Active voltage control presents a promising avenue for relieving power
congestion and enhancing voltage quality, taking advantage of the distributed
controllable generators in the power network, such as roof-top photovoltaics.
While Multi-Agent Reinforcement Learning (MARL) has emerged as a compelling
approach to address this challenge, existing MARL approaches tend to overlook
the constrained optimization nature of this problem, failing in guaranteeing
safety constraints. In this paper, we formalize the active voltage control
problem as a constrained Markov game and propose a safety-constrained MARL
algorithm. We expand the primal-dual optimization RL method to multi-agent
settings, and augment it with a novel approach of double safety estimation to
learn the policy and to update the Lagrange-multiplier. In addition, we
proposed different cost functions and investigated their influences on the
behavior of our constrained MARL method. We evaluate our approach in the power
distribution network simulation environment with real-world scale scenarios.
Experimental results demonstrate the effectiveness of the proposed method
compared with the state-of-the-art MARL methods.

基于多智能体强化学习的安全限制算法，用于解决分布式可控发电机在电力网络中主动电压控制问题，通过在电力分布网络模拟环境中进行实验，证明了该方法与最新的多智能体强化学习方法相比的有效性。