Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control

Understand

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.