Data-driven EV charging infrastructure with uncertainty based on a spatial–temporal flow-driven (STFD) models considering batteries

Abstract

Electric vehicles (EVs) play a vital role in meeting the United Nations Sustainable Development Goals by 2030, primarily by reducing emissions and enhancing air quality. Properly positioning EV charging infrastructure within urban settings is essential to facilitate a shift toward sustainable transportation. As EV adoption grows, microgrids (MGs) face new challenges, including increased power losses, deterioration of voltage profiles, and voltage stability issues. Incorporating energy storage systems (ESS) can help address these challenges by improving overall system efficiency. This research proposes a comprehensive planning methodology that optimizes the placement of EV charging stations by considering traffic flow patterns over space and time. Additionally, a stochastic modeling approach is introduced to identify the optimal locations for ESS, taking into account uncertainties such as variable electrical loads and the intermittent nature of renewable energy sources (RESs). The ESS placement is modeled as a multi-objective optimization problem, aiming to enhance voltage stability, reduce power losses, and improve voltage profiles. The developed mathematical frameworks and algorithms facilitate the optimal sizing and siting of both EV charging stations and ESS. An economic evaluation of the MG, including the costs associated with ESS integration, is also incorporated. The effectiveness of this integrated planning approach is demonstrated through a case study on a representative transportation network, showing its capacity to mitigate the adverse impacts impacts of EV integration on microgrid performance. Furthermore, the study employs a stochastic framework to simulate and analyze the uncertainties inherent to electrical loads and renewable energy generation. The obtained results show that integrating ESS significantly improved voltage stability, with the minimum voltage stability index (VSI) value increasing from 0.5848 to 0.8631. It also reduced power losses by 33.34%, decreased transformer loading by 19.5%, and enhanced economic efficiency. Additionally, Sodium-Nickel Chloride (Na-NiCl2) offered the highest savings of 6.99%, demonstrating significant technical and financial benefits for the MG.