计及分布式能源与电动汽车接入的空间负荷预测
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摘要
相较于传统负荷预测,空间负荷预测更加关注某一局部空间内的负荷分布情况,因而可以更好地确定电气设备的选型与空间布局。分布式能源及电动汽车的飞速发展,使城市空间负荷分布变得更为复杂,采用原有基于时间序列的负荷预测方法可能带来较大误差,不利于城市规划的经济性与可靠性。利用最小二乘支持向量机(least squares supportvectormachine,LS-SVM)的非线性映射能力,建立了计及分布式能源与电动汽车充电负荷的空间负荷预测模型,并通过我国中部某地区的实际算例验证了所提方法的有效性。
Compared with the traditional load forecasting,the spatial load forecasting pays more attention to the load distribution in a certain space, so it can better determine the selection and spatial layout of the electrical equipment. The rapid development of distributed energy and electric vehicles makes the urban spatial load distribution more complex. The original load forecasting method based on time series may bring large error, which is not conducive to the economy and reliability of urban power grid planning.Due to the nonlinear mapping ability of least squares support vector machine, a spatial load forecasting model for distributed and electric vehicle charging load is established. Finally, a practical example in a certain area of central China shows the effectiveness of the proposed method.
Compared with the traditional load forecasting,the spatial load forecasting pays more attention to the load distribution in a certain space, so it can better determine the selection and spatial layout of the electrical equipment. The rapid development of distributed energy and electric vehicles makes the urban spatial load distribution more complex. The original load forecasting method based on time series may bring large error, which is not conducive to the economy and reliability of urban power grid planning.Due to the nonlinear mapping ability of least squares support vector machine, a spatial load forecasting model for distributed and electric vehicle charging load is established. Finally, a practical example in a certain area of central China shows the effectiveness of the proposed method.
引文
[1]肖白,周潮,穆钢.空间电力负荷预测方法综述与展望[J].中国电机工程学报,2013,33(25):78-92.XIAO Bai, ZHOU Chao, MU Gang. Review and prospect of the spatial load forecasting methods[J]. Proceedings of the CSEE, 2013, 33(25):78-92.
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[10]郑蕊蕊,赵继印,赵婷婷,等.基于遗传支持向量机和灰色人工免疫算法的电力变压器故障诊断[J].中国电机工程学报,2011,31(7):56-63.ZHENG Ruirui, ZHAO Jiyin, ZHAO Tingting, et al. Power transformer fault diagnosis based on genetic support vector machine and gray artificial immune algorithm[J].Proceedings of the CSEE, 2011, 31(7):56-63.
[2]高亮,郎陈杰,夏能弘.分布式电源并网条件下配电网继电保护方案设计[J].电力系统保护与控制,2017,45(18):143-149.GAO Liang, LANG Chenjie, XIA Nenghong. Relay protection scheme design of distribution network under the condition of grid-connected distributed generation[J]. Power System Protection&Control, 2017, 45(18):143-149.
[3]尹晓敏,邵传军,王博,等.基于扩展空间负荷预测和市政控规的片区配电网规划[J].供用电,2016,33(5):12-15.YIN Xiaomin, SHAO Chuanjun, WANG Bo, et al. Area power distribution network planning based on extended spatial load forecast and municipal control planning[J].Distribution&Utilization, 2016, 33(5):12-15.
[4]蒋从伟,吴斌,张健,等.安徽省2020年电动汽车保有量及充电负荷预测探讨[J].电力需求侧管理,2018,20(2):29-33.JIANG Congwei, WU Bin, ZHANG Jian, et al. Discussion on forecasting of electric vehicle ownership and charging load in Anhui province in 2020[J]. Power Demand Side Management, 2018, 20(2):29-33.
[5]黄大为,潘波.考虑DG接入位置和容量的配电网保护综合改进方案[J].电力系统保护与控制,2016,44(17):64-70.HUANG Dawei, PAN Bo. Improved methods of distribution network protection scheme considering connection locations and capacity of DG[J]. Power System Protection&Control, 2016, 44(17):64-70.
[6]谭风雷,陈梦涛,汪龙龙.基于积温效应和优化支持向量机的短期电力负荷预测[J].电力需求侧管理,2018.20(5):33-36.TAN Fenglei, CHEN Mengtao, WANG Longlong. Shortterm electric power load forecasting based on accumulated temperature effect and optimized support vector machine[J]. Power Demand Side Management, 2018, 20(5):33-36.
[7]陈梦涛,张兆君,谭风雷,等.电动汽车充电站多阶段选址规划[J].电力工程技术,2017,36(6):117-121.CHEN Mengtao, ZHANG Zhaojun, TAN Fenglei, et al.Multi stage location planning of electric vehicle charging station[J]. Electric Power Engineering Teachnology, 2017,36(6):117-121.
[8]杨冰,王丽芳,廖承林.大规模电动汽车充电需求及影响因素[J].电工技术学报,2013,28(2):22-27.YANG Bing, WANG Lifang, LIAO Chenglin. Research on power-charging demand of large-scale electric vehicles and its impacting factors[J]. Transactions of China Electrotechnical Society, 2013, 28(2):22-27.
[9]杨延西,刘丁.基于小波变换和最小二乘支持向量机的短期电力负荷预测[J].电网技术,2005,29(13):60-64.YANG Yanxi, LIU Ding. Short-term load forecasting based on wavelet transform and least square support vector machines[J]. Power System Technology, 2005, 29(13):60-64.
[10]郑蕊蕊,赵继印,赵婷婷,等.基于遗传支持向量机和灰色人工免疫算法的电力变压器故障诊断[J].中国电机工程学报,2011,31(7):56-63.ZHENG Ruirui, ZHAO Jiyin, ZHAO Tingting, et al. Power transformer fault diagnosis based on genetic support vector machine and gray artificial immune algorithm[J].Proceedings of the CSEE, 2011, 31(7):56-63.