Survey on China Wind Power Prediction Based on Monsoons and Atmospheric Pressure Distribution
YANG Zhengling1,LIU Rengxiang2,LI Zhenzhen1
1．天津大学电气自动化与信息工程学院，天津 南开 300072 2．天津市过程检测与控制重点实验室(天津大学)，天津 南开 300072 1. School of Electrical and Information Engineering, Tianjin University, Nankai District, Tianjin 300072, China 2. Key Laboratory of Process Measurement and Control (Tianjin University), Nankai District, Tianjin 300072, China
In order to improve the accuracy and reliability of China's ultra-short-term and short-term wind power prediction, the elementary characteristics of winter monsoon and summer monsoon in China are reviewed. China's southeast coastal area locates on the main path of the winter monsoon and the summer monsoon, where the wind speed spatial correlation exceeds 2000 km and lag time exceeds 20 h for more than 7 months every year. The Siberian High not only causes the winter monsoon, but also dominates wind energy resource-rich areas in north and west of China. Although the overall weather forecast in China is more difficult than that in Europe and the United States, the winter monsoon, the summer monsoon and the Siberian High make it possible potentially to improve wind speed prediction obviously in these areas by spatial correlation. In particular, the Taiwan Strait region has the obvious potential of high-precision ultra-short-term wind speed prediction. Then to exactly solve wind speed-power curve precisely by the law of rotation of rigid body round a fixed axis, it is expected to obtain the high-performance ultra-short-term and short-term wind power prediction in the wind energy resource-rich areas in north and west of China by spatial correlation.
杨正瓴,刘仍祥,李真真. 基于季风和大气压分布的我国风电功率预测研究[J]. 分布式能源, 2018, 3(2): 29-38.
YANG Zhengling,LIU Rengxiang,LI Zhenzhen. Survey on China Wind Power Prediction Based on Monsoons and Atmospheric Pressure Distribution[J]. Distributed Energy, 2018, 3(2): 29-38.
ZHAO Junhua, DONG Zhaoyang, WEN Fushuan, et al. Data science for energy systems: theory, techniques and prospect [J]. Automation of Electric Power Systems, 2017, 41(4): 1-11, 19.
ZHANG Guorong, CHEN Xiaran. Future development of energy internet[J]. Electric Power Automation Equipment, 2017, 37(1): 7-13.
[7]
SU W C, HUANG A Q. 美国的能源互联网与电力市场[J]. 科学通报，2016, 61(11): 1210-1221.
[7]
SU W C, HUANG A Q. The energy internet and electricity market in the United States[J]. Chinese Science Bulletin, 2016, 61(11): 1210-1221.
[8]
MONESS M, MOUSTAFA A M. A survey of cyber-physical advances and challenges of wind energy conversion systems: prospects for internet of energy[J]. IEEE Internet of Things Journal, 2016, 3(2): 134-145.
[9]
KALYANARAMAN S. Back to the future lessons for Internet of energy networks[J]. IEEE Internet Computing, 2016, 20(1): 60-65.
[10]
BUI N, CASTELLANI A, ASTELLANI A P, et al. The internet of energy: a web-enabled smart grid system[J]. IEEE Network, 2012, 26(4): 39-45.
BAO Minglei, DING Yi, SHAO Changzheng. Reference of international energy system transformation to energy internet construction in China[J]. Distributed Energy, 2017, 2(2): 11-19.
XUE Yusheng, YU Chen, ZHAO Junhua, et al. A review on short-term and ultra-short-term wind power prediction[J]. Automation of Electric Power Systems, 2015, 39(6): 141-151.
XUE Yusheng, CHEN Ning, WANG Shumin, et al. Review on wind speed prediction based on spatial correlation[J]. Automation of Electric Power Systems, 2017, 41(10): 161-169.
QIAO Ying, LU Zongxiang, MIN Yong, et al.Research & application of raising wind power prediction accuracy[J]. Power System Technology, 2017, 41(10): 3161-3169.
[15]
HAGSPIEL S, PAPAEMANNOUIL A, SCHMID M, et al. Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid[J]. Applied Energy, 2012, 96: 33-44.
XUE Yusheng, YU Chen, ZHAO Junhua, et al. A review on short-term and ultra-short-term wind power prediction[J]. Automation of Electric Power Systems, 2015, 39(6): 141-151.
DING Huajie, SONG Yonghua, HU Zechun, et al. Probability density function of day-ahead wind power forecast errors based on power curves of wind farms[J]. Proceedings of the CSEE, 2013, 33(34): 136-144.
YE Lin, ZHAO Yongning. A review on wind power prediction based on spatial correlation approach[J]. Automation of Electric Power Systems, 2014, 38(14): 126-135.
[19]
JUNG J, BROADWATER R P. Current status and future advances for wind speed and power forecasting[J]. Renewable and Sustainable Energy Reviews, 2014(31): 762-777.
[20]
WANG J, WANG Y, JIANG P. The study and application of a novel hybrid forecasting model: a case study of wind speed forecasting in China[J]. Applied Energy, 2015, 143: 472-488.
QIAN Zheng, PEI Yan, CAO Lixiao, et al. Review of wind power forecasting method[J]. High Voltage Engineering, 2016, 42(4): 1047-1060.
[22]
OKUMUS I, DINLER A. Current status of wind energy forecasting and a hybrid method for hourly predictions[J]. Energy Conversion and Management, 2016, 123: 362-371.
[23]
DONG Q, SUN Y, LI P. A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: a case study of wind farms in China[J]. Renewable Energy, 2017, 102: 241-257.
[24]
GALLEGO-CASTILLO C, CUERVA-TEJERO A, LOPEZ-GARCIA O. A review on the recent history of wind power ramp forecasting[J]. Renewable and Sustainable Energy Reviews, 2015, 52: 1148-1157.
[25]
YE L, ZHAO Y, ZENG C, et al. Short-term wind power prediction based on spatial model[J]. Renewable Energy, 2017, 101: 1067-1074.
[26]
HERBERT G M, JOSELIN S, AMUTHA D. A review of technical issues on the development of wind farms[J]. Renewable & Sustainable Energy Reviews, 2014, 32: 619-641.
[27]
JHA S K, BILALOVIC J, JHA A, et al. Renewable energy: present research and future scope of artificial intelligence[J]. Renewable & Sustainable Energy Reviews, 2017, 77: 297-317.
YANG Zhengling, LIU Yang, ZHANG Ze, et al. Ultra-short-term wind speed prediction with spatial correlation using recent historical observations[J]. Power System Technology, 2017, 41(6): 1815-1822.
SHI Tong, JIANG Zhuo, XIAO Bai. Intrinsic time scale determination based on analyzing wind power output fluctuation features[J]. Distributed Energy, 2017, 2(4): 53-58.
YANG Zhengling, YANG Zhao, ZHANG Xi, et al. Improving optimal lag time of spatial correlation prediction by characteristics of monsoon[J]. Power System Protection and Control, 2016, 44(15): 33-38.
YANG Zhengling, FENG Yong, XIONG Dingfang, et al. Research prospects of improvement in wind power forecasting based on characteristics of monsoons[J]. Smart Grid, 2015, 3(1): 1-7.
[32]
陈隆勋，朱乾根，罗会邦，等. 东亚季风[M]. 北京：气象出版社，1991.
[33]
WANG B. The Asian monsoon[M]. New York: Springer-Verlag, 2006.
[34]
LAING A, EVANS J-L. Introduction to tropical meteorology, 2nd edition[EB/OL]. (2011-10)[2014-07-08]. .
[35]
PREETHI B, MUJUMDAR M, KRIPALANI R H, et al. Recent trends and tele-connections among South and East Asian summer monsoons in a warming environment[J]. Climate Dynamics, 2017, 48(7-8): 2489-2505.
[36]
LEE J-YI, WANG B. Future change of global monsoon in the CMIP5[J]. Climate Dynamics, 2014, 42(1-2): 101-119.
[37]
黄瑞芳，周园春，鞠永茂，等. 气象与大数据[M]. 北京：科学出版社，2017.
[38]
EFRON B, HASTIE T. Computer age statistical inference: algorithms, evidence, and data science[M]. Cambridge: Cambridge University Press, 2016.
[39]
PROIETTI T. Direct and iterated multistep AR methods for difference stationary processes[J]. International Journal of Forecasting, 2011, 27(2): 266-280.
[40]
MCELROY T. When are direct multi-step and iterative forecasts identical？[J]. Journal of Forecasting, 2015, 34(4): 315-336.
[41]
CHEVILLON G. Multistep forecasting in the presence of location shifts[J]. International Journal of Forecasting, 2016, 32(1): 121-137.
[42]
KHALID M, SAVKIN A V. Closure to discussion on “a method for short-term wind power prediction with multiple observation points”[J]. IEEE Transactions on Power Systems, 2013, 28(2): 1898-1899.
