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Multi-objective synergistic planning of EV fast-charging stations in the distribution system coupled with the transportation network

机译:结合交通网络的配电系统中电动汽车快速充电站的多目标协同规划

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The adoption of environmentally friendly electric vehicles (EVs) is increasing over the years due to the increased awareness of energy and environmental challenges. However, the current growth is slow because of lack of proper charging infrastructures. This study proposes a multi-objective synergistic planning model of an EV charging station considering the interaction between the distribution system and transportation networks as the fast charging of EVs affects the operation of both networks. The developed model minimises the power losses and voltage deviation of the distribution system and maximises the EV flow served by the fast charging station (FCS) simultaneously taking into account permissible waiting time and service radius of FCS. Multi-objective grey wolf optimiser (MOGWO) algorithm is used to obtain the non-dominated solutions and fuzzy satisfaction-based decision-making method is employed to reach final planning scheme. The effectiveness of the proposed model is investigated on the IEEE 123-bus distribution system coupled with a 25-node transportation network. The influence of different objectives, service radius and waiting time on the planning of FCS is also explored. Results reveal that the developed method can provide rational siting and sizing of FCS and it is also found that proper service radius and waiting time provide more convenience to the customer.
机译:近年来,由于人们越来越意识到能源和环境挑战,因此越来越多地采用环保型电动汽车(EV)。但是,由于缺乏适当的充电基础设施,当前的增长缓慢。这项研究提出了一个电动汽车充电站的多目标协同规划模型,该模型考虑了配电系统与交通网络之间的相互作用,因为电动汽车的快速充电会影响这两个网络的运行。考虑到允许的等待时间和FCS的服务半径,开发的模型可最大程度地减少配电系统的功率损耗和电压偏差,并最大化由快速充电站(FCS)服务的EV流量。采用多目标灰狼优化算法(MOGWO)获得非支配解,并采用基于模糊满意度的决策方法得出最终规划方案。在带有25个节点的运输网络的IEEE 123总线分配系统上研究了所提出模型的有效性。还探讨了不同目标,服务半径和等待时间对FCS规划的影响。结果表明,所开发的方法可以提供合理的FCS选址和规模,还发现适当的服务半径和等待时间为客户提供了更多便利。

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