DocumentCode :
2218697
Title :
LRGA for solving profit based generation scheduling problem in competitive environment
Author :
Logenthiran, T. ; Srinivasan, Dipti
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
fYear :
2011
fDate :
5-8 June 2011
Firstpage :
1148
Lastpage :
1154
Abstract :
Deregulated power industries increase the efficiency of electricity production and distribution, and offer higher quality, secure, and more reliable electricity at low prices. In a deregulated environment, utilities are not required to meet the total load demand. Generation companies (GENCOs) schedule the generators that produce less than the predicted load demand and reserve, but aim to deliver maximum profits. The scheduling of generators depends on the market price. More number of generating units are committed when the market price is higher. When more number of generating units are brought in the deregulated market, more profit can be achieved by producing higher amount of power. This paper present a hybrid algorithm to solve a profit based unit commitment problem in a deregulated environment. The proposed algorithm has been developed from generation company´s point of view. It maximizes the profit of the generation company in the deregulated power and reserve markets. A hybrid methodology between Lagrangian Relaxation and Generic Algorithm (LRGA) is used to solve generation scheduling in a day-ahead competitive electricity market. The results obtained are quite encouraging and useful in deregulated market optimization.
Keywords :
genetic algorithms; power generation dispatch; power generation economics; power generation scheduling; power markets; LRGA; Lagrangian relaxation and generic algorithm; day-ahead competitive electricity market; deregulated market optimization; deregulated power industries; electricity production efficiency; generation companies; market price; profit based generation scheduling problem; profit based unit commitment problem; reserve markets; Dynamic programming; Economics; Generators; Genetic algorithms; Optimization; Power systems; Scheduling; Generation scheduling; Generic algorithm; Lagrangian relaxation; Profit-based unit commitment; Regulated power system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
ISSN :
Pending
Print_ISBN :
978-1-4244-7834-7
Type :
conf
DOI :
10.1109/CEC.2011.5949746
Filename :
5949746
Link To Document :
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