Title :
A genetic-based algorithm for fuzzy unit commitment model
Author_Institution :
Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
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;
Conference_Titel :
Power Engineering Society Summer Meeting, 2000. IEEE
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-6420-1
DOI :
10.1109/PESS.2000.867526
