A Novel Expert System‐Based Optimization of Fast‐Charging Station Location Planning for Electric Buses

ABSTRACT The need for electric buses (e‐buses) has increased significantly due to the global shift towards sustainable urban mobility and low‐carbon transportation systems. The integration of e‐buses into public transportation has become an environmentally friendly urban development element due to their positive environmental impacts and potential to reduce private vehicle use. This study introduces a new locational planning model for fast‐charging stations (FCS) that optimally integrates them into the existing power network infrastructure to support the transition to e‐bus systems. It aims to minimize total operational, maintenance, installation and transportation costs to the charging station, as well as energy distribution line costs, while providing adequate charging services for e‐bus fleets. In the model, the binary version of the Walrus Optimization Algorithm has been proposed and compared with three well‐known metaheuristic algorithms: the Arithmetic Optimization Algorithm, the Grey Wolf Optimizer and the Whale Optimization Algorithm. A proposed model for charging stations has been evaluated using two real‐world datasets containing public transportation and power network data from Adana. The results of the proposed model demonstrate a more realistic, grid‐friendly deployment of fast‐charging stations at reduced cost.