Collaborative Optimization Strategy of Distributed Generators Based on Federated Reinforcement Learning for Privacy Preservation

Aiming at the privacy preservation and real-time decision-making problems of the optimal dispatch for distributed generators, a multi-agent distributed collaborative optimization strategy based on federated reinforcement learning is proposed. First, a distributed collaborative optimization framework for the distribution network based on federated reinforcement learning is constructed, which uses federated learning to avoid leaking private data in the process of multi-agent deep reinforcement learning. Under this framework, a multi-agent constrained policy optimization method is proposed, which uses off-line training to shorten the online decision-making time for supporting the real-time distributed decision-making of agents. At the same time, the proposed method constructs a feasible region for agents considering the constraints such as power flow equations, allowing them to explore freely during the training process, which improves the convergence speed and ensures that the real-time dispatch strategy can meet the power system security operation constraints. Finally, the simulation results show that each agent can achieve the global optimization only by using local information during off-line training, and the proposed method ensures the security of real-time decision-making and dispatch strategy. © 2023 Automation of Electric Power Systems Press. All rights reserved.