Robust Electric Vehicle Charging Station Planning Factoring Traffic Congestion Dynamics Adapting to Steady and Surging Charging Demand Increase

Electric vehicles (EVs) are increasingly recognized as a pivotal solution for curbing greenhouse gas emissions and reducing reliance on fossil fuels, addressing the growing environmental concerns. However, achieving widespread EV adoption necessitates establishing adequate charging infrastructure, especially along roadways. Consequently, planning charging locations and capacities has become a focal point of research. While considerable efforts have been devoted to this area, our study identifies two overlooked aspects in roadside electric vehicle charging station (EVCS) planning: the impact of traffic congestion propagation resulting from dynamic charging queues and the intricate charging demand uncertainty associated with evolving EV usage patterns due to market shifts or extreme events. In response to this gap, our research introduces a robust link-queue-based EV charging station (LQ-EVCS) planning model for finding an optimal EVCS location plan incorporating redundancy in charging and parking capacities, aiming to accommodate charging demand under short-term and long-term demand fluctuations while alleviating traffic congestion. To address the complexity of this robust optimization model, we propose a customized Distorted Greedy Pick (DGP) algorithm leveraging the proven submodular nature of the objective function. The DGP efficiently provides a solution with an optimality guarantee validated through rigorous mathematical proof. Numerical experiments affirm the efficacy of the DGP algorithm and the robustness of the LQ-EVCS model. These results underscore the importance of accounting for local traffic congestion dynamics in EVCS planning and highlight the model's resilience across diverse demand scenarios. The research not only contributes a practical solution to the under-explored aspects of EVCS planning but also emphasizes the need for a holistic approach that considers the intricate interplay among charging infrastructure, traffic dynamics, and evolving EV usage patterns.