Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can cause the grid violating voltage operation limits. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating real and/or reactive power at the connection point. Yet existing local autonomous control scheme of SIs is based on local information without coordination, which can lead to suboptimal performance. In this paper, a deep reinforcement learning (DRL) based algorithm is developed and implemented for coordinating multiple SIs. The reward scheme of the DRL is carefully designed to ensure voltage operation limits of the grid are met with more effective utilization of SI reactive power. The proposed DRL agent for voltage control can learn its policy through interaction with massive offline simulations, and adapts to load and solar variations. The performance of the DRL agent is compared against the local autonomous control on the IEEE 37 node system with thousands of scenarios. The results show a properly trained DRL agent can intelligently coordinate different SIs for maintaining grid voltage within allowable ranges, achieving reduction of PV production curtailment, and decreasing system losses.

本研究提出了一种基于深度强化学习的算法来协调多个智能逆变器，以更有效地利用逆变器反应功率来满足电网电压的操作限制，该算法能够通过与海量离线模拟的交互来学习其策略，并适应负载和太阳能变化。在IEEE 37节点系统上，训练良好的DRL代理可智能地协调不同的SI，以维持电网电压在允许范围内，实现减少PV生产限制和降低系统损失的目标。

基于深度强化学习的光伏智能逆变器协调控制实现电网电压调节