计及分布式能源影响的母线净负荷预测方法研究
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摘要
随着清洁能源应用的不断发展和大量的光伏、风电场建成投入使用,电力系统的负荷预测工作又面临了新的挑战,尤其是对体量较小的母线负荷预测而言,具有较强不确定性的风电接入使其准确预测的难度加大。以风电为例,首先研究了接入某母线的风电出力特性和历史预测误差分布情况。然后基于母线负荷需求的自适应预测策略,考虑风功率非平稳性而进行的风功率预测结果,采用两阶段还原的预测思路对母线净负荷进行预测。仿真算例分析表明,提出的预测方法用于风电接入的母线净负荷预测的预测效果较好。
With continuous development of clean energy and a large number of PV and wind farms built and put into operation,power system load forecasting is now faced with new challenges. For the relatively small volume of bus load forecasting in particular,wind power access of high uncertainty makes accurate forecast even more difficult. Taking wind power as example,it was firstly studied the characteristics of the output of wind power accessed to a certain bus as well as the distribution of historical forecast errors. Then,based on the adaptive prediction strategy of bus load demand,wind power forecasting results were presented under consideration of the non-stationarity of wind power. The two-phase reduction approach was adopted to forecast the net load of the bus. Analysis of the simulation example shows that the forecast method proposed in this paper can produce better effects for the bus net load forecast with wind power access.
With continuous development of clean energy and a large number of PV and wind farms built and put into operation,power system load forecasting is now faced with new challenges. For the relatively small volume of bus load forecasting in particular,wind power access of high uncertainty makes accurate forecast even more difficult. Taking wind power as example,it was firstly studied the characteristics of the output of wind power accessed to a certain bus as well as the distribution of historical forecast errors. Then,based on the adaptive prediction strategy of bus load demand,wind power forecasting results were presented under consideration of the non-stationarity of wind power. The two-phase reduction approach was adopted to forecast the net load of the bus. Analysis of the simulation example shows that the forecast method proposed in this paper can produce better effects for the bus net load forecast with wind power access.
引文
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[8]金义雄,段建民,杨俊强,等.含有山区小水电负荷的气象回归短期负荷预测技术[J].继电器,2007,35(14):54-58,69.
[9]赵海洲.分布式电源并网对电网负荷预测的影响[J].设备管理与维修,2015,36(S1):32-33.
[10]马亚辉.含分布式电源的综合负荷建模方法研究[D].湖南:湖南大学,2013.
[11]颜宏文,李欣然.基于差分进化的含分布式电源母线净负荷预测[J].电网技术,2013,37(6):1602-1606.
[12]刘熙哲.基于混沌理论的含分布式电源系统负荷预测研究[D].长沙:长沙理工大学,2011.
[13]徐玮,罗欣.用于小水电地区负荷预测的两阶段还原法[J].电网技术,2009,33(8):87-92.
[14]赵燃,陈新宇.母线负荷预测中的自适应预测技术及其实现[J].电网技术,2009,33(19):55-59.
[15]惠小健,王震.基于ARMA的风电功率预测[J],现代电子技术,2016,39(7):145-148.
[16]李婧娇,戴长春,王正风,等.基于规律性分析的超短期负荷预测方法研究[J].计算机仿真,2014,31(7):136-139.
[2]康重庆,周安石,王鹏,等.短期负荷预测中实时气象因素的影响分析及其处理策略[J].电网技术,2006,30(7):5-10.
[3]朱陶业,李应求,张颖,等.提高时间序列气象适应性的短期电力负荷预测算法[J].中国电机工程学报,2006,26(23):14-19.
[4]张凯,姚建刚,李伟,等.基于功率谱分解和实时气象因素的短期负荷预测[J].电网技术,2007,31(23):47-51.
[5]赵德应,李胜洪,张巧霞.气温变化对用电负荷和电网运行的影响初步探讨[J].电网技术,2000,24(1):55-58.
[6]刘敦楠,何光宇,范旻,等.数据挖掘与非正常日的负荷预测[J].电力系统自动化,2004,28(3):53-57.
[7]刘皓明,余昆,梁进国,等.特殊节假日的短期负荷预测新方法[J].电力需求侧管理,2006,8(5):14-16.
[8]金义雄,段建民,杨俊强,等.含有山区小水电负荷的气象回归短期负荷预测技术[J].继电器,2007,35(14):54-58,69.
[9]赵海洲.分布式电源并网对电网负荷预测的影响[J].设备管理与维修,2015,36(S1):32-33.
[10]马亚辉.含分布式电源的综合负荷建模方法研究[D].湖南:湖南大学,2013.
[11]颜宏文,李欣然.基于差分进化的含分布式电源母线净负荷预测[J].电网技术,2013,37(6):1602-1606.
[12]刘熙哲.基于混沌理论的含分布式电源系统负荷预测研究[D].长沙:长沙理工大学,2011.
[13]徐玮,罗欣.用于小水电地区负荷预测的两阶段还原法[J].电网技术,2009,33(8):87-92.
[14]赵燃,陈新宇.母线负荷预测中的自适应预测技术及其实现[J].电网技术,2009,33(19):55-59.
[15]惠小健,王震.基于ARMA的风电功率预测[J],现代电子技术,2016,39(7):145-148.
[16]李婧娇,戴长春,王正风,等.基于规律性分析的超短期负荷预测方法研究[J].计算机仿真,2014,31(7):136-139.