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Game and Strategy Analysis of Power Price Incentive Response Frequency Modulation in Load Aggregation Based on Reinforcement Learning
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Jing WU1, Wen-juan CHENG1, *, Xiao LIANG2, Zheng-feng WANG2, Hao TANG3
Science Technology and Engineering | 2025, 25(3) : 1087 - 1092
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Science Technology and Engineering | 2025, 25(3): 1087-1092
• Papers·Electrical Technology •
Game and Strategy Analysis of Power Price Incentive Response Frequency Modulation in Load Aggregation Based on Reinforcement Learning
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Jing WU1, Wen-juan CHENG1, *, Xiao LIANG2, Zheng-feng WANG2, Hao TANG3
Affiliations
  • 1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
  • 2. State Grid Anhui Electric Power Company, Hefei 230061, China
  • 3. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Published: 2025-01-28 doi: 10.12404/j.issn.1671-1815.2402574
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In order to solve the efficiency issues in distributed load responses to frequency regulation commands, an innovative strategy was introduced based on reinforcement learning for load aggregators’ pricing incentives in response to frequency commands. Within this strategy, a game-theoretic model between the load aggregators and load clusters was constructed, and the load aggregators adjust incentive prices based on frequency commands and their pricing strategies, while loads adjust their power consumption based on their own electricity costs to flexibly respond to the frequency commands. The multi-agent soft actor-critic (MASAC) algorithm was used to investigate the solution. The results show that the pricing incentive method enables effective load response to frequency commands, and the use of the MASAC algorithm not only optimizes the decision-making process but also significantly reduces computational complexity, achieving efficient dynamic adjustment. It is concluded that this method provides an effective solution for frequency regulation in power systems, offering significant theoretical significance and practical value.

load aggregator  /  game  /  secondary frequency regulation  /  reinforcement learning  /  price incentive
Jing WU, Wen-juan CHENG, Xiao LIANG, Zheng-feng WANG, Hao TANG. Game and Strategy Analysis of Power Price Incentive Response Frequency Modulation in Load Aggregation Based on Reinforcement Learning[J]. Science Technology and Engineering, 2025 , 25 (3) : 1087 -1092 . DOI: 10.12404/j.issn.1671-1815.2402574
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Year 2025 volume 25 Issue 3
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Article Info
doi: 10.12404/j.issn.1671-1815.2402574
  • Receive Date：2024-04-09
  • Online Date：2025-07-29
  • Published：2025-01-28
Article Data
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History
  • Received：2024-04-09
  • Revised：2024-07-18
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Affiliations
    1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
    2. State Grid Anhui Electric Power Company, Hefei 230061, China
    3. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
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