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.
Highlights
In the past decades large-scale wind power integration has become a trend [1]
The scenario results indicated that the overall fitness error of the adaptive multi-variable non-parametric kernel density estimation (AMNKDE) method was 8.81% and 11.6% lower than that of the traditional multivariate nonparametric kernel density estimation (MNKDE) method and the Copula-based parameter estimation method, respectively
For the proposed AMNKDE in this paper, the bandwidth was transformed from a traditional single parameter matrix, which contributed to the increasing difficulty of the solution
Summary
In the past decades large-scale wind power integration has become a trend [1]. As a result, a variety of uncertainties have been identified in the power systems [2,3,4,5,6,7]. Different from the PE method, the probability distributions of objects can be modeled directly, without the prior judgment process of function forms by the nonparametric kernel density estimation (NKDE) method It has higher accuracy and applicability and has been applied effectively in the field of probabilistic modeling in power systems [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31].
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call