Two-stage voltage regulation in power distribution system using graph convolutional network-based deep reinforcement learning in real time

The model-based voltage control is widely used to mitigate quick voltage fluctuations caused by renewable energy uncertainties. However, the accurate and complete parameters of the distribution system are rarely available in practice. A two-stage voltage regulation framework based on a mixed-integer second order cone optimization programming (MISOCP) model and graph convolutional network-based deep reinforcement learning (GCN-DRL) is proposed for active distribution system voltage regulation. Specifically, in the day-ahead stage, a MISOCP is proposed for hourly voltage regulation optimization with capacitor banks (CBs), on-load tap changers (OLTC), and energy storage systems (ESS). Then, a GCN-DRL method is proposed in the real-time stage for dispatching reactive power from the intelligent inverters connected to the photovoltaic systems to alleviate the voltage fluctuations. The proposed grid topological graph convolutional network (GTGCN) leverages the distribution system's graph structure information and the convolutional operation to capture and embed the graphical features among nodal measurements. Then, the deep deterministic policy gradient (DDPG) is innovatively proposed for GCN-DRL to learn the high-efficiency voltage regulation policies, which can be implemented in a real-time manner in practice. The proposed voltage regulation model is investigated on a modified IEEE 33-node distribution system and a 25-node unbalanced distribution system. The numerical results illustrate the high effectiveness and efficiency of the proposed adaptive robust operating model.