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Active Distribution Network Scheduling Based on Safe Deep Reinforcement Learning
编号：2
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更新：2025-07-30 20:15:11 浏览：151次
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摘要
Against the backdrop of high proportion penetration of new energy, the difficulty of scheduling optimization for active distribution networks is gradually increasing. This paper proposes a safe deep reinforcement learning framework integrating a data-driven power flow model to achieve the scheduling optimization of new energy. To address the challenge of active voltage control, a safe deep reinforcement learning strategy combined with a data-driven power flow model is designed, which maps reactive power to voltage amplitude through the Q2V strategy. Simulation results on the modified IEEE 33-bus system show that the optimization effect of this framework is significantly improved compared with the traditional Q strategy and V strategy. It achieves 46.2% and 64.9% reduction in line loss respectively, while strictly controlling the node voltage deviation within the range of ±5%.
关键词
Active distribution network,power flow model,safe deep reinforcement learning,scheduling optimization
报告人
Shao Zhou
Student Central South University稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
09月10日 2025
初稿截稿日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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