Distribution Network Hosting Capacity Assessment Method of Electric Vehicle Charging Stations Based on Multi-Zone Load Profiling

Abstract

Fast growth in electric vehicle (EV) charging stations is changing the way regional distribution networks are loaded. The difficulty is not only the size of the added demand, but also the fact that charging appears at different places, at different times, and under different voltage constraints. This paper considers the common planning situation in which station-level charging records are incomplete and only transformer-side aggregate measurements are available. A data-driven hosting capacity (HC) assessment method is developed for this setting. The method first constructs zone-specific daily load profiles and then separates EV charging components from mixed transformer curves through an improved ISODATA clustering method and an improved genetic algorithm (IGA). For planned electric vehicle charging stations (EVCSs) without historical measurements, Ordinary Kriging (OK) is used to infer charging profiles from nearby observed stations in the same functional zone. The calculated HC is then checked successively at the 10 kV, 35 kV, and 110 kV levels. When an upstream constraint is violated, an improved Entropy-weight TOPSIS (EW-TOPSIS) model reallocates the available capacity according to both network constraints and zone priority. The case study indicates that the method can identify upstream bottlenecks that are hidden in local assessments, preserve residential charging demand, and provide zone-specific guidance for EVCS expansion.