Capacity Market Trading Strategies of Generators Based on PER-MADDPG Algorithm

Authors

• Yanbin Li, School of Economics and Management, North China Electric Power University, Beijing 102206, China
• Zhaolun Pan, School of Economics and Management, North China Electric Power University, Beijing 102206, China
• Xinyue Ma, School of Economics and Management, North China Electric Power University, Beijing 102206, China
• Minghao Song, School of Economics and Management, North China Electric Power University, Beijing 102206, China
• Yujie Hu, School of Economics and Management, North China Electric Power University, Beijing 102206, China
• Xiaoda Xue, School of Economics and Management, North China Electric Power University, Beijing 102206, China

Abstract

Considering the issue of how power generators trade off their quantity and price bidding strategies to maximize profits in different capacity market environments, a capacity market bidding equilibrium model is constructed. Recognizing the limitations of traditional solution methods, which rely on the assumption of complete information and have low utilization of historical trading strategy information, a capacity market trading simulation method based on prioritized experience replay multi- agent deep deterministic policy gradient (PER-MADDPG) is proposed. The action space is constructed using quantity bidding strategy and price bidding strategy, and the state space is constructed using historical transaction strategies and winning bid information. Based on limited state information, each generator utilizes prioritized experience replay mechanism to allocate sampling probabilities according to the temporal difference error of the sample, ensuring that samples with larger errors are replayed more frequently during training.This effectively addresses the issue of amplified gradient noise caused by non- stationary interactions among multiple agents, thereby improving sample utilization efficiency and model convergence speed. Market simulation results indicate that the proposed method can help generators formulate optimal capacity market trading strategies under different market conditions to increase capacity revenue, and also can provide reference for capacity market builders in China to select capacity market clearing price mechanisms, thereby reducing grid capacity procurement costs. Compared to MADDPG, MAPPO, MASAC, MATD 3, and QMIX algorithms, the average rewards obtained by the proposed method for power generators increased by 2 853.08, 3 628.74, 2 167.11, 4 260.19, and 5 459.64 yuan, while the average algorithm runtime was reduced by 15.35%, 8.18%, 3.87%, 5.33%, and 31.03%, respectively.

Recommended Citation

Li, Yanbin; Pan, Zhaolun; Ma, Xinyue; Song, Minghao; Hu, Yujie; and Xue, Xiaoda (2026) "Capacity Market Trading Strategies of Generators Based on PER-MADDPG Algorithm," Journal of System Simulation: Vol. 38: Iss. 5, Article 18.
DOI: 10.16182/j.issn1004731x.joss.25-0456
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss5/18

First Page

1408

Last Page

1425

CLC

TM73; TP391

Recommended Citation

Li Yanbin, Pan Zhaolun, Ma Xinyue, et al. Capacity Market Trading Strategies of Generators Based on PER-MADDPG Algorithm[J]. Journal of System Simulation, 2026, 38(5): 1408-1425.

Corresponding Author

Pan Zhaolun

DOI

10.16182/j.issn1004731x.joss.25-0456