计及风电功率预测的飞轮储能配合风电场并网的有功功率控制
|
摘要
大规模风电并网后,风电场输出功率的波动对电力系统的安全稳定运行带来了巨大的挑战。其中,风电接入对系统功率平衡的影响是一个重要研究方面。进行风电功率预测和研究合适的风电场有功功率控制方式,有助于提高风电场的可调度性、降低风电功率波动对系统有功平衡的不利影响。
对于风电功率预测方面,本文以某风电场为研究对象。首先,根据《风电功率预测功能规范》设计并实现了风电数据预处理算法,对风电场原始数据中的不良数据进行了修正。其次,建立了基于BP神经网络的短期风电功率预测模型,提供预测时刻之后96点风电场有功曲线；建立了基于时间序列算法的超短期风电功率预测模型,提供预测时刻之后15min的风电场功率。并在Matlab平台上验证了预测算法的有效性。在此基础上,设计了相应的风电功率预测系统(WPPS)的功能和框架,采用风电场提供的历史数据测试运行表明系统能够可靠工作。对于风电场有功功率控制方面,本文在双馈感应电机(DFIG)风电场的交流侧接入飞轮储能系统(FESS),研究了一种新的风电场输出功率平滑控制策略。对Matlab/Simulink仿真结果的定量分析表明,所提出的控制策略能跟踪风电场预测功率并有效平滑风电场较小的输出功率波动。
With large-scale wind power integration into grid, the fluctuation of active power output of wind farm will become a big challenge for safe and stable operation of the power system. The influence of large-scale wind power integration on system power balance is a main area in research. In light of this issue, implement wind power prediction and study the appropriate active power control method of the wind farm will contribute to improve the schedulability of wind farm and avoid the adverse impact on system power balance due to the output fluctuation of wind farm.
The paper was based on the research object of a wind farm in the respect of wind power prediction. First, wind farm data preprocessing algorithm was proposed according to the Function Specification of Wind Power Prediction System. The bad data of wind farm source data was amended. Second, a short-term prediction model based on BP neural network was established, which provided96-points active predicted power curve of wind farm after prediction time was confirmed; Meanwhile, an ultrashort-term prediction model based on time-series algorithm was established, which provided rolling prediction curve of15minutes in advance of wind farm. The effectiveness of the wind power prediction algorithm was verified in MATLB. Based on this, the function and framework of corresponding wind power prediction system (WPPS) was designed. The WPPS was tested by the historical data from wind farm and it's shown that the WPPS could work reliably. In the respect of active power control of wind farm, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm. Anew smooth control strategy to level the wind power fluctuation was studied. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can track predicted power of wind farm and smooth the small active power fluctuation of wind farm effectively.
对于风电功率预测方面,本文以某风电场为研究对象。首先,根据《风电功率预测功能规范》设计并实现了风电数据预处理算法,对风电场原始数据中的不良数据进行了修正。其次,建立了基于BP神经网络的短期风电功率预测模型,提供预测时刻之后96点风电场有功曲线；建立了基于时间序列算法的超短期风电功率预测模型,提供预测时刻之后15min的风电场功率。并在Matlab平台上验证了预测算法的有效性。在此基础上,设计了相应的风电功率预测系统(WPPS)的功能和框架,采用风电场提供的历史数据测试运行表明系统能够可靠工作。对于风电场有功功率控制方面,本文在双馈感应电机(DFIG)风电场的交流侧接入飞轮储能系统(FESS),研究了一种新的风电场输出功率平滑控制策略。对Matlab/Simulink仿真结果的定量分析表明,所提出的控制策略能跟踪风电场预测功率并有效平滑风电场较小的输出功率波动。
With large-scale wind power integration into grid, the fluctuation of active power output of wind farm will become a big challenge for safe and stable operation of the power system. The influence of large-scale wind power integration on system power balance is a main area in research. In light of this issue, implement wind power prediction and study the appropriate active power control method of the wind farm will contribute to improve the schedulability of wind farm and avoid the adverse impact on system power balance due to the output fluctuation of wind farm.
The paper was based on the research object of a wind farm in the respect of wind power prediction. First, wind farm data preprocessing algorithm was proposed according to the Function Specification of Wind Power Prediction System. The bad data of wind farm source data was amended. Second, a short-term prediction model based on BP neural network was established, which provided96-points active predicted power curve of wind farm after prediction time was confirmed; Meanwhile, an ultrashort-term prediction model based on time-series algorithm was established, which provided rolling prediction curve of15minutes in advance of wind farm. The effectiveness of the wind power prediction algorithm was verified in MATLB. Based on this, the function and framework of corresponding wind power prediction system (WPPS) was designed. The WPPS was tested by the historical data from wind farm and it's shown that the WPPS could work reliably. In the respect of active power control of wind farm, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm. Anew smooth control strategy to level the wind power fluctuation was studied. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can track predicted power of wind farm and smooth the small active power fluctuation of wind farm effectively.
引文
[1]我国风电产业发展研究报告[R/OL].http://wenku.baidu.com/view/1cf4a7106c175f0e7cd1377f.
[2]贺益康,胡家兵,徐烈.并网双馈异步风力发电机运行控制[M].北京：中国电力出版社,2011：2-3.
[3]向荣.基于飞轮储能的并网风电场有功功率及频率控制[D].西南交通大学硕士论文,2011.
[4]2010中国风电装机容量统计出炉两项指标世界第一[R/OL].http://www.chin aequip.gov.cn/zhuanti/fengdian/2011-03/30/c_13804775.htm.
[5]李俊峰,施鹏飞,高虎.中国风电发展报告2010[R/OL].http://www.docin.com/p-88066770.html.
[6]姚伟,程时杰,文劲宇.直流输电技术在海上风电场并网中的应用[J].中国电力,2007,40(10)：70-74.
[7]黄玲玲,符杨,郭晓明.海上风电场集电系统可靠性评估[J].电网技术,2010,34(7)：169-174.
[8]葛川,何炎平,叶宇,杜鹏飞.海上风电场的发展、构成和基础形式[J].中国海洋平台,2008,23(6)：31-35.
[9]我国风电产业发展研究报告[R/OL].http://wenku.baidu.com/view/1cf4a7106c175f0e7cd1377f.html.
[10]雷亚洲.与风电并网相关的研究课题[J].电力系统自动化,2003,27(8)：84-89.
[11]傅旭,李海伟,李冰寒.大规模风电场并网对电网的影响及对策综述[J].陕西电力,2010,1：53-57.
[12]曹军,王虹富,邱家驹.变速恒频双馈风电机组频率控制策略[J].电力系统自动化,2009,33(13)：78-82.
[13]周双喜,鲁宗相.风力发电与电力系统[M].北京：中国电力出版社,2011：301-302.
[14]杨志凌.风电场功率短期预测方法优化的研究[D].华北电力大学博士论文,2011.
[15]MA Lei, LUAN Shi-yan, JIANG Chuan-wen, et al. A review on the forecasting of wind speed and generated power[J]. Renewable and Sustainable Energy Reviews,2009,13(4): 915-920.
[16]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算法[J].中国电机工程学报,2008,28(26)：87-91.
[17]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S, et al. Short-term forecasting of wind speed and related electrical power[J]. Solar Energy,1998,63(1):61-68.
[18]陈颖,周海,王文鹏,等.风电场输出功率超短期预测结果分析与改进[J].电力系 统自动化,2011,35(15)：30-33.
[19]卢静,牛若涛,冯双磊,等.风电功率预测技术及应用[J].新材料产业,2011,11：14-16.
[20]孟洋洋.风电场发电功率短期预测模型研究[D].重庆大学硕士论文,2011.
[21]冯双磊,王伟胜,刘纯,等.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2)：1-6.
[22]Landberg L. Prediktor: An on-line prediction system[C]. EUWEC Special Topic Conference, Kassel, Germany,2000:233-236.
[23]Nielsen T S, Madsen H. WPPT:a tool for wind power prediction[C]. EWEA Special Topic Conference, Kassel, Germany,2000:25-27.
[24]Zack J W, Brower M C, Bailey B H. Validating of the forewind model in wind forecasting application[C]. EUWEC Special Topic Conference Wind Power for the 21st Century, Kassel, Germany,2000.
[25]Lange M, Focken U, Heinemann D. Previento-regional wind power prediction with risk control[C]. World Wind Energy Conference, Berlin,2002.
[26]Lange B, Rohrig K, Ernst B, et al. Wind power prediction in Germany:Recent advances and future challenges[C]. European Wind Energy Conference, Athens,2006.
[27]国家电网公司调度通信中心.风电功率预测系统功能规范[S].2010.
[28]范高锋,王伟胜,刘纯.基于人工神经网络的风电功率短期预测系统[J].电网技术,2008,32(22)：72-76.
[29]刘兴杰.风电输出功率预测方法与系统[D].华北电力大学博士论文,2011.
[30]Hu P, Karki R, Billinton R. Reliability evaluation of generating systems containing wind power and energy storage[J]. Generation, Transmission & Distribution, IET,2009, 3(8):783-791.
[31]雷亚洲,Lightbody Gordon.国外风力发电导则及动态模型简介[J].电网技术,2005,25(12)：27-32.
[32]Toshiaki Kaneko, Tomonobu Senjyu, Atsushi Yona, et al. Output power coordination control for wind farm in small power system[C]. International Conference on Intelligent Systems Applications to Power Systems (ISAP 2007), Niigata, JAPAN,2007:1-6.
[33]Ahmad-Rami Al-Awaad. Advanced concept for participation of large wind farms in ancillary services[C]. International Conference on Renewable Energies and Power Quality (ICREPQ'10), Granada, Spain,2010:1-6.
[34]de Almeida R G, Peas Lopes J A. Participation of doubly fed induction wind generators in system frequency regulation[J]. Power Systems, IEEE Transactions on,2007,22(3): 944-950.
[35]Chowdhury B H, Ma H T. Frequency regulation with wind power plants[C]. Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century,2008:1-5.
[36]Panayiotis Moutis, Emmanouil Loukarakis, Stavros Papathanasiou, et al. Primary load-frequency control from pitch-controlled wind turbines [C]. IEEE Bucharest Power Tech Conference, Bucharest, Romania,2009:1-7.
[37]邹贤求.变速恒频风电机组参与一次调频的控制方法研究[D].湖南大学硕士论文,2010.
[38]迟永宁.大型风电场接入电网的稳定性问题研究[D].中国电力科学研究院博士论文,2006.
[39]王黎.飞轮储能技术应用于风力发电系统的仿真研究[D].大连理工大学硕士论文,2010.
[40]Cimuca G O, Saudemont C, Robyns B, et al. Control and performance evaluation of a flywheel energy-storage system associated to a variable-speed wind generator[J]. Industrial Electronics, IEEE Transactions on,2006,53(4):1074-1085.
[41]H Holttinen, R Hirvonen. Wind Power in Power Systems [M].1st Edition, John Wiley & Sons Ltd., Chichester,2005.
[42]熊倩,廖勇.含飞轮储能的直驱风电系统功率平滑控制策略研究[J].华东电力,2012,40(9)：1533-1539.
[43]薛迎成,邰能灵,宋凯,等.基于功率滤波的风力场功率平滑控制[J].华东电力,2011,39(3):454-458.
[44]Barton J P, Infield D G. Energy storage and its use with intermittent renewable energy[J]. Energy Conversion, IEEE Transactions on,2004,19(2):441-448.
[45]Changling L, Banakar H, Baike S, et al. Strategies to smooth wind power fluctuations of wind turbine generator[J]. Energy Conversion, IEEE Transactions on,2007,22(2): 341-349.
[46]廖勇,何金波,姚骏,等.基于变桨距和转矩动态控制的直驱永磁同步风力发电机功率平滑控制[J].中国电机工程学报,2009,29(18)：71-77.
[47]毕大强,葛宝明,王文亮,等.基于钒电池储能系统的风电场并网功率控制[J].电力系统自动化,2010,34(13)：72-78.
[48]洪海生,江全元,严玉婷.实时平抑风电场功率波动的电池储能系统优化控制方法[J].电力系统自动化,2013,37(1)：103-109.
[49]李蓓,郭剑波.平抑风电功率的电池储能系统控制策略[J].电网技术,2012,36(8)：38-43.
[50]阮军鹏,张建成,汪娟华.飞轮储能系统改善并网风电场稳定性的研究[J].电力科学与工程,2008,24(3)：5-8.
[51]胡雪松,孙才新,刘刃,等.采用飞轮储能的永磁直驱风电机组有功平滑控制策略[J].电力系统自动化,2010,34(13)：79-83.
[52]向荣,王晓茹,谭谨.基于飞轮储能的并网风电场有功功率及频率控制方法研究[J]. 系统科学与数学,2012,32(4)：438-449.
[53]吴桂联.短期风电功率预测技术研究[D].天津大学硕士论文,2012.
[54]田雨波.混合神经网络技术[M].北京：科学出版社,2009：20-47.
[55]蔡祯祺.基于数值天气预报NWP修正的BP神经网络风电功率短期预测研究[D].浙江大学硕士论文,2012.
[56]孟勇.风电功率预测系统的研究与开发[D].天津大学硕士论文,2010.
[57]叶晨.风电功率组合预测研究[D].华北电力大学(北京)硕士论文,2011.
[58]马华林,李翠风,张立燕.基于灰色模型和自适应过滤的网络流量预测[J].计算机工程,2009,35(1)：130-131.
[59]Takahashi R, Tamura J. Frequency control of isolated power system with wind farm by using Flywheel Energy Storage System [C].18th International Conference on Electrical Machines of the ICEM, Vilamoura,2008:1-6.
[60]汤新舟.永磁同步电机的矢量控制系统[D].浙江大学硕士论文,2005.
[61]孙春顺,王耀南,李欣然.飞轮辅助的风力发电系统功率和频率综合控制[J].中国电机工程学报,2008,28(29)：111-116.
[62]Wangdee W, Billinton R. Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment[J]. Energy Conversion, IEEE Transactions on,2006,21(3):734-741.
[63]李俊芳,张步涵,谢光龙,等.基于灰色模型的风速-风电功率预测研究[J].电力系统保护与控制,2010,38(19)：151-159.
[64]Tanabe T, Sato T, Tanikawa R, et al. Generation scheduling for wind power generation by storage battery system and meteorological forecast[C]. Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, 2008:1-7.
[65]PAATERO J V, LUND P D. Effect of energy storage on variations in wind power[J]. Wind Energy,2005,8(4):421-441.
[66]Chapelsky C, Salmon J, Knight A. Control of a high-inertia flywheel as part of a high capacity energy storage system[C]. Electrical and Computer Engineering (CCECE 2007), Canadian,2007:1437-1440.
[2]贺益康,胡家兵,徐烈.并网双馈异步风力发电机运行控制[M].北京：中国电力出版社,2011：2-3.
[3]向荣.基于飞轮储能的并网风电场有功功率及频率控制[D].西南交通大学硕士论文,2011.
[4]2010中国风电装机容量统计出炉两项指标世界第一[R/OL].http://www.chin aequip.gov.cn/zhuanti/fengdian/2011-03/30/c_13804775.htm.
[5]李俊峰,施鹏飞,高虎.中国风电发展报告2010[R/OL].http://www.docin.com/p-88066770.html.
[6]姚伟,程时杰,文劲宇.直流输电技术在海上风电场并网中的应用[J].中国电力,2007,40(10)：70-74.
[7]黄玲玲,符杨,郭晓明.海上风电场集电系统可靠性评估[J].电网技术,2010,34(7)：169-174.
[8]葛川,何炎平,叶宇,杜鹏飞.海上风电场的发展、构成和基础形式[J].中国海洋平台,2008,23(6)：31-35.
[9]我国风电产业发展研究报告[R/OL].http://wenku.baidu.com/view/1cf4a7106c175f0e7cd1377f.html.
[10]雷亚洲.与风电并网相关的研究课题[J].电力系统自动化,2003,27(8)：84-89.
[11]傅旭,李海伟,李冰寒.大规模风电场并网对电网的影响及对策综述[J].陕西电力,2010,1：53-57.
[12]曹军,王虹富,邱家驹.变速恒频双馈风电机组频率控制策略[J].电力系统自动化,2009,33(13)：78-82.
[13]周双喜,鲁宗相.风力发电与电力系统[M].北京：中国电力出版社,2011：301-302.
[14]杨志凌.风电场功率短期预测方法优化的研究[D].华北电力大学博士论文,2011.
[15]MA Lei, LUAN Shi-yan, JIANG Chuan-wen, et al. A review on the forecasting of wind speed and generated power[J]. Renewable and Sustainable Energy Reviews,2009,13(4): 915-920.
[16]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算法[J].中国电机工程学报,2008,28(26)：87-91.
[17]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S, et al. Short-term forecasting of wind speed and related electrical power[J]. Solar Energy,1998,63(1):61-68.
[18]陈颖,周海,王文鹏,等.风电场输出功率超短期预测结果分析与改进[J].电力系 统自动化,2011,35(15)：30-33.
[19]卢静,牛若涛,冯双磊,等.风电功率预测技术及应用[J].新材料产业,2011,11：14-16.
[20]孟洋洋.风电场发电功率短期预测模型研究[D].重庆大学硕士论文,2011.
[21]冯双磊,王伟胜,刘纯,等.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2)：1-6.
[22]Landberg L. Prediktor: An on-line prediction system[C]. EUWEC Special Topic Conference, Kassel, Germany,2000:233-236.
[23]Nielsen T S, Madsen H. WPPT:a tool for wind power prediction[C]. EWEA Special Topic Conference, Kassel, Germany,2000:25-27.
[24]Zack J W, Brower M C, Bailey B H. Validating of the forewind model in wind forecasting application[C]. EUWEC Special Topic Conference Wind Power for the 21st Century, Kassel, Germany,2000.
[25]Lange M, Focken U, Heinemann D. Previento-regional wind power prediction with risk control[C]. World Wind Energy Conference, Berlin,2002.
[26]Lange B, Rohrig K, Ernst B, et al. Wind power prediction in Germany:Recent advances and future challenges[C]. European Wind Energy Conference, Athens,2006.
[27]国家电网公司调度通信中心.风电功率预测系统功能规范[S].2010.
[28]范高锋,王伟胜,刘纯.基于人工神经网络的风电功率短期预测系统[J].电网技术,2008,32(22)：72-76.
[29]刘兴杰.风电输出功率预测方法与系统[D].华北电力大学博士论文,2011.
[30]Hu P, Karki R, Billinton R. Reliability evaluation of generating systems containing wind power and energy storage[J]. Generation, Transmission & Distribution, IET,2009, 3(8):783-791.
[31]雷亚洲,Lightbody Gordon.国外风力发电导则及动态模型简介[J].电网技术,2005,25(12)：27-32.
[32]Toshiaki Kaneko, Tomonobu Senjyu, Atsushi Yona, et al. Output power coordination control for wind farm in small power system[C]. International Conference on Intelligent Systems Applications to Power Systems (ISAP 2007), Niigata, JAPAN,2007:1-6.
[33]Ahmad-Rami Al-Awaad. Advanced concept for participation of large wind farms in ancillary services[C]. International Conference on Renewable Energies and Power Quality (ICREPQ'10), Granada, Spain,2010:1-6.
[34]de Almeida R G, Peas Lopes J A. Participation of doubly fed induction wind generators in system frequency regulation[J]. Power Systems, IEEE Transactions on,2007,22(3): 944-950.
[35]Chowdhury B H, Ma H T. Frequency regulation with wind power plants[C]. Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century,2008:1-5.
[36]Panayiotis Moutis, Emmanouil Loukarakis, Stavros Papathanasiou, et al. Primary load-frequency control from pitch-controlled wind turbines [C]. IEEE Bucharest Power Tech Conference, Bucharest, Romania,2009:1-7.
[37]邹贤求.变速恒频风电机组参与一次调频的控制方法研究[D].湖南大学硕士论文,2010.
[38]迟永宁.大型风电场接入电网的稳定性问题研究[D].中国电力科学研究院博士论文,2006.
[39]王黎.飞轮储能技术应用于风力发电系统的仿真研究[D].大连理工大学硕士论文,2010.
[40]Cimuca G O, Saudemont C, Robyns B, et al. Control and performance evaluation of a flywheel energy-storage system associated to a variable-speed wind generator[J]. Industrial Electronics, IEEE Transactions on,2006,53(4):1074-1085.
[41]H Holttinen, R Hirvonen. Wind Power in Power Systems [M].1st Edition, John Wiley & Sons Ltd., Chichester,2005.
[42]熊倩,廖勇.含飞轮储能的直驱风电系统功率平滑控制策略研究[J].华东电力,2012,40(9)：1533-1539.
[43]薛迎成,邰能灵,宋凯,等.基于功率滤波的风力场功率平滑控制[J].华东电力,2011,39(3):454-458.
[44]Barton J P, Infield D G. Energy storage and its use with intermittent renewable energy[J]. Energy Conversion, IEEE Transactions on,2004,19(2):441-448.
[45]Changling L, Banakar H, Baike S, et al. Strategies to smooth wind power fluctuations of wind turbine generator[J]. Energy Conversion, IEEE Transactions on,2007,22(2): 341-349.
[46]廖勇,何金波,姚骏,等.基于变桨距和转矩动态控制的直驱永磁同步风力发电机功率平滑控制[J].中国电机工程学报,2009,29(18)：71-77.
[47]毕大强,葛宝明,王文亮,等.基于钒电池储能系统的风电场并网功率控制[J].电力系统自动化,2010,34(13)：72-78.
[48]洪海生,江全元,严玉婷.实时平抑风电场功率波动的电池储能系统优化控制方法[J].电力系统自动化,2013,37(1)：103-109.
[49]李蓓,郭剑波.平抑风电功率的电池储能系统控制策略[J].电网技术,2012,36(8)：38-43.
[50]阮军鹏,张建成,汪娟华.飞轮储能系统改善并网风电场稳定性的研究[J].电力科学与工程,2008,24(3)：5-8.
[51]胡雪松,孙才新,刘刃,等.采用飞轮储能的永磁直驱风电机组有功平滑控制策略[J].电力系统自动化,2010,34(13)：79-83.
[52]向荣,王晓茹,谭谨.基于飞轮储能的并网风电场有功功率及频率控制方法研究[J]. 系统科学与数学,2012,32(4)：438-449.
[53]吴桂联.短期风电功率预测技术研究[D].天津大学硕士论文,2012.
[54]田雨波.混合神经网络技术[M].北京：科学出版社,2009：20-47.
[55]蔡祯祺.基于数值天气预报NWP修正的BP神经网络风电功率短期预测研究[D].浙江大学硕士论文,2012.
[56]孟勇.风电功率预测系统的研究与开发[D].天津大学硕士论文,2010.
[57]叶晨.风电功率组合预测研究[D].华北电力大学(北京)硕士论文,2011.
[58]马华林,李翠风,张立燕.基于灰色模型和自适应过滤的网络流量预测[J].计算机工程,2009,35(1)：130-131.
[59]Takahashi R, Tamura J. Frequency control of isolated power system with wind farm by using Flywheel Energy Storage System [C].18th International Conference on Electrical Machines of the ICEM, Vilamoura,2008:1-6.
[60]汤新舟.永磁同步电机的矢量控制系统[D].浙江大学硕士论文,2005.
[61]孙春顺,王耀南,李欣然.飞轮辅助的风力发电系统功率和频率综合控制[J].中国电机工程学报,2008,28(29)：111-116.
[62]Wangdee W, Billinton R. Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment[J]. Energy Conversion, IEEE Transactions on,2006,21(3):734-741.
[63]李俊芳,张步涵,谢光龙,等.基于灰色模型的风速-风电功率预测研究[J].电力系统保护与控制,2010,38(19)：151-159.
[64]Tanabe T, Sato T, Tanikawa R, et al. Generation scheduling for wind power generation by storage battery system and meteorological forecast[C]. Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, 2008:1-7.
[65]PAATERO J V, LUND P D. Effect of energy storage on variations in wind power[J]. Wind Energy,2005,8(4):421-441.
[66]Chapelsky C, Salmon J, Knight A. Control of a high-inertia flywheel as part of a high capacity energy storage system[C]. Electrical and Computer Engineering (CCECE 2007), Canadian,2007:1437-1440.