首页|Robust optimal energy management with dynamic price response: A non-cooperative multi-community aggregative game perspective

Robust optimal energy management with dynamic price response: A non-cooperative multi-community aggregative game perspective

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  • NETL
  • NSTL
  • Elsevier
Energy management system gradually becomes an effective means in smart grid for paving the way to low carbon economy. However, information privacy, a large population of users, and the uncertainties of renewable energy sources and consumers' behaviors have led to significant challenges for energy management system operation in terms of security and robustness. To conquer such difficulties, a bilevel energy management scheme for the day-ahead optimal scheduling of multi-community system combined with distributed energy sources is first proposed. Meanwhile, uncertainties induced by renewable energy sources generation and load consumption are handled through adjustable robust optimization. Secondly, a non-cooperative multi-community aggregative game is formulated to describe the interaction of numerous residential users which are coupled through the dynamic electricity price. Then, to seek the ε-Nash Equilibrium of the proposed game, an improved decentralized iterative algorithm based on Mean-Field control and consensus is presented which is benchmarked with centralized algorithm and a decentralized optimization method based on quadratic programming. Since each player in the proposed algorithm does not need to exchange information directly with other players, the information privacy is fully preserved. Also, the convergence of the proposed algorithm is provided. Case studies of a five-community system are conducted and the comparison results show that our proposed approach has better performance in terms of computational time and electricity cost.

Smart gridEnergy managementDemand responseAggregative gameRobust optimization

Dunfeng Zhang、Ruitian Han、Yanni Wan、Jiahu Qin、Lili Ran、Qichao Ma

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Department of Automation, University of Science and Technology of China, Hefei, 230027, China

School of Electronic and Electrical Engineering, Ningxia University, Yinchuan, 750021, China

Department of Automation, University of Science and Technology of China, Hefei, 230027, China, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China

2023

10.1016/j.ijepes.2023.109395

International journal of electrical power and energy systems

International journal of electrical power and energy systems

EI
ISSN：0142-0615
年,卷(期)：2023.154(Dec.)
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