| 引用本文: | 李成翔,龚楷程,朱益华,等.面向频率稳定的基于预生成经验池驱动的紧急切负荷智能在线决策[J].电力系统保护与控制,2026,54(03):132-143.[点击复制] |
| LI Chengxiang,GONG Kaicheng,ZHU Yihua,et al.Frequency stability-oriented intelligent online decision-making for emergency load shedding based on a pre-generated experience pool[J].Power System Protection and Control,2026,54(03):132-143[点击复制] |
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| 面向频率稳定的基于预生成经验池驱动的紧急切负荷智能在线决策 |
| 李成翔1,2,3,龚楷程4,朱益华1,2,3,梁卓航1,2,3,兰宇田4,韦善阳4,姚伟4 |
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| (1.直流输电技术全国重点实验室(南方电网科学研究院有限责任公司)，广东 广州 510663;2.国家能源
大电网技术研发(实验)中心，广东 广州 510663;3.广东省新能源电力系统智能运行与控制重点实验室，
广东 广州 510663;4.强电磁技术全国重点实验室(华中科技大学电气与电子工程学院), 湖北 武汉 430074) |
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| 摘要: |
| 随着电网规模的扩展和新能源渗透率的提高，电力系统频率稳定问题已成为当前研究的重点。紧急切负荷是应对电力系统频率失稳的有效控制手段，但传统紧急切负荷控制采用离线整定-在线匹配模式已难以适应复杂多变的故障场景。提出一种基于深度强化学习的紧急切负荷在线决策方法。首先，提出基于马尔可夫决策过程(Markov decision process, MDP)的紧急切负荷决策建模方法，同时采用分支竞争Q网络(branch dueling Q-network, BDQ)应对高维切负荷决策空间。其次，针对传统强化学习训练中时间与计算成本高昂的问题，通过解耦样本采集与模型训练环节，采用预生成经验池驱动的集中训练策略，实现智能体的高效训练。最后，基于10机39节点系统的算例验证表明，所提算法在决策有效性上较传统强化学习方法提升4.94%，训练所需时间仅为传统方法的8.82%。 |
| 关键词: 紧急切负荷 深度强化学习 频率安全 在线决策 |
| DOI：10.19783/j.cnki.pspc.250435 |
| 投稿时间：2025-04-22修订日期：2025-07-29 |
| 基金项目:国家自然科学基金项目资助(U22B20111)；南方电网科学研究院科技项目资助(SEPRI-K233009) |
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| Frequency stability-oriented intelligent online decision-making for emergency load shedding based on a pre-generated experience pool |
| LI Chengxiang1,2,3,GONG Kaicheng4,ZHU Yihua1,2,3,LIANG Zhuohang1,2,3,LAN Yutian4,WEI Shanyang4,YAO Wei4 |
| (1. State Key Laboratory of HVDC, Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China;
2. National Energy Power Grid Technology R&D Centre, Guangzhou 510663, China; 3. Guangdong Provincial Key Laboratory
of Intelligent Operation and Control for New Energy Power System, Guangzhou 510663, China; 4. State Key Laboratory
of Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China) |
| Abstract: |
| With the expansion of power grids and the increasing penetration of renewable energy, frequency stability in power systems has become a key research focus. Emergency load shedding is an effective control strategy to mitigate frequency instability; however, the traditional offline-setting and online-matching approach is increasingly inadequate for complex and evolving fault scenarios. To address this issue, a deep reinforcement learning-based method is proposed to achieve online decision-making for emergency load shedding. First, a decision modeling approach based on Markov decision process (MDP) is developed, and a branch dueling Q-network (BDQ) is introduced to handle the high-dimensional load shedding decision space. Furthermore, to overcome the high computational and time costs in traditional reinforcement learning training, a centralized training strategy driven by a pre-generated experience pool is adopted by decoupling sample collection from model training, thereby enabling efficient agent training. Finally, simulation results on a 10-machine 39-bus system demonstrate that the proposed algorithm improves decision effectiveness by 4.94% compared to traditional reinforcement learning methods, while reducing training time to only 8.82% of that of conventional approaches. |
| Key words: emergency load shedding deep reinforcement learning frequency security online decision-making |
