Locating charging stations for electric buses

Electric buses have emerged as a crucial component in the transition to low-carbon public transportation systems, owing to their potential to reduce greenhouse gas emissions, mitigate air pollution, and lower urban noise levels. This thesis presents a novel optimization-based spatial planning framework for the deployment of fast-charging stations, designed explicitly for e-bus networks. The primary objective of the proposed model is to facilitate the transition to sustainable public transportation by minimizing the total system cost, which encompasses installation costs, operation and maintenance costs, transportation costs between e-bus routes and charging stations, and infrastructure costs associated with energy distribution lines. To solve the complex optimization problem, this study proposes a binary version of the Walrus Optimization Algorithm (BWAOA), a recently developed nature-inspired metaheuristic technique. The performance of BWAOA is benchmarked against three metaheuristic algorithms, the Arithmetic Optimization Algorithm, the Grey Wolf Optimization, and the Whale Optimization Algorithm, to verify its efficiency and robustness. The proposed model is applied to two real-world case studies based on datasets from Adana, Turkey, which include both public transportation network data and electric power infrastructure data. Experimental results reveal that the BWAOA-based model achieves superior performance in terms of cost minimization, solution stability, and convergence behavior. Additionally, the spatial distribution of selected charging station locations demonstrates improved accessibility for the e-bus network.