Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control
This work addresses the challenge of decentralized control in power systems for improved efficiency and robustness, though it appears incremental as it builds on existing reinforcement learning methods.
The authors tackled the problem of Volt/VAR control in active distribution networks by proposing an online multi-agent reinforcement learning framework (OLDC) with a novel MACSAC algorithm, which eliminates the need for accurate models and real-time communication, achieving superior performance in numerical simulations on IEEE test cases.
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.