This paper proposes a multi-agent deep reinforcement learning-based approach for distribution system voltage regulation with high penetration of photovoltaics (PVs). The designed agents can learn the coordinated control strategies from historical data through the counter-training of local policy networks and centric critic networks. The learned strategies allow us to perform online coordinated control. Comparative results with other methods show the enhanced control capability of the proposed method under various conditions.

The multi-agent deep reinforcement learning-based approach enhances voltage regulation in distribution systems with high PV penetration, allowing for online coordinated control.

A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV Inverters

Key Takeaway: The multi-agent deep reinforcement learning-based approach enhances voltage regulation in distribution systems with high PV penetration, allowing for online coordinated control.