联邦分割强化学习驱动的配电网-建筑群隐私安全协同运行方法OA

As natural carriers of resources like heating,ventilation,air conditioning systems,and electric vehicles,buildings can provide significant flexibility for the distribution network operation.For the coordinated operation of the distribution network and buildings,although multi-agent deep reinforcement learning can address the parameter dependence of model-based methods,it faces the privacy leakage risk during centralized training.Thus,this paper proposes a federated split reinforcement learning method that avoids the interaction of privacy data among multiple agents.This method is implemented in a hierarchical solution framework that integrates optimization and reinforcement learning for coordinated scheduling on the premise of operational security and data privacy.Firstly,a model for the coordinated operation of the distribution network and buildings is established.Then,this paper constructs a hierarchical solution framework combining reinforcement learning and optimization to learn the decision policies of buildings while guaranteeing the safe and economic operation of the distribution network.Next,a federated reinforcement learning algorithm incorporating split learning is proposed,where split learning is used in deploying and optimizing the global value function in blocks via gradients,enabling privacy-preserving and model-free coordinated operation of buildings.Finally,the effectiveness of the proposed method is validated using an IEEE 3 3-bus test system with 20 connected buildings.