Coordinated Energy Storage Optimization for Power Quality in High-Renewable Distribution Networks

The increasing penetration of single-phase photovoltaic (PV) generation and electric vehicle (EV) charging has aggravated phase current asymmetry in low-voltage distribution networks. In contrast to voltage-oriented control strategies, this work focuses directly on mitigating current imbalance at the point of common coupling (PCC). A coordinated control framework based on multi-agent deep deterministic policy gradient (MADDPG) is developed to regulate distributed battery energy storage systems (BESS). The control objective is formulated in terms of the Current Unbalance Factor (IUF), derived from symmetrical component theory. A linearized DistFlow model is embedded in the learning environment to preserve physical consistency while maintaining computational tractability. Device-level constraints, including state-of-charge limits and ramp-rate bounds, are enforced through action projection, whereas network security limits are incorporated via reward penalties. Case studies on a modified residential feeder indicate that coordinated BESS control reduces the peak IUF from 2.75% to 2.50% under the studied operating condition. The maximum dominant-phase current decreases from 125 A to 110 A. The performance is close to that of centralized convex optimization while enabling decentralized real-time execution after offline training. These results suggest that multi-agent reinforcement learning can serve as a feasible alternative for phase imbalance mitigation in distribution networks with high renewable penetration.