Abstract
Multi-agent reinforcement learning (MARL) with “centralized training & decentralized execution” framework has been widely investigated to implement decentralized voltage control for distribution networks (DNs). However, a centralized training solution encounters privacy and scalability issues for large-scale DNs with multiple virtual power plants. In this letter, a decomposition & coordination reinforcement learning algorithm is proposed based on a federated framework. This decentralized training algorithm not only enhances scalability and privacy but also has a similar learning convergence with centralized ones.
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