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| Emergency load shedding decision-making using a branching dueling Q-network integrating grid topology information |
| DOI：10.19783/j.cnki.pspc.240501 |
| Key Words:simulation analysis transient voltage instability emergency load shedding decision-making deep reinforcement learning branching dueling Q-network power grid topology information graph convolution enhancement |
| Author Name | Affiliation | | PAN Xiaojie1 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China | | HU Ze2 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China | | YAO Wei2 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China | | LAN Yutian2 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China | | XU Youping1 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China | | WANG Yukun1 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China | | ZHANG Mujie1 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China | | WEN Jinyu2 | 1. Central China Branch of State Grid Corporation of China, Wuhan 430070, China
2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (School of Electrical and Electronic Engineering,
Huazhong University of Science and Technology), Wuhan 430074, China |
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| Abstract:The formulation of emergency control measures for transient voltage instability events is a crucial aspect of power system simulation analysis. Traditionally, emergency load shedding decisions are pre-determined offline and matched for execution in real-time. However, this process heavily relies on expert analysis of massive amounts of simulation data, which is both time-consuming and labor-intensive. To improve the efficiency of offline emergency load shedding decision-making, this paper presents a method for power system emergency load shedding decisions that integrates power grid topology information into a branching dueling Q-network (BDN) agent. First, an event-driven Markov decision process (MDP) is established to effectively guide the training of the deep reinforcement learning agents. Second, a BDN agent is designed, which exhibits superior training efficiency and decision-making capability compared to traditional non-branching networks. Then, to further enhance the agent’s training efficiency and decision-making performance, power grid topology information is integrated into the agent’s training process through graph convolutional networks (GCN). Finally, the proposed method is validated on the 8-machine 36-node system of the China Electric Power Research Institute. Compared to non-branching networks and deep reinforcement learning agents without integrated topology information, the proposed method demonstrates higher training efficiency and better decision-making performance. |
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