使用蒙特卡罗模拟连接充电站的基础设施

Muhammad Tayyab, Sebastian Helm, I. Hauer, Julius Brinken, Niels Schmidtke
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引用次数: 3

摘要

德国计划投资充电站的基础设施,到2022年达到5万个充电站。为了从传统汽车顺利过渡到电动汽车,必须规划几个电动汽车充电站。公共充电站的严格布置可能会导致电网的稳定性问题。这可以通过考虑交通和电网规划的智能电动汽车充电站(EVCS)来避免。高负荷充电带来的稳定性问题可以通过昂贵的电网扩建来解决。处理这种情况的另一种方法是通过规划算法来放置EVCS,以避免未来几年的电网扩张。本文提出了一种基于蒙特卡罗仿真的EVCS布局算法,该算法考虑了交通模型和网格建模。该算法考虑EVCS的不同数量/配置,确定最优EVCS连接节点。此外,根据所得到的配置,对多个EVCS的电动汽车充电功率进行了优化调度,以减少功率损失。通过交通建模确定了算法的输入、日充电需求、EVCS的数量和电动汽车的数量。该方法已在实际低压电网中得到了应用和验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Infrastructure linking for placement of Charging stations using Monte Carlo simulation
Germany plans to invest in the charging station's infrastructure to meet the 50,000 charging points by 2022. For a smooth transition from traditional to electrical vehicles, several electric vehicle charging stations must be planned. Rigorous placement of public charging stations may lead to stability problems in the power network grid. This can be avoided by intelligent Electric vehicle charging stations (EVCS) placement with consideration of traffic and grid planning. The stability problems associated with the electric vehicle charging due to high load may be solved by the expensive grid expansion. Another way to handle the situation is to place EVCS by planning algorithms to avoid the grid expansion for the coming years. In this paper, the authors present a new EVCS placement algorithm based on Monte Carlo simulation considering traffic model and grid modeling. The algorithm determines the optimal EVCS connection nodes taking into account different amounts/configurations of EVCS. Furthermore, the electric vehicles charging power of several EVCS has been scheduled optimally for the resulted configuration to reduce power losses. The input for the algorithm, the daily charge requirement, the amount of EVCS, and the number of electric vehicles has been determined by traffic modeling. The methodology has been implemented and tested in a low voltage network based on real data.
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