In the coming years, several transformations in the transport sector are expected, associated with the increase in electric vehicles (EVs). These changes directly impact electrical distribution systems (EDSs), introducing new challenges in their planning and operation. One way to assist in the desired integration of this technology is to allocate EV charging stations (EVCSs). Efforts have been made towards the development of EVCSs, with the ability to recharge the vehicle at a similar time than conventional vehicle filling stations. Besides, EVs can bring environmental benefits by reducing greenhouse gas emissions. However, depending on the energy matrix of the country in which the EVs fleet circulates, there may be indirect emissions of polluting gases. Therefore, the development of this technology must be combined with the growth of renewable generation. Thus, this proposal aims to develop a mathematical model that includes EVs integration in the distribution system. To this end, a mixed-integer linear programming (MILP) model is proposed to solve the allocation problem of EVCSs including renewable energy sources. The model addresses the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. Moreover, an EV charging forecast method is proposed, subject to the uncertainties related to the driver's behavior, the energy required by these vehicles, and the state of charge of the EVs. The proposed model was implemented in the AMPL modelling language and solved via the commercial solver CPLEX. Tests with a 24-node system allow evaluating the proposed method application.

中文翻译：

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电动汽车充电站与包括二氧化碳排放在内的可再生能源的联合优化配置

未来几年，随着电动汽车 (EV) 的增加，预计交通运输部门将发生一些转变。这些变化直接影响配电系统 (EDS)，为其规划和运营带来新的挑战。帮助实现该技术所需集成的一种方法是分配电动汽车充电站 (EVCS)。EVCS 已在努力开发，能够在与传统车辆加油站相似的时间为车辆充电。此外，电动汽车可以通过减少温室气体排放来带来环境效益。然而，根据电动汽车车队所在国家的能源矩阵，可能会间接排放污染气体。所以，这项技术的发展必须与可再生能源发电的增长相结合。因此，该提案旨在开发一个数学模型，将电动汽车集成到配电系统中。为此，提出了一种混合整数线性规划（MILP）模型来解决包括可再生能源在内的 EVCS 的分配问题。该模型解决了与需求（传统和电动汽车）和可再生能源发电相关的环境影响和不确定性。此外，提出了一种电动汽车充电预测方法，以考虑与驾驶员行为、这些车辆所需的能量以及电动汽车的充电状态相关的不确定性。所提出的模型是在 AMPL 建模语言中实现的，并通过商业求解器 CPLEX 进行求解。