基于混合博弈强化学习的虚拟电厂市场交易策略

郑峰; 孙电; 黄丽丽; 杨峰; 倪芸

doi:10.11930/j.issn.1004-9649.202506047

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基于混合博弈强化学习的虚拟电厂市场交易策略

电力市场 | 更新时间：2026-03-17

- 基于混合博弈强化学习的虚拟电厂市场交易策略
- Virtual power plant market trading strategy based on hybrid game reinforcement learning
- 中国电力 2026年59卷第3期页码：94-102
- 作者机构：
  
  1. 国家能源集团长源电力股份有限公司,湖北,武汉,430000
  2. 国能长源能源销售有限公司,湖北,武汉,430000
- 作者简介：
- 基金信息：
  
  国家能源集团长源电力股份有限公司科技项目（CYDL-2024-14）。
- DOI：10.11930/j.issn.1004-9649.202506047
  中图分类号：
- 网络首发：2026-03-16，
  
  纸质出版：2026-03-16
- 稿件说明：
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郑峰, 孙电, 黄丽丽, 杨峰, 倪芸. 基于混合博弈强化学习的虚拟电厂市场交易策略[J]. 中国电力, 2026, 59(3): 94-102.

ZHENG Feng, SUN Dian, HUANG Lili, et al. Virtual power plant market trading strategy based on hybrid game reinforcement learning[J]. 2026, 59(3): 94-102.
郑峰, 孙电, 黄丽丽, 杨峰, 倪芸. 基于混合博弈强化学习的虚拟电厂市场交易策略[J]. 中国电力, 2026, 59(3): 94-102. DOI： 10.11930/j.issn.1004-9649.202506047.

ZHENG Feng, SUN Dian, HUANG Lili, et al. Virtual power plant market trading strategy based on hybrid game reinforcement learning[J]. 2026, 59(3): 94-102. DOI： 10.11930/j.issn.1004-9649.202506047.

摘要

随着地区分布式能源快速发展，其单机装机容量小和出力随机性强的问题愈发凸显，导致分布式能源在单独参与市场交易时竞争力不足。为提升其市场参与能力，整合分布式能源形成虚拟电厂（virtual power plant，VPP）已成为一种有效途径。因此，针对含分布式能源的VPP市场交易策略进行研究，提出一种基于混合博弈强化学习的交易策略。首先，根据虚拟电厂内部单元的运行特性构建能源供应商和负荷聚合商的收益模型；然后，为了保证虚拟电厂内部运营商的整体收益建立社会福利最大化模型；最后，基于Stackelberg博弈和演化博弈的混合博弈强化学习算法求解该交易模型。算例分析表明，基于混合博弈强化学习算法的双层模型求解效果优于其他传统智能算法，求解时间减小近50%；此外，VPP同时参与能量市场和辅助服务市场时，可获得更高的收益。

Abstract

With the rapid development of regional distributed energy

the issues of small installed capacity and strong output variability have become increasingly prominent

resulting in insufficient competitiveness when distributed energy participates in market transactions independently. To enhance its market participation capabilities

integrating distributed energy resources into virtual power plant has emerged as an effective approach. Therefore

this study investigates market trading strategies for virtual power plant incorporating distributed energy resources and proposes a trading strategy based on hybrid game-based reinforcement learning. First

establish revenue models for energy suppliers and load aggregators based on the operational characteristics of internal units within the virtual power plant. Then

to ensure the overall profitability of operators within the virtual power plant

a social welfare maximization model is established. Finally

the transaction model is solved using a hybrid game-based reinforcement learning algorithm combining Stackelberg and evolutionary game theory. Case studies demonstrate that the two-layer model based on hybrid game-theoretic reinforcement learning algorithms outperforms traditional intelligent algorithms

reducing computation time by nearly 50%. Furthermore

when virtual power plants participate in both energy markets and ancillary service markets

they can achieve higher returns.

关键词

Keywords

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