Capacity planning and pricing design of charging station considering the uncertainty of user behavior

This paper proposes a methodological framework to optimize the capacity planning and pricing design of electric vehicle (EV) charging stations with renewable energy resources (RCS). Unlike existing literatures, our method takes account explicitly of the strategic behavior of EV users and its impact on the efficiency of RCS planning. As such, the problem is formulated as a game theoretic bi-level programming model, wherein the optimal capacity planning of the RCS and its operation/pricing schemes are determined at the upper level, while the lower level captures charging decisions by EV owners. Furthermore, a robust formulation is employed in this study to capture uncertain EV user behavior, wholesale energy prices and renewable energy output. Karush-Kuhn-Tucker (KKT) condition is used to transform the bi-level robust optimization problem to a single-level optimization problem optimization model. Then, column-and-constraint-generation (C&CG) algorithm is further utilized to solve the problem. Results from a case study show that the capacity planning and pricing design considering uncertainties is reasonable and practical.

Automated summary

This study proposes a methodological framework for optimizing the capacity planning and pricing design of electric vehicle charging stations with renewable energy resources, taking into account the strategic behavior of EV users. By using a game theoretic bi-level programming model and robust optimization approach, the researchers address this complex problem and demonstrate its feasibility and practicality through a case study.