Twostage symmetrical fuzzy modeling and optimization for the unit commitment in wind power systems

Authors

Jiaming CAI, School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University, Shanghai 200240 ,China ；Shanghai Electric Power Design Institute Co., Ltd.,Shanghai 200012 , China
Chengmin WANG, School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University, Shanghai 200240 ,China
Ning XIE, School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University, Shanghai 200240 ,China
Xin LI, School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University, Shanghai 200240 ,China

Keywords

symmetric fuzzy optimization, renewable energy, unit commitment, uncertainty

Abstract

In order to solve the problem of uncertainty caused by the inherent prediction error in the unit commitment decisionmaking involving the wind generation, this paper establishes a twostage symmetric fuzzy optimization model with both fuzzy constraints and fuzzy targets. The first stage of the model determines the fuzzy parameters of the system. While, in the second stage, the actual fuzzy solution and the corresponding fuzzy level are calculated, and the uncertainty of the unit commitment is quantified.Due to the introduction of unsolved fuzzy variables, a simplified operation based on the outerpoint iteration method is proposed. In case of the dayahead prediction, the 4 h rolling prediction in the day, the 1 h ultrashort rolling prediction, the adaptability to the forecasting error of this method is analyzed though comparing with the actual situation.The results show that the fuzzy optimization can reduce the scheduling error caused by the prediction error to a certain extent, so it is suitable for the usage of system scheduling with a high uncertainty.

DOI

10.19781/j.issn.16739140.2020.06.005

First Page

36

Last Page

45

Recommended Citation

CAI, Jiaming; WANG, Chengmin; XIE, Ning; and LI, Xin (2021) "Twostage symmetrical fuzzy modeling and optimization for the unit commitment in wind power systems," Journal of Electric Power Science and Technology: Vol. 35: Iss. 6, Article 5.
DOI: 10.19781/j.issn.16739140.2020.06.005
Available at: https://jepst.researchcommons.org/journal/vol35/iss6/5