Federated duelling deep Q-network based collaborative energy scheduling for a power distribution network

Abstract

The collaborative energy scheduling of source-load-energy storage has great potential to meet the active control requirements of power-distribution networks. In this study, a federated deep reinforcement learning framework was developed to facilitate collaborative energy scheduling and maximize the total economic benefit in a distribution network. Then, considering the application of Markov decision processes for energy scheduling, a spatial temporal graph convolutional network transformer based power generation packaging model for renewable energy sources was presented, and a collaborative energy scheduling strategy based on a federated duelling deep Q-network was designed. The simulation results indicate that the developed collaborative scheduling strategy can maximize the economic benefits of a power distribution network while ensuring data privacy.