A Reinforcement Learning Based Approach to Solve Voltage Issues in Distribution Networks

This paper proposes a Proximal Policy Optimization (PPO)-based reinforcement learning approach to solve overvoltage problem in power distribution networks. The approach aims to minimize the voltage deviations and to keep voltage magnitudes in the allowed ranges. The numerical simulations are performed on a modified unbalanced 123 node network. The modified test system includes a total number of 34 single phase Photovoltaics (200 kVA) connected to three phases. We modified the base case load profile based on real-world daily variations obtained from EPIAS. The PV generation profile was modeled according to a typical sunny day. Using OpenDSS and Python, we implemented PPO-based RL to optimize the setpoints of smart inverters and voltage regulators. The model was trained with load and solar profiles at 09:00, 12:00, and 16:00 to derive optimal voltage regulation strategies for these time points. From the simulation results, we observed that the proposed PPO-based RL approach significantly reduces voltage deviations across all phases, which may help efficient operation of the distribution networks.