一种新的数据驱动的多剂增强学习方法,用于在弱电网支持下控制电压
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了一种多剂深度强化学习 (MADRL) 方法,用于弱光伏 (PV) 电网中的活性电压控制. 这种方法确保了电压合规性,并减少了网络损失,优于传统方法.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力系统 电力系统
背景情况:
- 在高光伏集成的配电网络中,弱电压支持带来了挑战.
- 活性电压控制对于电网的稳定性和效率至关重要.
研究的目的:
- 为光伏集群开发基于多代理深度强化学习 (MADRL) 的协调控制.
- 为了提高弱电网的电压合规性和能源效率.
主要方法:
- 制定了电压控制作为一个分散的部分可观察的马尔科夫决策过程 (Dec-POMDP).
- 采用了集中式培训与分散执行 (CTDE) 框架.
- 设计的屏障功能用于奖励塑造,以平衡电压和效率.
主要成果:
- 该MADRL框架确保了电压合规性,并减少了网络损失.
- MADDPG算法实现了91.9%的可控制率 (CR),功耗较低 (0.0695 p.u.). ) 的情况.
- 与最佳功率流 (OPF) 和垂落控制相比,证明了卓越的性能.
结论:
- 拟议的MADRL方法有效地提高了电压稳定性和能源效率.
- 该方法在无模型和通信受限制的弱电网条件下是稳健的.
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