Review of Active Distribution Network Planning: Elements in Optimization Models and Generative AI Applications

Active distribution networks (ADNs) are rapidly evolving with the integration of distributed energy resources, flexible loads, and energy storage systems. Traditional planning methods, based on passive upgrades and worst-case scenarios, are no longer adequate for high DER penetration and dynamic system behavior. This review highlights the key evolution needs that will drive the evolution towards a more dynamic and optimized active distribution planning. Furthermore, this work reviews the core elements in ADN planning, covering time horizons, objectives, decision variables, uncertainty approaches, and optimal power flow formulations. This work also reviews recent generative AI models applied to active distribution networks, presenting a structured classification and definitions of each generative AI category.