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Active Distribution Network Scheduling Based on Safe Deep Reinforcement Learning
ID：2 View Protection：ATTENDEE Updated Time：2025-07-30 20:15:11 Hits：483 Poster Presentation

Start Time：2025-11-09 09:00（Asia/Shanghai）

Duration：1min

Session：P Poster presentation » P33.Power system and automation

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Abstract
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%.
Keywords
Active distribution network,power flow model,safe deep reinforcement learning,scheduling optimization
Speaker
Shao Zhou
Student Central South University

Submission Author
Shao Zhou Central South University
Dongran Song Central South University
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
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
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