A Multilevel Optimization Approach for Locating Public-Owned Charging Stations in Collusive Charging-as-a-Service Markets

Electric vehicle charging-as-a-service (CaaS) economy promotes EV diffusion around the globe, while the efficiency of such markets could be hampered by subtle pricing manipulation among collusive service providers. To address this issue, this paper proposes a hierarchical multi-objective optimization approach for locating publicly-owned charging stations (POCS) within a collusive EV charging-as-a-service market. The approach trades off the users’ welfare against the market in a tri-level leader-follower structure: The lower level allocates users’ choice via a variational inequality model, depending on the service prices and placement of facilities determined in the middle and upper level. To solve this problem, we design a double-loop iterative method, which evolutionarily optimizes the location in the outer loop and exactly calculates the collusive market model in the remaining part. Numerical studies validate the performance of our method in terms of quality of solution sets. The findings show that investing POCS could effectively handle the collusive behavior and so as to promote the sustainability of CaaS economy.