Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review

The increasing integration of distributed energy resources has led to heightened complexity in distribution network models, posing challenges of uncertainty and volatility to the operation and control of distribution networks. Simultaneously, the widespread deployment of metering and sensing devices has furnished data-driven approaches with copious data sources. As a potent tool for data mining, artificial intelligence (AI) algorithms have the capacity to harness extensive datasets, thus furnishing comprehensive energy services for distribution grids. This paper provides a comprehensive exposition of AI techniques applied in the realm of distribution network voltage control (DNVC). Initially, a brief overview is provided for the four voltage control frameworks and the data calibration of DNVC, serving as the foundation for various voltage control algorithms. Subsequently, in ascending order of algorithmic advancement, the application of improved heuristic algorithms, deep learning (DL) algorithms structured around artificial neural networks (ANNs), and deep reinforcement learning (DRL) algorithms in the DNVC domain are sequentially introduced, and the relationships among the three AI technologies in framework, control principles, and performance are systematically analyzed. Additionally, attention is directed toward the algorithmic progress made in addressing specific challenges within the DNVC process. Lastly, building upon extant research, the paper summarizes four prominent developmental directions for AI in DNVC: safety exploration, information privacy, multi-feeder collaboration, and heightened uncertainty. Corresponding solutions to each of these directions are individually discussed.