Artificial Intelligence-Empowered Smart Distribution Networks: Modeling, Operation and Resilience

Smart distribution networks are becoming more active and complex as distributed photovoltaics, energy storage, electric vehicles, flexible loads and power electronic devices are widely integrated. These changes increase uncertainty, bidirectional power flows and operational constraints at the medium- and low-voltage levels, making conventional rule-based planning, monitoring and control methods increasingly difficult to apply at scale. Recent advances in artificial intelligence, data-driven modeling, edge computing and digital twins provide new opportunities to improve situational awareness, forecasting, optimization, protection and self-healing in smart distribution grids. However, practical deployment still faces challenges related to data quality, interpretability, cyber-physical security, real-time implementation and coordination with grid operation rules.The Research Topic aims to address how artificial intelligence can be designed, validated and deployed to support safe, efficient and resilient operation of smart distribution networks. It seeks contributions that move beyond isolated algorithmic improvements and demonstrate how AI can solve practical grid problems, such as uncertainty-aware forecasting, active distribution network control, voltage/var optimization, fault diagnosis, outage restoration, distributed energy resource coordination and demand-side flexibility management. A key goal is to connect AI methods with power-system constraints, engineering interpretability and operational reliability. By collecting methodological advances, application studies and field-oriented experiences, this Research Topic will promote trustworthy AI-enabled distribution grids and identify pathways for integrating intelligent decision support into future low-carbon power systems.We welcome Original Research, Reviews, Mini Reviews, Perspectives, Methods, and Case Study manuscripts. Topics of interest include, but are not limited to: - AI-based load, renewable and electric-vehicle charging forecasting; - Graph learning and spatiotemporal modeling for distribution networks; - Reinforcement learning and optimization for voltage regulation, network reconfiguration and energy management; - Digital twins and edge intelligence for real-time operation; - Fault detection, localization, isolation and service restoration; - Cyber-physical security, privacy-preserving learning and robust AI; - Coordination of distributed photovoltaics, storage, flexible loads and electric vehicles;- Explainable, physics-informed or safety-constrained AI for grid applications. Studies combining realistic datasets, simulation platforms, hardware-in-the-loop validation or field demonstrations are particularly encouraged. Topic editors: Leijiao Ge, Tao Huang, Xiaobing Liao, Bo Jie, JIANGJIAO XU, Guanlong Jia Source: https://www.frontiersin.org/research-topics/82030/artificial-intelligence-empowered-smart-distribution-networks-modeling-operation-and-resilience