Cakir, Muhammed TurhanNayir, HasanDemir, AlperKaya, HuseyinCeylan, Oguzhan2025-07-252025-07-252025979833151518897983315151712166-9546https://doi.org/10.1109/CPE-POWERENG63314.2025.11027244https://hdl.handle.net/20.500.14365/6300This paper proposes a Proximal Policy Optimization (PPO)-based reinforcement learning approach to solve over-voltage 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.eninfo:eu-repo/semantics/closedAccessUnbalanced Distribution NetworksVoltage RegulationOptimizationReinforcement LearningConference Object10.1109/CPE-POWERENG63314.2025.110272442-s2.0-105009412374