期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 282, 期 2, 页码 753-771出版社
ELSEVIER
DOI: 10.1016/j.ejor.2019.09.035
关键词
OR in energy; Peer-to-peer energy trading; Preferences; Variational Equilibrium; Generalized Nash Equilibrium
资金
- ANR project PACMAN [ANR-16-CE05-0027]
- Agence Nationale de la Recherche (ANR) [ANR-16-CE05-0027] Funding Source: Agence Nationale de la Recherche (ANR)
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. (C) 2019 Elsevier B.V. All rights reserved.
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