Research Status and Prospects of Distribution Network Planning Technology Based on Artificial Intelligence

[Objective] With large-scale access to distributed power sources， new energy storage， charging facilities， etc.， the physical， digital， and commercial forms of distribution networks have undergone profound changes. The traditional planning method based on manual decision-making hinders distribution network optimization due to massive factors， complex structures， and numerous pieces of equipment. Artificial intelligence technology provides a feasible solution for overcoming the technical bottlenecks of distribution network planning. [Methods] In this context， this study analyzes the challenges faced by the distribution network planning process under new circumstances， including the precise spatiotemporal prediction of source-load， probabilistic balance of power and energy， coordinated planning of source-grid-load-storage， and empowerment of digitalization and intelligence. It elaborates on the current research status of artificial intelligence-based distribution network planning， focusing on key aspects such as knowledge graph construction， source-load scenario generation， power-energy balance， planning demand reduction， and intelligent network planning. [Results] This study summarizes and analyzes the issues in artificial intelligence-based distribution network planning technologies， including difficulties in processing unstructured and semi-structured data， limited scenario applicability， low accuracy in demand deduction， lack of interpretability， and high-dimensional solution spaces for planning schemes. It proposes potential solutions in technical research， such as graph learning， transfer learning， multimodal fusion， enhanced interpretability， and human-machine hybrid intelligence enhancement. [Conclusions] Compared with traditional distribution network planning methods， artificial intelligence-based distribution network planning demonstrates significant advantages of strong generalization， applicability， and scalability. However， it still faces critical issues， such as insufficient model accuracy and poor quality of generated solutions. In future work， we will continue to investigate artificial intelligence-based distribution network planning methods based on technical prospects， aiming to address the key challenges involved. This will provide references and insights for the development and digital-intelligent transformation of distribution network planning technology systems under the new power system framework.