Optimal scheduling and uncertainty-aware planning of electric vehicles charging stations for enhanced power distribution network performance

This paper introduces a dual-optimization framework designed to integrate Electric Vehicle (EV) charging stations into distribution networks, aiming to maximize economic returns while simultaneously improving network performance. Initially, a GA-based optimal scheduling framework is developed to maximize the collective profit for both EV owners and charging stations, incorporating financial penalties for service failures. Subsequently, a robust statistical simulation is utilized to account for uncertainties in vehicle availability and generate realistic station load profiles. These loads are then applied to the IEEE 33-bus system to evaluate key network performance indicators, including loss costs and voltage drops at the buses. The findings demonstrate that the proposed planning strategy, combined with strategic bus placement, effectively reduces network loss costs and enhances voltage profiles. Specifically, the loss-to-energy ratio improves from 4.36 % to 4.309 %, highlighting the framework's capability to balance economic objectives with enhanced grid efficiency.