Robust game-theoretic optimization for energy management in community-based energy system

• Published in: Electric power systems research 2023-01, Vol.214, p.108939, Article 108939
• Main Authors: Liu, Xiaofeng, Ji, Zhenya, Sun, Wangqing, He, Qinman
• Format: Article
• Language: English
• Subjects: Community-based energy system; Demand response; Robust game-theoretic optimization; Uncertainty
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2022.108939

•A system scheme is proposed to optimize the ESS capacity and energy consumption for multi-community in an individual-oriented way considering the uncertainty of distributed generation.•A robust non-cooperative game approach is formulated for the proposed system scheme, oriented from the pursuit of the economical operation facing with the uncertainty.•To solve the optimization problem, the distributed algorithm is proposed by combining PSO and C&CG algorithm, which can converge to the robust Nash equilibrium. It is significant to schedule energy consumption in community-based energy system consisting of distributed generation, energy storage, multi-community. In this paper, a robust game-theoretic approach is proposed to optimize the storage capacity and energy consumption considering the uncertainty of distributed generation. Energy management with energy storage optimization is studied by considering the cost of distributed generations, cost of energy storage, and bidirectional energy trading. In order to search the robust solution for Nash equilibrium and optimal storage capacity, a distributed algorithm combining column and constraint generation and particle swarm optimization is designed. Simulation results show that the proposed approach has a well performance in reducing operation cost of energy system and tackling the uncertainty of distributed generation.