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Applications of Game Theory to Design and Operation of Modern Power Systems: A Comprehensive Review

Author

Listed:
  • Aviad Navon

    (The Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

  • Gefen Ben Yosef

    (The Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

  • Ram Machlev

    (The Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

  • Shmuel Shapira

    (The Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

  • Nilanjan Roy Chowdhury

    (The Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

  • Juri Belikov

    (Department of Software Science, Tallinn University of Technology, Akadeemia tee 15a, 12618 Tallinn, Estonia)

  • Ariel Orda

    (The Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

  • Yoash Levron

    (The Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

Abstract

In this work, we review papers that employ game theoretic tools to study the operation and design of modern electric grids. We consider four topics in this context: energy trading, energy balancing, grid planning, and system reliability, and we demonstrate the advantages of using game-theoretic approaches for analyzing complex interactions among independent players. The results and conclusions provide insights regarding many aspects of design and operation, such as efficient methodologies for expansion planning, cyber-security, and frequency stability, or fair-benefit allocation among players. A central conclusion is that modeling the system from the perspective of one entity with unlimited information and control span is often impractical, so correct modeling of the selfish behavior of independent players may be critical for the development of future power systems. Another conclusion is that correct usage of incentives by appropriate regulation or sophisticated pricing mechanisms may improve the social welfare, and, in several cases, the results obtained are as good as those obtained by central planning. Using an extensive content analysis, we point to several trends in the current research and attempt to identify the research directions that are currently at the focus of the community.

Suggested Citation

  • Aviad Navon & Gefen Ben Yosef & Ram Machlev & Shmuel Shapira & Nilanjan Roy Chowdhury & Juri Belikov & Ariel Orda & Yoash Levron, 2020. "Applications of Game Theory to Design and Operation of Modern Power Systems: A Comprehensive Review," Energies, MDPI, vol. 13(15), pages 1-35, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3982-:d:393495
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    References listed on IDEAS

    as
    1. Limpens, Gauthier & Jeanmart, Hervé, 2018. "Electricity storage needs for the energy transition: An EROI based analysis illustrated by the case of Belgium," Energy, Elsevier, vol. 152(C), pages 960-973.
    2. Zhou, Qun & Tesfatsion, Leigh & Liu, Chen-Ching & Chu, Ron F. & Sun, Wei, 2013. "A Nash Approach to Planning Merchant Transmission for Renewable Resource Integration," Staff General Research Papers Archive 36496, Iowa State University, Department of Economics.
    3. Srinivasan, Dipti & Rajgarhia, Sanjana & Radhakrishnan, Bharat Menon & Sharma, Anurag & Khincha, H.P., 2017. "Game-Theory based dynamic pricing strategies for demand side management in smart grids," Energy, Elsevier, vol. 126(C), pages 132-143.
    4. Yu, Nanpeng & Tesfatsion, Leigh & Liu, Chen-Ching, 2012. "Financial Bilateral Contract Negotiation in Wholesale Electricity Markets Using Nash Bargaining Theory," ISU General Staff Papers 201201010800001470, Iowa State University, Department of Economics.
    5. Huang, Yu Wen & Kittner, Noah & Kammen, Daniel M., 2019. "ASEAN grid flexibility: Preparedness for grid integration of renewable energy," Energy Policy, Elsevier, vol. 128(C), pages 711-726.
    6. João Soares & Tiago Pinto & Fernando Lezama & Hugo Morais, 2018. "Survey on Complex Optimization and Simulation for the New Power Systems Paradigm," Complexity, Hindawi, vol. 2018, pages 1-32, August.
    7. Zhang, Xian & Chan, K.W. & Wang, Huaizhi & Hu, Jiefeng & Zhou, Bin & Zhang, Yan & Qiu, Jing, 2019. "Game-theoretic planning for integrated energy system with independent participants considering ancillary services of power-to-gas stations," Energy, Elsevier, vol. 176(C), pages 249-264.
    8. Han, Xiaojuan & Ji, Tianming & Zhao, Zekun & Zhang, Hao, 2015. "Economic evaluation of batteries planning in energy storage power stations for load shifting," Renewable Energy, Elsevier, vol. 78(C), pages 643-647.
    9. Kristiansen, Martin & Korpås, Magnus & Svendsen, Harald G., 2018. "A generic framework for power system flexibility analysis using cooperative game theory," Applied Energy, Elsevier, vol. 212(C), pages 223-232.
    10. Tengfei Ma & Junyong Wu & Liangliang Hao & Huaguang Yan & Dezhi Li, 2018. "A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach," Energies, MDPI, vol. 11(10), pages 1-19, October.
    11. Klessmann, Corinna & Held, Anne & Rathmann, Max & Ragwitz, Mario, 2011. "Status and perspectives of renewable energy policy and deployment in the European Union—What is needed to reach the 2020 targets?," Energy Policy, Elsevier, vol. 39(12), pages 7637-7657.
    12. Nian Wang & Jing Zhang & Yu He & Min Liu & Ying Zhang & Chaokuan Chen & Yerui Gu & Yongheng Ren, 2020. "Load-Frequency Control of Multi-Area Power System Based on the Improved Weighted Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 13(2), pages 1-17, January.
    13. Su, Wencong & Huang, Alex Q., 2014. "A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers," Applied Energy, Elsevier, vol. 119(C), pages 341-350.
    14. Somi Jung & Dongwoo Kim, 2017. "Pareto-Efficient Capacity Planning for Residential Photovoltaic Generation and Energy Storage with Demand-Side Load Management," Energies, MDPI, vol. 10(4), pages 1-20, March.
    15. Arnob Ghosh & Vaneet Aggarwal, 2020. "Penalty Based Control Mechanism for Strategic Prosumers in a Distribution Network," Energies, MDPI, vol. 13(2), pages 1-14, January.
    16. Lo Prete, Chiara & Hobbs, Benjamin F., 2016. "A cooperative game theoretic analysis of incentives for microgrids in regulated electricity markets," Applied Energy, Elsevier, vol. 169(C), pages 524-541.
    17. Esteban, Miguel & Zhang, Qi & Utama, Agya, 2012. "Estimation of the energy storage requirement of a future 100% renewable energy system in Japan," Energy Policy, Elsevier, vol. 47(C), pages 22-31.
    18. Banez-Chicharro, Fernando & Olmos, Luis & Ramos, Andres & Latorre, Jesus M., 2017. "Beneficiaries of transmission expansion projects of an expansion plan: An Aumann-Shapley approach," Applied Energy, Elsevier, vol. 195(C), pages 382-401.
    19. Arezou Gholizad & Loza Ahmadi & Erfan Hassannayebi & Mehrdad Memarpour & Masoud Shakibayifar, 2017. "A System Dynamics Model for the Analysis of the Deregulation in Electricity Market," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 6(2), pages 1-30, April.
    20. Bohao Sun & Yong Tang & Lin Ye & Chaoyu Chen & Cihang Zhang & Wuzhi Zhong, 2018. "A Frequency Control Strategy Considering Large Scale Wind Power Cluster Integration Based on Distributed Model Predictive Control," Energies, MDPI, vol. 11(6), pages 1-19, June.
    21. Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
    22. Wang, Jianhui & Zhou, Zhi & Botterud, Audun, 2011. "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand," Energy, Elsevier, vol. 36(5), pages 3459-3467.
    23. Leach, Gerald, 1992. "The energy transition," Energy Policy, Elsevier, vol. 20(2), pages 116-123, February.
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    Cited by:

    1. Pfeifer, Antun & Feijoo, Felipe & Duić, Neven, 2023. "Fast energy transition as a best strategy for all? The nash equilibrium of long-term energy planning strategies in coupled power markets," Energy, Elsevier, vol. 284(C).
    2. Yuly V. Garcia & Oscar Garzon & Carlos J. Delgado & Jan L. Diaz & Cesar A. Vega Penagos & Fabio Andrade & Adriana C. Luna & J. C. Hernandez, 2023. "Overview on Transactive Energy—Advantages and Challenges for Weak Power Grids," Energies, MDPI, vol. 16(12), pages 1-19, June.
    3. Marcel Clemens & Torsten Clemens, 2022. "Scenarios to Decarbonize Austria’s Energy Consumption and the Role of Underground Hydrogen Storage," Energies, MDPI, vol. 15(10), pages 1-23, May.
    4. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    5. Filipe Bandeiras & Álvaro Gomes & Mário Gomes & Paulo Coelho, 2023. "Application and Challenges of Coalitional Game Theory in Power Systems for Sustainable Energy Trading Communities," Energies, MDPI, vol. 16(24), pages 1-42, December.
    6. Noppada Teera-achariyakul & Dulpichet Rerkpreedapong, 2022. "Optimal Preventive Maintenance Planning for Electric Power Distribution Systems Using Failure Rates and Game Theory," Energies, MDPI, vol. 15(14), pages 1-19, July.
    7. Gustavo Chica-Pedraza & Eduardo Mojica-Nava & Ernesto Cadena-Muñoz, 2021. "Boltzmann Distributed Replicator Dynamics: Population Games in a Microgrid Context," Games, MDPI, vol. 12(1), pages 1-18, January.

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