OPTIMISATION OF ELECTRIC VEHICLE CHARGING STATION LOCATIONS: A CASE STUDY IN DELHI NCT, INDIA

Abstract

The Electric Vehicles (EV) market in India is expected to be a $206 billion opportunity by 2030, and the country has set a goal of having at least 30% of new vehicle sales be electric by that same year. However, the deployment of a network of public charging stations is crucial for increasing the market share of EVs in India. Overall, this report provides a comprehensive overview of the potential for EVs in India and emphasizes the need for a methodology for data-driven optimization of charging station placement to support their growth. The objective is to ensure comprehensive coverage for various vehicle types while minimizing deployment costs. A Mixed-Integer Linear Programming (MILP) model solved using greedy search, Chvátal's algorithm, and genetic algorithms is employed on 11 zones of South Delhi region generated by Rapidex tool. The MILP approach provides a precise mathematical formulation, ensuring all constraints and objectives were explicitly considered. It was complemented by heuristic methods. Chvátal's algorithm improved upon the greedy method by considering cost-efficiency, yielding better solutions in reasonable time frames. The genetic algorithm provided a flexible heuristic, making it ideal for large and complex instances. The primary objective was to ensure that each zone's charging demand was adequately met while considering the constraints of distance and vehicle types' willingness to travel for charging. The number of charging ports are also acquired by obtaining the peak hour demand of charging for different vehicle types. Future work could focus on integrating real-time data and dynamic demand models to further enhance the robustness and applicability of the proposed methods in real-world scenarios.