Optimizing the Placement of Dc Fast Charging Stations in Tourism Corridors Using Modified Constrained Simulated Annealing

The use of motor vehicles has led to a rise in petroleum demand and greenhouse gas emission concerns. Electric Vehicles (EVs) offer a promising solution to these environmental challenges. Supporting EV adoption along scenic roadways is crucial for promoting sustainable ecotourism and economic growth. However, the limited driving range and insufficient charging infrastructure hinder widespread adoption, particularly for intercity travel. Launched in 2022, the Lake Michigan Circuit initiative aims to expand charging infrastructure along the coastline. This study presents an optimization framework for DC Fast Charging station placement and charger allocation considering stochastic queuing delays, budget constraints, and real-world grid upgrade cost. A heuristic charging station assignment algorithm is proposed to model EV charging behavior during tourism trips, accounting for multiple charging options. This study introduces a Modified Constrained Simulated Annealing (MCSA) method, which utilizes a decomposition technique to separately optimize station locations and the number of chargers, while incorporating a dynamic penalty method for effective constraint handling. Additionally, the study compares the performance of MCSA against a Genetic Algorithm (GA) approach that integrates advanced constraint-handling mechanisms. The results demonstrate that both approaches effectively optimize the charging infrastructure, with MCSA outperforming GA in solution quality while requiring lower computational effort and runtime. Both methods were validated on a small case study using implicit enumeration. Sensitivity analyses under varying budget constraints highlight the trade-offs between infrastructure investment and user service levels. While a lower budget reduces the number of chargers, a minimum number of stations is required to ensure trip feasibility.