A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans

Jian Yang, Yu Liu, Shangguang Jiang, Yazhou Luo, Nianzhang Liu and Deping Ke
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Jian Yang: North China Branch of State Grid Corporation of China, Beijing 100053, China
Yu Liu: North China Branch of State Grid Corporation of China, Beijing 100053, China
Shangguang Jiang: North China Branch of State Grid Corporation of China, Beijing 100053, China
Yazhou Luo: North China Branch of State Grid Corporation of China, Beijing 100053, China
Nianzhang Liu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Deping Ke: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2022, vol. 15, issue 7, 1-21

Abstract: How to consider both the influence of weather and wind power in the modeling process of probability distribution of wind power forecast error (WPFE), and to emphasize the application value of conditional modeling, is rarely studied at present. This paper proposes a novel method of conditional probability distribution modeling for WPFE. This method uses a proposed MNSGA-II-Kmeans algorithm to perform multi-objective clustering of multi-dimensional influencing factors (MDIF), including weather and wind power. It can maximize the difference between the probability distributions of each MDIF mode’s WPFE while clustering, thus ensuring the application value of the conditional modeling way. Based on the clustering results, by using the versatile distribution to simulate the probability distribution of WPFE and the support vector machine to realize the recognition of MDIF modes, the specific conditional probability distribution function of WPFE can be provided to stochastic economic dispatch by identifying the forecast MDIF data. A wind plant of north China with historical data is selected for calculation. The results verify the effectiveness of the proposed method, and by comparison with the non-conditional probability distribution of WPFE that does not consider MDIF, it can effectively increase the wind power consumption of the power system.

Keywords: wind power forecast error; conditional probability distribution; multi-dimensional influencing factors; multi-objective clustering (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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