基于时序谷时段充电的小区电动汽车负荷预测
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摘要
针对传统负荷预测模型的不足,提出一种基于时序谷时段充电的小区私家电动汽车负荷预测模型,在满足居民小区内大规模私家电动汽车有序充电的同时进行负荷预测,并为小区充电站的规划及配电网的优化调度提供理论基础。首先分析了小区私家电动汽车历史出行规律、居民小区生活用电规律及历史用电数据;其次,基于峰谷分时电价引导并充分利用谷时段进行电动汽车有序充电,从而得出该小区的电动汽车总充电负荷;最后对兰州市某个具有代表性的居民小区电动汽车充电负荷进行仿真验证。结果表明,该方法不仅能有效降低电网负荷峰谷差率及小区配网过载率,同时能够更加方便准确地预测出整个小区电动汽车的总充电负荷,具有较强的实用性。
Aiming at the shortcomings of traditional load forecasting model, a residential private electric vehicle load forecasting model is proposed based on time series valley charging in the paper, which can predict the electric vehicle load, make the orderly charging of large-scale private electric vehicles in the residential community, provide a theoretical basis for the planning of the charging station and the optimal scheduling of the distribution network. Firstly, the historical travel rules of residential private electric vehicles, the rules of residential electricity consumption and historical electricity consumption data are amalyzed. Secondly, based on the peak-to-valley time-of-use electricity price guide and making full use of the valley period for the orderly charging of electric vehicles, the charging load of the electric vehicle in the community is obtained. Finally, the electric vehicle charging load of a residential area in Lanzhou is simulated and verified. The results show that the method can more effectively and accurately predict the charging load of electric vehicles while effectively reducing the peak-to-valley difference of the grid load and the network distribution network overload rate, which has strong practicability.
Aiming at the shortcomings of traditional load forecasting model, a residential private electric vehicle load forecasting model is proposed based on time series valley charging in the paper, which can predict the electric vehicle load, make the orderly charging of large-scale private electric vehicles in the residential community, provide a theoretical basis for the planning of the charging station and the optimal scheduling of the distribution network. Firstly, the historical travel rules of residential private electric vehicles, the rules of residential electricity consumption and historical electricity consumption data are amalyzed. Secondly, based on the peak-to-valley time-of-use electricity price guide and making full use of the valley period for the orderly charging of electric vehicles, the charging load of the electric vehicle in the community is obtained. Finally, the electric vehicle charging load of a residential area in Lanzhou is simulated and verified. The results show that the method can more effectively and accurately predict the charging load of electric vehicles while effectively reducing the peak-to-valley difference of the grid load and the network distribution network overload rate, which has strong practicability.
引文
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[13]苏海锋,梁志瑞.基于峰谷电价的家用电动汽车居民小区有序充电控制方法[J].电力自动化设备,2015,35(6):17-22.Su Haifeng,Liang Zhirui.Ordered charging control method for residential electric vehicle residential quarters based on peak-to-valley electricity price[J].Electric Power Automation Equipment,2015,35(6):17-22.
[2]王玮,李睿,姜久春.面向能源互联网的配电系统规划关键问题研究综述与展望[J].高电压技术,2016,42(7):2028-2036.Wang Wei,Li Rui,Jiang Jiuchun.Summary and prospects of key issues in power distribution system planning for energy internet[J].High Voltage Engineering,2016,42(7):2028-2036.
[3]曾鸣,杨雍琦,李源非,等.能源互联网背景下新能源电力系统运营模式及关键技术初探[J].中国电机工程学报,2016,36(3):681-691.Zeng Ming,Yang Yiqi,Li Yuanfei,et al.Study on the operation mode and key technologies of new energy power system under the background of energy internet[J].Proceedings of the CSEE,2016,36(3):681-691.
[4]张洪财,胡泽春,宋永华,等.考虑时空分布的电动汽车充电负荷预测方法[J].电力系统自动化,2014,38(1):13-20.Zhang Hongcai,Hu Zechun,Song Yonghua,et al.Prediction method of electric vehicle charging load considering time and space distribution[J].Automation of Electric Power Systems,2014,38(1):13-20.
[5]党杰,汤奕,宁佳,等.基于用户意愿和出行规律的电动汽车充电负荷分配策略[J].电力系统保护与控制,2015,43(16):8-15.Dang Jie,Tang Wei,Ning Jia,et al.Electric vehicle charging load distribution strategy based on user’s will and travel law[J].Power System Protection and Control,2015,43(16):8-15.
[6]Tang D,Wang P.Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles[J].IEEE Transactions on Smart Grid,2016,7(2):627-636.
[7]黄小庆,陈颉,陈永新,等.大数据背景下的充电站负荷预测方法[J].电力系统自动化,2016,40(12):68-74.Huang Xiaoqing,Chen Wei,Chen Yongxin,et al.Charge station load forecasting method under the background of big data[J].Automation of Electric Power Systems,2016,40(12):68-74.
[8]李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域--大数据的研究现状与科学思考[J].中国科学院院刊,2012,27(6):647-657.Li Guojie,Cheng Xueqi.Big data research:a major strategic field of future science and technology and economic and social development--research status and scientific thinking of big data[J].Chinese Journal of Academy of Sciences,2012,27(6):647-657.
[9]刘世成,张东霞,朱朝阳,等.能源互联网中大数据技术思考[J].电力系统自动化,2016,40(8):14-21.Liu Shicheng,Zhang Dongxia,Zhu Chaoyang,et al.Thinking of big data technology in energy internet[J].Automation of Electric Power Systems,2016,40(8):14-21.
[10]曲朝阳,陈帅,杨帆,等.基于云计算技术的电力大数据预处理属性约简方法[J].电力系统自动化,2014,38(8):67-71.Qu Chaoyang,Chen Shuai,Yang Fan,et al.A method for power big data preprocessing attribute reduction based on cloud computing technology[J].Automation of Electric Power Systems,2014,38(8):67-71.
[11]黄彦浩,于之虹,谢昶,等.电力大数据技术与电力系统仿真计算结合问题研究[J].中国电机工程学报,2015,35(1):13-22.Huang Yanhao,Yu Zhihong,Xie Wei,et al.Research on the combination of power big data technology and power system simulation and calculation[J].Proceedings of the CSEE,2015,35(1):13-22.
[12]郭春林,肖湘宁.电动汽车充电基础设施规划方法与模型[J].电力系统自动化,2013,37(13):70-75.Guo Chunlin,Xiao Xiangning.Planning method and model of electric Vehicle charging infrastructure[J].Automation of Electric Power Systems,2013,37(13):70-75.
[13]苏海锋,梁志瑞.基于峰谷电价的家用电动汽车居民小区有序充电控制方法[J].电力自动化设备,2015,35(6):17-22.Su Haifeng,Liang Zhirui.Ordered charging control method for residential electric vehicle residential quarters based on peak-to-valley electricity price[J].Electric Power Automation Equipment,2015,35(6):17-22.