New Energy Bound-Based Model for Optimal Charging of Electric Vehicles with Solar Photovoltaic Considering Low-Voltage Network's Constraints

This directory contains the following datasets: SOURCE CODE Three scripts in Python are included to obtain the parameters of the whole network and test the proposed optimisation approach. SENSITIVITY MATRICES OF THE 905-NODES LOW-VOLTAGE FEEDER * dVcal.txt: voltage sensitivity matrix of household nodes due to the addition of load, expressed in V/kW * dVGen.txt: voltage sensitivity matrix of household nodes due to the addition of generation, expressed in V/kW * dLline.txt: loading sensitivity matrix of the feeder's main cable due to the addition of load, expressed in kW/kW * dLlineGen.txt: loading sensitivity matrix of the feeder's main cable due to the addition of generation, expressed in kW/kW * dStcal.txt: loading sensitivity matrix of the distribution transformer due to the addition of load, expressed in kW/kW * dStGen.txt: loading sensitivity matrix of the distribution transformer due to the addition of generation, expressed in kW/kW INITIAL VOLTAGES AND LOADING LEVELS These data were obtained for a test period of 30 hours with a 10-minute interval (i.e., 180 slots of time). * Vini.txt: time-series of voltage per phase for every household node obtained from the unbalanced quasi-dynamic power flow (QDPF) * PLini.txt: time-series of loading per phase for the feeder's main cable obtained from the unbalanced QDPF * STini.txt: time-series of loading per phase for the distribution transformer obtained from the unbalanced QDPF INPUT AND ADDITIONAL PARAMETERS Both folders "Winter parameters" and "Summer parameters" contain 100 text files, which correspond to each simulated scenario, numbered from one to one hundred for the following variables: * DtX.txt: the daily travelled distance of each EV for scenario X * e_arrX.txt: the arrival energy level of each EV for scenario X * taIntX.txt: the arrival time interval of each EV for scenario X * tpIntX.txt: the number of time intervals of each EV parked at home for scenario X * e_reqX.txt: the objective energy level of each EV for scenario X * xX.txt: the availability of each EV for scenario X, which is based on taIntX.txt and tpIntX.txt * eLowerX.txt: the lower energy boundary of each EV for scenario X * eUpperX.txt: the upper energy boundary of each EV for scenario X * PpvX.txt: time-series of PV power output for scenario X It is also included the IEEE LV network in PowerFactory V15.1 with a series of EVs and PVs. These have a time-series profile assigned from the proposed optimisation problem results. Additionally, the parameters and results obtained by comparing the addition of network constraints are included.本目录包含如下数据集： 一、源代码 包含3个Python脚本，用于获取全网络参数并测试所提出的优化方法。 二、905节点低压馈线灵敏度矩阵 - dVcal.txt：负荷新增时用户节点的电压灵敏度矩阵，单位为V/kW - dVGen.txt：分布式电源新增时用户节点的电压灵敏度矩阵，单位为V/kW - dLline.txt：负荷新增时馈线主电缆的负载灵敏度矩阵，单位为kW/kW - dLlineGen.txt：分布式电源新增时馈线主电缆的负载灵敏度矩阵，单位为kW/kW - dStcal.txt：负荷新增时配电变压器的负载灵敏度矩阵，单位为kW/kW - dStGen.txt：分布式电源新增时配电变压器的负载灵敏度矩阵，单位为kW/kW 三、初始电压与负载水平 此类数据取自时长30小时、时间间隔为10分钟的测试周期（共180个时间槽）： - Vini.txt：基于非平衡准动态潮流（quasi-dynamic power flow, QDPF）计算得到的各用户节点各相电压时序数据 - PLini.txt：基于非平衡准动态潮流（quasi-dynamic power flow, QDPF）计算得到的馈线主电缆各相负载时序数据 - STini.txt：基于非平衡准动态潮流（quasi-dynamic power flow, QDPF）计算得到的配电变压器各相负载时序数据 四、输入与附加参数 "Winter parameters"（冬季参数）与"Summer parameters"（夏季参数）两个文件夹均包含100个文本文件，分别对应编号1至100的模拟场景下的如下变量： - DtX.txt：场景X下每台电动汽车（Electric Vehicle, EV）的每日行驶里程 - e_arrX.txt：场景X下每台电动汽车的到达荷电状态 - taIntX.txt：场景X下每台电动汽车的到达时间区间 - tpIntX.txt：场景X下每台电动汽车在家停放的时间区间数 - e_reqX.txt：场景X下每台电动汽车的目标荷电状态 - xX.txt：场景X下每台电动汽车的可用状态，其生成依据为taIntX.txt与tpIntX.txt - eLowerX.txt：场景X下每台电动汽车的荷电状态下限 - eUpperX.txt：场景X下每台电动汽车的荷电状态上限 - PpvX.txt：场景X下的光伏（Photovoltaic, PV）出力时序数据 此外，本目录还包含PowerFactory V15.1中的IEEE低压电网模型，内置多台电动汽车与光伏装置，其时序曲线取自所提出优化问题的求解结果。同时还包含加入网络约束前后对比得到的参数与仿真结果。