Physics-Informed Multi-Agent DRL-Based Active Distribution Network Zonal Balancing Control Strategy for Security and Supply Preservation

When large-scale and clustered distributed photovoltaic devices are connected to an active distribution network, the safe and stable operation of the distribution network is seriously threatened, and it is difficult to satisfy the demand for preservation of supply. Multi-agent reinforcement learning provides an idea of zonal balance control, but it is difficult to fully satisfy operation constraints. In this paper, a multi-level control framework based on a local physical model and a multi-agent sequential update algorithm is proposed. The framework generates parameters through an upper-layer reinforcement learning algorithm, which are passed into the objective function of the lower-layer local physical model. The lower-layer local physical model will incorporate safety constraints to determine the power setpoints of the devices; meanwhile, the sequential updating algorithm is integrated into a centralized training–decentralized execution framework, which can increase the efficiency of the sample utilization and promote the monotonic improvement of the strategies. The modified 10 kV IEEE 69-node system is studied as an example, and the results show that the proposed framework can effectively reduce the total operating cost of the active distribution network, while meeting the demand of the system to preserve the supply and ensure the safe and stable operation of the system.