Graph Learning-Based Voltage Regulation in Distribution Networks With Multi-Microgrids

Microgrids (MGs), as localized small power systems, can effectively provide voltage regulation services for distribution networks by integrating and managing various distributed energy resources. Existing literature employs model-based optimization approaches to formulate the voltage regulation problem of multi-MGs, which require complete system models. However, this assumption is normally impractical due to time-varying environment and privacy issues. To fill this research gap, this paper suggests a data-driven decentralized framework for the cost-effective voltage regulation of a distribution network with multi-MGs. A novel multi-agent reinforcement learning method featuring an augmented graph convolutional network and a proximal policy optimization algorithm is proposed to solve this problem. Furthermore, the techniques of critical bus and electrical distance enhance the capability of feature extractions from the distribution network, allowing for the decentralized training with privacy preserving. Simulation results based on modified IEEE 33-bus, 69-bus, and 123-bus networks are developed to validate the effectiveness of the proposed method in enabling multi-MGs to provide distribution network voltage regulation.