Optimizing DC fast charger placement along tourism corridor under budget constraints

Abstract

With the rise in electric vehicle (EV) adoption, enabling long-distance EV travel along scenic routes offers a promising path to boost regional tourism and expand EV use beyond urban areas. However, limited driving range and insufficient charging infrastructure remain significant barriers to widespread adoption for touristic travel. Launched in 2022, the Lake Michigan Circuit initiative aims to expand charging infrastructure along the coastline. This study presents an optimization framework for siting and sizing DC Fast Charging stations, accounting for budget constraints, EV user behavior, stochastic queuing delays, and real-world grid upgrade costs. To model flexible EV user behavior during tourism trips, a charging station assignment algorithm is proposed using a buffer range that includes multiple charging options. A Modified Constrained Simulated Annealing (MCSA) algorithm is introduced, which decomposes the problem into location and sizing subproblems and incorporates a dynamic penalty method for constraint handling. MCSA’s performance is benchmarked against a Genetic Algorithm (GA) and validated through implicit enumeration on a small-scale case study. Results demonstrate that MCSA can yield higher-quality solutions with reduced runtime and computational effort. Comprehensive sensitivity analyses explore the effects of budget limits, charger power levels, and user behavior assumptions. Findings show that while lower budgets increase queuing time and reduce charger availability, a minimum number of stations is required to ensure trip feasibility. Also, 150 kW chargers offer the best performance under limited available funding. The study underscores the importance of calibrating EV user charging behavior, as behavioral assumptions significantly impact infrastructure configuration and investment requirements.