NON-PARAMETRIC KERNEL DENSITY ESTIMATION AND ANALYSIS OF GUANGDONG OFFSHORE WIND POWER OUTPUT BASED ON OPTIMAL BANDWIDTH

Rao Zhi; Wang Ke; Tan Junfeng; Li Jiaming; Yang Zaimin; Meng Wenchuan

doi:10.19912/j.0254-0096.tynxb.2022-1325

PDF(1596 KB)

Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (12) : 274-282. DOI: 10.19912/j.0254-0096.tynxb.2022-1325

NON-PARAMETRIC KERNEL DENSITY ESTIMATION AND ANALYSIS OF GUANGDONG OFFSHORE WIND POWER OUTPUT BASED ON OPTIMAL BANDWIDTH

Rao Zhi^1,2, Wang Ke³, Tan Junfeng², Li Jiaming³, Yang Zaimin¹, Meng Wenchuan¹

Author information +

History +

Abstract

To support offshore wind power planning and grid security scheduling, a non-parametric kernel density estimation model based on the optimal bandwidth parameter is proposed, which does not need to rely on a priori parameter results and can be adapted to offshore wind power with multi-modal output distribution. The case study verifies that the fitting effect of the proposed model can have the characteristics of smooth curve and reflect the characteristics of spikes, reducing the error of fitting estimation. And by analyzing the non-parametric kernel density estimation model of offshore wind power, development suggestions are formed, which can provide reference value for offshore wind power planning and power grid security dispatch in Guangdong Province.

Key words

offshore wind power / non-parametric kernel density estimation / optimal bandwidth / multiple spatiotemporal scales

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Rao Zhi, Wang Ke, Tan Junfeng, Li Jiaming, Yang Zaimin, Meng Wenchuan. NON-PARAMETRIC KERNEL DENSITY ESTIMATION AND ANALYSIS OF GUANGDONG OFFSHORE WIND POWER OUTPUT BASED ON OPTIMAL BANDWIDTH[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 274-282 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1325

References

[1] 樊肖杰, 迟永宁, 马士聪, 等. 大规模海上风电接入电网关键技术与技术标准的研究及应用[J]. 电网技术, 2022, 46(8): 2859-2870.
FAN X J, CHI Y N, MA S C, et al.Research and application of key technologies and technical standards for large-scale offshore wind farms connecting to power grid[J]. Power system technology, 2022, 46(8):2859-2870.
[2] 唐英杰, 张哲任, 徐政. 基于单向电流型AAMC的海上风电直流送出方案[J]. 电力系统自动化, 2022, 46(14): 129-139.
TANG Y J, ZHANG Z R, XU Z.Offshore wind power DC transmission scheme based on unidirectional current alternate-arm multilevel converter[J]. Automation of electric power systems, 2022, 46(14):129-139.
[3] 苏向敬, 周汶鑫, 李超杰, 等. 基于双重注意力LSTM神经网络的可解释海上风电出力预测[J]. 电力系统自动化, 2022, 46(7): 141-151.
SU X J, ZHOU W X, LI C J, et al.Interpretable offshore wind power output forecasting based on long short-term memory neural network with dual-stage attention[J]. Automation of electric power systems, 2022, 46(7):141-151.
[4] 符杨, 任子旭, 魏书荣, 等. 基于改进LSTM-TCN模型的海上风电超短期功率预测[J]. 中国电机工程学报, 2022, 42(12): 4292-4303.
FU Y, REN Z X, WEI S R, et al.Ultra-short-term power prediction of offshore wind power based on improved LSTM-TCN model[J]. Proceedings of the CSEE, 2022, 42(12):4292-4303.
[5] 余光正, 陆柳, 汤波, 等. 考虑转折性天气的海上风电功率超短期分段预测方法研究[J]. 中国电机工程学报, 2022, 42(13): 4859-4871.
YU G Z, LU L, TANG B, et al.Research on ultra-short-term subsection forecasting method of offshore wind power considering transitional weather[J]. Proceedings of the CSEE, 2022, 42(13):4859-4871.
[6] 杨楠, 王波, 刘涤尘, 等. 计及大规模风电和柔性负荷的电力系统供需侧联合随机调度方法[J]. 中国电机工程学报, 2013, 33(16): 63-69, 17.
YANG N, WANG B, LIU D C, et al.An integrated supply-demand stochastic optimization method considering large-scale wind power and flexible load[J]. Proceedings of the CSEE, 2013, 33(16): 63-69, 17.
[7] FABBRI A, ROMAN T G S, ABBAD J R, et al. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market[J]. IEEE transactions on power systems, 2005, 20(3): 1440-1446.
[8] BLUDSZUWEIT H, DOMINGUEZ-NAVARRO J A, LLOMBART A. Statistical analysis of wind power forecast error[J]. IEEE transactions on power systems, 2008, 23(3): 983-991.
[9] BLUDSZUWEIT H, DOMINGUEZ-NAVARRO J A. A probabilistic method for energy storage sizing based on wind power forecast uncertainty[J]. IEEE transactions on power systems, 2011, 26(3): 1651-1658.
[10] 丁华杰, 宋永华, 胡泽春, 等. 基于风电场功率特性的日前风电预测误差概率分布研究[J]. 中国电机工程学报, 2013, 33(34): 136-144, 22.
DING H J, SONG Y H, HU Z C, 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, 22.
[11] 林卫星, 文劲宇, 艾小猛, 等. 风电功率波动特性的概率分布研究[J]. 中国电机工程学报, 2012, 32(1): 38-46, 20.
LIN W X, WEN J Y, AI X M, et al.Probability density function of wind power variations[J]. Proceedings of the CSEE, 2012, 32(1): 38-46, 20.
[12] 吕晓禄, 梁军, 贠志皓, 等. 风电场出力的纵向时刻概率分布特性[J]. 电力自动化设备, 2014, 34(5): 40-45.
LYU X L, LIANG J, YUN Z H, et al.Longitudinal instant probability distribution of wind farm output power[J]. Electric power automation equipment, 2014, 34(5): 40-45.
[13] 徐询, 谢丽蓉, 叶林, 等. 基于非参数核密度估计的风电场有功功率双层优化模型[J]. 电力系统自动化, 2022, 46(2): 43-55.
XU X, XIE L R, YE L, et al.Bi-level optimization model of active power for wind farm based on nonparametric kernel density estimation[J]. Automation of electric power systems, 2022, 46(2):43-55.
[14] 刘晓楠, 周介圭, 贾宏杰, 等. 基于非参数核密度估计与数值天气预报的风速预测修正方法[J]. 电力自动化设备, 2017, 37(10): 15-20.
LIU X N, ZHOU J G, JIA H J, et al.Correction method of wind speed prediction based on non-parametric kernel density estimation and numerical weather prediction[J]. Electric power automation equipment, 2017, 37(10):15-20.
[15] 杨楠, 黄禹, 叶迪, 等. 基于自适应多变量非参数核密度估计的多风电场出力相关性建模[J]. 中国电机工程学报, 2018, 38(13): 3805-3812, 4021.
YANG N, HUANG Y, YE D, et al.Modeling of output correlation of multiple wind farms based on adaptive multivariable nonparametric kernel density estimation[J]. Proceedings of the CSEE, 2018, 38(13):3805-3812, 4021.
[16] 吴振威, 蒋小平, 马会萌, 等. 基于非参数核密度估计法的光伏出力随机分布模型[J]. 中国电力, 2013, 46(9): 126-130.
WU Z W, JIANG X P, MA H M, et al.Random distribution model of photovoltaic output based on non-parametric kernel density estimation method[J]. Electric power, 2013, 46(9):126-130.
[17] 饶志, 杨再敏, 蒙文川, 等. 基于改进型非参数核密度估计法的南方区域风电出力特性分析[J]. 电网与清洁能源, 2022, 38(6): 81-88, 97.
RAO Z, YANG Z M, MENG W C, et al.An analysis of wind power output characteristics in Southern China region based on improved non-parametric kernel density estimation[J]. Power system and clean energy, 2022, 38(6): 81-88, 97.
[18] 刘思, 傅旭华, 叶承晋, 等. 应用聚类分析与非参数核密度估计的空间负荷分布规律[J]. 电网技术, 2017, 41(2): 604-610.
LIU S, FU X H, YE C J, et al.Spatial load distribution based on clustering analysis and non-parametric kernel density estimation[J]. Power system technology, 2017, 41(2): 604-610.
[19] 张晓英, 张晓敏, 廖顺, 等. 基于聚类与非参数核密度估计的风电功率预测误差分析[J]. 太阳能学报, 2019, 40(12): 3594-3604.
ZHANG X Y, ZHANG X M, LIAO S, et al.Prediction error analysis of wind power based on clustering and non-parametric kernel density estimation[J]. Acta energiae solaris sinica, 2019, 40(12): 3594-3604.
[20] 兰博, 田喆, 牛纪德, 等. 基于TRNSYS仿真数据的平板型太阳能集热器净得热非参数核密度估计概率模型[J]. 太阳能学报, 2021, 42(2):1-7.
LAN B, TIAN Z, NIU J D, et al.Probabilistic model of nonparametric kernel density estimation for useful energy gain of flat-plate solar collector based on trnsys simulation data[J]. Acta energiae solaris sinica, 2021, 42(2):1-7.