引用本文: | 张琳娟,许长清,王利利,等.基于OD矩阵的电动汽车充电负荷时空分布预测[J].电力系统保护与控制,2021,49(20):82-91.[点击复制] |
ZHANG Linjuan,XU Changqing,WANG Lili,et al.OD matrix based spatiotemporal distribution of EV charging load prediction[J].Power System Protection and Control,2021,49(20):82-91[点击复制] |
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摘要: |
电动汽车(Electric Vehicle, EV)出行存在时间、空间上的不确定性,考虑时空分布的EV负荷预测是研究其与电网之间的交互影响、电动汽车充电站选址定容、实现有序充电的重要基础。以电动私家车为研究对象,提出基于出行起讫点矩阵(Origin-Destination Matrix, OD 矩阵)考虑时空分布的EV负荷预测方法。首先根据电动汽车充电模式等影响充电负荷的因素,建立充电负荷基础参数的概率模型。其次由实际路网建立其拓扑结构模型,由OD矩阵结合Floyd算法模拟电动汽车最短距离出行轨迹,采用车速—流量关系模型计算用户在既定起讫点时的行驶时间。然后考虑电池荷电状态的连续变化,基于蒙特卡洛方法(Monte Carlo method)建立EV充电负荷预测模型。最后采用所提方法计算包含居民区、商业区和工作区的某市辖区EV充电负荷时空分布。算例计算结果表明,不同功能区域的EV充电负荷在充电时间、充电方式及充电量上具有不同特征,居民区的大部分充电负荷充电需求在19:00至次日05:00,商业区和工作区的充电负荷集中在日间11:00—17:00,同时EV充电负荷加大了配电网的负荷峰值,影响了配电网的安全运行。所提出的EV充电负荷预测方法可为后续有序充电策略及充电站选址定容研究提供基础数据。 |
关键词: OD矩阵 时空分布 电动汽车 负荷预测 蒙特卡洛方法 |
DOI:DOI: 10.19783/j.cnki.pspc.201535 |
投稿时间:2020-12-11修订日期:2021-06-21 |
基金项目:国家自然科学基金项目资助(51307152) |
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OD matrix based spatiotemporal distribution of EV charging load prediction |
ZHANG Linjuan,XU Changqing,WANG Lili,LI Jingli,CHEN Xing,YANG Xuchen,SHI Yongkai |
(1. Economic and Technological Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450000, China;
2. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China) |
Abstract: |
There are uncertainties in time and space for electric vehicle travel. Research on load prediction of EVs considering the spatial and temporal distribution is the basis of future research on the interaction between EVs and the power grid and an orderly charging control strategy. Taking electric private cars as the research object, this paper proposes a method for load prediction of electric vehicles based on an OD matrix and considering spatial and temporal distribution. First, a probability model of basic parameters of EV charging load is established according to the influencing factors of EV user travel habits, charging behavior characteristics and charging mode. Secondly, a topology model is established according to the actual road network. The OD matrix is combined with a Floyd algorithm to simulate the shortest distance travel trajectory of electric vehicles. Then, considering the continuous change of the battery charge state, a charging load prediction model of EV is established based on the Monte Carlo method. Finally, the method is used to calculate the spatial and temporal distribution of EV charging load in a city including residential, business and working areas. Results show that the electric vehicle charging load is obviously regional, with residential area charging load mainly concentrated in the day after 19:00 to 05:00, and commercial and workspace concentrated in the day, from 11:00—17:00. The electric vehicle charging load increases the peak load of the distribution network, and total load affects the safe operation of the distribution network. The results provide base data for strategic research of the orderly charging of electric vehicles and the locating and sizing of charging stations.
This work is supported by the National Natural Science Foundation of China (No. 51307152). |
Key words: OD matrix spatial and temporal distribution electric vehicles load forecasting Monte Carlo method |