Optimization of Electric Vehicles Charging Stations in Urban Areas: A Huff Model and Genetic Algorithm Study of Chandigarh, India

Abstract

With the rapid adoption of electric vehicles (EVs) in India, a coherent approach in developing public charging infrastructure is essential. In this paper, we propose a hybrid framework integrating the Huff Model with a Genetic Algorithm (GA) to determine the optimal locations of EV charging stations in Chandigarh city. Chandigarh is the best choice for study because of its highest charger-to-vehicle ratio in the country. The Huff Model approximates the probability of individual demand nodes (residential and commercial) to select specific charging facilities in view of site attractiveness and location decay. The GA optimization process is iterative in search of the most efficient station configuration with the aim of maximizing aggregated accessibility and minimizing energy deficiency given the constraints of solar energy and battery storage based on Chandigarh 500 kWp photovoltaic (PV) chargers. A synthetic demand of 10 destinations in Chandigarh is used to simulate the demand of urban traffic with the load variation, solar production, and grid reliance. Computational evidence shows that the convergence of the GA fitness score between 0.45 and 0.95 with respect to multiple generations, indicating a satisfactory equilibrium in charging demand versus solar energy utilisation.  With the inclusion of the PV capacity and battery state of charge in the algorithm we can reduce grid dependence by about 28 per cent compared to non-solar optimization runs. The resultant spatial distribution outlines the strategically ideal areas within the Sector 17, Sector 35, and the Industrial Phase I and the IT Park in respect to technical efficiency and enhanced consumer experience with a provision of accessibility, shorter wait time and greater dependability of the charging services delivery. This is the first study to offer a hybrid and scalable model of an EV charging infrastructure. In future, testing the model with real-time data can be insightful to develop the sustainable charging infrastructure across diverse cultures.