The deployment of fast charging stations (FCSs) can tackle one of the main barriers to the widespread adoption of plug-in electric vehicles (PEVs), i.e., the otherwise long charging time of PEVs. Moreover, feeding the demand of FCSs from renewable energy sources (RESs) can maximize the positive environmental impact of PEVs and decrease the energy costs associated with charging. However, due to their stochastic behavior, RESs are usually insufficient to meet the energy demand of FCSs in a cost-effective way and need to be coupled with energy storage systems (ESSs). This article proposes a new probabilistic mixed integer linear programing (MILP) formulation for determining the optimal capacity and type of renewable generation and energy storage to minimize the energy costs associated with an FCS, while meeting its performance requirements. The proposed formulation accounts for renewable generation uncertainties as well as a wide range of technical and operational characteristics of different energy storage technologies (e.g., depth of discharge (DoD), number of allowable charging/discharging cycles, and calendric lifetime) and allows for choosing the optimal combination of ESSs and RESs. A set of numerical studies has been conducted in GAMS environment to validate the feasibility and effectiveness of the proposed formulation.

中文翻译：

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用于快速充电站中可再生能源和储能集成的新 MILP 配方

快速充电站 (FCS) 的部署可以解决广泛采用插电式电动汽车 (PEV) 的主要障碍之一，即 PEV 的充电时间过长。此外，从可再生能源 (RES) 满足 FCS 的需求可以最大限度地提高 PEV 对环境的积极影响，并降低与充电相关的能源成本。然而，由于其随机行为，RES 通常不足以以具有成本效益的方式满足 FCS 的能源需求，需要与储能系统 (ESS) 耦合。本文提出了一种新的概率混合整数线性规划 (MILP) 公式，用于确定可再生发电和储能的最佳容量和类型，以最大限度地降低与 FCS 相关的能源成本，同时满足其性能要求。拟议的公式考虑了可再生能源发电的不确定性以及不同储能技术的广泛技术和操作特性（例如，放电深度 (DoD)、允许的充电/放电循环次数和日历寿命），并允许选择ESS 和 RES 的最佳组合。在 GAMS 环境中进行了一组数值研究，以验证所提出的公式的可行性和有效性。