基于分布式光伏典型日曲线的统调负荷预测方法
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摘要
电力负荷预测是电力系统的一项重要工作,分布式光伏电源大规模接入电网后极大地增加了电网负荷预测的难度。在分布式光伏电源出力数据采集汇总困难的情况下,采用选取2个特定日的统调负荷曲线相减得到差值曲线并对其进行修正的方法,近似模拟出分布式光伏电源典型日曲线。实践证明,利用分布式光伏电源典型日曲线辅助电网统调负荷预测,能够有效提高统调负荷预测的准确率。
Power load forecasting is an important task in power system. With the large-scale integration of distributed photovoltaic(PV) power into power grid, power grid load forecasting becomes increasingly difficult. It is difficult to collect data of distributed photovoltaic power output, so two daily unified dispatch load curves are selected and subtracted to conclude a difference curve and correct it to approximately simulate a typical daily curve of PV power. It is proved that grid load forecasting via typical daily curve of distributed PV power can improve unified dispatch load forecasting accuracy.
Power load forecasting is an important task in power system. With the large-scale integration of distributed photovoltaic(PV) power into power grid, power grid load forecasting becomes increasingly difficult. It is difficult to collect data of distributed photovoltaic power output, so two daily unified dispatch load curves are selected and subtracted to conclude a difference curve and correct it to approximately simulate a typical daily curve of PV power. It is proved that grid load forecasting via typical daily curve of distributed PV power can improve unified dispatch load forecasting accuracy.
引文
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[15]王惠中,刘轲,周佳,等.电力系统短期负荷预测建模仿真研究[J].计算机仿真,2016,33(2):175-177.
[2]刘伟,彭冬,卜广全,等.光伏发电接入智能配电网后的系统问题综述[J].电网技术,2009,33(19):1-6.
[3]杨超颖,王金浩,王硕,等.基于拟牛顿法小波神经网络的光伏发电系统短期功率预测模型[J].中国电力,2014,47(6):117-123.
[4]王保义,赵硕,张少敏.基于云计算和极限学习机的分布式电力负荷预测算法[J].电网技术,2014,38(4):526-529.
[5]李鹏梅,臧传治,王侃侃.基于相似日和神经网络的光伏发电预测[J].可再生能源,2013,31(10):1-4.
[6]刘念,张清鑫,李小芳,等.基于核函数极限学习机的分布式光伏短期功率预测[J].农业工程学报,2014,30(4):15-157.
[7]兰华,廖志民,赵阳.基于ARMA模型的光伏电站出力预测[J].电测与仪表,2011,48(54):31-34.
[8]陈昌松,段善旭,蔡涛,等.基于模糊识别的光伏发电短期预测系统[J].电工技术学报,2011,26(7):83-89.
[9]王守相,张娜.基于灰色神经网络组合模型的光伏短期出力预测[J].电力系统自动化,2012,36(19):37-41.
[10]李萌,程浩忠,杨宗麟,等.采用分形插值的典型日负荷曲线改进预测方法[J].电力系统及其自动化学报,2015,27(3):36-39.
[11]何淼,叶鹏,何宁宁,等.电力系统饱和负荷预测研究综述[J].沈阳工程学院学报,2017,13(4):340-344.
[12]李健,张植华,林毓,等.配电网负荷预测应用研究[J].电网与清洁能源,2014,30(4):31-35.
[13]袁晓玲,施俊华,徐杰彦.基于BP神经网络的光伏发电短期出力预测[J].可再生能源,2013,31(7):11-16.
[14]丁明,王磊,毕锐.基于改进BP神经网络的光伏发电系统输出功率短期预测模型[J].电力系统保护与控制,2012,40(11):93-99.
[15]王惠中,刘轲,周佳,等.电力系统短期负荷预测建模仿真研究[J].计算机仿真,2016,33(2):175-177.