基于云理论的风电场群长期出力区间预测
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摘要
风电的不确定性给风电集群式开发和并网带来极大挑战,风电功率的点预测已很难满足电网长期灵活规划的实际需求。针对风电场群的长期风电功率区间预测问题,提出了一种基于云理论的D藤PairCopula-GARCH-t模型,用于预测风电场群的出力区间。GARCH-t模型较为准确地反映了预测误差的尖峰厚尾特性,提高了风电功率预测的精度。D藤Pair Copula模型有效地描述了风电场群之间出力的相关性。以期望、熵和超熵为数字特征的云模型预测出的风电功率区间,不仅能反映风电的随机性和模糊性,也能合理地描述两者之间的关联性,为规划人员作长期风电并网规划提供参考。
The uncertainty of wind power poses great challenges to the cluster development and grid connection of wind power. The point prediction of wind power is difficult to meet the actual need of flexible planning of the power network. Regarding to the problem of interval prediction for long-term wind power of wind farms clusters, the D-vine Pair Copula-GARCH-t model based on cloud theory is proposed to predict the output range of wind farms. The GARCH-t model can accurately reflect the leptokurtic characteristics of prediction errors and thus improve the precision of wind power prediction. Meanwhile, the D-vine Pair Copula model effectively describes the correlation between the wind power of wind farm clusters. Based on such cloud model with the digital characteristics of expectation, entropy and hyper entropy, the interval wind power can not only reflect the randomness and fuzziness, but also describe reasonably the relationship between them. The interval prediction for wind power could provide a reference for planner to do the long-term planning of wind farms.
The uncertainty of wind power poses great challenges to the cluster development and grid connection of wind power. The point prediction of wind power is difficult to meet the actual need of flexible planning of the power network. Regarding to the problem of interval prediction for long-term wind power of wind farms clusters, the D-vine Pair Copula-GARCH-t model based on cloud theory is proposed to predict the output range of wind farms. The GARCH-t model can accurately reflect the leptokurtic characteristics of prediction errors and thus improve the precision of wind power prediction. Meanwhile, the D-vine Pair Copula model effectively describes the correlation between the wind power of wind farm clusters. Based on such cloud model with the digital characteristics of expectation, entropy and hyper entropy, the interval wind power can not only reflect the randomness and fuzziness, but also describe reasonably the relationship between them. The interval prediction for wind power could provide a reference for planner to do the long-term planning of wind farms.
引文
[1]国家发改委能源研究所.中国2050高比例可再生能源发展情景暨路经研究[R].2015.
[2]白建华,辛颂旭,刘俊,等.中国实现高比例可再生能源发展路径研究[J].中国电机工程学报,2015,35(14):3699-3702.BAI Jianhua,XIN Songxu,LIU Jun,et al.Roadmap of realizing the high penetration renewable energy in China[J].Proceeding of the CSEE,2015,35(14):3699-3702.
[3]国家发改革委.可再生能源发展“十三五”规划[R].2016.
[4]康重庆,姚良忠.高比例可再生能源电力系统的关键科学问题与理论研究框架[J].电力系统自动化,2017,41(9):2-11.KANG Chongqing,YAO Liangzhong.Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):2-11.
[5]程浩忠,李隽,吴耀武,等.考虑高比例可再生能源的交直流输电网规划挑战与展望[J].电力系统自动化,2017,41(9):19-27.CHENG Haozhong,LI Jun,WU Yaowu,et al.Challenges and prospects for AC/DC transmission expansion planning considering high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):19-27.
[6]黎静华,桑川川,甘一夫,等.风电功率预测技术研究综述[J].现代电力,2017,34(3):1-11.LI Jinghua,SANG Chuanchuan,GAN Yifu,et al.Areview on wind power prediction technology[J].Modern Electric Power,2017,34(3):1-11.
[7]张颖超,王雅晨,邓华,等.基于IAFSA-BPNN的短期风功率预测[J].电力系统保护与控制,2017,45(7):58-63.ZHANG Yingchao,WANG Yachen,DENG Hua,et al.Short-term wind power prediction based on IAFSA-BPNN[J].Power System Protection and Control,2017,45(7):58-63.
[8]田波,朴在林,郭丹,等.基于改进EEMD-SE-ARMA的超短期风功率组合预测模型[J].电力系统保护与控制,2017,45(4):72-79.TIAN Bo,PIAO Zailin,GUO Dan,et al.Combined prediction model of ultra short term wind power based on improved EEMD-SE-ARMA[J].Power System Protection and Control,2017,45(4):72-79.
[9]李燕青,袁燕舞,郭通.基于AMD-ICSA-SVM的超短期风电功率组合预测[J].电力系统保护与控制,2017,45(14):113-120.LI Yanqing,YUAN Yanwu,GUO Tong.Combined prediction model of ultra short term wind power based on AMD-ICSA-SVM[J].Power System Protection and Control,2017,45(14):113-120.
[10]LI Z,YE L,ZHAO Y N,et al.Short-term wind power prediction based on extreme learning machine with error correction[J].Protection and Control of Modern Power Systems,2016,1(1):9-16.DOI:10.1186/s41601-016-0016-y.
[11]范磊,卫志农,李慧杰,等.基于变分模态分解和蝙蝠算法-相关向量机的短期风速区间预测[J].电力自动化设备,2017,37(1):93-100.FAN Lei,WEI Zhinong,LI Huijie,et al.Short-term wind speed interval prediction based on VMD and BA-RVMalgorithm[J].Electric Power Automation Equipment,2017,37(1):93-100.
[12]兰飞,桑川川,梁浚杰,等.基于条件Copula函数的风电功率区间预测[J].中国电机工程学报,2016,36(增刊1):79-86.LAN Fei,SANG Chuanchuan,LIANG Junjie,et al.Interval prediction for wind power based on conditional copula function[J].Proceedings of the CSEE,2016,36(S1):79-86.
[13]杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(增刊1):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction intervals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(S1):146-153.
[14]LI X,ZHANG X,WU L.Transmission line overload risk assessment for power systems with wind and load-power generation correlation[J].IEEE Transactions on Smart Grid,2015,6(3):1233-1242.
[15]VALIZADEH H H,LOTFIFARD S.Spatiotemporal modeling of wind generation for optimal energy storage sizing[J].IEEE Transactions on Sustainable Energy,2015,6(1):113-121.
[16]张玥,王秀丽,曾平良,等.基于Copula理论考虑风电相关性的源网协调规划[J].电力系统自动化,2017,41(9):102-108.ZHANG Yue,WANG Xiuli,ZENG Pingliang,et al.Source network coordination planning based on Copula theory considering wind power correlation[J].Automation of Electric Power Systems,2017,41(9):102-108.
[17]徐玉琴,陈坤,李俊卿,等.Copula函数与核估计理论相结合分析风电场出力相关性的一种新方法[J].电工技术学报,2016,31(13):92-100.XU Yuqin,CHEN Kun,LI Junqing,et al.A new method analyzing output correlation of multi-wind farms based on combination of Copula function and kernel estimation theory[J].Transactions of China Electrotechnical Society,2016,31(13):92-100.
[18]吴巍,汪可友,李国杰,等.基于Pair Copula的多维风电功率相关性分析及建模[J].电力系统自动化,2015,39(16):37-42.WU Wei,WANG Keyou,LI Guojie,et al.Correlation analysis and modeling of multiple wind power based on Pair Copula[J].Automation of Electric Power Systems,2015,39(16):37-42.
[19]王国胤,李德毅,姚一豫,等.云模型与粒计算[M].北京:科学出版社,2012.
[20]兰飞,农植贵,黎静华.风电功率序列的时空相关性研究[J].电力系统及其自动化学报,2016,28(1):24-31.LAN Fei,NONG Zhigui,LI Jinghua.Research on spatial and temporal correlation of wind power sequence[J].Proceedings of the CSU-EPSA,2016,28(1):24-31.
[21]黄红梅.应用时间序列分析[M].北京:清华大学出版社,2016.
[22]田书欣,程浩忠,曾平良,等.大型集群风电接入输电系统规划研究综述[J].中国电机工程学报,2014,34(10):1566-1574.TIAN Shuxin,CHENG Haozhong,ZENG Pingliang,et al.Review of transmission planning for integrating large clusters of wind power[J].Proceedings of the CSEE,2014,34(10):1566-1574.
[23]秦志龙,李文沅,熊小伏.考虑风速相关性的发输电系统可靠性评估[J].电力系统自动化,2013,37(16):47-52.QIN Zhilong,LI Wenyuan,XIONG Xiaofu.Reliability assessment of composite generation and transmission system considering wind speed correlation[J].Automation of Electric Power Systems,2013,37(16):47-52.
[2]白建华,辛颂旭,刘俊,等.中国实现高比例可再生能源发展路径研究[J].中国电机工程学报,2015,35(14):3699-3702.BAI Jianhua,XIN Songxu,LIU Jun,et al.Roadmap of realizing the high penetration renewable energy in China[J].Proceeding of the CSEE,2015,35(14):3699-3702.
[3]国家发改革委.可再生能源发展“十三五”规划[R].2016.
[4]康重庆,姚良忠.高比例可再生能源电力系统的关键科学问题与理论研究框架[J].电力系统自动化,2017,41(9):2-11.KANG Chongqing,YAO Liangzhong.Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):2-11.
[5]程浩忠,李隽,吴耀武,等.考虑高比例可再生能源的交直流输电网规划挑战与展望[J].电力系统自动化,2017,41(9):19-27.CHENG Haozhong,LI Jun,WU Yaowu,et al.Challenges and prospects for AC/DC transmission expansion planning considering high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):19-27.
[6]黎静华,桑川川,甘一夫,等.风电功率预测技术研究综述[J].现代电力,2017,34(3):1-11.LI Jinghua,SANG Chuanchuan,GAN Yifu,et al.Areview on wind power prediction technology[J].Modern Electric Power,2017,34(3):1-11.
[7]张颖超,王雅晨,邓华,等.基于IAFSA-BPNN的短期风功率预测[J].电力系统保护与控制,2017,45(7):58-63.ZHANG Yingchao,WANG Yachen,DENG Hua,et al.Short-term wind power prediction based on IAFSA-BPNN[J].Power System Protection and Control,2017,45(7):58-63.
[8]田波,朴在林,郭丹,等.基于改进EEMD-SE-ARMA的超短期风功率组合预测模型[J].电力系统保护与控制,2017,45(4):72-79.TIAN Bo,PIAO Zailin,GUO Dan,et al.Combined prediction model of ultra short term wind power based on improved EEMD-SE-ARMA[J].Power System Protection and Control,2017,45(4):72-79.
[9]李燕青,袁燕舞,郭通.基于AMD-ICSA-SVM的超短期风电功率组合预测[J].电力系统保护与控制,2017,45(14):113-120.LI Yanqing,YUAN Yanwu,GUO Tong.Combined prediction model of ultra short term wind power based on AMD-ICSA-SVM[J].Power System Protection and Control,2017,45(14):113-120.
[10]LI Z,YE L,ZHAO Y N,et al.Short-term wind power prediction based on extreme learning machine with error correction[J].Protection and Control of Modern Power Systems,2016,1(1):9-16.DOI:10.1186/s41601-016-0016-y.
[11]范磊,卫志农,李慧杰,等.基于变分模态分解和蝙蝠算法-相关向量机的短期风速区间预测[J].电力自动化设备,2017,37(1):93-100.FAN Lei,WEI Zhinong,LI Huijie,et al.Short-term wind speed interval prediction based on VMD and BA-RVMalgorithm[J].Electric Power Automation Equipment,2017,37(1):93-100.
[12]兰飞,桑川川,梁浚杰,等.基于条件Copula函数的风电功率区间预测[J].中国电机工程学报,2016,36(增刊1):79-86.LAN Fei,SANG Chuanchuan,LIANG Junjie,et al.Interval prediction for wind power based on conditional copula function[J].Proceedings of the CSEE,2016,36(S1):79-86.
[13]杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(增刊1):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction intervals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(S1):146-153.
[14]LI X,ZHANG X,WU L.Transmission line overload risk assessment for power systems with wind and load-power generation correlation[J].IEEE Transactions on Smart Grid,2015,6(3):1233-1242.
[15]VALIZADEH H H,LOTFIFARD S.Spatiotemporal modeling of wind generation for optimal energy storage sizing[J].IEEE Transactions on Sustainable Energy,2015,6(1):113-121.
[16]张玥,王秀丽,曾平良,等.基于Copula理论考虑风电相关性的源网协调规划[J].电力系统自动化,2017,41(9):102-108.ZHANG Yue,WANG Xiuli,ZENG Pingliang,et al.Source network coordination planning based on Copula theory considering wind power correlation[J].Automation of Electric Power Systems,2017,41(9):102-108.
[17]徐玉琴,陈坤,李俊卿,等.Copula函数与核估计理论相结合分析风电场出力相关性的一种新方法[J].电工技术学报,2016,31(13):92-100.XU Yuqin,CHEN Kun,LI Junqing,et al.A new method analyzing output correlation of multi-wind farms based on combination of Copula function and kernel estimation theory[J].Transactions of China Electrotechnical Society,2016,31(13):92-100.
[18]吴巍,汪可友,李国杰,等.基于Pair Copula的多维风电功率相关性分析及建模[J].电力系统自动化,2015,39(16):37-42.WU Wei,WANG Keyou,LI Guojie,et al.Correlation analysis and modeling of multiple wind power based on Pair Copula[J].Automation of Electric Power Systems,2015,39(16):37-42.
[19]王国胤,李德毅,姚一豫,等.云模型与粒计算[M].北京:科学出版社,2012.
[20]兰飞,农植贵,黎静华.风电功率序列的时空相关性研究[J].电力系统及其自动化学报,2016,28(1):24-31.LAN Fei,NONG Zhigui,LI Jinghua.Research on spatial and temporal correlation of wind power sequence[J].Proceedings of the CSU-EPSA,2016,28(1):24-31.
[21]黄红梅.应用时间序列分析[M].北京:清华大学出版社,2016.
[22]田书欣,程浩忠,曾平良,等.大型集群风电接入输电系统规划研究综述[J].中国电机工程学报,2014,34(10):1566-1574.TIAN Shuxin,CHENG Haozhong,ZENG Pingliang,et al.Review of transmission planning for integrating large clusters of wind power[J].Proceedings of the CSEE,2014,34(10):1566-1574.
[23]秦志龙,李文沅,熊小伏.考虑风速相关性的发输电系统可靠性评估[J].电力系统自动化,2013,37(16):47-52.QIN Zhilong,LI Wenyuan,XIONG Xiaofu.Reliability assessment of composite generation and transmission system considering wind speed correlation[J].Automation of Electric Power Systems,2013,37(16):47-52.