论文标题
在电压分配网络中，共识多代理增强学习用于伏特的控制
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks
论文作者
论文摘要
VAR-VAR控制（VVC）是主动分配网络管理系统中的关键应用，可减少网络损耗并改善电压曲线。为了消除对不准确和不完整网络模型的依赖性，并提高了针对通信或控制器故障的弹性，我们提出了共识多代理的深入强化学习算法来解决VVC问题。 VVC问题被提出为网络多代理马尔可夫决策过程，该过程使用最大的熵增强学习框架和新型的沟通效率共识策略来解决。所提出的算法允许各个代理使用本地奖励学习组控制策略。对IEEE分配测试馈线的数值研究表明，我们提出的算法与单格强化学习基准的性能相匹配。另外，提出的算法被证明是有效且有弹性的。
Volt-VAR control (VVC) is a critical application in active distribution network management system to reduce network losses and improve voltage profile. To remove dependency on inaccurate and incomplete network models and enhance resiliency against communication or controller failure, we propose consensus multi-agent deep reinforcement learning algorithm to solve the VVC problem. The VVC problem is formulated as a networked multi-agent Markov decision process, which is solved using the maximum entropy reinforcement learning framework and a novel communication-efficient consensus strategy. The proposed algorithm allows individual agents to learn a group control policy using local rewards. Numerical studies on IEEE distribution test feeders show that our proposed algorithm matches the performance of single-agent reinforcement learning benchmark. In addition, the proposed algorithm is shown to be communication efficient and resilient.
