Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

Lefeng Cheng, Xin Wei, Manling Li, Can Tan, Meng Yin, Teng Shen () and Tao Zou ()
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Lefeng Cheng: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Xin Wei: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Manling Li: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Can Tan: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Meng Yin: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Teng Shen: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Tao Zou: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2024, vol. 12, issue 20, 1-56

Abstract: With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q -learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

Keywords: smart grids; user-side electricity market; evolutionary game theory; deep Q -learning network (DQN); multi-agent systems; strategy optimization; real-time decision making; supply–demand fluctuations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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