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Placement of Public Fast-Charging Station and Solar Distributed Generation with Battery Energy Storage in Distribution Network Considering Uncertainties and Traffic Congestion

机译:考虑不确定性和交通拥堵

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摘要

In this paper, a sustainable solution for the allocation of Public Fast-Charging Stations (PFCSs) and Solar Distributed Generations (SDGs) along with Battery Energy Storages (BESs) and its scheduling is proposed. The solution is obtained with minimization of Energy loss, voltage deviation index, and investment as well as operation maintenance costs of PFCS, SDG, BES, considering battery degradation. Moreover, the associate relevant factors such as; number of charging ports, capacities of the PFCS and EV flow captured by the PFCS are evaluated. Two-stage optimization is employed to getting the solutions. The first stage of optimization deals with PFCS's location, SDG's locations with sizes and BES scheduling. On the other hand, second stage looks after the assignment of EVs to the apt PFCSs considering the shortest distances with traffic congestions in view of reducing energy consumption of the EVs. As a test case, a 33 node radial distribution network is chosen with the corresponding traffic network. The allocation problem is solved by using Harris Hawks Optimization (HHO) and Grey Wolf Optimizer (GWO). Four other established optimization techniques are used to authenticate the solutions. The EV assignment problem is tackled by Integer Linear Programming (ILP). 2m Point Estimation Method (2m PEM) is applied to deal with the uncertainties associated with EVs, traffic flow and SDG.
机译:在本文中,提出了一种可持续的解决公共快速充电站(PFCSS)和太阳分布代(SDGS)以及电池能量存储器(BESS)及其调度的可持续解决方案。通过最小化能量损失,电压偏差指数和投资以及PFCS,SDG,BES的操作维护成本,可以最小化解决方案,考虑到电池劣化。此外,缔合的相关因素如;评估充电端口数,PFCS捕获的PFC和EV流的容量进行了评估。采用两阶段优化来获得解决方案。优化的第一阶段涉及PFCS的位置,SDG具有大小和BES调度的位置。另一方面,考虑到降低EVS的能量消耗,第二阶段在考虑到具有交通拥堵的最短距离的APT PFC的分配之后。作为测试用例,使用相应的业务网络选择33节点径向分配网络。通过使用Harris Hawks Optimization(HHO)和灰狼优化器(GWO)来解决分配问题。其他四种既定的优化技术用于验证解决方案。通过整数线性编程(ILP)来解决EV分配问题。应用2M点估计方法(2M PEM)来处理与EVS,交通流量和SDG相关的不确定性。

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