  The distributed Volt/Var control (VVC) methods have been widely studied for
active distribution networks(ADNs), which is based on perfect model and
real-time P2P communication. However, the model is always incomplete with
significant parameter errors and such P2P communication system is hard to
maintain. In this paper, we propose an online multi-agent reinforcement
learning and decentralized control framework (OLDC) for VVC. In this framework,
the VVC problem is formulated as a constrained Markov game and we propose a
novel multi-agent constrained soft actor-critic (MACSAC) reinforcement learning
algorithm. MACSAC is used to train the control agents online, so the accurate
ADN model is no longer needed. Then, the trained agents can realize
decentralized optimal control using local measurements without real-time P2P
communication. The OLDC with MACSAC has shown extraordinary flexibility,
efficiency and robustness to various computing and communication conditions.
Numerical simulations on IEEE test cases not only demonstrate that the proposed
MACSAC outperforms the state-of-art learning algorithms, but also support the
superiority of our OLDC framework in the online application.
