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