A genetic-based algorithm for fuzzy unit commitment model

Author

Mantawy, A.H.

Author_Institution

Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia

Volume

1

fYear

2000

fDate

2000

Firstpage

250

Abstract

This paper presents a fuzzy model for the unit commitment problem (UCP). The model takes the uncertainties in the forecasted load demand and the spinning reserve constraints in a fuzzy frame. The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. In the implementation for the GA, coding of the UCP solutions is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units plus a penalty term determined due to the fuzzy load and spinning reserve membership functions. Numerical results showed an improvement in the solutions costs compared to the results reported in the literature and the GA with crisp UCP model

Keywords

fuzzy set theory; genetic algorithms; load (electric); power generation scheduling; fitness function; forecasted load demand; fuzzy unit commitment model; generating units; genetic algorithm; genetic-based algorithm; penalty term; spinning reserve constraints; total operating cost; uncertainties; Cost function; Fuzzy logic; Genetic algorithms; Load forecasting; Minerals; Petroleum; Predictive models; Spinning; Testing; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society Summer Meeting, 2000. IEEE

Conference_Location

Seattle, WA

Print_ISBN

0-7803-6420-1

Type

conf

DOI

10.1109/PESS.2000.867526

Filename

867526