Deep reinforcement learning-driven bidding strategy for wind-storage systems in energy and frequency regulation markets

LI Zhongping

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Deep reinforcement learning-driven bidding strategy for wind-storage systems in energy and frequency regulation markets

更新时间：2025-10-16

- Deep reinforcement learning-driven bidding strategy for wind-storage systems in energy and frequency regulation markets
- Vol. 44, Issue 3, Pages: 30-42(2025)
- 作者机构：
  
  四川大学电气工程学院,四川,成都,610065
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM732
- Published：2025
- 稿件说明：
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LI Zhongping. Deep reinforcement learning-driven bidding strategy for wind-storage systems in energy and frequency regulation markets[J]. 2025, 44(3): 30-42.
DOI：

LI Zhongping. Deep reinforcement learning-driven bidding strategy for wind-storage systems in energy and frequency regulation markets[J]. 2025, 44(3): 30-42. DOI：

摘要

在电力市场环境下，风储系统通过参与能量市场和调频市场实现经济性提升和电网调频调峰辅助功能，但竞价策略需要解决风储竞价能量-调频双市场协同优化等关键问题。为此，文中提出一种基于深度强化学习驱动的风储系统参与能量-调频市场竞价策略，以应对不完全信息市场环境下的风储系统竞价策略。首先，构建风储系统参与能量-调频市场交易框架，阐明各市场主体的竞价与运营策略；然后，针对不同调频资源的响应能力差异，引入实时调频性能得分模型，并建立风储系统竞价模型；最后，为求解不完全信息市场环境下的多主体随机博弈问题，采用具备无模型学习能力的多智能深度强化学习方法，处理多主体竞价博弈关系。仿真结果表明，文中所提方法能够有效为风储系统参与能量-调频市场制定竞价策略，在保证高收敛稳定性的同时显著提升经济性收益，并有效支持电网的调频调峰需求。

Abstract

In the power market environment

participation of wind-storage system in both the energy market and the frequency regulation market is essential to enhance economic efficiency and support grid frequency regulation and peak shaving. However

key issues such as formulating bidding strategies for wind-storage systems in energy-frequency regulation dual markets need to be addressed. A bidding model driven by deep reinforcement learning is proposed in this paper to formulate bidding strategies in an incomplete information market environment. Firstly

a framework for wind-storage systems participating in the energy and frequency regulation markets is established to clarify the bidding operation strategies of each market entity. Then

a real-time frequency regulation performance scoring model is introduced to address the differences in frequency regulation response capabilities among various resources. Based on this

a bidding model for wind-storage systems is developed. Finally

a multi-agent deep reinforcement learning method with strong model-free learning capabilities is employed to solve the stochastic game problem in an incomplete information market environment and to handle the multi-agent bidding game relationship. Simulation results indicate that the proposed method can effectively formulate bidding strategies for wind-storage systems participating in the energy and frequency regulation markets. The method achieves high returns while ensuring high convergence stability. As a result

the economic efficiency of wind-storage systems is enhanced

and grid frequency regulation and peak shaving are effectively supported.

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