Modeling of Collusion Behavior in the Electrical Market Based on Deep Deterministic Policy Gradient

Yifeng Liu, Jingpin Chen, Meng Chen, Zhongshi He, Ye Guo () and Chenghan Li ()
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Yifeng Liu: Hubei Electric Power Co., Ltd., Power Exchange Center, Wuhan 430073, China
Jingpin Chen: Hubei Electric Power Co., Ltd., Power Exchange Center, Wuhan 430073, China
Meng Chen: Hubei Electric Power Co., Ltd., Power Exchange Center, Wuhan 430073, China
Zhongshi He: Hubei Electric Power Co., Ltd., Power Exchange Center, Wuhan 430073, China
Ye Guo: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Chenghan Li: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China

Energies, 2024, vol. 17, issue 22, 1-9

Abstract: The evolution of the electricity market has brought the issues of market equilibrium and collusion to the forefront of attention. This paper introduces the Deep Deterministic Policy Gradient (DDPG) algorithm on the IEEE three-bus electrical market model. Specifically, it simulates the behavior of market participants through reinforcement learning (DDPG), and Nash equilibrium and the collusive equilibrium of the power market are simulated by setting different reward functions. The results show that, compared with the Nash equilibrium, collusion equilibrium can increase the price of nodal marginal electricity and reduce total social welfare.

Keywords: electricity market; market equilibrium; deep reinforcement learning; collusion; bidding strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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