DocumentCode :
1195990
Title :
Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints
Author :
Venkatesh, P. ; Gnanadass, R. ; Padhy, Narayana Prasad
Author_Institution :
Dept. of Electr. & Electron. Eng., Thiagarajar Coll. of Eng., Madurai, India
Volume :
18
Issue :
2
fYear :
2003
fDate :
5/1/2003 12:00:00 AM
Firstpage :
688
Lastpage :
697
Abstract :
Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost and optimal emission of generating units, respectively. Combined economic emission dispatch (CEED) problem is obtained by considering both the economy and emission objectives. This biobjective CEED problem is converted into a single objective function using a price penalty factor approach. A novel modified price penalty factor is proposed to solve the CEED problem. In this paper, evolutionary computation (EC) methods such as genetic algorithm (GA), micro GA (MGA), and evolutionary programming (EP) are applied to obtain ELD solutions for three-, six-, and 13-unit systems. Investigations showed that EP was better among EC methods in solving the ELD problem. EP-based CEED problem has been tested on IEEE 14-, 30-, and 118-bus systems with and without line flow constraints. A nonlinear scaling factor is also included in EP algorithm to improve the convergence performance for the 13 units and IEEE test systems. The solutions obtained are quite encouraging and useful in the economic emission environment.
Keywords :
air pollution; evolutionary computation; genetic algorithms; load flow; power generation dispatch; power generation economics; IEEE 118-bus system; IEEE 14-bus systems; IEEE 30-bus system; biobjective CEED problem; combined economic emission dispatch; economic load dispatch; evolutionary computation; evolutionary programming; genetic algorithm; line flow constraints; optimal fuel cost; price penalty factor; Cogeneration; Cost function; Environmental economics; Evolutionary computation; Fuel economy; Genetic algorithms; Genetic programming; Propagation losses; Quadratic programming; System testing;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/TPWRS.2003.811008
Filename :
1198303
Link To Document :
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