Abstract 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.

中文翻译：

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考虑用户行为不确定性的充电站容量规划与定价设计

摘要 本文提出了一种方法论框架，以优化具有可再生能源 (RCS) 的电动汽车 (EV) 充电站的容量规划和定价设计。与现有文献不同，我们的方法明确考虑了电动汽车用户的战略行为及其对 RCS 规划效率的影响。因此，该问题被表述为博弈论的双层规划模型，其中 RCS 的最佳容量规划及其操作/定价方案在上层确定，而下层则捕获 EV 所有者的充电决策。此外，本研究采用了稳健的公式来捕捉不确定的电动汽车用户行为、批发能源价格和可再生能源输出。Karush-Kuhn-Tucker (KKT) 条件用于将双层鲁棒优化问题转化为单层优化问题优化模型。然后，进一步利用列和约束生成（C&CG）算法来解决该问题。案例研究结果表明，考虑不确定性的容量规划和定价设计是合理和实用的。