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Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium

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  • Le Cadre, Hélène
  • Jacquot, Paulin
  • Wan, Cheng
  • Alasseur, Clémence

Abstract

We consider a network of prosumers involved in peer-to-peer energy exchanges, with differentiation price preferences on the trades with their neighbors, and we analyze two market designs: (i) a centralized market, used as a benchmark, where a global market operator optimizes the flows (trades) between the nodes, local demand and flexibility activation to maximize the system overall social welfare; (ii) a distributed peer-to-peer market design where prosumers in local energy communities optimize selfishly their trades, demand, and flexibility activation. We first characterize the solution of the peer-to-peer market as a Variational Equilibrium and prove that the set of Variational Equilibria coincides with the set of social welfare optimal solutions of market design (i). We give several results that help understanding the structure of the trades at an equilibrium or at the optimum. We characterize the impact of preferences on the network line congestion and renewable energy surplus under both designs. We provide a reduced example for which we give the set of all possible generalized equilibria, which enables to give an approximation of the price of anarchy. We provide a more realistic example which relies on the IEEE 14-bus network, for which we can simulate the trades under different preference prices. Our analysis shows in particular that the preferences have a large impact on the structure of the trades, but that one equilibrium (variational) is optimal. Finally, the learning mechanism needed to reach an equilibrium state in the peer-to-peer market design is discussed together with privacy issues.

Suggested Citation

  • Le Cadre, Hélène & Jacquot, Paulin & Wan, Cheng & Alasseur, Clémence, 2020. "Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium," European Journal of Operational Research, Elsevier, vol. 282(2), pages 753-771.
    • Handle: RePEc:eee:ejores:v:282:y:2020:i:2:p:753-771
    DOI: 10.1016/j.ejor.2019.09.035
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    9. Tsaousoglou, Georgios & Ellinas, Petros & Varvarigos, Emmanouel, 2023. "Operating peer-to-peer electricity markets under uncertainty via learning-based, distributed optimal control," Applied Energy, Elsevier, vol. 343(C).
    10. Spiliopoulos, Nikolas & Sarantakos, Ilias & Nikkhah, Saman & Gkizas, George & Giaouris, Damian & Taylor, Phil & Rajarathnam, Uma & Wade, Neal, 2022. "Peer-to-peer energy trading for improving economic and resilient operation of microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 517-535.
    11. Rahman Khorramfar & Osman Y. Özaltın & Karl G. Kempf & Reha Uzsoy, 2022. "Managing Product Transitions: A Bilevel Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2828-2844, September.
    12. Liu, Junhong & Long, Qinfei & Liu, Rong-Peng & Liu, Wenjie & Hou, Yunhe, 2023. "Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading," Applied Energy, Elsevier, vol. 331(C).
    13. João Mello & Cristina de Lorenzo & Fco. Alberto Campos & José Villar, 2023. "Pricing and Simulating Energy Transactions in Energy Communities," Energies, MDPI, vol. 16(4), pages 1-22, February.
    14. Wicak Ananduta & Sergio Grammatico, 2022. "Equilibrium Seeking and Optimal Selection Algorithms in Peer-to-Peer Energy Markets," Games, MDPI, vol. 13(5), pages 1-13, October.
    15. Schwidtal, J.M. & Piccini, P. & Troncia, M. & Chitchyan, R. & Montakhabi, M. & Francis, C. & Gorbatcheva, A. & Capper, T. & Mustafa, M.A. & Andoni, M. & Robu, V. & Bahloul, M. & Scott, I.J. & Mbavarir, 2023. "Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    16. Moret, Fabio & Pinson, Pierre & Papakonstantinou, Athanasios, 2020. "Heterogeneous risk preferences in community-based electricity markets," European Journal of Operational Research, Elsevier, vol. 287(1), pages 36-48.
    17. Xiang, Liu, 2022. "A large-scale equilibrium model of energy emergency production: Embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors," Energy, Elsevier, vol. 259(C).
    18. Tsaousoglou, Georgios & Giraldo, Juan S. & Paterakis, Nikolaos G., 2022. "Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    19. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    20. Bogdan-Constantin Neagu & Ovidiu Ivanov & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A New Vision on the Prosumers Energy Surplus Trading Considering Smart Peer-to-Peer Contracts," Mathematics, MDPI, vol. 8(2), pages 1-27, February.
    21. Roman Le Goff Latimier & H. Ben Ahmed, 2023. "Peer to peer electricity markets," Post-Print hal-04268639, HAL.

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