基于核密度估计和Copula函数的风、光出力场景生成

Abstract
Figure/Table
References
Related Citation (0)

Download: PDF (1315 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The randomness, volatility and intermittent nature of new energy resources bring troubles to power system planning. A reasonable description of how wind power and photovoltaic output behave and generating typical output scene is a common method for new energy planning. A method for generating typical scene of relevant wind power and photovoltaic output is proposed. This paper firstly fits a large number of sample data based on kernel density estimation, and performs fitting and pre-test to obtain a kernel density estimation expression of wind and photovoltaic power output. This paper builds a variety of combined distribution models of wind and photovoltaic power based on Copula functions, and then judges the fitness of each model. The Kendall and Spearman correlation coefficients of each model are considered to select the optimal Copula function as wind power, photovoltaic joint probability distribution. Finally, the annual power output of wind and photovoltaic power is generated based on the optimal Copula joint probability distribution. Case analysis shows that the simulation results of annual output of wind and photovoltaic power meets their relevance, and has higher accuracy in wind and photovoltaic power output in the reaction. There must be a certain reference value for the reliability analysis of power system and grid planning.

Key words： kernel density estimation Copula function scene generation complementary characteri- stics difference coefficient

Received: 30 August 2021

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	SONG Yu
	LI Han

Cite this article:

SONG Yu,LI Han. Typical scene generation of wind and photovoltaic power output based on kernel density estimation and Copula function[J]. Electrical Engineering, 2022, 23(1): 56-63.

URL:

http://dqjs.cesmedia.cn/EN/Y2022/V23/I1/56

[1] HOICKA C E, ROWLANDS I H.Solar and wind resource complementarity: advancing options for renewable electricity integration in Ontario, Canada[J]. Renewable Energy, 2011, 36(1): 97-107.
[2] 乔延辉, 韩爽, 许彦平, 等. 基于天气分型的风光出力互补性分析方法[J]. 电力系统自动化, 2021, 45(2): 82-88.
[3] 白桦, 王正用, 李晨, 等. 面向电网侧、新能源侧及用户侧的储能容量配置方法研究[J]. 电气技术, 2021, 22(1): 8-13.
[4] 钟嘉庆, 李茂林, 江静, 等. 基于Copula理论的风/光出力预测误差分析方法的研究[J]. 电工电能新技术, 2017, 36(6): 39-46.
[5] 偲默, 苗世洪, 霍雪松, 等. 基于动态Copula的风光联合出力建模及动态相关性分析[J]. 电力系统保护与控制, 2019, 47(5): 35-42.
[6] HAGHI H V, BINA M T, GOLKAR M A, et al.Using copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran[J]. Renewable Energy, 2010, 35(9): 1991-2000.
[7] 周泽人, 舒印彪, 董存, 等. 基于混合威布尔分布的风能资源分布统计分析研究[J]. 数理统计与管理, 2020, 39(4): 584-594.
[8] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
[9] 李鹏. 电力需求响应机制下含电池储能系统的风光互补发电系统经济调度研究[J]. 电气技术, 2015, 16(3): 57-60.
[10] 白顺明, 陈磊, 姜飞, 等. 考虑风电最大化消纳的电力系统多目标优化[J]. 电气技术, 2020, 21(1): 7-11.
[11] 吴治均, 李明昆. 基于馈线路径集合法的并网型微电网可靠性评估[J]. 电气技术, 2016, 17(10): 17-22.
[12] 黎静华, 文劲宇, 程时杰, 等. 考虑多风电场出力Copula相关关系的场景生成方法[J]. 中国电机工程学报, 2013, 33(16): 30-36, 21.
[13] 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.
[14] ARIK I, KANTAR Y M, USTA I.The new odd-Burr Rayleigh distribution for wind speed characterization[J]. Wind & Structures, 2019, 28(6): 369-380.
[15] 车兵, 李轩, 郑建勇, 等. 基于LHS与BR的风电出力场景分析研究[J]. 电力工程技术, 2020, 39(6): 213-219.
[16] 周博, 艾小猛, 方家琨, 等. 计及超分辨率风电出力不确定性的连续时间鲁棒机组组合[J]. 电工技术学报, 2021, 36(7): 1456-1467.