An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems

Nguyen Gia Minh Thao and Kenko Uchida
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Nguyen Gia Minh Thao: Research Center for Smart Vehicles and Electromagnetic Energy System Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan
Kenko Uchida: Department of Electrical Engineering and Bioscience, Waseda University, Tokyo 169-8555, Japan

Energies, 2018, vol. 11, issue 3, 1-26

Abstract: This paper proposes an improved interval fuzzy modeling (imIFML) technique based on modified linear programming and actual boundary points of data. The imIFML technique comprises four design stages. The first stage is based on conventional interval fuzzy modeling (coIFML) with first-order model and linear programming. The second stage defines reference lower and upper bounds of data using MATLAB. The third stage initially adjusts scaling parameters in the modified linear programming. The last stage automatically fine-tunes parameters in the modified linear programming to realize the best possible model. Lower and upper bounds approximated by the imIFML technique are closely fitted to the reference lower and upper bounds, respectively. The proposed imIFML is thus significantly less conservative in cases of large variation in data, while robustness is inherited from the coIFML. Design flowcharts, equations, and sample MATLAB code are presented for reference in future experiments. Performance and efficacy of the introduced imIFML are evaluated to estimate solar photovoltaic, wind and battery power in a demonstrative renewable energy system under large data changes. The effectiveness of the proposed imIFML technique is also compared with the coIFML technique.

Keywords: interval fuzzy modeling; linear programming; lower bound; upper bound; boundary points; min-max optimization; automatic-tuning scheme; photovoltaic/wind/battery power system. (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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