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

doi:10.16182/j.issn1004731x.joss.25-0456

Abstract

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, MATD3, 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.

Key words: capacity market, generator, bidding equilibrium model, PER-MADDPG algorithm, trading strategy

CLC Number:

TM73

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

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名称	装机容量/MW	单位容量成本/(万元/MW)
G1	1 000	2.8
G2	1 000	3.4
G3	600	3.3
G4	600	3.5
G5	600	3.6
G6	600	4.1
G7	300	3.2
G8	300	3.3
G9	300	3.4
G10	300	3.7
G11	300	4.2
G12	100	4.0

参数	数值
智能体数量	12
Actor网络层数	4
Critic网络层数	4
经验池大小	10 000
迭代次数	5 000
学习率	0.000 1
奖励折扣因子	0.90
每次采样经验样本数	256
ε-贪婪策略探索率	0.15
目标网络软更新参数	0.01

场景编号	算法总耗时	每轮训练平均耗时
Ⅰ	2 828.09	0.56
Ⅱ	2 552.61	0.51
Ⅲ	2 916.69	0.58
Ⅳ	3 449.42	0.68
Ⅴ	3 814.01	0.76
Ⅵ	3 625.13	0.72

场景	场景Ⅰ		场景Ⅱ		场景Ⅲ		场景Ⅳ		场景Ⅴ		场景Ⅵ
发电商	中标容量/ MW	出清价格/ (万元/MW)	中标容量/ MW	出清价格/ (万元/MW)	中标容量/ MW	出清价格/ (万元/MW)	中标容量/ MW	出清价格/ (万元/MW)	中标容量/ MW	出清价格/ (万元/MW)	中标容量/ MW	出清价格/ (万元/MW)
G1	993	3.34	972	3.85	1 000	4.93	993	3.63	997	3.62	984	3.62
G2	986	3.34	953	3.85	1 000	4.93	986	3.86	1 000	3.86	982	3.86
G3	600	3.34	592	3.85	600	4.93	583	3.83	596	3.83	600	3.85
G4	600	3.34	600	3.85	600	4.93	390	3.9	598	3.9	596	4.6
G5	600	3.34	591	3.85	581	4.93	583.63	3.87	540	3.95	600	3.94
G6	89	3.34	600	3.85	599	4.93	475.55	3.59	600	4.17	600	4.14
G7	299	3.34	300	3.85	300	4.93	300	3.78	293.97	4.17	300	3.78
G8	297	3.34	292	3.85	300	4.93	300	3.83	294.64	4.05	297	4.47
G9	298	3.34	226	3.85	298	4.93	232	3.88	295	3.86	298	4.54
G10	298	3.34	292	3.85	297	4.93	294.64	3.67	293	3.99	297	4.72
G11	0	3.34	295	3.85	295	4.93	0	0	294.64	4.11	297	4.18
G12	100	3.34	100	3.85	97	4.93	0	0	0	0	99	4.84