配置储能装置的光伏预测配网优化研究
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摘要
在分析区域光伏并入配网时交流母线PCC点电压波动机理的前提下,提出一种配置储能装置的光伏预测配网优化运行方法。该方法分析光伏发电输出功率与多种气象因素的相关性,将多种气象因素作为多个信息源处理,运用信息融合理论将其加权为一个综合影响因子λ,建立以λ为输入的BP神经网络预测模型,将模型输出的预测值实时传送给储能装置,采用基于滤波原理的光伏发电输出功率平滑控制,实现光伏功率平滑输出,这种方法可以和分时电价有效结合,既提高配网运行经济性的同时稳定节点电压,降低节点电压越限的可能。研究结果表明:所述预测模型具有较高的预测精度,对配网的安全优化运行有一定作用。
An optimized operation method of PV forecasting distribution network with energy storage device was proposed based on the analysis of the voltage fluctuation mechanism of AC busbar PCC point when regional PV was incorporated into the distribution network. The method analyzed the correlation between photovoltaic power output and various meteorological factors. Treating various meteorological factors as multiple information sources, and using information fusion theory to weight it into a comprehensive impact factor λ, BP neural network prediction was established taking λ as an input. The model transmitted the predicted value of the model output to the energy storage device in real time, and adopted the smoothing control of the photovoltaic power generation output power based on the filtering principle to realize the smooth output of the photovoltaic power. The results show that the method can be effectively combined with the time-sharing electricity price, and the distribution network operation is improved. Theeconomical stability of the PCC point voltage at the same time reduces the possibility that the node voltage exceeds the limit. The prediction model has higher prediction accuracy and has a certain effect on the safety optimization operation of the distribution network.
An optimized operation method of PV forecasting distribution network with energy storage device was proposed based on the analysis of the voltage fluctuation mechanism of AC busbar PCC point when regional PV was incorporated into the distribution network. The method analyzed the correlation between photovoltaic power output and various meteorological factors. Treating various meteorological factors as multiple information sources, and using information fusion theory to weight it into a comprehensive impact factor λ, BP neural network prediction was established taking λ as an input. The model transmitted the predicted value of the model output to the energy storage device in real time, and adopted the smoothing control of the photovoltaic power generation output power based on the filtering principle to realize the smooth output of the photovoltaic power. The results show that the method can be effectively combined with the time-sharing electricity price, and the distribution network operation is improved. Theeconomical stability of the PCC point voltage at the same time reduces the possibility that the node voltage exceeds the limit. The prediction model has higher prediction accuracy and has a certain effect on the safety optimization operation of the distribution network.
引文
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[20]潘泉,王增福,梁彦,等.信息融合理论的基本方法与进展(Ⅱ)[J].控制理论与应用,2012,29(10):1233-1244.PAN Quan,WANG Zengfu,LIANG Yan,et al.The basic method and development of information fusion theory(II)[J].Control Theory&Applications,2012,29(10):1233-1244.
[2]李建宁,刘其辉,李赢,等.光伏发电关键技术的研究与发展综述[J].电气应用,2012,31(5):70-76.LI Jianning,LIU Qihui,LI Ying,et al.Research and development of key technologies of photovoltaic power generation[J].Electrotechnical Application,2012,31(5):70-76.
[3]ZHAO Hailong.Photovoltaic industry outlook in 2016[EB/OL].[2015-12.14].http://www.pvnews.cn/chanyepinglun/.
[4]GLASSLEY W,KLEISSL J,SHIU H,et al.Current state of the art in solar forecasting,final report:appendix A California renewable energy forecasting,resource data and mapping[R].California:California Institute for Energy and Environment,2010:112-117.
[5]赵书强,王明雨,胡永强,等.基于不确定理论的光伏出力预测研究[J].电工技术学报,2015,30(16):213-220.ZHAO Shuqiang,WANG Mingyu,HU Yongqiang,et al.Research on the prediction of PV output based on uncertainty theory[J].Transactions of China Electrotechnical Society,2015,30(16):213-220.
[6]龚莺飞,鲁宗相,乔颖,等.光伏功率预测技术[J].电力系统自动化,2016,40(4):140-151.GONG Yingfeng,LU Zongxiang,QIAO Ying,et al.An overview of photovoltaic energy system output forecasting technology[J].Automation of Electric Power Systems,2016,40(4):140-151.
[7]叶林,陈政,赵永宁,等.基于遗传算法-模糊径向基神经网络的光伏发电功率预测模型[J].电力系统自动化,2015,39(16):16-22.YE Ling,CHEN Zheng,ZHAO Yongning,et al.Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis function neural network[J].Automation of Electric Power Systems,2015,39(16):16-22.
[8]嵇灵,牛东晓,汪鹏.基于相似日聚类和贝叶斯神经网络的光伏发电功率预测研究[J].中国管理科学,2015,23(3):118-122.JI ling,NIU Dongxiao,WANG Peng.Photovoltaic load forecasting based on the similar day and bayesian neural network[J].Chinese Journal of Management Science,2015,23(3):118-122.
[9]袁晓玲,施俊华,徐杰彦.计及天气类型指数的光伏发电短期出力预测[J].中国电机工程学报,2013,33(34):57-64.YUAN Xiaoling,SHI Junhua,XU Jieyan.Short-term power forecasting for photovoltaic generation considering weather type index[J].Proceedings of the CSEE,2013,33(34):57-64.
[10]代倩,段善旭,蔡涛,等.基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J].中国电机工程学报,2011,31(34):28-35.DAI Qian,DUAN Shanxu,CAI Tao,et al.Short-term PVgeneration system forecasting model without irradiation based on weather type clustering[J].Proceedings of the CSEE,2011,31(34):28-35.
[11]丁明,徐宁舟.基于马尔可夫链的光伏发电系统输出功率短期预测方法[J].电网技术,2011,35(1):152-157.DING Ming,XU Ningzhou.A method to forecast short-term output power of photovoltaic generation system based on Markov chain[J].Power System Technology,2011,35(1):152-157.
[12]MANDAL P,MADHIRA S T S,MENG J,et al.Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques[J].Procedia Computer Science,2012,12(3):332-337.
[13]高阳,张碧玲,毛京丽,等.基于机器学习的自适应光伏超短期出力预测模型[J].电网技术,2015,39(2):307-311.GAO Yang,ZHANG Biling,MAO Jingli,et al.Machine learning based adaptive very-short-term forecast model for photovoltaic power[J].Power System Technology,2015,39(2):307-311.
[14]程莎莎,张琦,黄湘源.用于平抑风电波动功率的混合储能系统及其控制系统[J].电工电气,2013,5(8):17-21.CHENG Shasha,ZHANG Qi,HUANG Xiangyuan.Hybrid energy storage system and control system for balance and inhibition of wind power fluctuation in power output[J].Electrotechnics Electric,2013,5(8):17-21.
[15]严干贵,朱星旭,李军徽,等.内蕴运行寿命测算的混合储能系统控制策略设计[J].电力系统自动化,2013,37(1):110-114.YAN Gangue,ZHU Xingxu,LI Junhui,et al.Control strategy design for hybrid energy storage system with intrinsic operation life measurement and calculation[J].Automation of Electric Power Systems,2013,37(1):110-114.
[16]夏向阳,唐伟,冉成科,等.基于DSP控制的单相光伏并网逆变器设计[J].电力科学与技术学报,2011,26(3):52-56.XIA Xiangyang,TANG Wei,RAN Chengke,et al.Design of single phase photovoltaic grid-connected inverter based on DSPcontrol[J].Journal of Electric Power Science and Technology,2011,26(3):52-56.
[17]李清然,张建成.含分布式光伏电源的配电网电压越限解决方案[J].电力系统自动化,2015,39(22):117-123.LI Qingran,ZHANG Jiancheng.Solutions of voltage beyond limits in distribution network with distributed photovoltaic generators[J].Automation of Electric Power Systems,2015,39(22):117-123
[18]康重庆,夏清,刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007:208-213.KANG Chongqing,XIA Qing,LIU Mei.Power system load forecasting[M].Beijing:China Electric Power Press,2007:208-213.
[19]YONA A,SENJYU T,FUNABASHI T.Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system[C]//IEEE Power Engineering Society General Meeting.Tampa,FL,USA:Institute of Electrical and Electronics Engineers,2007:1-6.
[20]潘泉,王增福,梁彦,等.信息融合理论的基本方法与进展(Ⅱ)[J].控制理论与应用,2012,29(10):1233-1244.PAN Quan,WANG Zengfu,LIANG Yan,et al.The basic method and development of information fusion theory(II)[J].Control Theory&Applications,2012,29(10):1233-1244.