Robust Planning of Electric Vehicle Charging Stations Considering Demand Uncertainty and Facility Failures

Carbon reduction policies and the increasing trend toward transportation electrification have spurred the rapid development of electric vehicles (EVs). However, the uncertainties of charging demands and facility failures, resulting from factors such as power disruptions, equipment damage, and inefficient operation, pose challenges in balancing the economic efficiency and service reliability of EV charging stations (EVCSs). This article proposes a robust planning model in the coupled traffic-power network, aiming to minimize the investment costs of EVCSs and the operation costs, including detour costs for EV users, penalty costs for unmet demands, and generation costs while maintaining a controllable risk level. To solve the model, we employ the column-and-constraint generation algorithm along with the alternative directions method (C&CG-AD). Furthermore, a second-order cone programming (SOCP)-based branch flow model (BFR) is incorporated to repair the duality gap of BFR and ensure the convergence of the C&CG-AD algorithm. A comprehensive case study is conducted to validate the effectiveness of the proposed model and solution methods. The findings demonstrate that the robust solution considering both uncertainties offers superior cost-effectiveness, operational reliability, and resilience against various failures compared to strategies that either average out uncertainties or overlook facility failures.