引用本文:唐昊,张庆虎,方道宏,朱虹,吴寅涛.需求响应下基于深度强化学习的综合能源系统能量管理策略[J].控制理论与应用,2026,43(1):205~215.[点击复制]
TANG Hao,ZHANG Qing-hu,FANG Dao-hong,ZHU Hong,WU Yin-tao.Energy management strategy of integrated energy system considering demand response by using deep reinforcement learning[J].Control Theory & Applications,2026,43(1):205~215.[点击复制]
需求响应下基于深度强化学习的综合能源系统能量管理策略
Energy management strategy of integrated energy system considering demand response by using deep reinforcement learning
摘要点击 215  全文点击 27  投稿时间：2023-04-23  修订日期：2025-07-31
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DOI编号  10.7641/CTA.2024.30263
  2026,43(1):205-215
中文关键词  综合能源系统  多能互补  需求响应  调度优化  深度强化学习
英文关键词  integrated energy systems  multiple complementation  demand response  optimized scheduling  deep rein forcement learning
基金项目  国家自然科学基金项目(62273130),安徽省自然科学基金项目(2108085UD01),安徽省电力设计院有限公司项目(W2021JSFW1132)资助.
作者单位E-mail
唐昊* 合肥工业大学电气与自动化工程学院 htang@hfut.edu.cn 
张庆虎 合肥工业大学电气与自动化工程学院  
方道宏 合肥工业大学电气与自动化工程学院  
朱虹 中国能源建设集团安徽省电力设计院有限公司  
吴寅涛 合肥工业大学电气与自动化工程学院  
中文摘要
      含光伏、储能及燃气轮机等分布式能源的综合能源系统(IES)具有多能协调、互补共济的能源利用形式,能 够在参与电网需求响应时发挥重要作用.针对IES如何有效响应电网调峰需求的问题,文中将多能耦合转化与内部 用户负荷响应作为IES的能量管理手段,提出了考虑多能互补与内部用户响应特性的IES日内调度优化方法.首先, 在IES多能耦合运行架构的基础上分析了内部用户的响应特性,分别通过补贴价格与负荷削减量来改变内部用户的 电负荷需求,进而,构建了光伏出力与负荷不确定下IES参与电网需求响应的能量管理策略优化模型;然后,运用基 于TD3的深度强化学习算法实现了IES能量管理策略的求解;最后,通过算例表明,所提能量管理策略优化模型与策 略优化方法能够合理制订系统内部的能量转换控制和需求响应方案以充分挖掘系统的响应潜力,从而,有效完成电 网的调峰需求响应目标.
英文摘要
      An integrated energy system (IES) that incorporates distributed energy resources such as photovoltaics, ener gy storage, and gas turbines has the potential to provide a multi-energy coordinated and complementary energy utilization form, which can play an important role in participating in grid demand response. To effectively respond to grid peak regulation demands, this paper proposes an optimization method for IES intraday scheduling considering multi-energy complementarity and internal user response as the energy management means of IES. Firstly, based on the multi-energy coupling operation architecture of IES, the response characteristics of internal users are analyzed. The electric load demand of internal users is changed by subsidy price and load reduction respectively, and then the energy management strategy optimization model of IES participating in power grid demand response under photovoltaic output and load uncertainty is constructed. Secondly, the deep reinforcement learning algorithm based on TD3 is used to solve the IES energy man agement strategy. Finally, the case study shows that the proposed energy management strategy optimization model and strategy optimization method can reasonably formulate the energy conversion control and demand response scheme within the system to fully tap the response potential of the system and effectively achieve the peak regulation demand response goal of the grid.