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Impact of Electric Vehicles on the Expansion Planning of Distribution Systems considering Renewable Energy, Storage and Charging Stations

机译:电动车对考虑可再生能源,储存和充电站的分配系统扩展规划的影响

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Energy storage systems (ESS) have adopted a new role with the increasing penetration of electric vehicles (EVs) and renewable energy sources (RES). EVs introduce new charging demands that change the traditional demand profiles and RES are characterized by their high variability. This paper presents a new multistage distribution expansion planning model where investments in distribution network assets, RES, ESS and EV charging stations (EVCS) are jointly considered. The charging demand necessary for EVs transportation is performed using a vehicle model based on travel patterns. The variability associated with RES along with the demand requires the incorporation of uncertainty, which is characterized through a set of scenarios. These scenarios are generated by the k-means++ clustering technique that allows keeping the correlation in the information of the uncertainty sources. The resulting stochastic program is driven by the minimization of the present value of the total expected cost including investment, maintenance, production, losses and non-supplied energy. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program, which can be solved by commercial software. Numerical results are presented for an illustrative 54-node test system.
机译:能源存储系统(ESS)采用了新的作用,随着电动车辆(EVS)和可再生能源(RES)的普及。 EVS引入了改变传统需求配置文件和RE的新的充电需求,其特点是其高可变性。本文介绍了一个新的多级分布扩展规划模式,共同考虑了分销网络资产,RES,ESS和EV充电站(EVC)的投资。 EVS运输所需的充电需求是使用基于旅行模式的车型进行的。与RES相关的可变性以及需求需要纳入不确定性,其特征在于一系列场景。这些方案由K-Means ++聚类技术生成,允许保持不确定性源的信息中的相关性。由此产生的随机计划通过最小化预期成本的最小化,包括投资,维护,生产,损失和非提供能源的总价值。相关的场景的确定性等效物被配制为混合整数线性程序,可以通过商业软件解决。提出了用于说明性54节点测试系统的数值结果。

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