electricity • Outages

• Self-generation • Clean energy • Increase system reliability

• Restrictions on non-returnable energy

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The annual energy balance of a conventional building and NZEB can be determined from the relationship between the electrical energy supplied from the building’s self-power generation and the utility grid, and the electrical load consumption, as shown in (1) and (2). The net annual energy of a building can be determined in (3). E Supply = E Cons 365 ∑

E Supply,i =

i=1

E N et =

365 ∑

(1)

E Cons,i

(2)

i=1 365 ∑ i=1

E Supply,i −

365 ∑

E Cons,i

(3)

i=1

where E Supply Yearly electrical energy supplied from the building and the grid (kWh/ year), E Cons Yearly electrical load consumption (kWh/year), E Supply,i Electrical energy supplied at day ith (kWh/day), E Cons,i Electrical load consumption at day ith (kWh/day), E N et Net annual energy of the building (kWh/year). For stand-alone building configuration, the energy balance equation of conventional buildings and NZEB can be shown in (4). RES B E SS Load B E SS E Supply + E Supply = E Cons + E Cons

(4)

where RES E Supply Yearly electrical energy supplied from the RES self-generation (kWh/ year), B E SS E Supply Yearly electrical energy supplied from the BESS (kWh/year), Load E Cons Yearly electrical load consumption (kWh/year), B E SS E Cons Yearly electrical energy consumed by the BESS (kWh/year). For grid-connected building configuration, the energy balance equations of conventional buildings and NZEB can be shown in (5) and (6), respectively. Grid Load E Supply = E Cons

(5)

RES B E SS Grid Load B E SS E Supply + E Supply + E Supply = E Cons + E Cons

(6)

where Grid E Supply Yearly electrical energy supplied from the grid (kWh/year).

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2.2 Hybrid Energy Sources for NZEB Solar PV systems are the most popular in generating electricity for buildings in urban areas due to their ease of installation, maintenance, and availability of an energy resource. In addition, PV systems can also be easily increased in installed capacity according to the load’s power consumption. The integration of PV systems with a building can be divided into two schemes: building-applied photovoltaic (BAPV) and building-integrated photovoltaic (BIPV). Building-Applied Photovoltaic. The BAPV installation scheme is to install additional solar panels on the roof of buildings (See: Fig. 3), such as a sloping or flat roof. The main advantages of this scheme are ease of use and high efficiency since the installation angle of the PV panel can be adjusted to receive sunlight directly [16]. However, this PV installation scheme can only be installed on the roof of a building, making buildings with limited roof space unable to produce enough PV power to meet the load’s demand. Building-Integrated Photovoltaic. The BIPV installation scheme combines PV material with building materials (See: Fig. 4), such as roof tiles, skylights, roof cladding, and parapets. The advantages of this scheme are durability, aesthetics of the building, and reduced installation space constraints compared to BAPV [17]. However, BIPV has limitations in determining the installation angle of the panels because installation depends on the building structure, which affects solar radiation exposure. Therefore, determining the PV system’s installation schemes should consider the building’s structure and the installation area. The initially estimated PV sizing is determined from the building’s electrical demand data in (7), then select the appropriate size of BAPV and BIPV to achieve the desired total capacity. P VSi ze =

E Load (E Solar × η P V )

(7)

where Sloping roof

Fig. 3 Configuration of BAPV installation

Flat roof

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Skylight

Canopy

Facade

Parapet

Fig. 4 Configuration of BIPV installation

P VSi ze PV installed capacity (kWp), E Solar Specific solar energy (kWh/year/kWp), η P V PV system efficiency (%). Battery Energy Storage System. The BESS stores energy when the PV produces power more than the load’s demand, and it supplies energy to the load during times when PV cannot produce power. The BESS reduces the PV system’s power fluctuations, enabling the system to deliver more uninterrupted power and improve system reliability. The initially estimated BESS sizing is calculated from the excess energy produced by the PV that is greater than the load demand shown in (8). B E SSsi ze =

E E xcess × D η I nv. × DoD

(8)

where B E SSsi ze BESS installed capacity (kWh), E E xcess Excess energy produced by the PV system (kWh/day), DoD Battery depth of discharge (%), η I nv. Inverter efficiency (%), D Days of autonomy (day).

2.3 Grid Interaction of NZEB The grid interaction of NZEB is necessary for the design process as it affects the energy balance and sizing of both RES and BESS. There are two configurations of NZEB and grid interaction: stand-alone and grid-connected buildings (See: Fig. 5). In the case of stand-alone NZEB equipped with PV and BESS, the energy balance equations and the net energy of the building can be expressed in (9) and (10), respectively.

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PV

EPVUtilized

EGB

EPVUtilized

PV

Electricity Grid

Electricity Grid

EGB

EPVUtilized

EBG

EBG Inverter

Inverter

E

discharge BESS

(a)

E

charge BESS

E

MDB

BESS

discharge BESS

(b)

ELoad

BESS

Inverter

E

charge BESS

E

MDB

discharge BESS

(c)

ELoad

BESS

E

charge BESS

MDB

ELoad

Fig. 5 Grid interaction configurations: a stand-alone, b grid-connected with returnsable energy, and c grid-connected with non-returnable energy PV B E SS Load B E SS E Supply + E Supply = E Cons + E Cons

  Load   PV B E SS B E SS − E Cons + E Cons E N et = E Supply + E Supply

(9) (10)

Grid-connected NZEBs can be classified into two types: those that can return energy to the grid and those that cannot return energy to the grid. The NZEB, with a returnable energy ability, can receive energy from the grid to supply its loads and, conversely, can return or sell the building’s surplus energy to the grid. Therefore, the energy balance equation of this NZEB type can be expressed in (11). On the contrary, the NZEB, with non-returnable energy constraints, can only receive energy from the grid to supply the load and cannot return or sell the excess energy to the grid. Therefore, the energy balance equation of this NZEB type can be expressed in (12). 

   Load PV B E SS B E SS + E Supply + [E B2G − E G2B ] = E Cons + E Cons E Supply 

   Load PV B E SS B E SS + E Supply + [−E G2B ] = E Cons + E Cons E Supply

(11) (12)

where E B2G Transferred energy from the building to the grid (kWh/year), E G2B Transferred energy from the grid to the building (kWh/year). The annual net energy in (13) can occur in positive, negative, and zero net energy values. – The positive value indicates that the building can generate more electricity than the load’s consumption. – The negative value indicates that the building can generate less electricity than the load’s consumption, which needs energy from the grid to offset. – The zero value indicates that the building can generate electricity in balance with the load’s consumption, thereby achieving a net-zero annual energy building.  E N et =

   PV B E SS Load B E SS E Supply + E Supply + [E B2G − E G2B ] − E Cons + E Cons E Cons

(13)

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323 Start

- Physical Data - Electrical Energy Analysis - Energy Audit

Building data collection : 1

Collect building data

- EPVUtilized - EExcess

Calculate initial PV sizing

-The efficiency of BAPV (0.8) and BIPV (0.6) -Environmental factors (0.8)

Determine the effect of PV

- EPVGenerated - ELoad - Emissing

PV and BESS sizing design : 2

Plot daily load and PV generation curve Yes

No

Reduce consumption?

No

Install BESS?

No

Yes No

EPVGenerated cover ELoad?

Yes

Yes

No

No

Connect grid?

Connect grid?

Yes Yes

Return energy to grid?

Return energy to grid?

No

Yes EPVUtilized = ELoad ?

Yes

Yes

Error ±10 kWh/day

EPVGenerated = ELoad ?

Yes

Yes

Error ±10 kWh/day

No

No

Error ±10 kWh/day

No

EPVGenerated < ELoad ? Yes

No Step down PV 1 kWp

EExcess = 2(EMissing) ?

No

EPVUtilized < ELoad ? Yes Step up PV 1 kWp

EPVGenerated = ELoad ?

Error ±10 kWh/day

EExcess < 2(EMissing) ?

No Step down PV 1 kWp

Step up PV 1 kWp

Yes

No

Step up PV 1 kWp

Step down PV 1 kWp

Estimate PV and BESS size

Estimate PV size

Yes

Update information

No

Simulate project using PVsyst software

Energy analysis : 3

Model project using Autodesk Revit software

Building model : 4

Consider NZEB?

No

Yes

Unable NZEB

End

Fig. 6 The proposed NZEB design algorithm

where E N et Proportion of net annual energy of the building.

3 NZEB Design Methodology The solution of the proposed NZEB design approach is to determine the optimal BAPV, BIPV, and BESS installed capacities. The NZEB design algorithm (See: Fig. 6) can be separated into four parts as follows:

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Table 2 Configurations of grid interaction and hybrid energy sources installed in the building

3.1 Building Data Collection Renovating an existing conventional building requires physical and electrical data to create a primary database. The physical data include location, building size, and weather conditions. Electrical data consist of the load curve, the grid interaction configuration, the electrical system of the building, and results from an energy audit.

3.2 PV and BESS Sizing Design The building designs are divided into six configurations, as shown in Table 2. First, the size of PV installed on a building is selected based on the building’s electricity demand. The size of the PV system is then determined based on the load missing energy and excess energy to obtain the optimum PV size for each configuration. In addition, the BESS size is calculated from the excess energy twice the missing energy obtained from a 40% DoD allowance and 70% state of charge (SoC) energy efficiency due to battery loss considerations, as recommended in [18].

3.3 Annual Net Energy Analysis NZEB energy analysis with the PVsyst software simulates the electrical system from building electrification using PV and BESS installations. The key workflows are specifying the solar irradiation location, determining the installation angle of the panels, sizing the PV system, determining the temperature dissipation, and sizing the BESS system. An analysis of the energy flows occurring within the building shows the study’s results in a simulation report and energy loss diagram for determining the net annual energy.

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3.4 Building Modeling The building information modeling (BIM) implemented with Autodesk Revit software is used to validate the hybrid energy source installation, forecast construction, and reduce redundant installation errors in structural, architectural, and electrical systems. The level of development (LOD) is 200 (See: Fig. 7), which displays the design of the building plan and installation of PV and BESS in the building. The modeling begins with project documentation, building plans, electrical systems, PV structures, and battery placement. In addition, the simulation results are displayed in 2D and 3D formats to analyze and simulate additional electrical installations in the building before actual construction.

4 Case Studies and Results To evaluate the proposed design method, an education building located at Chiang Mai University, Thailand, is used as a case study to be renovated into NZEB. The key design process includes building data collection, sizing hybrid energy sources, energy analysis, and simulating the installation of building-applied photovoltaic (BAPV), building-integrated photovoltaic (BIPV), and battery energy storage system (BESS) in the building. The case studies are divided into six scenarios according to the configurations shown in Table 2 as follows: case 1: Installing only PV in the stand-alone building, case 2: Installing both PV and BESS in the stand-alone building, case 3: Installing PV in the returnable energy grid-connected building (REGB), case 4: Installing PV and BESS in the REGB, case 5: Installing PV in the non-returnable energy grid-connected building (nREGB), case 6: Installing PV and BESS in the nREGB.

LOD 0

Fig. 7 Level of development

LOD 100

LOD 200

LOD 300

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Table 3 Building information Physical Data

Building Details

Location

• Latitude 18.79483°, longitude 98.95168°

Building Structure

• Reinforced concrete building with four floors

Size

• Width 10 m, length 60 m, height 13.8 m

Area

• Total area 3,877 m2 , roof area 969.25 m2

Types of rooms

• 10 classrooms, 3 meeting rooms, 2 co-working space

Electrical Data

Building Details

Energy consumption of the electrical system

• Air conditioning system (split-type) 79.1%, lighting system (LED T8) 17.8%, others 3.1%

Configurations of grid interaction

• Grid-connected with non-returnable energy to the grid

Annual electric energy

• 164,229 kWh/year

Maximum demand

• 85.0 kW

4.1 Building Data Collection The physical and electrical data of the building are compiled in Table 3.

4.2 NZEB Design and Energy Analysis for the Stand-Alone Building In case 1, designing a PV system for the stand-alone building cannot determine PV sizing in accordance with the electrical demand of the load. Because the PV only produces electricity during the day (PV period), but the load consumes electricity throughout the day, as shown in Fig. 8. Renovating the stand-alone building into NZEB is necessary to install a BESS in conjunction with the PV system. In case 2, installing 122 kWp BAPV and 12 kWp BIPV together with 3,319 kWh BESS can renovate the building into NZEB, as shown in Table 4. The excess PV energy is stored in BESS to supply power to the load outside the PV period. However, the building required the installation of a large BESS resulting in a high investment.

4.3 NZEB Design and Energy Analysis for the Grid-Connected Building The simulated grid-connected building can be divided into two types: the returnable energy grid-connected building (REGB) and the non-returnable energy gridconnected building (nREGB).

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(b)

Fig. 8 Daily load and generation curve of the stand-alone building in a case 1 and b case 2

Table 4 The results of the stand-alone building Case study

PV system (kWp)

BESS system (kWh)

Case 1

Unable to be NZEB

Case 2

BAPV: 122 BIPV: 12

3,319

Days of autonomy (day)

3

Energy from grid (kWh/year)

Energy to grid (kWh/ year)

Performance ratio

Proportion of net annual energy

0

0

0.68

0

Returnable Energy Grid-Connected Building. In case 3, the installation of 112 kWp BAPV can renovate the building into NZEB, as shown in Table 5. The excess PV energy can be returned to the grid during the PV period, and the load can receive energy from the grid outside the PV period (See: Fig. 9). However, the building still has a high energy consumption from the electricity grid, resulting in high energy costs. Therefore, in case 4, installing 116 kWp BAPV and 97.3 kWh BESS can enable the building to be NZEB, reducing grid demand and increasing the reliability of the building’s electrical system. However, for some countries with restrictions on non-returnable energy from buildings to electricity grids, the NZEB design needs to be re-evaluated for installing PV and BESS. Non-Returnable Energy Grid-Connected Building. In case 5, installing 122 kWp BAPV and 124 kWp BIPV cannot enable the building to be NZEB. This is because the installed BAPV and BIPV cannot supply energy to the load throughout the day, leading to energy consumption from the grid outside the PV period. Receiving large amounts of energy from the grid causes an imbalance between supplied and consumed energy (See: Fig. 10), resulting in negative annual net energy, as shown in Table 6. Therefore, installing a BESS with the PV system is necessary to renovate the building into NZEB. Thus, in case 6, installing 122 kWp BAPV and 44 kWp BIPV combined with 729 kWh BESS can enable the building to be NZEB. Such a large BESS results

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Table 5 The results of the grid-connected building with returnable energy to the grid Case study

PV system (kWp)

Case 3

BAPV: 112

Case 4

BAPV: 116

BESS system (kWh)

Days of autonomy (day)

Energy from grid (kWh/year)

Energy to grid (kWh/ year)

Performance ratio

Proportion of net annual energy

-

-

37,879

40,963

0.79

0.0188

97.3

1

21,829

25,099

0.77

0.0199

Fig. 9 Daily load and generation curve of the REGB in a case 3 and b case 4

from the design criteria that allows BESS to back up electricity for up to 3 days to compensate in case of no sunlight for the PV system. Although the building cannot return energy to the grid, installing the BESS will reduce energy imports and create a balance between supplied and consumed energy. Therefore, the simulated building in case 6 is chosen as a suitable case for the PV and BESS installations consistent with the existing building grid connection scheme that cannot return energy to the grid.

Fig. 10 Daily load and generation curve of the nREGB in a case 5 and b case 6

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Table 6 The results of the grid-connected building with non-returnable energy to the grid Case study

PV system (kWp)

BESS system (kWh)

Days of autonomy (day)

Energy from grid (kWh/year)

Energy to grid (kWh/ year)

Performance ratio

Proportion of net annual energy

Case 5

BAPV: 122 BIPV: 124

-

-

14,692

0

0.42

−0.0895

Case 6

BAPV: 122 BIPV: 44

729

3

1,612

0

0.59

−0.0098

Rooftop BAPV

Electrical room

Battery room

Facade BIPV

(a)

(b)

Fig. 11 The installation of a PV and b BESS

4.4 Building Modeling The BIM concept can be used to simulate the building equipped with PV and BESS in case 6, conducted with Autodesk Revit software to demonstrate the possibilities of building electrical installations. The installation of 122 kWp BAPV on the rooftop and 44 kWp BIPV on the south wall of the building occupies 808 m2 and 88 m2 , respectively. By installing both BAPV and BIPV, the PV systems can sufficiently generate electricity and store energy in the BESS to meet the load requirements throughout the day (see: Fig. 11).

5 Conclusion Renovating urban buildings into NZEB is challenging due to the limited installation space of self-generation systems and limitations on returning electricity from buildings to grids. This paper presents a new NZEB design methodology with installing hybrid energy sources from BAPV, BIPV, and BESS, considering grid interactions. Energy analysis using the PVsyst software can effectively assess the NZEB energy generation and consumption. In addition, the BIM concept is used to simulate the

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installation of hybrid energy sources for NZEB to evaluate and verify the proposed design approach. The simulation results show that the proposed design approach can be used to develop NZEB for stand-alone building, REGB, and nREGB configurations. Installing only PV cannot convert stand-alone buildings and nREGB to NZEB. By installing PV with BESS, existing buildings can be converted to NZEB by balancing the supplied and consumed energy. The NZEB design approach can be utilized for renovating urban buildings with the limited installation space of selfgeneration systems and limitations on returning electricity from buildings to electricity grids. Adopting the NZEB design concept makes it possible to increase the energy resilience of buildings, reduce dependence on electricity grids, promote the use of renewable energy, and reduce environmental impacts. Acknowledgements The authors would like to appreciate the financial support from the CMU Junior Research Fellowship Program, Teaching Assistants and Research Assistants Scholarship Program, Faculty of Engineering, and Graduate School from Chiang Mai University.

References 1. Dong Z, Zhao K, Liu Y, Ge J (2021) Performance investigation of a net-zero energy building in hot summer and cold winter zone. J Build Eng 43:103192 2. Harkouss F, Fardoun F, Biwole PH (2019) Optimal design of renewable energy solution sets for net zero energy buildings. Energy 179:1155–1175 3. Gong H, Rallabandi V, Ionel DM, Colliver D, Duerr S, Ababei C (2020) Dynamic modeling and optimal design for net zero energy houses including hybrid electric and thermal energy storage. Trans Indust Appl 56(4):4102–4113 4. Keteng J, Li H, Lei Y, et al. (2022) Photovoltaic optimal configuration of net zero energy building based on whole-process energy efficiency. In: 5th International electrical and energy conference (CIEEC), pp 4842–4847. IEEE, China (2022). 5. Mehrjerdi H, Hemmati R, Shafie-khah M, Catalão JPS (2021) Zero energy building by multicarrier energy systems including hydro, wind, solar, and hydrogen. Trans Indust Inform 17(8):5474–5484 6. Ahmed A, Ge T, Peng J, Yan WC, Tee BT, You S (2022) Assessment of the renewable energy generation towards net-zero energy buildings: a review. Energy Build 256:111755 7. Cillari G, Franco A, Fantozzi F (2021) Sizing strategies of photovoltaic systems in nZEB schemes to maximize the self-consumption share. Energy Rep 7:6769–6785 8. Agostino DD, Minelli F, D’Urso M, Minichiello F (2022) Fixed and tracking PV systems for net zero energy buildings: comparison between yearly and monthly energy balance. Renew Energy 195:809–824 9. Liu J, Zhou Y, Yang H, Wu H (2022) Net-zero energy management and optimization of commercial building sectors with hybrid renewable energy systems integrated with energy storage of pumped hydro and hydrogen taxis. Appl Energy 321:119312 10. Mottaghizadeh P, Jabbari F, Brouwer J (2022) Integrated solid oxide fuel cell, solar PV, and battery storage system to achieve zero net energy residential nanogrid in California. Appl Energy 323:119577 11. Adeyemo AA, Amusan OT (2022) Modelling and multi-objective optimization of hybrid energy storage solution for photovoltaic powered off-grid net zero energy building. J Energy Stor 55:105273

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12. Yan HW, Narang A, Tafti HD, Farivar GG, Ceballos S, Pou J (2021) Minimizing energy storage utilization in a stand-alone dc microgrid using photovoltaic flexible power control. Trans Smart Grid 12(5):3755–3764 13. Ntube N, Li H (2021) Optimal sizing of battery energy storage system in a PV-battery system for a net zero home. In: Innovative smart grid technologies conference - Latin America (ISGT Latin America), pp. 1–5. IEEE PES, Peru 14. Tumminia G et al (2020) Grid interaction and environmental impact of a net zero energy building. Energy Convers Manage 203:112228 15. Feng W et al (2019) A review of net zero energy buildings in hot and humid climates: experience learned from 34 case study buildings. Renew Sustain Energy Rev 114:109303 16. Suanjan K (2018) Design of support structure for solar tracking photovotaic system. MS Thesis, 25605809031551OFI (unpublished). Thammasat University, Bangkok, Thailand (2018) 17. Chivelet NM, Kapsis K, Wilson HR, Delisle V, Yang R, Olivieri L et al (2022) Buildingintegrated photovoltaic (BIPV) products and systems: a review of energy-related behavior. Energy Build 262:1–8 18. Rand DAJ, Moseley, PT, Garche J (2015). Electrochemical energy storage for renewable sources and grid balancing, Elsevier, Amsterdam

Use of Mine Tailings as a Substrate in Microbial Fuel Cells for Electric Energy Generation F. Silva-Palacios, A. Salvador-Salinas, S. Rojas-Flores, M. De La Cruz-Noriega, R. Nazario-Naveda, M. Gallozzo-Cardenas, D. Delfin-Narciso, and Félix Díaz

Abstract In order to find an alternative for the reuse of mining effluents and contribute with eco-friendly technologies that cover the high demand for energy, this research evaluated the use of mine tailings as a substrate for the generation of electrical energy through microbial fuel cells. (MFC). A single chamber microbial fuel cell with air cathode was constructed, using a copper foil as anode electrode and a graphite plate as cathode. For the characterization of the cells, physicochemical parameters such as voltage, electric current, pH, turbidity and electrical conductivity were measured for 30 days and at room temperature (18 ± 2.2 ºC). The voltage, current and turbidity, reached peak values of 0.65 ± 0.02 V, 1.83 ± 0.04 mA and 981.5 ± 13.44 NTU respectively, in addition the mine tailings operated at an acid pH and conductivity values greater than 146 mS/cm. These results demonstrated that it is possible to use mining tailings as a substrate in microbial fuel cells for sustainable electric power generation. Keywords microbial fuel cell · bioelectricity · mine tailing · wastewater · substrate F. Silva-Palacios (B) · A. Salvador-Salinas School of Environmental Engineering, Faculty of Engineering, Universidad César Vallejo, Trujillo 13001, Peru e-mail: [email protected] S. Rojas-Flores · M. De La Cruz-Noriega · R. Nazario-Naveda Vicerrectorado de Investigación, Universidad Autónoma del Perú, Lima 15842, Peru M. Gallozzo-Cardenas Universidad Tecnológica del Perú, Trujillo 13001, Peru D. Delfin-Narciso Grupo de Investigación en Ciencias Aplicadas Y Nuevas Tecnologías, Universidad Privada del Norte, Trujillo 13007, Peru F. Díaz Professional Academic School of Human Medicine, Universidad Norbert Wiener, Lima 15046, Peru © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_24

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1 Introduction The crisis due to energy shortages and the contamination of water resources are global problems that harm the development of society. In Peru, this occurs mainly in the Andean areas, where mining activity takes place; generating rampant contamination of natural resources and causing great concern to the entire population. Effluents from mining industries contain a wide variety of heavy metal ions [1], which are relatively dense, toxic and present in high concentrations, which constitutes a great danger both for ecosystems and for human health [2]. For this reason, we highlight that the contamination of water by mining tailings is one of the most worrying environmental problems, and since it does not have an added value of reuse as a raw material for another activity, it has been a latent threat [3, 4]. There are several studies that focus on the treatment of wastewater with the presence of heavy metals and the generation of electrical energy; however, they are oriented to the treatment of these waters. The use of real mine tailings in the generation of electrical energy through Microbial Fuel Cells (MFCs) is a field with great potential and that deserves more attention. MFCs are an excellent and promising technology to address this problem and at the same time treat contaminated water [5]. A Microbial Fuel Cell is a bioreactor in which bioelectrochemical bacteria transfer electrons to perform bioremediation, biorecovery of metals and pure materials, as well as the processing of substances, treatment of environmental contaminants, production of electrical energy, etc. [6, 7]. Some of the advantages of this technology are the direct use of substrates for electricity generation, adaptable operating conditions, without the need for gas treatment, without using energy for the aeration process, and its ease of application in rural areas [8]. The substrate is a fundamental part of the MFCs functioning process, its chemical composition and concentration affects the delivery of electrons by the bacteria, therefore, the generation of electricity [9]. Among the substrates are the simple ones; like glucose, acetate, butyrate, starch, lactose, molasses, among others, stand out, since they are commonly studied; and complexes such as wastewater, sludge, sediment, organic waste, leachate from food waste, among others [10]. In wastewater there are heavy metals that have a considerable redox potential (reduction and oxidation) which successfully bioaccumulate in MFCs, therefore, they also increase the efficiency in electric power generation; among them are copper, silver, gold, chrome, cobalt, vanadium, selenium, mercury, zinc, etc. [3]. Likewise, microorganisms play an important role absorbing the substrate (simple, complex, mixed substances) and catalyze it through redox reactions for the generation of clean energy [11]. Electrochemically active microorganisms are considered to be those with a high capacity for electron discharge and extracellular transfer of electrons [9, 11]; states that MFCs are recognized as an excellent option for the simultaneous application of bioenergy generation and wastewater treatment. Electrogenic microbial consortia generate energy thanks to the biodegradation of organic matter present in wastewater. Rojas, et al. [12]; In his study of bioelectricity generation from wastewater using low-cost microbial fuel cells, a maximum voltage of 0.349 V was obtained. Progressive increase in voltage in the initial days is mainly

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due to acclimatization and degradation of the substrate used by microorganisms that generate electricity in their metabolism was concluded. Also, it was successfully verified that using agro-industrial wastes to generate bioelectricity with maximum voltage and current peaks of 0.389 V and 1.179 mA, respectively [13]. Due to the fact that water and energy are an essential part of human activities, the use of innovative technologies to obtain them that allow minimizing ecological impacts are necessary [14, 15]. In the search for an alternative to the reuse of mining effluents to mitigate the ecological impacts it causes; This research aims to study the physicochemical parameters on the generation of electrical energy using mine tailings as a substrate through the single-chamber MFCs on a laboratory scale.

2 Materials and Methods 2.1 Substrate Preparation A specific site was selected for the sampling of mine tailings for use as a substrate in microbial fuel cells. In the La Libertad Region, Santiago de Chuco Province, Quiruvilca District, exactly in the Santa Catalina tailings dam in the town of Shorey at an altitude of 3818 m above sea level, on the slopes of the eastern flank of the Western Andes mountain range, the source area of the River Moche. The specific sampling point is located within the Santa Catalina tailings dam, at a latitude of 8º1' 39.954”S and longitude of 78º18' 36.852”W approximately. A 2-L sample of mine tailings was collected in a high-density polyethylene bottle. For the conservation of this sample, it was stored at 4 ºC and then transported to the laboratory where the corresponding analyzes were carried out [16]. Two types of samples were used in this work, mine tailings under normal conditions (pristine) and mine tailings sterilized (sterile) by autoclaving at 130 ºC.

2.2 Microbial Fuel Cells Fabrication For this investigation, the MFCs were fabricated with a single air cathode chamber, a prototype of transparent borosilicate glass of 100 ml capacity was used. For electrodes, a 3 cm wide and 5.50 cm long of copper plate was used as anode and a 1 cm radius of graphite plate as cathode. Each electrode was connected to a 0.5 mm thick copper wire at the ends. 8 single-chamber MFCs with air cathode were fabricated, which were subsequently sterilized under a UV light lamp in a biosafety cabinet (JSR Biological Safety Level Class II Type A2) for a period of 20 min [17].

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2.3 Characterization of Microbial Fuel Cells Daily monitoring of voltage and electric current was carried out by means of a Prasek Premium PR-85 brand multimeter; pH (pH-meter 110 Series Oakton), electrical conductivity (conductivity meter CD-4301), turbidity (Lutron TU - 2016 turbidimeter) and resistance using an energy sensor (Vernier- ±30 V & ±1000 mA) [13].

3 Results and Analysis Figure 1(a) shows the average values of the generated voltages of MFCs during a period of 30 days. These values increase rapidly until reaching a maximum on days 4, 5 and 6 with 0.65 ± 0.02 V of the pristine mining tailings, followed by the sterile mining tailings reaching 0.64 ± 0.01 V on the fifth day. On the other hand, the minimum registered voltage values were presented on first day with a value of 0.55 ± 0.02 V for pristine, followed by the sterile reaching a minimum generation of 0.25 ± 0.02 V in the first day. The results obtained in the pristine MFCs, can be given by the presence of microorganisms and the redox reactions generated by the metals present in the substrate. This is an important biological factor in the generation of electrical energy and therefore affects the performance of the MFCs. The substrates are metabolized through oxidation–reduction processes where electrons will be generated and which also influence the increase in voltage values [8]. In addition, voltage losses are related to the metabolism of microorganisms, this decrease is inevitable because energy is obtained from the oxidation of the substrates [12]. Observed voltages values with sterile MFCs are given by the redox reactions of the metal ions present. It should be noted that metabolites play an important role in bioelectrochemical systems, where the most important step is the transfer of electrons from bacteria to the electrodes. Therefore, bacteria capable of expressing the components of the electron chain in their outer walls may be well suited for use in microbial fuel cells, as they provide easy transport channels for higher currents and enhanced electron transfer [18]. The electrical current monitoring record is shown in Fig. 1(b), the maximum values occurred on fifth day for sterile samples with 1.26 ± 0.02 mA, and for pristine sample a maximum current of 1.83 ± 0.04 mA was achieved on first day. Regarding the minimum values reached, they occurred on first day for sterile samples with 0.63 ± 0.01 mA, and for pristine one it was generated continuously for three days (28, 29 and 30) with values of 0.79 ± 0.02. The current behavior differences observed in the pristine MFCs compared to the sterile MFCs, may be due to the greater presence of organic matter in the substrate. Under normal conditions, when all conditions are activated, they include aerobic microorganisms for the decomposition of organic compounds, while the respiration that occurs in MFCs is anaerobic [19]. Taking this into account, microbes may need a longer adaptation time to consume organic

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0.7 2.0

a)

1.8

0.6

Sterile Pristine

b)

1.6 1.4

Current (mA)

Voltage (V)

0.5

0.4

0.3

1.2 1.0 0.8 0.6 0.4

Sterile Pristine

0.2 0

5

10

15

20

25

0.2 0

30

5

10

15

20

25

30

Time (days)

Time (days)

Fig. 1 Values of a voltage and b current of MFCs

matter in large quantities. On the other hand, some authors prepare an inoculum with iron-enriched medium before its use in MFCs, which allows bacteria with extracellular respiration to multiply, since the previous enrichment of the medium inhibits methanogenesis and promotes efficient iron conversion. chemical energy of organic matter [20]. Another important parameter is the pH, where the values obtained in the monitoring of the MFCs are found in Fig. 2 (a). An acid medium is observed, the maximum pH reached was 4.85 ± 0.06 on day 22 and a minimum value of 2.73 ± 0.00 on first day for the sterile samples. Also, for pristine samples, a maximum measurement of 4.72 ± 0.03 was achieved on day 12, while a minimum of 2.95 ± 0.00 was obtained on first day. In both, the sterile and pristine MFCs, the pH values increase after one or two days of operation, since the exchange of protons will be in a lower proportion than their production, in other words, the pH is conditioned to cathodic reactions by oxidizing oxygen [21].

Sterile Pristine

6

pH

250

Conductivity (mS/cm)

a)8

4

2

Sterile Pristine

b)

200

150

100

50

0 0

5

10

15

20

25

30

0

5

10

Time (days)

Fig. 2 Monitoring of the values of a pH and b conductivity of MFCs

15

Time (days)

20

25

30

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The electrical conductivity values from the monitoring of the MFCs are shown in Fig. 2(b), the maximum electrical conductivity of the sterile samples was recorded on first day, achieving values of 220 ± 0.00 mS/cm and a minimum of 137 ± 4.24 mS/ cm on day 29. In other hand, a maximum value of 160.5 ± 0.71 mS/cm was obtained on eighth day and a minimum of 146.5 ± 2.12 mS/cm on the 27th day of monitoring for pristine ones. In both types of samples MFCs, conductivity decreases due to the reactions generated by the influence of the substrate pH. In both types of MFCs samples, the conductivity decreases due to the reactions generated by the influence of the pH of the substrate. The physical and chemical characteristics of heavy metals in aqueous solution depend to a large extent on the pH of the water, with neutral or acid values, heavy metals exist in their cationic state and tend to be more soluble and mobile in aqueous solutions [21]. As the pH rises above neutrality, the heavy metals form solids that precipitate. An example of this is observed in Chromium, as the pH increases, it changes from Cr (III), its most stable form, to Cr (VI), its most toxic form. Another example is that of lead, which precipitates in aqueous media with alkaline pH, while in acidic media it is found in the form of free ions [22]. On the other hand, turbidity monitoring is observed in Fig. 3, obtaining a maximum of 981.5 ± 13.44 NTU on day 29 and a minimum of 217.5 ± 13.44 NTU on third day of monitoring for pristine substrate MFCs; while, for sterile substrate MFCs, a maximum of 963.5 ± 37.48 NTU and a minimum of 786 ± 45.25 NTU were reached on day 19 and 8 of monitoring systems, respectively. Turbidity in the waters is generally due to the presence of suspended solids [23], the increase of these value observed could be due to reactions of the tailings compounds when heat is supplied when sterilizing in the autoclave, as happened in the research by Li et al. [24], who considered the increase in the turbidity during the operation time of the MFCs, explaining that carbon oxidation could arise to produce graphene oxide, so that the increase in suspended particles was due to the fragmented carbon fibers of the electrode. Figure 4 shows the graphs that comply with Ohm’s Law, which can be expressed by the formula V = RI, where V is the potential difference or voltage (V), R is the resistance (Ω), and I is the current (mA) [25]. Referring to this formula, the voltage is directly proportional to the current, so a linear function (y = mx + b) can be established [26], and from the slope (m) the resistance (Ω) of the MFC can be calculated [27]. This methodology uses polarization curves (graph of electric current (I) against voltage (V)) and is the most widely accepted to analyse the electrical efficiency of a MFCs [28]. According to this, in the pristine substrate MFCs, the resistance was 95.6245 ± 9.8745 Ω (Fig. 4(a)). While the resistance in the sterile substrate MFCs was 162.3548 ± 15.2467 Ω. In these studies, copper anodes were used, which is a metallic material that has low resistance and good conductivity, allowing the flow of electrons through it [29], however, despite show good results in the generation of bioelectricity, its main limitation is its corrosion [18]. The internal resistance of MFCs depends on the decomposition of the substrates used for power generation because the electrons that are released in the oxidation process in the anode chamber flow freely throughout the system when the internal resistance is low, which also influences the formation of

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Sterile Pristine

1000

Turbidity (NTU)

800

600

400

200 0

5

10

15

20

25

30

Time (days) Fig. 3 Turbidity values from monitoring MFCs

Pristine Linear Fit of Sheet1 B

a) Voltage (mV)

0.02

y = a + b*x Equation B Plot No Weighting Weight 0.00248 ± 1.4241 Intercept 95.6245 ± 9.8745 Slope 0.00726 Residual Sum of S 0.06789 Pearson's r 0.00461 R-Square (COD) 0.00295 Adj. R-Square

0.01

5

Sterile Linear Fit of Sheet1 B

4 3

b)

2

Voltage (V)

0.03

1

Equation Plot Weight Intercept Slope Residual Sum of Squ Pearson's r R-Square (COD) Adj. R-Square

y = a + b*x B No Weighting -1.80961 ± 0.14 162.3548 ± 15. 969.26554 0.15113 0.02284 0.02121

0 -1 -2 -3

0.00

-4 -5

-3000

-2000

-1000

0

Current (mA)

1000

2000

3000

0

1

2

3

4

5

Current (A)

Fig. 4 Internal resistance of a pristine and b sterile mine tailings substrate MFCs

anodic biofilms of the MFCs [30]. So, the low internal resistance exposed can be affected by the adhesion of the microorganisms present in the substrate with the anode electrode and confirms the high current values shown by the cells [31, 32], as well as by the transfer of electrons, which will occur more efficiently from the anode to the cathode; therefore, microorganisms may prefer more direct forms of electron transfer due to their genetics [33]. The lack of oxidative capacity of the substrate significantly reduces the efficiency of “extraction” of electrons and, therefore, the generation of electrical energy. In fact, some microorganisms have greater electroactivity and greater capacity to generate high current densities in pure and mixed cultures. On the other hand, the mechanism of electron transfer differs from

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one microorganism to another, which is crucial to determine the efficiency of the process [34].

4 Conclusions This study has successfully demonstrated the efficiency of using gold mine tailings from the Relavera Santa Catalina - Shorey as a substrate for the generation of electrical energy through a single-chamber microbial fuel cell with air cathode, which was operated and monitored for 30 days at room temperature. In the microbial fuel cells with mine tailings as substrate, peak values of 0.65 ± 0.02 V, 1.83 ± 0.04 mA, 981.5 ± 13.44 NTU were achieved, respectively, for voltage, current and turbidity, in addition the mine tailings operated at an acidic pH and conductivity values greater than 146 mS/cm. This research paper provides an ecological solution for the reuse of mine tailings.

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Energy Consumption Characteristics of Wall-Hanging Gas Boilers in Hot Summer–Cold Winter Zone of China Yubo Zhou, Xiaomei Huang, and Zhuojun Hu

Abstract With socioeconomic development, the consumption of natural gas heating has increased in regions characterized by hot summers and cold winters. To explore the characteristics of heating energy consumption in residential buildings, original natural-gas consumption data were obtained from residential households in Nanjing, China and a cluster analysis was conducted based on the level of energy consumption and residents’ reliance on heating. The findings indicate that (1) natural-gas heating users can be classified into three clusters: low heating energy consumption and low reliance on heating, medium heating energy consumption and high reliance on heating, and high heating energy consumption and high reliance on heating; (2) heating energy consumption is inversely related to air temperature and a high heating energy consumption is more sensitive to air temperature; and (3) the heating energy consumption is higher on non-working days compared to that for workdays, and low heating energy consumption during workdays strongly influences that on nonworking days. These findings will act as reference for building engineers and urban planners to realize the demand characteristics of gas heating in urban settlements situated in hot summer–cold winter zones. Keywords Building energy consumption · Residential natural gas consumption · Household heating energy · Resident buildings

1 Introduction China is a vast country and the climate varies significantly across its regions. According to the climatic characteristics, regions in China can be classified into severe cold zones, cold zones, hot summer–cold winter zones, hot summer–warm Y. Zhou · X. Huang (B) School of Civil Engineering, Chongqing University, Chongqing 400044, China e-mail: *[email protected] Z. Hu Zhuohui (Jiangsu) Energy Engineering Technology Co., LTD., Nanjing 210022, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_25

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winter zones, and temperate zones. Cities built in severe cold and cold zones consider residential heating requirements at the initial stage of construction unlike those located in hot summer–cold winter zones. With the recent development of China’s national economy and the increased requirements for individuals’ quality of life, an increasing number of residents in the hot summer–cold winter zone are using heating in winter [1–5]. The hot summer–cold winter zone prevails in the middle and lower reaches of the Yangtze River and its surrounding areas in China. These areas are densely populated and economically developed; further, they cover ~1.8 million km2 , house a population of ~550 million, and contribute to ~48% of the country’s GDP [6]. This zone contributes to more than one-third of the country’s natural gas consumption [7]. Wall-hanging gas boilers are favored over decentralized heating systems because of their high thermal comfort and ease of installation and use. Thus, an increasing number of residents use wall-hanging gas boilers for heating. Compared with other decentralized heating systems, wall-hanging gas boilers utilize clean energy, emit the least amount of pollutants, deliver superior environmental performance, and involve low primary-energy consumption [8–12]. According to the China Gas Appliance Industry’s “14th Five-Year Plan” Development Report, China’s wall-hanging gas boiler market is in a stable growth phase. Between 2016 and 2020, the annual market sales for wall-hanging gas boilers increased from 2.1 to 4.2 million units. Among these sales, the combined sales in east, central, and southwest China, represented by decentralized heating in the South, accounted for approximately 16% of the total annual sales. This market share has been increasing annually, and it was 46% in 2020 [13]. In future, hot summer–cold winter zones are expected to become essential markets for wall-hanging gas boilers. In the hot summer–cold winter zone, natural gas consumed by heating users has emerged as a vital component of household heating-energy consumption. However, only a few studies have explored the characteristics of the energy consumption of gas heating users in this zone. The original gas consumption data of wallhanging gas boiler users in Nanjing was obtained for this study. Then, the users were classified using cluster analysis to determine the characteristics of their heating energy consumption for understanding how multiple factors affect heating energy consumption.

2 Data and Methods 2.1 Data We collected gas consumption data from natural gas users of four communities in Nanjing, which is a representative city of the hot summer–cold winter zone, located in the lower reaches of the Yangtze River. In 2020, Nanjing ranked 10th in the country in terms of regional GDP. The average daily temperatures in Nanjing are in the range

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0–10 °C during winter, and the average indoor temperature without heating is less than 12 °C [14, 15]. A gas meter is a device used to measure the volume of gas consumption, and it is installed in the household of every gas consumer. With the development of IoT technology, gas NB meters have gradually replaced traditional membrane gas meters. The Nanjing Gas Company now actively promotes the installation of gas NB meters. These meters include an NB-IoT module installed on the base meter, and they can record the consumer’s gas meter readings at regular intervals and upload them to the cloud. The gas company can easily obtain the uploaded data through the IoT module, which facilitates data analysis and consumer management [16]. The residential gas consumption data from four communities with gas NB meters installed in their households were collected in this study. Several residents of these four communities use wall-hanging gas boilers for heating. Overall, we collected hourly gas consumption data from 2009 residents for three months (from the beginning of December 2021 to the end of February 2022), which is the period in which gas heating is primarily used.

2.2 Methods The gas NB meter measures the gas consumption of all residential gas appliances simultaneously. As the residential wall-hanging gas boiler is not monitored, its heating usage can be identified through other methods. During heating, the gas consumption is considerably higher and the boiler operates for a longer period, i.e., the daily gas consumption is considerably higher if the boiler is on compared to that when it is not in use. Therefore, the usage of the wall-hanging gas boiler by a resident for heating can be derived based on the gas consumption for a given day. Accordingly, the daily gas consumption data for a resident are collated and used to identify whether they are using the boiler for heating. Combining empirical judgment with knowledge from existing research on the gas consumption characteristics of gas heating users [17–21], we can determine if the resident used heating on that day based on whether the daily gas consumption is greater than 3 m3 /d; if it is lower than 3 m3 /d, the resident did not use heating on that day. Cluster analysis groups data into clusters with similar characteristics, where the distance between any data point within a cluster and its cluster center is less than its distance from other cluster centers. In principle, cluster analysis can simplify the analysis of extensive data by preserving its characteristics and reducing computational workload. In this research, the selected residents were classified using two-step clustering performed using IBM SPSS. In the first step, the BIRCH algorithm was used to obtain several small sub-clusters. The second step involved the clustering phase in which the preprocessed subclusters were merged according to the minimum distance principle. The BIRCH algorithm clusters data by scanning the database and constructing a tree structure based on the distance between the data using two

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concepts: clustering feature (CF) and CF tree. The algorithm is well suited to handle the large amounts of low-dimensional data [22, 23]. − → CF(N, LS, ,SS) uses three characteristics to represent the feature of a cluster. Let →  − → − → xi , and SS = i xi2 represent a multidimensional data simple, the xi , N, L S = i − number of data simples in each CF, the linear vector sum of data, and the sum of − → squares of data, respectively. LS/N represents the center of a CF. A new data sample − → → → can be easily integrated into CF using CF + − xi , SS+xi2 ), and two xi = (N +1, LS+ −  −→ −→ CFs can be quickly merged using CF1 + CF2 = N1 + N2 , LS1 + LS2 , SS1 + SS2 . Figure 1 illustrates the basic structure of the CF tree. Each leaf node contains a set of CFs representing one or many data points, while non-leaf nodes store the aggregated CFs of each of its children. The CF tree has three characteristics: B limits the numbers of CFs within each non-leaf node, L limits the numbers of CFs within each leaf node, and t represents the threshold value to determine whether two CFs are close to each other. The CF tree is built by sequentially inserting data points. The insertion leaf is found by choosing the “nearest” CF at each level. Within the leaf node, the data point is added to the best CF if it is within t; otherwise, a new CF is added to the leaf. Leaf nodes that exceed the maximum capacity are split so that an additional entry is inserted into the parent. This procedure can propagate to higher levels of the tree and cause the tree to grow when the root node overflows. The gas consumption data was preprocessed to extract the CFs from an extensive amount of raw gas consumption data before performing cluster analysis. The focus is on the daily gas consumption of users and the number of heating days. The daily gas consumption on a heating day reflects the residential energy consumption level, i.e., a higher daily gas consumption translates to a higher energy consumption level. The number of heating days indicates the dependence of residents on gas heating.

Fig. 1 Basic structure of a CF tree

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Residents with greater reliance on gas heating in cold weather and higher willingness to use gas heating use heating for a greater number of days. Therefore, these characteristics that reflect the residential heating energy consumption can be used as clustering indicators for cluster analysis in various clusters.

3 Results and Discussion 3.1 Cluster Analysis Results The two-step cluster analysis was performed on the acquired data using two indicators: average daily gas consumption on heating days and the number of heating days of each residential household. In total, five class clusters were obtained. The scatter diagrams of each type of cluster member are plotted in Fig. 2. The characteristics of residents in each type of cluster are depicted in Fig. 3, and the percentage share of each cluster type is charted in Fig. 4. Cluster 1: This residential group exhibited low daily gas consumption and heating days, with an average daily gas consumption of 0.39 m3 /d and an average number of heating days of 0.25 d. This group of residents barely used heating, and even in case of usage, the daily gas consumption was considerably less than the average heating usage. Although this group of residents comprised the greatest share (67.8%) of the study sample, they can be qualified as non-users of gas heating.

Fig. 2 Members of each cluster

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Fig. 3 Characteristics of each cluster Fig. 4 Percentage of each cluster

Cluster 2: The daily gas consumption and number of heating days of this residential group were slightly less than those of heating users, with an average daily gas consumption of 8.58 m3 /d and an average number of heating days of 26.24 d. Overall, this group accounted for 11.7% of all households, which was second only to cluster 3 among all heating users. Although the heating energy consumption and number of heating days were low among the residents of this cluster, their proportion is larger among heating users. Cluster 3: The residents in this group used heating for the longest periods, with an average daily gas consumption of 11.29 m3 /d and an average number of heating days of 69.13 d. More importantly, this group accounted for 16.5% and included approximately half of all heating users. Therefore, this resident cluster type can be regarded as the most critical type among all heating users.

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Cluster 4: This resident cluster exhibited extremely high daily gas consumption and long heating days, with an average daily gas consumption of 18.1 m3 /d and an average number of heating days of 60.21 d. These residents were more dependent on heating and had extremely high daily gas consumption, thereby constituting the highest energy consumers in building heating. Although this group accounted for a low share of 2.8% of all heating users, their high daily gas consumption and long heating days cannot be neglected. Cluster 5: The average daily gas consumption of this group was 17.99 m3 /d, and the average number of heating days was 14.96 d, i.e., the residents in this group used heating less frequently, but the level of energy consumption was high during use. Overall, this resident cluster accounted for only 1.1% of all heating users, which was relatively low. As residents consume gas using gas stoves or domestic hot water in their daily-life activities, their daily gas consumption level indicates their residential occupancy, i.e., zero daily gas consumption implies household vacancy. The results revealed that the majority of these residents lived in their households only during certain periods, and possibly, turned on heating only during the Spring Festival holidays.

3.2 Characteristics of Cluster Class A box plot representing the sum of the gas consumption by various resident clusters during heating days is illustrated in Fig. 5. The highest heating gas consumption was observed for cluster 4, with an average heating gas consumption of 1091.11 m3 / household. This was followed by cluster 3, with an average heating gas consumption of 791.08 m3 /household. In comparison, the average heating gas consumption of cluster 5 was 275.33 m3 /household because of the low number of heating days. Similarly, the heating gas consumption of cluster 2 was considerably less than that of clusters 3 and 4 because of the low daily gas consumption and number of heating days, with an average heating gas consumption of 229.15 m3 /household. For all clusters, the residential average daily gas consumption and air temperature are plotted on a line graph in Fig. 6, which indicates that the lowest air temperature occurred on December 26. The daily gas consumption of clusters 2, 3, and 4 displayed Fig. 5 Box plot of gas consumption of each cluster

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Fig. 6 Daily gas consumption and air temperature of each cluster

a significant peak on December 26, with average daily gas consumptions of 9.71, 13.49, and 20.32 m3 /d, respectively. In addition, clusters 2 and 3 experienced peak daily gas consumptions on February 7, with an average daily gas consumption of 10.63 m3 /d and 13.96 m3 /d, respectively, which was higher than their daily gas consumption on December 26. Although Cluster 4 exhibited a similar daily gas consumption peak during the Spring Festival holiday from January 29 to February 7, it was less than the daily gas consumption on December 26. The negligibly small number of samples of heating users in clusters 1 and 5 did not display a clear pattern of daily gas consumption. The number of daily heating users from all clusters is plotted in Fig. 7. The number of heating users in clusters 2, 3, and 4 starts to increase rapidly in late December and decrease rapidly at the end of February. In cluster 3, the residents who turned on the heating at an earlier stage had already turned on the heating in November. The number of heating users in clusters 3 and 4 were more dependent on heating and their usage showed low fluctuations, whereas that in cluster 2 was less dependent and their usage showed more fluctuations. Among all factors, the heating usage of residents in cluster 2 was more strongly influenced by air temperature. The hourly gas consumption of residents from various clusters on heating days is depicted in Fig. 8. The hourly gas consumption of residents in cluster 1 varied from that of the remaining four clusters, and the lowest gas consumption of this cluster was recorded at night. Therefore, most residents in this cluster did not use heating at night. Gas consumption by the residents of this cluster reached its peak at 19:00– 20:00 h, with an average hourly gas consumption of 0.38 m3 /h. In comparison, the hourly gas consumption of residents in cluster 2 was less than that of residents in

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Fig. 7 Heating user number of each cluster

cluster 3, and it peaked at 18:00–19:00 h, with an average hourly gas consumption of 0.5 m3 /h. For residents in cluster 3, the hourly gas consumption peaked during 21:00–22:00 h, with an average hourly gas consumption of 0.57 m3 /h. The hourly gas consumption of residents from clusters 4 and 5 were relatively similar, i.e., the peak gas consumption occurred at 21:00–22:00 h and 22:00–23:00 h, with an average hourly gas consumption of 0.81 m3 /h and 0.86 m3 /h, respectively. The gas consumption among residents in cluster 2 peaked earlier than that of other heating users, probably because their daily gas consumption is relatively low and residential cooking behavior influences their hourly gas consumption peaks. In contrast, the residents of clusters 2, 3, 4, and 5 exhibited the highest gas consumption in the evening and lower gas consumption at night and in the afternoon, with residents in clusters 2 and 5 recording the lowest gas consumption at night and those in clusters 3 and 4 recording the lowest gas consumption in the afternoon. This is presumably because the time of day with the lowest gas consumption is related to the residential reliance on gas heating. Residents with less dependence on gas heating may opt to operate their heating systems at a lower temperature at night. Although a lower outdoor temperature prevails during night, the lower operating temperature reduces gas consumption.

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Fig. 8 Hourly gas consumption on heating days

3.3 Effect of Various Factors on Daily Gas Consumption Figure 6 shows that the daily gas consumption is negatively correlated with the air temperature on heating days, i.e., a lower air temperature requires more heat to maintain a stable indoor temperature, which increases the daily gas consumption. The scatter plots of daily gas consumption and air temperature for various clusters were plotted and a linear regression analysis was performed. The scatter plots are illustrated in Fig. 9, and the linear regression analysis table is presented in Table 1. According to Pearson’s correlation coefficient, the relationship between daily gas consumption and air temperature is insignificant for residents in clusters 1 and 5, which is potentially caused by the small sample size. For residents in clusters 2, 3, and 4, a significant negative correlation exists between the daily gas consumption and air temperature. The effect of air temperature on the daily gas consumption differs across the residents of cluster 2, 3, and 4, with the slightest impact on residents in cluster 2 and the most significant effect on residents in cluster 4. Thus, the air temperature strongly influences the daily gas consumption of residents. In addition to air temperature, household activities are related to daily gas consumption because the daily gas consumption increases with the time spent by residents in their household, and this is further associated with the resting pattern of the residents. Excluding retired family members and those who work at home, individuals spend less time at home on workdays and more on non-working days. The daily gas consumption data for various clusters on non-working days and workdays are plotted in a box plot in Fig. 10. The daily gas consumption in clusters 1, 2, 3,

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Fig. 9 Scatter plot of daily gas consumption and air temperature

Table 1 Table of linear regression analysis cluster 1

cluster 2

cluster 3

cluster 4

slope

−0.24

−0.44

−0.66

intercept

9.54

13.45

21.03

−0.69

−0.88

−0.85

0.47

0.76

0.72

Pearson’s r Adjusted R-square

−0.09

cluster 5

0.16

and 4 was higher on non-working days than that on workdays. In comparison, the daily gas consumption of cluster 5 was higher on workdays than that on non-working days. Compared to workdays, the daily gas consumption of the five clusters on nonworking days increased by 5.32%, 7.84%, 5.68%, 3.22%, and −3.66%, respectively. Disregarding the small sample size of clusters 1 and 5, we can presume from the results of clusters 2, 3, and 4 that a higher daily gas consumption will weakly impact that on the non-working days because residents with higher daily gas consumption have more family members occupying the household on workdays, which diminishes the influence of non-working days. The effect of non-working days on heating energy consumption is reflected in the daily gas consumption and number of heating users, i.e., a greater number of heating users on a given day implies that residents are more likely to use heating on that day. Figure 7 shows that the number of heating users among the residents of clusters 2 and 3 decreased significantly during the Spring Festival holidays. However, the air temperature did not rise significantly, and the number of heating users in cluster 4

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Fig. 10 Comparison of daily gas consumption between workdays and non-working days

declined marginally, which suggests that certain residents turned off heating during the Spring Festival. Presumably, this segment of residents was influenced by the Spring Festival and did not occupy the household because they were attending family reunions at a different house, which consequently reduced the number of heating users.

3.4 Discussion Based on the cluster analysis, the residents are segmented into five class clusters, among which class cluster 1 included residents who do not use heating. Class cluster 5 included a low proportion of residents with short heating days. For gas heating energy consumption, we should focus on residents in class clusters 2, 3, and 4. Among them, residents in class clusters 2 and 3 cover the majority of gas heating users. Under the combined influence of heating energy consumption level and heating reliance, the building heating energy consumption of residents in class cluster 3 was considerably larger than that of residents in class cluster 2. Overall, residents in class cluster 3 were the most influential group, because they comprised a more significant proportion of the population with higher building heating energy consumption. The heating use of most residents starts from mid- to late-December and ends in late February. This study entailed an investigation of the effect of air temperature and residents’ resting schedule on the daily gas consumption of various resident clusters. The

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daily gas consumption is negatively correlated with air temperature, i.e., a higher air temperature indicates lower daily gas consumption. The quantitative analysis was separately conducted for each resident clusters. The results revealed that residents with a high daily gas consumption were more sensitive to air temperature. The daily gas consumption on non-working days was higher than that on workdays, and in the case of low daily gas consumption, the residents were more sensitive toward non-working days.

4 Conclusions We acquired the hourly gas consumption data of 2009 residents in Nanjing from November 2021 to February 2022 for cluster analysis based on residential heating energy consumption to understand the heating characteristics and factors influencing gas heating usage. Upon studying the heating energy consumption characteristics of the various clusters of residents and the effects of air temperature, non-working days, residential economic level, and building age on heating energy consumption, the following conclusions were obtained. 1) Based on cluster analysis, the residents were segmented into five clusters. The vast majority of heating users were categorized under clusters 2, 3, and 4, which formed the primary focus of the analysis. The residents’ average daily gas consumption in these three clusters was 8.58, 11.29, and 18.1 m3 /d, and the average number of heating days were 26.24, 69.13, and 60.21 d, respectively. Overall, cluster 3 constituted the highest proportion of residents with a high level of heating energy consumption and was considered the most crucial resident cluster. 2) Owing to the air temperature and holidays, residents in clusters 2, 3, and 4 experienced peak heating gas consumption on December 26 and February 7, whereas hourly peak gas consumption occurred in the evening, with the lowest consumption in the afternoon or night. 3) The heating usage of most users started from mid- to late-December and ceased in late February. 4) The daily gas consumption is inversely related to air temperature. For each 1 °C rise in the air temperature, the daily gas consumption of the residents from class clusters 2, 3, and 4 decreased by 0.24, 0.44, and 0.66 m3 /d on average. A higher daily gas consumption of the residents translates to a higher sensitivity to air temperature. 5) The daily gas consumption of residents increased on non-working days. Compared to workdays, the daily gas consumption of residents in class clusters 2, 3, and 4 on non-working days increased by 7.84%, 5.68%, and 3.22%, respectively. Overall, residents with a higher daily gas consumption are less affected by non-working days.

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Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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20. Dong J, Li Y, Zhang W, Zhang L, Lin Y (2020) Impact of residential building heating on natural gas consumption in the south of China: taking Wuhan city as example. Energy Built Environ 1(4):376–384. https://doi.org/10.1016/j.enbenv.2020.04.002 21. Li L, Li J, Li K, Luo X, Jiao J (2022) Climatic impacts on residential natural gas consumption: evidence from Hefei, China. Energy Build 275. https://doi.org/10.1016/j.enbuild.2022.112488 22. Lang A, Schubert E (2022) BETULA: fast clustering of large data with improved BIRCH CF-Trees. Inf Syst 108. https://doi.org/10.1016/j.is.2021.101918 23. Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD international conference on management of data, pp 103–114. https://doi.org/10.1145/233269.233324

Engineering Example of Vertical Zoning Reconstruction of Municipal Solid Waste Landfill for Solidified Fly Ash Landfill Yanxue Chen, Xuren Zhou, Xingjian Wang, Zhijie Tan, Taihui Xiao, Hailin Chen, and Tianyu Qin

Abstract A municipal solid waste landfill in Chongqing was reconstructed vertically to be used for fly ash solidification landfill and solving the problem that fly ash solidification landfill occupies a large amount of land resources. By conducting the analysis of the overall stability of the reservoir area, the design of the foundation, to solve the problems of insufficient foundation bearing capacity after loading the fly ash solidification, and the difficulties of uneven settlement of the foundation. The paper aims to design for the landfill gas drainage and treatment system. The paper introduces the overall technical scheme and economic benefit analysis of this project and providing references for other similar projects. Keywords Municipal solid waste landfill (MSWL) · Vertical partition · Fly ash solidification · Stability · Landfill gas

1 Introduction According to the statistics of E20 Research Institute, in 2016, the amount of waste incineration was 68.11 million tons, and the amount of fly ash generated was up to 3.95 million tons. The amount of fly ash generated from waste incineration in China is seriously inconsistent with the amount of fly ash generated. At present, at least 50% of the fly ash has not been properly treated. The safe disposal of fly ash has become the weakest link in the whole process of pollution control and risk management of municipal waste incineration [1]. As fly ash is enriched with various heavy metals and organic toxicants generated in the incineration process of municipal waste, all countries in the world list it as a hazardous waste, and China’s corresponding technical Y. Chen (B) · X. Zhou · X. Wang · Z. Tan · T. Xiao · H. Chen · T. Qin CMCU Engineering Co., Ltd., Chongqing 400039, China e-mail: [email protected] X. Zhou College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518000, Guangdong, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_26

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policies also manage and treat fly ash as a hazardous waste [2]. In the past, in terms of specific treatment methods, fly ash was mainly put into hazardous waste landfill after stabilization treatment according to the requirements of Standard for Pollution Control on the security Landfill site for hazardous wastes (GB18598-2001) [3]. In April 2008, China issued the Standard for Pollution Control on the Landfill site of municipal solid waste (GB16889-2008) [4], which proposed that municipal waste incineration fly ash and other wastes that have undergone certain treatment and meet the standard requirements can enter the municipal waste landfill, which will greatly promote the construction of municipal waste incineration facilities in China. At present, there are three kinds of fly ash treatment methods in China [5]. Sanitary landfill after solidification and stabilization is the most economical and convenient method at present, and has been applied in most waste incineration power plants. The solidified fly ash entering the municipal waste landfill must be landfilled separately in different areas and cannot be mixed with municipal waste. At present, horizontal zoning of landfills is widely used, while the engineering application of vertical zoning is almost blank.

2 Project Overview and Site Condition Analysis 2.1 Overview of the Original Municipal Waste Landfill This municipal solid waste landfill is a typical valley type landfill, with a designed storage capacity of 2.44 million m3 , a treatment capacity of 270 tons per day, a design service life of 21 years. It was put into operation in 2004 and suspended in 2018, with an actual treatment capacity of 168 tons per day. Before it is stopped, 950 thousand tons of municipal waste (including 86 thousand tons of sludge transferred from the other district to treat, and no industrial waste or industrial solid waste is buried) were treated, and the remaining storage capacity was 1.05 million m3 . At present, the final site is not covered, and 1.0 mm HDPE film is used for temporary coverage. The original landfill area was set with gas guide gabions, but now most of them have been silted up and the drainage effect is not ideal.

2.2 Main Problems and Difficulties in Transformation Due to the great difference between the characteristics of municipal waste and solidified fly ash, the following problems exist when loading solidified fly ash in combination with the actual situation of this municipal waste landfill: Firstly, whether the overall stability of the landfill area meets the specification requirements and whether there is a risk of sliding and collapse. What’s more, the bearing capacity of the foundation is insufficient and uneven settlement. The bearing capacity of the foundation

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in the current reservoir area is about 30 kPa, which is far lower than the load required by the design landfill (5 m high, about 65 kPa). At the same time, due to the operation characteristics of municipal waste landfill, there may be uneven settlement of the foundation under the condition of loading fly ash solidified material. In addition, most of the original gas conducting gabions are clogged, the pumping effect is poor, and the high level of leachate affects the gas conducting performance of the garbage dump, and there are potential safety hazards such as explosion after loading solidified fly ash.

3 Overall Technical Proposal In view of the above analysis, this design focuses on solving the following problems: ➀ overall stability analysis of the reservoir area; ➁ Foundation treatment works (including ground-water lowering measures); ➂ Design of landfill gas drainage and treatment system.

3.1 Overall Stability Analysis of Reservoir Area 3.1.1

Sliding Stability Analysis

As shown in Fig. 1 below, the possible sliding surfaces are the contact surface between solidified fly ash (intended to fill) and anti-seepage membrane (sliding surface I), the contact surface between municipal waste (filled) and anti-seepage membrane (sliding surface II), and the contact surface between rock and soil (sliding surface III).

Fig. 1 Structural Calculation Section

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Table 1 Physical and Mechanical Property Parameters Geotechnical name Weight

natural

Garbage soil

sludge

KN/m3

7.3*

12.5

kPa

76

25

kPa

5*

3*

2*

1.5*

23*

12*

20*

10*

saturated Characteristic value of bearing capacity Cohesion

natural

13.0

saturated Internal Friction Angle

natural saturated

3.1.2

°

Design Parameters

a. Geological exploration parameters. The interface shear parameters of artificial fill and silty clay adopt the shear parameters of silty clay soil. The shear resistance parameters of the interface between silty clay and rock stratum are 18.0 kPa in natural state, 12.0° in internal friction angle. Saturated cohesive is 12.0 kPa and 8.0° in internal friction angle; The natural cohesion of the interface parameters between artificial fill and rock stratum is 7.0 kPa, the internal friction angle is 20.0°, the saturated cohesion is 5.0 kPa, and the internal friction angle is 17.0°. Refer to Table 1 for detailed geological exploration parameters. b. Interface strength index of geosynthetics (Interface strength index of geosynthetics F, c’). According to the rough geomembrane/clay process of residual strength index of the Technical Code for Geotechnical Engineering of Municipal Waste Sanitary Landfill (CJJ 176-2012) [4], the residual interface strength index is taken as the natural working condition: internal friction angle F take 12°, and cohesion c’ as 0 kPa. Under saturated condition: underwater internal friction angle F take 9°, and the underwater cohesion c’ is taken as 0 kPa. Taking the peak interface strength index as the natural working condition: internal friction angle F take 9°, and the cohesion c’ is taken as 0 kPa. Under saturated condition: underwater internal friction angle F take 7°, and the underwater cohesion c’ is taken as 0 kPa. According to Article 6.3.4 of Technical Code for Geotechnical Engineering of Municipal Waste Sanitary Landfill (CJJ176-2012), residual interface strength index shall be used for areas with a bottom slope of 10° or more, and peak interface strength index shall be used for areas with a bottom slope of less than 10°.

3.1.3

Calculation Results

According to the above calculation, the minimum safety factor is 1.81, which is greater than 1.35 required by the specification, so the overall stability of the reservoir area meets the requirements (Table 2).

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Table 2 Stability calculation results of the reservoir area after fly ash landfill Design conditions

Slip surface I

Sliding surface II

Sliding surface III

Conventional operating conditions

2.45

4.05

7.29

Abnormal operating condition I

1.81

3.02

4.82

3.2 Foundation Treatment 3.2.1

Current Situation of the Reservoir Area

The current municipal waste has been landfilled for about 15 years, and the self weight consolidation is far from completed. The current landfill site has a high groundwater level, which slows down the consolidation progress of municipal solid waste. The consolidation coefficient of municipal solid waste is small, and the garbage dump is more likely to sink. There are many kinds of municipal garbage, the coefficient of self-weight consolidation of different municipal garbage varies greatly, and the possibility of uneven settlement in the reservoir area is large. Complete separation must be achieved between the solidified fly ash to be filled in the upper part and the filled municipal garbage.

3.2.2

Selection of Isolation Scheme

a. Hard isolation scheme and soft isolation scheme can be adopted for isolation scheme Hard isolation scheme: set a reinforced concrete bottom plate on the bottom surface of the solidified fly ash to be filled, and set a bored pile foundation under the bottom plate to the bedrock. Soft isolation scheme: set geogrid and clay soft isolation layer on the solidified fly ash bottom plate to be filled. b. Scheme comparison and selection. The pile foundation drilling depth of the hard isolation scheme is up to 40 m, and it is drilled in the municipal garbage, the groundwater level is high, and the drilling difficulty is too high; The pile foundation drilling in the hard isolation scheme will pierce the anti-seepage membrane at the bottom of the reservoir area, which is difficult to repair with the existing technology, and may cause the leakage of municipal waste to penetrate into the stratum, causing groundwater pollution; The cost of hard isolation scheme is more than 5 times that of soft isolation scheme. Based on the above, soft isolation is recommended.

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Recommended Scheme Design

a. Overall settlement treatment During the construction period, the groundwater level is lowered by 7 m, the effective dead weight stress of the municipal waste layer is increased by 70 kPa, and the consolidation progress of municipal waste is accelerated; After the construction of the isolation layer is completed, the underwater water level is restored to the original height, and the increased stress of solidified fly ash loading is basically offset by the reduction of effective stress in the municipal waste layer due to the rise of the groundwater level. Before soft isolation construction, moderately weathered sand and mudstone shall be dumped and filled, and heavy machinery shall be used for rolling. The surface pressure coefficient shall be 0.90 or above. The above two measures can effectively reduce the overall settlement risk of the reservoir area. b. Partial settlement treatment The isolation layer is a layer of two-way geogrid added to each 300 mm thick cohesive soil. The compaction coefficient of backfill is 0.95. Three layers are designed in total. The calculation of geogrid can be conducted in accordance with the specifications of Technical Code for Geotechnical Engineering of Municipal Waste Sanitary Landfill (CJJ176-2012) [6] and Technical Code for Application of Geosynthetics (GB/T50290-2014 [7]). Basic parameters: Waste slag weight : r = 15k N /m 3 Waste slag thickness: H = 5m Weight of settlement adjustment layer: r = 20kN/m3 Ground overload: q = 20kN/m2 According to the Technical Code for Geotechnical Engineering of Municipal Waste Sanitary Landfill (CJJ176-2012), the parameter of each layer of geogrid is 25 kN/m. According to the calculation of Technical Code for Application of Geosynthetics GB/T50290-2014, the design allowable stress of each layer of geogrid is 38.28 kN/ m, and the ultimate tensile strength of geogrid is T = 95.7 kN/m. Since the stresses in both directions are consistent, two-way geogrid is used, and the ultimate tensile strength of geogrid is T = 100 kN/m. The strength of the geogrid calculated by the Technical Code for Geotechnical Engineering of Municipal Waste Sanitary Landfill (CJJ176-2012) is relatively small. Due to the special importance of the geogrid in this project, the calculation value of the Technical Code for Application of Geosynthetics (GB/T50290-2014) is adopted in this design. Therefore, three layers of geogrid are set in the design, and the ultimate tensile strength of each layer of geogrid T = 100 kN/m, and two-way geogrid is adopted.

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3.3 Design of Landfill Gas Emission and Treatment System 3.3.1

Status Quo and Problems of Landfill Gas Emission

The emission effect of the original vertical air conduction system is not ideal, and most gabions cannot operate normally. The liquid level of leachate is high, most of which operate at medium and high liquid levels, and the gas collection rate is low. The original landfill gas emission system has a long transmission distance, and many areas are passive drainage. In addition, the uneven settlement of municipal waste causes uneven gas transmission, and bulges in some areas, which has potential safety hazards. In order to discharge the landfill gas in the site smoothly, a new gas transmission collection system is required. The design adopts the combination of “horizontal gas transmission and vertical gas transmission” to guide and discharge landfill gas.

3.3.2

Gas Production Calculation and Equipment Selection

According to the Technical Specifications for Sanitary Landfill Treatment of Municipal Garbage [8], Technical Specifications for Landfill Gas Collection, Treatment and Utilization Engineering of Municipal Garbage Landfill Site [9] and other specifications, the tenth year is the peak value of landfill gas, and the landfill gas production is 4,482,687 m3 /a (estimated 511.72 m3 /h). The main pipe flow at the end is 434.96 m3 /h (85% of the maximum annual hourly gas production). The rated flow of air extraction equipment (fan) is 522 m3 /h. (Take 1.2 of the end main pipe flow). After calculation, the pressure loss of the system is about 2560 Pa (including the pressure loss of the pipeline about 460 Pa and the pressure loss of the flare equipment about 2100 Pa), so the selection pressure of the system fan is 2560 × 1.2 = 3100 Pa, the system fan adopts frequency conversion explosion-proof roots blower (with explosion-proof function).

3.3.3

Scheme Design

a. Design ideas If the original gabion is used for external connection, the transmission effect is not good, and the transportation distance is long, there is a certain safety risk. In addition, the external connection of the original gabion and the penetration of the membrane will lead to the interaction between municipal waste and solidified fly ash. Considering the operation of the landfill area comprehensively, the gas inside the landfill site in this design is led to the horizontal gas diversion blind ditch at the top of the garbage through the garbage layer or vertical shaft, and then finally led to the

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Fig. 2 Detail drawing of horizontal air guide blind ditch with vertical air guide gabion

external collection main pipe through the connecting branch pipe and collected to the flare system for treatment. b. Design content (the design is shown in Fig. 2). The bottom elevation of the horizontal gas blind ditch is consistent with that of the existing warehouse. The horizontal gas conducting layer is arranged on the reinforced layer in the combined form of “gravel + plastic” gas conducting pipe. The section of the air guide blind ditch is trapezoid, which is divided into upper trapezoid and lower trapezoid by three layers of reinforcement. A DN150 plastic blind ditch pipe is arranged between the bottom reinforcement layer and the middle reinforcement layer, and a DN150 plastic blind ditch pipe is arranged between the middle reinforcement layer and the top reinforcement layer. Gravel graded crushed stone shall be used for protection within 100 mm around the plastic blind ditch pipe, and 20–40 mm crushed stone shall be used for filling other areas of the section. The top and left and right sides of the upper trapezoidal section shall be protected with geotextile first, and then all sides shall be wrapped with geogrid mesh, and the left and right sides of the lower trapezoidal section are first protected by geotextile, and then all sides are wrapped by geogrid mesh; The horizontal blind ditch connects each independent vertical gas gathering well to form a horizontal gas collecting layer. At the same time, considering the high-water level of leachate in the current garbage dump, the bottom of the horizontal gas transmission blind ditch longitudinally distributed in this design is provided with a simultaneous drainage and exhaust blind ditch. When the water level is high, it will be used as the leachate drainage blind ditch, and the leachate will be discharged into the pumping shaft. After the water level effectively drops, it can be used as the gas transmission blind ditch.

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Vertical gas conduction system: vertical gas conduction generally refers to burying the gas conducting perforated pipe in the vertical direction of the garbage dump, drilling the garbage dump for exhaust, with an average depth of 15 m, a distance of more than 5 m from the bottom of the site, and a hole diameter of 1000 mm. After drilling, put the lead wire mesh cage with a diameter of 1000 mm into the hole, and then insert the DN110HDPE perforated pipe into the mesh cage, and fill the middle with gravel with a particle size of 40–60 mm. The vertical shaft and blind ditch are connected by a hollow gravel layer. The top of the gabion is located under the horizontal blind ditch, and a hollow cylindrical area with a diameter of 600 is separated by a double-layer geogrid net 1.0 m below the top. The gas conducting pipe passes through this area and is connected with the connecting branch pipe through a hose. The spacing of gas gabion is temporarily designed as 50 m (further adjusted according to the survey results), and a total of 11 gas gabion wells are set. Collection system: the collection system is composed of connecting branch pipes and off-site collection main pipes. The connecting branch pipes are used to connect the horizontal drainage system in the landfill site to the off-site collection main pipes (HDPE non porous pipes). The connecting branch pipe is buried in the intermediate reinforcement layer. In consideration of the pile settlement, DN100 flexible pipe with 10 mm wall thickness and stiffening ring is used for welding when the connecting branch pipe is laid from the edge of the site bottom to the slope. The connection between the branch pipe and the horizontal gas transmission blind ditch can be divided into two situations: The one is to discharge the landfill gas in the horizontal gas transmission blind ditch, the pipe should run through the horizontal gas transmission blind ditch, and the part buried in the horizontal gas transmission blind ditch should be perforated by 10%. The other one is to discharge the landfill gas in shaft gabion. At this time, flange should be used to connect the top hose of the shaft, and all connecting branch pipes shall be installed with manual regulating butterfly valves, manual ball valves and plastic hoses and connected with flanges. All other exposed pipe sections shall be welded to ensure that the air leakage rate of the exposed pipe is less than 5%.

4 Safety and Environment Analysis (1) Security analysis Through calculation, the minimum safety factor of the reservoir area is greater than the specification requirements (see Sect. 3.1 for details), so the overall stability of the reservoir area meets the requirements. In this design, measures such as dewatering, dumping and filling, rolling of undisturbed overburden, laying of geogrid and 300 mm thick backfill are taken to make the bearing capacity of the foundation meet the requirements of the specification. The ultimate tensile strength of the selected geogrid is greater than the strength requirements of Technical Code for geotechnical Engineering of municipal Waste Sanitary Landfill (CJJ176-2012) and Technical

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Code for application of geosynthetics (GB/T 50, 290-2014), meeting the requirements. The type selection pressure of the fan is greater than the pressure loss of the landfill system. The fan adopts a frequency conversion explosion-proof roots blower with explosion-proof function. In conclusion, the safety of this design meets the requirements of the specification. (2) Environmental analysis The solidified fly ash of this project is treated to meet the requirements of Article 6.3 of CB16889-2008 and then enters the landfill site. It is vertically zoned with the original municipal waste landfill system, and anti-seepage measures are designed. Therefore, the possibility of infiltration after loading the solidified fly ash is small, and the impact on the regional soil environment is small. There is a set of leachate emergency treatment facilities in the landfill site, and a set of biochemical treatment system, supporting facilities and equipment, as well as concentrated solution treatment system will be built later to meet the operation requirements after the transformation. Therefore, there is no risk of sewage discharge in the project. The project has designed a safe and reliable landfill gas collection, drainage and terminal treatment system (see Sect. 3.3 for details) to ensure a very low air leakage rate and minimize the possibility of odor diffusion during operation. To sum up, there are few sensitive points around the landfill site, so the Project has little impact on the regional environment.

5 Project Benefit Analysis The engineering cost of this project is 20%–50% lower than that of similar projects. The economic costs of similar projects (horizontal partition reconstruction) in China are listed in Table 3. Table 3 Economic cost analysis of similar projects Project

Area covered / m2

Total investment / million RMB

Investment per unit/ RMB·m−2

Saving investment cost compared with similar projects /%

This project

44,856.7

2882.88

642

—

Fengxin Municipal Waste Incineration Solidification Fly Ash Landfill Project

18,000

1978.39

1099

41.5

Lechang Municipal Waste Landfill Fly Ash Landfill Area Project

12,060

968.87

803

20.0

Hechi Municipal Waste Treatment Plant Fly Ash Landfill Project

22,096

1899.14

859

25.2

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In addition, the implementation of the project can produce good environmental and social benefits, save land resources, make fly ash harmless and safe disposal, and improve the overall environment of the landfill site, which will greatly improve the environmental health.

6 Conclusion (1) The vertical partition reconstruction of the original municipal waste landfill is used for solidified fly ash landfill. The space of the original landfill is used to solve the terminal problem of solidified fly ash, saving land resources and investment. (2) The problem of insufficient bearing capacity and uneven settlement of the foundation after the vertical partition reconstruction is solved through the foundation treatment measures of dewatering, dumping and filling, rolling compaction of the undisturbed overburden surface, laying of geogrid and 300 mm thick backfill. Through structural calculation and design such as overall stability analysis of the reservoir area, key problems such as stability and safety in the process of vertical partition reconstruction are solved. When loading solidified fly ash on the original municipal waste landfill area, it is necessary to consider the characteristics of the original municipal waste and the structural problems such as sliding and insufficient foundation bearing capacity that may exist in the landfill area. At the same time, it is necessary to calculate the structural mechanics problems after loading solidified fly ash. (3) The horizontal + vertical gas transmission system is completed under the geogrid to realize active drainage, prevent the risk of mixing garbage and fly ash caused by the gabion penetrating the membrane, and eliminate the potential safety hazards caused by poor drainage of landfill gas; Manually adjust the butterfly valve, manual ball valve and plastic hose and adopt flange connection to solve the problem of uneven guide discharge caused by long transmission distance; The lower trapezoidal (1 m deep) buffer layer is connected with a flexible hose to solve the problem that the gas conduction system is affected by the uneven settlement of the municipal waste layer; Flexible space shall be reserved for the connecting pipe with flexible hose, and the clay layer shall be set to prevent the branch pipe from being pulled to cause poor exhaust. Acknowledgements I would like to express my gratitude to all those who helped me during the writing of this thesis. Lucid waters and lush mountains are invaluable assets. Environmental protection is not easy, but it’s critical and important. It is not easy to solve difficult problems in the industrial production process with economic methods, therefore, we need to constantly explore and summary in engineering practice. Thank those people and companies who helped me along the way.

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References 1. Luo X, Wang Y, Gong X et al (2018) Problems and countermeasures in solidification and stabilization fly ash landfill disposal of waste incineration. Chin J Environ Eng 12(10):2717–2724 2. Zhao G, Li H, Zhao Z et al (2005) Basic properties of fly ash from incineration of municipal solid waste. J Fuel Chem Technol 02:184–188 3. Lu W, Zai Z (2013) Practice of refuse landfill reconstruction based on fly ash sanitary landfill – taking Changshu Nanhu municipal waste landfill as an example. J Green Sci Technol 12:191–193 4. GB 16889-2008. Standard for pollution control on the landfill site of municipal solid waste 5. Jiang X, Chang W (2015) Review for treatment and application of municipal solid waste incineration fly ash. J Zhejiang Univ Technol 43(01):7–17 6. CJJ 176-2012. Technical code for geotechnical engineering of municipal solid waste sanitary landfill 7. GB/T 50290-2014. Technical code for application of geosynthetics 8. GB 50869-2013. Technical code for municipal solid waste sanitary landfill 9. CJJ 133-2009. Technical code for projects of landfill gas collection, treatment and utilization

Energy Harvesting and Power Transmission

Effect of Metamaterial Application on Coupling Coefficient of Wireless Power Transfer Pharida Jeebklum, Prasenjit Dey, Phumin Kirawanich, and Chaiyut Sumpavakup

Abstract An investigation on the effect of metamaterial applications on the coupling coefficient of wireless power transfer. The coupling coefficient is the magnetic compatibility of the coil pair that depends on the material of the core. The wireless power transfer has air as the core. The coupling coefficient of air cores can be as low that depending on the gap between the two coils. Thus, the coupling coefficient affects the efficiency of the wireless power transfer. The wireless power transfer was designed following the Society of Automotive Engineer standard (SAE). The gap between the transmitter and the receiver coil is 0.15 m. The coupling coefficients were simulated on ANSYS Maxwell 3D. The simulation was divided into 2 main cases that the wireless power transfer without metamaterial slab and the wireless power transfer with metamaterial slab. The wireless power transfer with metamaterial slab is simulated with 2 types that the edge metamaterial slab and the symmetrical metamaterial slab. The gap between the metamaterial slab and the transmitter coil is adjusted to 0.01–0.10 m. The result was found that in the case of the wireless power transfer without metamaterial slab, the coupling coefficient was 0.4056. The symmetrical metamaterial slab at a gap between the transmitter coil and the metamaterial slab of 0.01 m gives a maximum coupling coefficient of 0.4062. Therefore, the wireless power transfer with metamaterial slab gives a greater coupling coefficient than the wireless power transfer without metamaterial slab. Keywords Coupling Coefficient · Metamaterial · Wireless Power Transfer

P. Jeebklum Power Engineering Technology, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand P. Dey · P. Kirawanich Faculty of Engineering, Mahidol University, Nakhon Pathom 737170, Thailand C. Sumpavakup (B) Research Centre for Combustion Technology and Alternative Energy – CTAE and College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_27

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1 Introduction There are two types of charging electric vehicles: wired charging and wireless charging. The wired charging is using a conductor to transfer power to the battery. The wired charging may have different types of sockets or plugs according to the charging power level. In addition, the conductor can lead to leakage of electricity and is dangerous for users. As a result, the wireless charging technology is very attractive because it is safer than wired charging and is more convenient to use. The electric vehicle is parked in the area with the wireless charging, the system will charge the battery. The wireless charging or wireless power transfer is the inductance between the transmitter and receiver coil in which air is the intermediate. The transmitter coil, which is buried or placed on the floor, generates the magnetic flux. The receiver coil is mounted on the bottom of the vehicle. The wireless power transfer method is convenient and safe for the users. The users do not have to touch the power transfer system. However, there is a power loss in wireless power transfer. This is caused by the misalignment or too much the gap between the transmitter and the receiver coil. These causes the magnetic flux to leak more and reduce efficiency. Metamaterial application in conjunction with wireless power transfer is one of the ways to improve wireless power transfer efficiency. The metamaterial slab can extend the power transfer gap [1]. The metamaterial application causes the magnetic field of the power transmitter section to be better directed towards the power receiver section even though the transmitter and receiver coil are misaligned. As a result, power transfer efficiency is increased [2–4]. The metamaterial slab is an artificially engineered materials with negative refractive characteristics. The negative refractive characteristic is due to both the permittivity and the permeability being negative. This results in the electromagnetic waves incident on the metamaterial slab refracted to converge [5]. This paper presents the design of a wireless power transfer in conjunction with metamaterial slab. The metamaterial slab was designed in two forms. The objective was to study the effect of metamaterial application on the coupling coefficient of wireless power transfer. The parameters for the simulation include the gap varied at 0.01–0.10 m. The current of metamaterial cells is excited as maximum input power 3.7 kW. The resonance frequency complies with The J2954 wireless power transfer standards set forth by the SAE [6] for PHEVs and EVs for light-duty and passenger vehicles. There are a total of six primary sections in this essay. The principles of wireless power transfer are explained in section two. Section three illustrates metamaterial for wireless power transfer. Designing of a wireless power transfer in conjunction with metamaterial slab is described in Section four. Section five gives the result and discussion of the simulation. The last section is the conclusion.

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2 Principles of Wireless Power Transfer The wireless power transfer system (see Fig. 1) consists of two main parts: the power transmitter section and the power receiver section. The power transmitter section is either embedded or placed on the ground. The power receiver section is attached to the electric vehicle. The power transmitter section consists of an AC power supply, rectifier circuit, inverter circuit, and power transmitter. The AC power supply has frequency of 50–60 Hz, which is insufficient for wireless power transfer. The rectifier circuit converts AC to DC with frequency filter. The inverter circuit converts DC to AC at high frequencies so that power can be transferred. The frequency level that can transfer power is approximately kHz level and above. However, there is a standard of the SAE specifying a frequency range of 81.38–90 kHz [7, 8]. The power transmitter is used as a resonant circuit. The resonant frequency: f can be calculated from Eq. (1). The power receiver section consists of a power receiver, High-frequency AC to DC conversion circuit or charger, and battery. The SAE has classified the power level of the wireless power transfer 4 level: Level 1 (WPT1) has maximum input power 3.7 kW, Level 2 (WPT2) has maximum input power 7.7 kW, Level 3 (WPT3) has maximum input power 11 kW, and Level 4 (WPT4) has maximum input power 22 kW [7, 8]. f =

2π

1 √

LC

(1)

where L is inductance of the coil and C is capacitance in the power transmitter or the power receiver. To comprehend the connection between the coupling coefficient and the WPT’s efficiency, a circuit analysis is presented in this section. Because the metamaterial slab’s spacing between the transmitter and receiver can alter the coupling coefficient. Hence, the coupling coefficient and efficiency are strongly connected [9]. It is possible to express the power transfer from the transmitter section to the receiving section by (2). P = 2π f M I P I S sin φ P S

Fig. 1 The wireless power transfer system

(2)

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where M, I P , I S , and φ P S are mutual inductance of the transmitter and receiver coil that the mutual inductance can be expressed by (3), amplitude of transmitter coil current, amplitude of receiver coil current, and phase difference between the transmitter current and receiver current respectively. the mutual inductance showed the relation between and √ M = k LP LS (3) where k, L P , and L S are coupling coefficient of two coils, transmitter section inductance, and receiver section inductance respectively. The power loss is caused by the transmitter section resistance R P and the receiver section resistance R S . .. The power loss can be evaluated as (4) PLoss = I P2 R P + I S2 R S

(4)

The efficiency η of the wireless power transfer can be roughly measured as (5) η=

I 2 R P + I S2 R S Pout Ploss =1− =1− √P P P 2π f k L P L S I P I S sin φ P S

(5)

One of the elements impacting the effectiveness of wireless power transfer is the coupling coefficient between the transmitter and receiver coils. The magnetic compatibility of the coil pair is measured by the coupling coefficient. The coupling coefficient depends on the material of the core, e.g. iron or ferrite cores can be very high approximately 0.99. However, the coupling coefficient of air cores in the wireless power transfer can be as low as 0.4 to 0.8 depending on gap the two coils. Lu and Ngo (2019) proposed a larger ferrite core than the coil mounted on the back of the coil. The results was found that the ferrite core improves the coupling efficiency and prevents magnetic leakage [10]. Ahmad A and Alam (2019) analyzed of the magnetic field pattern of ferrite core-based coils was also presented. The results was shown that the coupling coefficient between the transmitter and receiver coil was highest when alignment. The ferrite cores improved the coupling coefficient and nearby materials are not subjected to magnetic fields [11]. The ferrite cores in the receiver coil is proposed by Tejeda, Carretero, Boys, and Covic (2017) [12]. The ferrite cores in the receiver coil is used to control the leakage of the magnetic field. The results was found that the ferrite cores had lower magnetic leakage than without using ferrite. However, the disadvantage of ferrite cores is that cause core loss or eddy currents and the system will also be larger [12]. Lee, Ahn, and Jang (2017) proposed wireless power transfer system for trains which provides high efficiency and reduce the ferrite cores on the transmitter section. The results was found that the ferrite cores can improve the coupling efficiency. However, the ferrite core at high power can become magnetic saturation [13]. Therefore, the ferrite cores can improve coupling efficiency and prevent magnetic leakage. Nevertheless, the ferrite cores will cause core loss or eddy current and saturation of the ferrite core.

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3 Metamaterial for Wireless Power Transfer The metamaterial is an artificially engineered materials with negative refractive characteristics. The negative refractive characteristic is due to both the permittivity and the permeability being negative. This results in the electromagnetic waves incident on the metamaterial refracted to converge [5]. The metamaterial slab has the opposite properties of most materials in nature with both the permittivity and the permeability being positive. The electromagnetic wave strikes any material with positive refractive characteristics, the electromagnetic wave is diverged. However, the electromagnetic wave strikes the metamaterial slab with negative refractive characteristics, the electromagnetic wave is focused [5]. In the wireless power transfer, the metamaterial slab is applied to extend the power transfer gap. Even though the transmitter and receiver coil are misaligned, the magnetic flux is better directed toward the power receiver section. As a result, the power transfer efficiency and the gap are increased [1–4]. The metamaterial slab is made from composite materials such as metal and plastic or PCB which are often arranged in a repeating pattern. The metamaterial slab properties are not derived from the basic material properties but from the newly designed structure, precise shape, shape, size, orientation, and arrangement. This gives the metamaterial some ingenious properties that can focus electromagnetic waves. Thus, the metamaterial advantages over conventional materials [14, 15]. The previous research on the wireless power transfer system in conjunction with the metamaterial slab has been applied to small devices such as mobile phones and light bulbs. However, the metamaterial slab has not been applied to the electric vehicles yet. Zheng, Fang, Zheng, and Feng (2022) proposed self-resonant dual-band negative permeability metamaterial design. The result was found that the metamaterial can be used in a wide frequency band. The frequency is changed, the metamaterial can improve the power transfer efficiency [16]. Li, Wang, Yao, Zhang, and Tang (2016) simulated to investigate a 6 × 6 planar array metamaterial slab. The platform with metamaterials slab is utilized to light a 15W bulb. The results were found that the wireless power transfer without metamaterial slab, the power transfer efficiency was 16%. The wireless power transfer with single metamaterial slab, the power transfer efficiency was 29.3%. The wireless power transfer with two metamaterials slab, the power transfer efficiency was 36.7% [17]. Wang, Teo, Nishino, Yerazunis, Barnwell, and Zhang (2011) proposed an anisotropic metamaterial slab arrangement using two surfaces for a 40 W light bulb. The result was found that the power transfer efficiency was increased from 17 to 47% [18]. A design of hybrid metamaterial slab with zero and negative permeability was proposed by Cho, Lee, Jeong, Kim, Song, Yoon, Song, Kong, Yun, and Kim (2016) [2]. The edge area of the slab portion has a constructed metamaterial cell for the negative permeability. The center area of the slab has been constructed to have zero permeability. Comparing the hybrid metamaterials slab to the single permeability metamaterials slab, the power efficiency may be increased by 21.4% at a 20 cm gap [2]. Cho, Lee, Kim, Kim, Song, Kong, Park, Seo, and Kim (2018) suggested that the met-amaterial cells in the middle region of the hybrid metamaterial slab have zero relative permeability and

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straighten the magnetic field direction. As a result of a magnetic boundary condition, the metamaterial cells at the borders of the hybrid metamaterial slab shift the direction of the emitted magnetic fields. The power transmission efficiency increased from 34.5% to 41.7% as a result, according to the findings [19]. Lee, Cho, Jeong, Hong, Sim, Kim, and Kim (2019) presented a hybrid metamaterial slab with two stacks. The hybrid metamaterial slab is made up of cells with both zero and negative magnetic permeability. The efficiency of the wireless power transfer was improved compared to the without metamaterial slab and the one metamaterial slab [20]. This increases the gap between the power transmitter coil and the power receiver coil. The metamaterial application causes the magnetic field of the power transmitter section to be better directed towards the power receiver section. There are 3 types of the arrangement of metamaterial cells (see Fig. 2): a symmetrical metamaterial slab has a single permeability that are arranged in full slab, the hybrid metamaterial slab has two types of permeability, and an edge metamaterial slab has a negative permeability that are arranged only in the edge slab. The metamaterial cell as shown in Fig. 3. The magnetic permeability of the metamaterial cell can be calculated from Eq. (6). [21]  μ=1− F 1−

1 R +j 2 2π f LM (2π f ) L M C M

−1 (6)

The inductance of the metamaterial cell can be calculated from Eq. (7). [3]

Fig. 2 The arrangement of metamaterial cells a) symmetrical metamaterial slab b) hybrid metamaterial slab c) edge metamaterial slab Fig. 3 The metamaterial cell

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   5 DM + dM L M = 6.025x10−7 (D M + d M )(N M + 1) 3 ln 4 DM − dM

(7)

The capacitance of the metamaterial cell can be calculated from Eq. (8). [21] C M = ε0 εr

wM sM

(8) πw 2

where F, w M , s M , R, D M , d M , N M , εr , and ε0 are cell volume; F = s 2 M , conductor M width of the metamaterial cell, conductor spacing of the metamaterial cell, resistance, outer width of the metamaterial cell; D M = d M + 2N M w M + 2s M (N M − 1), internal width of the metamaterial cell, number of turns of metamaterial cell, vacuum permittivity, and dielectric constant of the material respectively. This paper investigates symmetry metamaterial slab and edge metamaterial slab effect to coupling coefficient of the wireless power transfer. The wireless power transfer was designed following the SAE standard both power transfer level and standard frequency.

4 Designing the Wireless Power Transfer with the Metamaterial WPT1 of the wireless power transfer has maximum input power 3.7 kW. The design of the resonant circuit is following the standard frequency range of the SAE. From Eq. (1), the capacitance of the available capacitor is 2 nF. The range of inductance of the inductor is 1563–1912 µH. In this paper, the inductance of 1900 µH was chosen. Thus, the resonant frequency was calculated to be 81.64 kHz. The inductors are created from copper wire No. 27 S.W.G. that can withstand a current of 0.432 A and has a diameter of 0.4 mm. The 50 strands of copper wire can withstand a current of 21.6 A and have a diameter of 0.003 m. The transmitter coil has an internal diameter of 0.10 m, 73 turns, and an outer diameter of 0.80 m. The receiver coil has an internal diameter of 25 cm, 60 turns, and an outer diameter of 0.82 m (see Fig. 4). The current excitement to the coils are 17 A. The gap between the transmitter and receiver coil is 0.15 m. The following Table 1 gives a summary of the parameters of the coils. The metamaterial cell is designed to have an internal width of 0.05 m, conductor width of 0.01 m, conductor spacing of 0.01 m, and 5 turns. Thus, the outer width of the metamaterial cell is 0.23 m. The permeability of the metamaterial cell is $-$2.16. The following Table 2 gives a summary of the parameters of metamaterial cell. The edge metamaterial slab used 12 metamaterial cells and a cell spacing of 0.01 m. The edge metamaterial slab has a width of 0.95 m. The symmetrical metamaterial slab used 16 metamaterial cells and a cell spacing of 0.01 m. The symmetrical metamaterial slab has a width of 0.95 m (see Fig. 5). The following Table 3 gives a summary of the parameters of metamaterials.

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Fig. 4 The transmitter and receiver coil on ANSYS Maxwell 3D

Table 1 Parameters of coil

Table 2 Parameters of metamaterial cell

Parameters

Symbol

Values

Internal diameter of the transmitter coil

diT

0.10 m

Number of turns in the transmitter coil

NT

73 turn

Outer diameter of the transmitter coil

DT

0.80 m

Current in the transmitter coil

IT

17 A

Internal diameter of the receiver coil

diR

0.25 m

Number of turns in the receiver coil

NR

60 turn

Outer diameter of the receiver coil

DR

0.82 m

Current in the receiver coil

IR

17 A

Conductor diameter

w

0.003 m

Air gap distance between the coils

d

0.15 m

Parameters of cell

Symbol

Values

Internal width of the metamaterial cell

dM

0.05 m

Conductor width of the metamaterial cell

wM

0.01 m

Conductor spacing of the metamaterial cell

sM

0.01 m

Number of turns in the metamaterial cell

NM

5 turn

Outer width of the metamaterial cell

DM

0.23 m

Permeability of the metamaterial cell

μ

−2.16

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Fig. 5 The metamaterial slab on ANSYS Maxwell 3D a) edge metamaterial slab b) symmetrical metamaterial slab

Table 3 Parameters of metamaterial slab

Parameters of metamaterial

Edge metamaterial slab

Symmetrical metamaterial slab

Number of the metamaterial cell

12

16

Spacing of the metamaterial cell

0.01 m

0.01 m

Width of the metamaterial slab

0.95 m

0.95 m

5 Result and Discussion The simulation on ANSYS Maxwell 3D to investigate the coupling coefficient is divided into 2 main cases: the wireless power transfer without metamaterial slab and the wireless power transfer with metamaterial slab. The wireless power transfer with metamaterial slab is simulated with 2 types: the edge metamaterial slab and the symmetrical metamaterial slab. The edge metamaterial slab is excited current to each cell of the metamaterial with 3 values: no excite, 1.416 A (17 A/12 cells), and 17 A. The symmetrical metamaterial slab is excited current to each cell of the metamaterial with 3 values: no excite, 1.0625 A (17 A/16 cells), and 17 A. The metamaterial cells were excited to verify the coupling coefficient simulation. The gap between the metamaterial slab and the transmitter coil; mz is adjusted to 0.01– 0.10 m (see Fig. 6). The magnetic flux density of the wireless power is simulated (see Fig. 7). The following Table 4 gives a summary of the coupling coefficient of the wireless power transfer (see Fig. 8). From the simulation results, the coupling coefficient of the wireless power transfer without metamaterial slab is 0.4056. The wireless power transfer with the edge metamaterial slab and without excitation of the metamaterial cell has a maximum coupling coefficient of 0.4059 at a gap of 0.03 m. The wireless power transfer with the edge metamaterial slab that excitation of the metamaterial cell with a current of 1.416

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Fig. 6 The transmitter and the receiver coil with a) the edge metamaterial slab b) the symmetrical metamaterial slab

Fig. 7 The magnetic flux density of a) the edge metamaterial slab no excites current b) the symmetrical metamaterial slab no excites current c) without metamaterial slab

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Table 4 Coupling coefficient (k) of the wireless power transfer mz (m) Without metamaterial With metamaterial slab slab Edge metamaterial slab no excite 1.416 A 17 A 0.01

0.4056

Symmetrical metamaterial slab no excite 1.0625 A 17 A

0.4058

0.4059

0.4059 0.4062

0.4060

0.4058

0.02

0.4058

0.4061

0.4059 0.4057

0.4063

0.4061

0.03

0.4059

0.4060

0.4059 0.4056

0.4060

0.4058

0.04

0.4056

0.4059

0.4059 0.4057

0.4060

0.4059

0.05

0.4057

0.4060

0.4059 0.4057

0.4060

0.4059

0.06

0.4058

0.4060

0.4059 0.4058

0.4061

0.4059

0.07

0.4057

0.4060

0.4059 0.4059

0.4060

0.4059

0.08

0.4057

0.4060

0.4059 0.4058

0.4060

0.4059

0.09

0.4058

0.4060

0.4059 0.4057

0.4061

0.4060

0.10

0.4058

0.4060

0.4059 0.4057

0.4061

0.4059

Fig. 8 The coupling coefficient of the wireless power transfer

A has a maximum coupling coefficient of 0.4061 at a gap of 0.02 m. The wireless power transfer with the edge metamaterial slab that excitation of the metamaterial cell with a current of 17 A has the coupling coefficient of 0.4059. The coupling coefficient of the edge metamaterial slab that excitation of the metamaterial cell with a current of 17 A changes very little, so appears to be constant. The simulation results were found that the wireless power transfer with the edge metamaterial slab can increase the coupling coefficient. The greatest coupling coefficient for wireless power transfer with the symmetrical metamaterial slab and without excitation of the metamaterial cell is 0.4062 at a gap of 0.01 m. The wireless power transfer with the

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symmetrical metamaterial slab that excitation of the metamaterial cell with a current of 1.0625 A has a maximum coupling coefficient of 0.4063 at a gap of 0.02 m. The wireless power transfer with the symmetrical metamaterial slab that excitation of the metamaterial cell with a current of 17 A has a maximum coupling coefficient of 0.4061 at a gap of 0.02 m. The simulation results were found that the symmetrical metamaterial slab can increase the coupling coefficient in the wireless power transfer. The gap between the transmitter coil and the metamaterial slab that gives the highest coupling coefficient is in the range of 0.01–0.03 m. The metamaterial slab is closer to the transmitter coil than the receiver coil. Therefore, the metamaterial slab should be placed close to the transmitter coil.

6 Conclusion The coupling coefficients were simulated on the ANSYS Maxwell 3D at a gap between the transmitter and receiver coil of 0.15 m. The result was found that in the case of the wireless power transfer without metamaterial slab, the coupling coefficient was 0.4056. In the case of the wireless power transfer with metamaterial slab, the coupling coefficient was largely higher than the wireless power transfer without metamaterial slab. The symmetrical metamaterial slab gives a higher coupling coefficient than the edge metamaterial slab. The symmetrical metamaterial slab gives the highest coupling coefficient of 0.4063 at a gap between the transmitter coil and the metamaterial slab 0.02 m and the metamaterial cell is excited current 1.0625 A. However, in testing the wireless power transfer system with metamaterial slab is not excited current to the metamaterial slab. Thus, the symmetrical metamaterial slab at a gap between the transmitter coil and the metamaterial slab of 0.01 m gives a maximum coupling coefficient of 0.4062. The metamaterial slab should be placed close to the transmitter coil. Acknowledgements This project is funded by National Research Council of Thailand (NRCT) and Pan drives Company Limited (N41A650434).

References 1. Corrêa DC, Resende UC, Bicalho FS (2019) Experiments with a compact wireless power transfer system using strongly coupled magnetic resonance and metamaterials. IEEE Trans Magn 55(8):1–4 2. Cho Y, et al (2016) Hybrid metamaterial with zero and negative permeability to enhance efficiency in wireless power transfer system. In: Proceedings of the 2016 IEEE wireless power transfer conference. aveiro, portugal, pp 1–3 3. Hussain N, Park J, Jeong M, Choi D, Hong I, Kim N (2020) Design of metamaterial for efficiency improvement of magnetic resonant WPT system. In: Proceedings of the 2020 IEEE wireless power transfer conference, Seoul, Korea (South), pp 234–237

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4. Alphones A, Sampath JPK (2015) Metamaterial assisted wireless power transfer system. In: Proceedings of the 2015 Asia-Pacific microwave conference, Nanjing, China, pp 1–3 5. Lee W, Yoon YK (2020) Wireless power transfer systems using metamaterials: a review. IEEE Access 8:147930–147947 6. Panchal C, Stegen S, Lu J (2018) Review of static and dynamic wireless electric vehicle charging system. Eng Sci Technol 21(5):922–937 7. Jayalath S, Khan A (2021) Design, challenges, and trends of inductive power transfer couplers for electric vehicles: a review. IEEE J Emerg Sel Top Power Electron 9(5):6196–6218 8. Shi L, Martin-Segura G, Fonseca J, Rivas, HA, García O, Fernandez A (2022) Analysis and modelling of normative WPT3 system based on standard SAE J2954. In: Proceedings of the 2022 wireless power week, Bordeaux, France, pp 857–860 9. Ahmad A, Alam MS, Rafat Y, Shariff S (2020) Designing and demonstration of misalignment reduction for wireless charging of autonomous electric vehicle. eTransportation 4:1–11 10. Lu M, Ngo KDT (2019) Analytical calculation of proximity-effect resistance for planar coil with litz wire and ferrite plate in inductive power transfer. IEEE Trans Ind Appl 55(3):2984–2991 11. Ahmad A, Alam MS (2019) Magnetic analysis of copper coil power pad with ferrite core for wireless charging application. Trans Electr Electron Mater 20:165–173 12. Tejeda A, Carretero C, Boys JT, Covic GA (2017) Ferrite-less circular pad with controlled flux cancelation for EV wireless charging. IEEE Trans Power Electron 32(11):8349–8359 13. Lee SB, Ahn S, Jang IG (2017) Simulation-based feasibility study on the wireless charging railway system with a ferriteless primary module. IEEE Trans Veh Technol 66(2):1004–1010 14. Metamaterial. https://en.wikipedia.org/wiki/Metamaterial. Accessed 21 Dec 2021 15. Wojciech JK, Thanh NC (2018) Metamaterials in application to improve antenna parameters. INTECH 12:63–85 16. Zheng Z, Fang X, Zheng Y, Feng H (2022) A wireless power transfer system based on dual-band metamaterials. IEEE Microwave Wirel Compon Lett 32(6):615–618 17. Li W, Wang P, Yao C, Zhang Y, Tang, H: Experimental investigation of 1D, 2D, and 3D metamaterials for efficiency enhancement in a 6.78 MHz wireless power transfer system. In: Proceedings of the 2016 IEEE wireless power transfer conference, Aveiro, Portugal, pp 1–4 18. Wang B, Teo KH, Nishino T, Yerazunis W, Barnwell J, Zhang J (2011) Experiments on wireless power transfer with metamaterials. Appl Phys Lett 98(25):1–3 19. Cho Y, Lee S, Kim DH, Kim H, Song C, Kong S, Park J, Seo C, Kim J (2018) Thin hybrid metamaterial slab with negative and zero permeability for high efficiency and low electromagnetic field in wireless power transfer systems. IEEE Trans Electromagn Compat 60(4):1001–1009 20. Lee S, et al (2019) High efficiency wireless power transfer system using a two-stack hybrid metamaterial slab. In: Proceedings of the 2019 IEEE wireless power transfer conference, London, UK, pp 616–619 21. Dong, Y., Li, W., Yang, X., Yao, C., Tang, H. (2017) Design of unit cell for metamaterials applied in a wireless power transfer system. In: Proceedings of the 2017 IEEE PELS workshop on emerging technologies: wireless power transfer, Chongqing, China, pp 143–147

Effects of Flow Velocity and Length-to-Depth Ratio on Low-Speed Rectangular Cavity Flow Oscillations for Clean Energy Harvesting Abhishek Singh, Varun Thangamani, Foo Ngai Kok, and Christina Vanderwel

Abstract Low-speed cavity flow oscillations have been found to be a potential source for harvesting energy from flow-induced vibrations. An investigation was conducted using wind tunnel experiments to analyze the effect of flow velocity and cavity length-to-depth ratio on low-speed cavity flow oscillations. This study aims to find the most suitable cavity and flow velocity configuration to harvest clean oscillatory energy from the flow. Cavities with five length-to-depth (L/D) ratios of 1, 2, 3, 4 and 6 have been considered with flow velocities at 10, 15, 20, 25 and 30 m/s. The cavity tone amplitudes showed a general increase in amplitude with an increase in velocity up to 25 m/s and slightly dropped at 30 m/s, except for the cavity with L/D = 2. The Total Spreaded Acoustic Power (TSAP) was found to increase with an increase in velocity. The results showed that cavities with L/D = 1 and 6 do not exhibit cavity tones, and the noise in the frequency spectra was found to be broadband. Cavities with L/D = 2, 3 and 4 showed high-amplitude flow oscillations with frequencies that matched Rossiter’s semi-empirical model. Cavity with L/D = 3 showed flow oscillations with the highest amplitude, although the frequency peak of the cavity with L/D = 2 had a better Quality (Q) factor. Overall, the cavity with L/D = 3, at a flow velocity of 25 m/s, was found to be the best choice for energy harvesting. Keywords oscillation · acoustic · energy · vibration etc.

A. Singh · V. Thangamani (B) · F. N. Kok University of Southampton Malaysia, Iskandar Puteri, Malaysia e-mail: [email protected] C. Vanderwel University of Southampton, Southampton, UK © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Gaber (ed.), Proceedings of the 5th International Conference on Clean Energy and Electrical Systems, Lecture Notes in Electrical Engineering 1058, https://doi.org/10.1007/978-981-99-3888-9_28

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1 Introduction Energy harvesting from ambience has been a topic that has been gaining momentum amongst scientific communities owing to their positive impacts on the environment as well as their potential to counteract the sharp increase in battery-consuming devices over the past decades. Technologies like Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have ushered a steep rise in the use of sensors and MEMS-based devices. This evolution of technology has resulted in the exploration of energy harvesting systems that are able to power these devices and sensors, removing the need for battery replacement and maintenance. One of the energy harvesting sources whose studies have consistently been attaining traction is fluid flow-based oscillations. In energy harvesting from fluid-based oscillations, structural or fluid oscillations generated from fluid dynamic or aeroelastic phenomena are used to vibrate a harvester that generates electrical power. The harvester used can be based on electromagnetic or piezoelectric principles. Some classifications of motions utilized for such power generation include galloping, flutter, buffeting, and vortex-induced vibrations. Such energy harvesting methods could potentially find applications in powering sensors, and their output varies from a few microwatts to milliwatts depending on the scale of the harvester. The galloping method generally consists of attaching a prismatic body to the free end of a cantilever beam and subjecting it to axial flows. As the flow velocity crosses a cut-in value, the prism oscillates transversely with large amplitudes. Sirohia and Mahadik [1] used equilateral triangular prisms to generate power of 53 mW using piezoelectric material attached to the cantilever. Few other studies have used a square prism to generate power from galloping-based oscillations [2, 3]. When certain aerodynamic bodies, like a wing, are exposed to velocities above a threshold value, the aeroelastic vibrations exceed well above the internal damping of the material, causing the structure to oscillate significantly. This phenomenon is called flutter, and the principle has been utilized to harvest energy. In the method of harvesting energy from vortex-induced vibrations, the vortices shed from a bluff body are used to oscillate a piezoelectric or electromagnetic harvester to generate power. Wen et al. [4] used a piezoelectric cantilever beam in the wake region of a cuboidal bluff body to generate a power of 1 µW at a flow velocity of 2 m/s. Studies have also been conducted using a cylinder [5] and a transverse flat plate [6]. Using vortices shed from a cylinder flow, Akaydin [5] was able to generate a peak power of 4 µW at a Reynolds number of 14,800. The review article by Abdelkefi [7] provides further details of the various energy harvesting methods from flow-induced vibrations. This paper focuses on energy harvesting from flow oscillations generated by flow over a rectangular cavity. Under some conditions, flow over certain cavity geometries are known to generate self-sustained flow oscillations that can be rather severe. They are created by a feedback mechanism first proposed by Rossiter in 1964 [8]. According to the Rossiter model, vortices created at the cavity’s leading-edge travel downstream and interact with the rear wall, causing pressure fluctuations. These

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fluctuations then travel upstream and generate additional, amplified vortices at the leading edge, resulting in a self-sustaining feedback cycle. The frequency of oscillations is determined by various factors such as the geometry of the cavity, incoming boundary layer and Mach number. They are undesirable in most applications like aircraft wheel wells, aircraft weapons bays, car sunroofs, inter-coach train gaps etc. They cause mechanical vibrations to the parts located within and around these areas, and extensive damage occurs when their natural frequency matches the flow frequencies. Several studies have focused on suppressing the cavity flow oscillations to avoid these pernicious effects. The control methods aim to disrupt the feedback loop and thus weaken the flow oscillation amplitudes. Some of the suppression methods are passive [9], wherein external energy is not involved, while others are active [10] and use external energy. Although deemed undesirable in several situations, cavity flow oscillations can be beneficially used by harvesting the oscillatory flow energy. Such an energy harvesting method can potentially have multiple applications, such as pipelines, vehicles, and rivers, wherein they can be used to power small devices and sensors. By installing the harvesters in rivers as arrays and scaling them up, they have the potential to generate a significant amount of power. Thangamani and Kok [11] explored the potential of using cavity flow oscillations for energy harvesting by placing a PZT-5H piezoelectric cantilever beam inside cavities of length-to-depth (L/D) ratios of 2 and 3. They did experimental and computational studies to find the best location to place the harvester and found that the location close to the trailing edge was most suitable. The beam was mounted transversely to the aft wall, as shown in Fig. 1, and the cavity was exposed to a flow of 30 m/s. The natural frequency of the piezoelectric beam was also tuned to match the flow frequencies closely to obtain peak amplitudes through resonance. They were able to harvest an average and peak power of 32.16 µW and 403.46 µW, respectively. This is a significant amount of power compared with other comparable studies on energy harvesting from vortex-induced vibrations. Figure 2 shows the power variation across different resistor load values obtained in the study for L/D = 3 cavity. Later, Thangamani [12] described a single-degree-of-freedom model for the energy harvesting method, the predictions of which matched the experimental results reasonably well. While the pilot studies by Thangamani and Kok were able to establish the potential of cavity flow oscillations as a source of energy harvesting, the studies were limited to a single flow velocity and two cavity dimensions (L/D = 2, 3) only. It is essential Fig. 1 Piezoelectric energy harvester attached transversely to the aft wall of the cavity for energy harvesting [11]

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Fig. 2 Power generated by the harvester placed in a cavity of L/D = 3 at 30 m/s, across different resistor loads

to study the effect of cavity dimensions and flow velocities on the oscillations as they are primary factors affecting the flow frequencies and amplitudes. Tracy and Plentovich [13] studied the physical mechanism of the tone generation inside the cavity. Based on the observation from their study they categories the flow field in to three types open flow, closed flow and transition flow. These flow fields depend on the cavity L/D ratios and flow speed and generate different oscillation patterns for different flow type. Although their study is based on high speed flows an observation can be made that a similar phenomenon may also occurs in low-speed flows. In general, cavities with L/D < 7 exhibit open cavity flow type and show self-sustained oscillations. This study aims to experimentally and methodically study the effects of cavity length-to-depth ratio (L/D) and incoming velocity on flow oscillations. Five L/D ratios (1, 2, 3, 4 and 6) and flow velocities (10, 15, 20, 25 and 30 m/s) were chosen for this purpose. These specific L/D ratios were chosen so that they cover the open cavity flow type that are shown to exhibit oscillations. The velocities chosen are appropriate for low speed energy harvesting applications. The results of the cavity’s unsteady behavior have been presented to study the effect of these parameters and to choose the optimal cavity L/D ratio and velocity for energy harvesting. Two criteria were considered while choosing the optimal cavity for energy harvesting – (i) high amplitude of oscillations across the chosen range of velocities and (ii) predictability of the frequency of oscillations, i.e., their match to the Rossiter model.

2 Experimental Details All the experiments discussed in this study were performed on the TecQuipment AF1300 subsonic wind tunnel located in the Aerospace laboratory at the University of Southampton Malaysia. The AF1300 has a maximum wind speed capability of 36 m/s, houses a semi-anechoic exhaust, and has a test section of 10 mm thick acrylic to visualize the experiments. Dimensions of the test section of this wind tunnel are L × W × D = 600 mm × 300 mm × 300 mm. The test section has a pitot-static probe, pressure taps and removable doors. For the data acquisition and graphic interface, this machine is equipped with the Versatile Data Acquisition System (VDAS), which is connected to multiple instruments such as differential pressure transducers, static

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pressure taps, three-component force balance etc. For the study of flow-induced oscillations, the cavity used is installed on the side of a customized door of the test section. Fluctuating static pressure is measured on the cavity walls to study the oscillation tones. PCB-manufactured microphones were used for the fluctuating pressures, and TecQuipment differential pressure transducers were used for the static pressure measurement. Static pressure and flow velocity data were acquired using the VDAS, while fluctuating pressures were acquired using the National Instrument’s Data Acquisition card NI USB-4431. Boundary layer measurement in this study has been done using a flat mouth boundary layer probe mounted on a bespoke holder made of 10 mm thick acrylic. A more detailed description of the boundary layer profile measurements, pressure oscillation measurement, and machine background noise analysis have been presented in Sects. 3.1 and 3.2. A more detailed description of the boundary layer profile measurements, pressure oscillation measurement, and machine background noise analysis have been presented in Sects. 1.2, 1.3, and 1.4, respectively. Free stream velocities used in these experiments were 10, 15, 20, 25 and 30 m/s. These velocities were measured and set for the experiments using a pitot-static probe placed along the test section axis.

2.1 Model Description A modular 3-D cavity (Fig. 3(a)) capable of alterable dimensions (L, D) was manufactured using a 10 mm thick transparent PVC acrylic board and mounted on the middle of the test section door (Fig. 3(b)). This transformable cavity has a maximum length and depth of 150 mm each, and by placing additional parts, its length and depth could be altered. This arrangement makes possible a total of 18 configurations with L/D ratios varying from 0.33 to 6 and L/W ratios from 0.5 to 1.5. This current study investigates cavities with L/D ratios 1, 2, 3, 4, and 6. Pressure taps are made on the walls of the cavity to acquire the static and fluctuating pressures developed due to the fluid flow and unstable shear layer interaction. The taps were spaced in such a way that for every configuration, the normalized distance (x/L, y/D, z/W) of the first tap from the leading edge (LE) and trailing edge (TE) was the same. Nomenclature for the five different cavity models is given based on their L/D ratios such that the cavity with L/D = n is expressed as Cn.

2.2 Boundary Layer Measurement Setup The velocity profile of the boundary layer entering the cavity was measured using a flat mouth pitot probe installed on a custom-built holder equipped with a micrometer. The pitot was installed such that the measurement point was close to the leading

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Fig. 3 (a) Schematic of the CAD design of the transformable cavity.; (b) Clean cavity installed on the AF1300 subsonic wind tunnel with pressure taps closed with 3d printed buttons

edge of the cavity. The vertical displacement of the pitot can be measured by the micrometer, which has a least count of 0.01 mm. Using this setup, the pitot was traversed in the vertical direction, and readings for the stagnation pressures were acquired along the line. The distance between the data points in the experiment was chosen to be 0.5 mm for better accuracy of the profile. The stagnation pressure was measured in the vertical direction until 99% of the freestream velocity was reached. For boundary layer velocity calculation, the static pressure was measured using a pressure tap on the test section. Data Acquisition and Post-Processing As discussed in Sect. 1, the wind tunnel is equipped with a data acquisition system called Versatile Data Acquisition System (VDAS) to acquire the pressure outputs from the cavity and the test section. The VDAS is connected to two digital differential pressure transducers (DDPT), which can read differential pressures of up to 7 kPa. Stagnation and static pressures from the pitot and test section were measured using the two different DDPTs. Pressures for each data point were acquired with a sample size of 120 and a sampling rate of 2 Hz. Acquired data were averaged to minimize errors. After data acquisition, all the data post-processing was done using MATLAB.

2.3 Oscillation Tone Measurement Setup Pressure oscillations in the cavity were measured using microphones flush mounted on the pressure taps of the cavity wall, as shown in Fig. 4. The effectiveness of the tones inside the cavity is expected to be different at different locations [13, 14]. In this study, fluctuating pressures are acquired using one tap each at the aft wall, cavity floor and front wall. Centre coordinates (x/L, y/D, z/W) of all the three taps for C1 are (0, −0.1, 0), (0.5, −1, 0) and (1, −0.1, 0) for aft wall, cavity floor and front wall respectively. Fluctuating pressures inside the cavity were measured using the PCB reamplified microphones 130F21 and 130F20. These microphones have a sensitivity of 45mv/

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Fig. 4 Experimental setup installed with microphones and schematic of the same.

Pa and 50mv/Pa, respectively. The 130F21 and 130F20 microphones have very low inherent noise of 29 dB, yielding better accuracy. Data from these microphones were acquired using a sound pressure module in time signal format using the NI-DAQ. The schematic of the cavity geometries and the experimental setup are shown in Fig. 4. Data Acquisition and Post-Processing Fluctuating pressure data were acquired using the National Instrument data acquisition card NI USB-4431 which has a 24-bit resolution and maximum sampling rate of 102.4 kHz. The NI USB-4431 is a four-channel analogue-input device in which all these channels can be used simultaneously. For fluctuating pressure acquisition, ten sets of data with 327,680 samples, each acquired at a sampling rate of 32,768 Hz, have been taken. The spectra from the ten sets were averaged out during the post-processing to minimize random errors. Connections between microphones and NI USB-4431 are made using NI-BNC cables that have low noise interference. LabVIEW 2021 was used as the GUI for the DAQ. Post-processing of the acquired data for the frequency spectrum was done using Fast Fourier Transform (FFT) of the time series in MATLAB using Welch’s method. During the post-processing using Welch’s method, hamming window with a window size of 8192 and 50% of overlapping has been chosen for better accuracy. This configuration gives a frequency resolution of 4 Hz for the frequency spectra.

2.4 Background Noise During the wind tunnel operation, the machine has some level of inherent noise due to vibration. To attenuate this background noise to some level, the wind tunnel is

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equipped with semi-anechoic exhaust and corrugated tunnel wall materials. However, achieving 100% attenuation of the machine noise is very difficult. Hence, to account for this noise in the frequency spectra, the wind tunnel background noise was acquired at all tested velocities for reference. A test section wall without any cavity was installed to acquire this background noise with a microphone. The same data acquisition technique and post-processing used for the cavity oscillation studies were used for this noise acquisition. The frequency spectrum of background noise is plotted along the cavity flow-induced oscillation for reference to understand the effect of noise. It can be noticed that the energy level of the background noise is low compared to the cavity spectra.

3 Result and Discussion 3.1 Boundary Layer Measurement The incoming boundary layer plays a key role in determining the cavity acoustics, and hence, its characterization is important. Results from the boundary layer measurement show that the incoming Boundary Layer Profile (BLP) is turbulent for all cases. The boundary layer thickness measured based on 99% of freestream velocities were very close and estimated to be 15.5, 16.5, 16.5, 15 and 16.5 mm for 10, 15, 20, 25 and 30 m/s, respectively. In order to validate the BLP, the results in the current study have been compared with the turbulent BLP model presented in the literature. The boundary layer profile obtained for all five velocities – 10, 15, 20, 25 and 30 m/s are validated with the velocity defect chart method (VDCM) [15] of measuring the turbulent boundary layer profile using the frictional velocity aspect. Djenidi et al. [16] and Talluru et al. [17] have shown in their studies that if the mean velocity profile in the form of velocity defect (Uinf – U) normalized by frictional velocity was plotted against non-dimensional wall distance, then all the tested experimental data available in literature collapsed into a single curve (universal profile) independent of surface roughness and Reynold’s number. Djinedi et al. extracted a best-fit polynomial (Eq. 1) for this universal profile using the VDCM. They also proposed a technique to find the frictional velocity using the curve fitting of the experimental data on the polynomial graph. The polynomial profile has been used to compare the results of the current study with the universal profile. In Eq. (1), p1 – p6 and q1 – q5 are constants given by Djinedi et al. [15] for the polynomial. 

p1 x 5 + p2 x 4 + p3 x 3 + p4 x 2 + p5 x + p6  f (x) =  5 x + q1 x 4 + q2 x 3 + q3 x 2 + q4 x + q5

 (1)

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Fig. 5 Validation of boundary layer profiles using curve fitting on the universal profile

Figure 5 shows the curve fitting of the current experimental data on the universal profile by adjusting the frictional velocity (Uτ ) for the best fit. Frictional velocity is also known as shear velocity, and it depends on two factors – wall shear stress (τw ) and density (ρ) of the fluid as expressed in Eqs. (2) and (3). Uτ =

√ τw /ρ

(2)

dU dy

(3)

τw = μ

The wall shear stress increases with flow velocity, thus increasing frictional velocity as they are related by Eq. (2). Figure 5 shows that the BLP measured is in good agreement with the universal profile described by VDCM.

3.2 Cavity Flow Oscillations Results from the current experimental study show a variation in the Total Spreaded Acoustic Power (TSAP) and oscillation tones corresponding to the change in flow velocity and cavity dimension. TSAP is calculated using the area under the frequency spectrum curve and indicates the total energy present in the spectrum. The area under the curve is calculated by employing a numerical integration technique using MATLAB. Frequency spectra for studying TSAP and oscillation tones were acquired following the procedure explained in Sect. 1.3. The oscillation tones obtained from the experiments are compared with the analytical model presented by the Rossiter

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Fig. 6 Power spectral density plot showing the maximum oscillation amplitude at TE

for the prediction of the tonal frequency and were found to be in good agreement. This agreement validates that the oscillations observed in the cavity are flow-induced oscillations caused by Rossiter feedback. Frequency spectra obtained at the aft wall, front wall and cavity floor are observed closely to find the location experiencing the tones with maximum amplitude. These spectra show maximum acoustic powers at the microphone mounted near the trailing edge (TE) for all cavity configurations. For example, Fig. 6 shows the frequency spectra obtained by microphones placed at the trailing edge (TE), Cavity Floor (CF) and Leading Edge (LE), respectively, from C2 exposed to a free stream velocity of 25 m/s. As can be observed from the plot, the point of maximum energy is near the TE. Hence, in the following sections, all the observations will be made on the spectra obtained at the TE. Consequently, it follows that the location near the TE is ideal for placing the energy harvester. This study analyses the effect of flow velocity and cavity L/D ratio on the oscillation tones and associated power and attempts to find the best cavity configuration with maximum acoustic power for energy harvesting through oscillation tone. Results of the study show that with the increase in the free stream velocity, the TSAP of the frequency spectra increases sharply. Figure 7 shows the variation of the TSAP with the free stream velocity for C1, C2, C3, C4, and C6. Another observation that can be made from Fig. 7 is that as the cavity becomes shallower (L/D increases), the TSAP increases up to C5 and then decreases abruptly. This is most likely because, as L/D become greater than 5, the flow inside the cavity could be switching to transitional flow. This would mean that the shear layer in cavity flow behaves differently in C6. Tracy and Plentovich [13] showed in their study that as the cavity becomes shallower, the attachment point of the shear layer is altered, resulting in different frequency spectra and unsteady behavior. Fig. 7 Total spreaded acoustic power in the frequency spectra for all the cavity configurations

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Flow visualization techniques and static pressure measurement studies are required to confirm and investigate this further and are part of future work. Preliminary results of the cavity flow oscillation showed that the oscillation tone or high amplitude narrow bands (concentrated acoustic energy) are not available for every cavity configuration. The study presented in this paper shows that out of five cavities tested, only three cavities C2, C3 and C4 have oscillation tones for low-speed flow. Various researchers [8, 13, 14] have shown that the flow profile inside the cavities depends on multiple factors such as Mach number, cavity dimension, boundary layer profile and other ambient parameters. Different flow profiles lead to different interaction patterns of the shear layer and aft wall inside the cavity. This means that not all cavity L/D ratios exhibit cavity tones. Rossiter [8], from his study, gives an analytical model given by Eq. (4) to predict the cavity oscillation tones using the cavity dimension, flow parameter and some empirical constants. f =

U (m − γ )   L 1k + M

(4)

where f is the frequency, U is the free stream velocity, L is the cavity length, m is the mode number, M is the Mach number, k and γ are empirical constants. k and γ are found from the experimental data using the best fit. In the current study, these constants’ values were found to be 0.215 and 0.55, respectively, from best-fit. The frequency of oscillations found in the experiments was found to be in good agreement with Rossiter’s semi-empirical model, and the maximum variation between them was found to be 14.3%. Only the first mode of oscillation was found to be prominent in all the cases. Velocity Effect This section focuses on the effect of velocity on the oscillation tone generation in low-speed cavity flows. Tracy and Plentovich [13] showed in their study that flow profiles generated inside a cavity are of three types – open flow, closed flow, and transition flow. Because of the different flow profiles, these flow types result in different transient and mean environments inside the cavity. They also showed in their study that switching between the flow type occurs with a change in cavity dimension and flow velocity. Although their studies are based on high Mach number flows (transonic and supersonic), the current study shows the effect in low-speed flows shows a similar variation. Figure 7 shows that with the increase in the free stream velocity, TSAP increases for each cavity; however, the results also show that concentrated acoustic power or tonality does not follow the same trend. The frequency spectrum in Fig. 8 shows that cavities with a mean flow speed of 25 m/s have the maximum amplitude oscillation tones. Amplitudes of the cavity oscillation tones at different free stream velocities for C2, C3 and C4 are shown in Table 1. To harvest this concentrated acoustic energy effectively, piezo harvesters need to be tuned to the frequencies of these tones. So, future work would entail tuning the designed harvester to the cavity frequencies observed in Table 1.

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Fig. 8 Effect of flow velocity on cavity oscillation tones in different cavities: a).C1; b).C2; c).C3; d).C4; e).C5; and f). variation of maximum tonal amplitude with free stream velocity Table 1 Tonal frequency and associated magnitude Cavity

U∞ (m/s)

Tonal frequency (Hz)

Rossiter frequency (Hz)

Difference (%)

Amplitude (Pa2 / Hz)

C2

10

28

28

0

0.3

C2

15

44

42

4.8

1.68

C2

20

60

56

7.1

3.3

C2

25

76

69

10.1

5.2

C2

30

92

82

12.2

5.53

C3

10

28

28

0

0.67

C3

15

40

42

4.8

2.32

C3

20

56

56

0

4.8

C3

25

68

69

1.4

6.3

C3

30

88

82

7.3

5.3

C4

10

24

28

14.3

0.5

C4

15

40

42

4.8

2.121

C4

20

52

56

7.1

4.1

C4

25

68

69

1.4

5.4

C4

30

80

82

2.4

4.6

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The spectra for C1 and C6 do not exhibit cavity tones and mostly show broadband noise. The amplitudes are also relatively lesser than those for C2, C3 and C4. This implies that the flow profile inside the cavity is different for these two sets of cavities, and C1 and C6 are unsuitable for energy harvesting using resonant harvesters since they don’t possess tones in their spectra. Length-to-Depth Ratio Effect As discussed in the velocity effect section, Tracy and Plentovich’s [13] study shows that the cavity flow profile also depends on the cavity dimensions for high Mach numbers. This section will discuss its effect on low-speed flow from the context of energy. harvesting. The focus is to find the cavity configuration that possesses the maximum acoustic power in a narrow band or at some specific frequency so that the harvester can be tuned in that frequency to harvest energy effectively. Experimental results show that TSAP in the frequency spectra increases up to cavity L/D ratio four and then decreases abruptly. Figure 9 shows the effect of the cavity L/D ratio on the TSAP for all the test velocities. Tracy and Plentovich [13] have stated this phenomenon as the switching of flow type, as explained in the velocity effect section. Their study showed that when the depth of the cavity was decreased while keeping the free stream velocity constant, the flow profile changed inside the cavity. As the L/D was increased, the shear layer dipped into the cavity and attached and separated from the cavity floor. Such a profile creates two recirculation regions inside the cavity and decreases the tonality of the cavity flow oscillations. This section is focused on the effect of changing the depth while keeping all the other parameters constant. Results from the power spectrum plots for cavity configuration show that by keeping the velocity constant and changing the depth, C3 shows cavity tone with maximum amplitude, as seen in Fig. 8. From experimental results, it is observed that although C3 possesses the maximum amplitude, its power is spread in a wider band compared to C2, which has a sharper peak. This means that C2 has a higher Q-factor (Quality factor) when compared to C3. Comparison plots for all the cavities keeping the velocity constant are shown in Fig. 9.

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Fig. 9 Effect of cavity dimension on the oscillation tones for different flow speeds: a). U = 10 m/ s; b). U = 15 m/s; c). U = 20 m/s; d). U = 25 m/s; e). U = 30 m/s; and f). variation of TSAP with cavity dimension

4 Conclusion Cavities of five different length-to-depth ratios are tested for five different low-speed flows to analyze the effect of velocity and L/D ratio on the cavity flow oscillation. From the cavity flow experiments, L/D ratio and velocity were found to significantly affect the cavity unsteadiness and tonality of the flow oscillations. With an increase in velocity, the amplitudes of oscillations increased until 25 m/s and then dropped at 30 m/s for all cavities except the cavity with L/D = 2. The Total Spreaded Acoustic Power (TSAP) was found to increase with an increase in velocity for all cavities. The cavity flow unsteadiness showed significant changes with the L/D ratio. The cavity flow unsteadiness showed significant changes with the L/D ratio. Cavities with L/D = 1 and 6 were not shown to exhibit cavity tones and were found to have broadband spectra. Cavities with L/D = 2, 3 and 4 showed high amplitude tones which matched well with Rossiter’s semi-empirical model. Of the different configurations tested, the cavity with L/D = 3 at a velocity of 25 m/s, was determined to be the best configuration for energy harvesting. At this configuration, the peak amplitude was found to be 6.3 Pa2 /Hz, the highest among all. A further in-depth analysis for and a more resolved velocity and cavity geometry for optimal energy harvesting will be carried out in future studies.

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Acknowledgement This conference material was presented with the support of the University of Southampton Malaysia Conference Fund (UoSM/CONF2022/7), and the authors would like to express their gratitude to RMC, UoSM for their generous support.

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