A Distributed Deep Reinforcement Learning Approach for Reactive Power Optimization of Distribution Networks

Abstract

An actor-critic based distributed deep reinforcement learning approach is proposed to optimize the reactive power of the distribution network under the access of distributed photovoltaics, wind turbines and other power sources. This approach can optimize and dispatch the resources of the distribution network in real time under the change of power output such as distributed photovoltaics and wind turbines, so as to optimize the reactive power of the distribution network. First, this paper builds an optimization model with the objective function of minimizing the reactive power of the distribution network, and considers the operating constraints. Then, the agents of the proposed approach are trained, and the well-trained agents can schedule and optimize the resources of the distribution network in real time. Finally, based on the actual source-load output data in a certain place, reactive power optimization simulation experiments are carried out on the IEEE 33-bus, IEEE 123-bus simulation systems and the actual power distribution system in a region of China. Simulation results show that the proposed distributed deep reinforcement learning approach (DDRLA) can optimize distribution network reactive power online in real time.

Keywords

• Actor-critic
• distributed power generation
• distribution network
• reactive power optimization
• distributed deep reinforcement learning