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Financial Bilateral Contract Negotiation in Wholesale Electricity Markets Using Nash Bargaining Theory

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  • Yu, Nanpeng
  • Tesfatsion, Leigh
  • Liu, Chen-Ching

Abstract

Bilateral contracts are important risk-hedging instruments constituting a major component in the portfolios held by many electric power market participants. However, bilateral contract negotiation is a complicated process as it involves risk management, strategic bargaining, and multi-market participation. This study analyzes a financial bilateral contract negotiation process between a generation company and a load-serving entity in a wholesale electric power market with congestion managed by locational marginal pricing. Nash bargaining theory is used to model a Pareto-efficient settlement point. The model predicts negotiation outcomes under various conditions and identifies circumstances in which the two parties might fail to reach an agreement. Both analysis and simulation are used to gain insight regarding how these negotiation outcomes systematically vary in response to changes in the participants' risk preferences and price biases.

Suggested Citation

  • 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.
    • Handle: RePEc:isu:genstf:201201010800001470
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    References listed on IDEAS

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    1. Junjie Sun & Leigh Tesfatsion, 2007. "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 291-327, October.
    2. Deng, S.J. & Oren, S.S., 2006. "Electricity derivatives and risk management," Energy, Elsevier, vol. 31(6), pages 940-953.
    3. Nash, John, 1950. "The Bargaining Problem," Econometrica, Econometric Society, vol. 18(2), pages 155-162, April.
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    1. Kandpal, Bakul & Backe, Stian & Crespo del Granado, Pedro, 2024. "Enhancing bargaining power for energy communities in renewable power purchase agreements using Gaussian learning and fixed price bargaining," Energy, Elsevier, vol. 309(C).
    2. Jian Zhang & Yanan Zheng & Mingtao Yao & Huiji Wang & Zhaoguang Hu, 2019. "An Agent-Based Two-Stage Trading Model for Direct Electricity Procurement of Large Consumers," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    3. 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.
    4. Shen, Jian-jian & Cheng, Chun-tian & Jia, Ze-bin & Zhang, Yang & Lv, Quan & Cai, Hua-xiang & Wang, Bang-can & Xie, Meng-fei, 2022. "Impacts, challenges and suggestions of the electricity market for hydro-dominated power systems in China," Renewable Energy, Elsevier, vol. 187(C), pages 743-759.
    5. 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.
    6. Marí, L. & Nabona, N. & Pagès-Bernaus, A., 2017. "Medium-term power planning in electricity markets with pool and bilateral contracts," European Journal of Operational Research, Elsevier, vol. 260(2), pages 432-443.
    7. Liu, Songsong & Papageorgiou, Lazaros G., 2018. "Fair profit distribution in multi-echelon supply chains via transfer prices," Omega, Elsevier, vol. 80(C), pages 77-94.
    8. Fotouhi Ghazvini, Mohammad Ali & Faria, Pedro & Ramos, Sergio & Morais, Hugo & Vale, Zita, 2015. "Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market," Energy, Elsevier, vol. 82(C), pages 786-799.
    9. Edward J. Anderson & Andrew B. Philpott, 2019. "Forward Commodity Trading with Private Information," Operations Research, INFORMS, vol. 67(1), pages 58-71, January.
    10. Xiao, Yunpeng & Wang, Xifan & Wang, Xiuli & Wu, Zechen, 2016. "Trading wind power with barrier option," Applied Energy, Elsevier, vol. 182(C), pages 232-242.
    11. 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.
    12. Kandpal, Bakul & Backe, Stian & Crespo del Granado, Pedro, 2024. "Power purchase agreements for plus energy neighbourhoods: Financial risk mitigation through predictive modelling and bargaining theory," Applied Energy, Elsevier, vol. 358(C).
    13. Nurcan Yarar & Yeliz Yoldas & Serkan Bahceci & Ahmet Onen & Jaesung Jung, 2024. "A Comprehensive Review Based on the Game Theory with Energy Management and Trading," Energies, MDPI, vol. 17(15), pages 1-29, July.
    14. Kharrati, Saeed & Kazemi, Mostafa & Ehsan, Mehdi, 2016. "Equilibria in the competitive retail electricity market considering uncertainty and risk management," Energy, Elsevier, vol. 106(C), pages 315-328.
    15. Kai Ma & Congshan Wang & Jie Yang & Qiuxia Yang & Yazhou Yuan, 2017. "Economic Dispatch with Demand Response in Smart Grid: Bargaining Model and Solutions," Energies, MDPI, vol. 10(8), pages 1-17, August.

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