Title :
Solution of unit commitment problem using enhanced genetic algorithm
Author :
Singhal, Prateek K. ; Naresh, R. ; Sharma, Veena ; Kumar, N. Goutham
Author_Institution :
Dept. of Electr. Eng., Nat. Inst. of Technol., Hamirpur, India
Abstract :
In this paper, enhanced genetic algorithm (EGA) is used to solve the short-term unit commitment problem (UCP) and the enhanced lambda iteration (ELI) method is used to solve the economic dispatch (ED) sub-problem. Based on EGA, the problem specific operators have been integrated in the simple GA algorithm and thus, enhanced the quality of the solution. Performance of EGA is tested on 2 test systems comprising of 4-unit and 10-unit over the scheduling time horizon of 8 hours and 24 hours respectively. Results demonstrate that the proposed method is superior to the other reported methods in the literature.
Keywords :
genetic algorithms; iterative methods; power generation dispatch; power generation economics; power generation scheduling; ED subproblem; EGA; ELI method; UCP; economic dispatch subproblem; enhanced genetic algorithm; enhanced lambda iteration method; operation unit commitment problem; scheduling time horizon; Biological cells; Genetic algorithms; Power generation; Schedules; Sociology; Spinning; Statistics; economic dispatch; genetic algorithm; unit commitment;
Conference_Titel :
Power Systems Conference (NPSC), 2014 Eighteenth National
DOI :
10.1109/NPSC.2014.7103861
