An Optimal Scheduling Method for Power Grids in Extreme Scenarios Based on an Information-Fusion MADDPG Algorithm

Xun Dou, Cheng Li, Pengyi Niu, Dongmei Sun, Quanling Zhang () and Zhenlan Dou
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Xun Dou: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Cheng Li: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Pengyi Niu: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Dongmei Sun: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Quanling Zhang: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Zhenlan Dou: State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

Mathematics, 2025, vol. 13, issue 19, 1-26

Abstract: With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system.

Keywords: reinforcement learning; distribution network; power grid voltage security; experience fusion strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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