报告详情
Active Distribution Network Scheduling Based on Safe Deep Reinforcement Learning
编号：2
访问权限：仅限参会人
更新：2025-07-30 20:15:11 浏览：192次
张贴报告
报告开始：2025年11月09日 09:00（Asia/Shanghai）
报告时间：1min
所在会场：[P] Poster presentation [P3] 3.Power system and automation
暂无文件
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
全部评论
重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月30日 2025
初稿截稿日期
-
11月10日 2025
注册截止日期
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
发表评论