居民区场景下计及负荷预测的有序充电策略  

Ordered Charging Strategy Considering Load Forecasting in Residential Area Scenarios

作　　者：盛东旭 高辉[1] 龙羿 SHENG Dongxu;GAO Hui;LONG Yi(College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing,Jiangsu 210003,China;State Grid Chongqing Electric Power Company Marketing Service Center,Chongqing 400023,China)

机构地区：[1]南京邮电大学自动化学院/人工智能学院,江苏南京210003 [2]国网重庆市电力公司营销服务中心,重庆400023

出　　处：《广东电力》2023年第8期70-78,共9页Guangdong Electric Power

基　　金：国家自然科学基金项目(52077107)。

摘　　要：近年来我国新能源汽车保有量急剧增长,电动汽车大量接入配电网。为保障电网运行安全性与电动汽车用户充电经济性,首先,基于居民区电动汽车充电场景,以减小电网负荷波动并降低电动汽车用户充电成本为目标,统计电动汽车用户出行规律,分析包括电动汽车并网充电开始、结束时刻与充电电量的电动汽车充电规律,利用马尔科夫链蒙特卡洛方法实现居民区电动汽车充电负荷预测;其次,计及负荷预测数据进行数据评估分级,制订负荷分级电价,并根据峰平谷分时电价制订加权分时电价,建立以加权分时电价为理论的有序充电模型;最后,以华东地区某居民区充电桩为例,运用MATLAB验证基于马尔科夫链蒙特卡洛方法的充电负荷预测,建立变功率有序充电策略模型,利用多种群遗传算法求解模型。试验结果表明,采用所提策略可使负荷波动率下降至33.80%(比无序充电下降18.93%),用户单位充电价格下降至1.346元/kWh(比无序充电下降10.27%),可实现电网运行负荷波动与用户充电成本的降低。In recent years,the number of new energy vehicles in China has rapidly increased,and large numbers of electric vehicles(EVs) have been connected to the distribution network.To ensure the safety of power grid operation and the charging economy of EV users,firstly based on the charging scenarios of EVs in residential areas,with the goal of reducing grid load fluctuations and the charging costs of EV users,this paper analyzes the travel patterns of EV users,including the starting and ending times of EV grid-connection charging and the charging capacity.It also uses Markov chain Monte Carlo method to predict the charging load of EVs in residential areas.Secondly,considering the load forecasting data for data evaluation and grading,the paper formulates a load grading electricity price and a weighted time of use electricity price based on the peak to valley time of use electricity price.It also establishes an orderly charging model based on the weighted time of use electricity price theory.Finally,taking a charging station in a residential area in East China as an example,the paper uses MATLAB to verify charging load prediction based on Markov chain Monte Carlo method,and builds a variable power orderly charging strategy model solved by using multiple population genetic algorithm.The experimental results show that the proposed strategy can reduce the load fluctuation rate to 33.80%(a year-on-year decrease of 18.93% for disordered charging),and the unit charging price of users can be reduced to 1.346 yuan/kWh(a year-on-year decrease of 10.27% for disordered charging),achieving a reduction in grid operation load fluctuation and user charging cost.

关 键 词：电动汽车 马尔科夫链蒙特卡洛方法 负荷预测 有序充电 分时电价 多种群遗传算法 

分 类 号：TM715.1[电气工程—电力系统及自动化] U469.72[机械工程—车辆工程]