A Conceptual Framework for Planning Active Distribution Systems under Accelerated Expansion of Electric Vehicle Charging Infrastructure

(ADNs). This transition is mainly driven by the widespread deployment of distributed energy resources (DER), advances in monitoring and control technologies, and, more recently, the rapid growth of electric vehicle (EV) adoption. In this context, the increasing demand for fastcharging infrastructure introduces new technical and planning challenges for distribution utilities.

The growing penetration of electric vehicle charging stations (EVCS) modifies load patterns in ways that conventional planning approaches are not designed to address. Traditional methods struggle to represent the stochastic nature of EV adoption, the clustering of fast-charging events, and the impact of uncoordinated connections at specific network locations. As a result, distribution planners are increasingly required to adopt methodologies capable of assessing flexible, progressive, and deferred investment strategies under uncertainty, especially in scenarios where localized load growth may trigger premature network reinforcements.

This work proposes a conceptual framework for the flexible expansion planning of ADNs under accelerated EVCS growth. The methodology incorporates Approximate Dynamic

Programming (ADP) to manage the curse of dimensionality associated with multi-year planning problems, allowing the representation of uncertain demand trajectories and the evaluation of investment decisions involving DER and Distributed Energy Storage Systems (DESS).

Overall, this study contributes to the development of planning methodologies that combine stochastic forecasting, scenario analysis, flexibility-enabled resources, and advanced optimization techniques, providing distribution utilities with a structured approach to address the challenges introduced by the rapid expansion of EV charging infrastructure.