Adaptive Nonparametric Kernel Density Estimation Approach for Joint Probability Density Function Modeling of Multiple Wind Farms

Nan Yang, Yu Huang, Dengxu Hou, Songkai Liu, Di Ye, Bangtian Dong and Youping Fan
Additional contact information
Nan Yang: New Energy Micro-grid Collaborative Innovation Centre of Hubei Province (China Three Gorges University), Yichang 443002, China
Yu Huang: Yichang Power Supply Company, State Grid Hubei Electric Power Company, Yichang 443002, China
Dengxu Hou: New Energy Micro-grid Collaborative Innovation Centre of Hubei Province (China Three Gorges University), Yichang 443002, China
Songkai Liu: New Energy Micro-grid Collaborative Innovation Centre of Hubei Province (China Three Gorges University), Yichang 443002, China
Di Ye: New Energy Micro-grid Collaborative Innovation Centre of Hubei Province (China Three Gorges University), Yichang 443002, China
Bangtian Dong: New Energy Micro-grid Collaborative Innovation Centre of Hubei Province (China Three Gorges University), Yichang 443002, China
Youping Fan: School of Electrical Engineering, Wuhan University, Wuhan 430000, China

Energies, 2019, vol. 12, issue 7, 1-15

Abstract: The uncertainty of wind power brings many challenges to the operation and control of power systems, especially for the joint operation of multiple wind farms. Therefore, the study of the joint probability density function (JPDF) of multiple wind farms plays a significant role in the operation and control of power systems with multiple wind farms. This research was innovative in two ways. One, an adaptive bandwidth improvement strategy was proposed. It replaced the traditional fixed bandwidth of multivariate nonparametric kernel density estimation (MNKDE) with an adaptive bandwidth. Two, based on the above strategy, an adaptive multi-variable non-parametric kernel density estimation (AMNKDE) approach was proposed and applied to the JPDF modeling for multiple wind farms. The specific steps of AMNKDE were as follows: First, the model of AMNKDE was constructed using the optimal bandwidth. Second, an optimal model of bandwidth based on Euclidean distance and maximum distance was constructed, and the comprehensive minimum of these distances was used as a measure of optimal bandwidth. Finally, the ordinal optimization (OO) algorithm was used to solve this model. The scenario results indicated that the overall fitness error of the AMNKDE method was 8.81% and 11.6% lower than that of the traditional MNKDE method and the Copula-based parameter estimation method, respectively. After replacing the modeling object the overall fitness error of the comprehensive Copula method increased by as much as 1.94 times that of AMNKDE. In summary, the proposed approach not only possesses higher accuracy and better applicability but also solved the local adaptability problem of the traditional MNKDE.

Keywords: kernel density estimation; multiple wind farms; joint probability density; ordinal optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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