Safe and Fair PV Curtailment for Voltage Control in Unbalanced Active Distribution Network

The integration of photovoltaic (PV) systems into the grid has increased, resulting in various opportunities and challenges related to voltage fluctuation and the control of distributed energy resources (DERs). Smart inverters can protect against voltage violations by implementing active and reactive power control. Each smart inverter can be represented as an agent in deep reinforcement learning (DRL), which uses data to learn control actions for a given distribution network. The agent’s training must address the challenges of achieving stable learning and ensuring fair PV curtailment, while the voltage unbalance in a practical three-phase unbalanced distribution network must remain within the acceptable range. The proposed model-free method effectively addresses the problems by adopting a hybrid approach that combines gradient and evolutionary parameter update schemas to learn a robust policy for safe and fair PV curtailment. We successfully implemented the proposed method to achieve voltage regulation close to an ideal 1 per unit (p.u.) and a voltage unbalance factor within a 2% range. The PVC is guaranteed to be regulated fairly, implying that there will be a proportional but minimal to no reduction in low PV generation periods.