Federated Deep Reinforcement Learning for Energy Scheduling in Privacy-Sensitive PV-EV Charging Networks

The large-scale adoption of electric vehicles (EVs) improves transport sustainability but creates severe peak-time stress on distribution grids. In PV-assisted charging networks, station operators must jointly decide retail charging prices and energy-storage dispatch under uncertain demand and generation conditions. This paper develops a distributed federated deep reinforcement learning framework for multi-station scheduling, where each station trains a local soft actor–critic (SAC) policy and only model parameters are exchanged with a global aggregator. To better adapt prices to local supply–demand conditions, we introduce a sales-factor-based correction mechanism that links the announced price to demand pressure and storage status. The objective combines station revenue, operating expenses, and user-discomfort-related penalties under operational constraints. Simulation results on a five-station setting show stable convergence and consistent gains over benchmark methods, with profit improvements of 3.90–39.00%. The framework keeps raw operational data local and supports collaborative optimization across stations.