A Reinforcement Learning Based Voltage Regulation Strategy for Active Distribution Networks

The penetration of renewable energy in active distribution networks is gradually increasing. Conventional analytical-based numerical solution methods face a serious challenge with the need for faster response times for intermittent PV power generation. In this paper, a recurrent neural network strategy is proposed to optimize the reactive power value of a PV inverter to achieve a fast solution to the voltage overrun problem. This model-free fast optimization strategy formulates the reactive power optimization problem as an observable Markov decision process. The recurrent neural network in the hidden layer of the model-free strategy successfully integrates time series information into the continuous control of the distribution network. A case study of a typical 33-bus system validates the effectiveness and good performance of the method.