Continuous Genetic Algorithm to Solve Economic Environmental Dispatch (EED)

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


This paper presents the Continuous Genetic Algorithm (CGA) to solve the Economic Environmental Dispatch (EED) problem. The equality constraints of power balance and the inequality generator capacity constraints are considered. The EED problem is a biobjective non linear optimization problem since it is obtained by considering both the economy and emission objectives. This biobjectives problem is converted into a single objective function using a price penalty factor approach. In this paper CGA are tested on six generators system and the results are compared. The solutions are quite encouraging and useful in the EED.
Copyright © 2015 Praise Worthy Prize - All rights reserved.


Genetic Algorithm; Continuous Codage; Optimization Problem; Transmission Line Loss; Emission and Economic Dispatch

Full Text:



M.A.Abido , A novel multiobjective evolutionary algorithm for enviremental/economic power dispatch , Elsevier Electric power systems research., vol. 65 n. 3, November 2003, pp. 71-81.

M.A.Abido, A niched pareto genetic algorithm for multiobjective enviromental/economic dispatch, Elsevier Electric power and energy system, vol 25, April 2002, pp 97-105.

G.W.Stagg and A.H.El Abiadh, Computer methods in power systems analysis, (McGraw-Hill, 1968).

Robert T.F. Ah King, Harry C.S.Rughooputh and Kalyanmoy Deb, Evolutionary Multi-Objective Environmental/ Economic Dispatch: Stochastic vs. Deterministic Approaches, Int. J. Circuit Theory Approaches, Vol 2, Jun 1974, pp 163-174.

T. Denise King, M. E. El-Hawary and Feria1 El-Hawary, Optimal Environmental Dispatching Of Electric Power Systems Via An Improved Hopfield Neural Network Model, IEEE Trans. Power Syst, Vol 10, n 2 August 1995.

L. Benasla, Contribution to the study of the stability and the optimal economic dispatch, University of science and technology, University of science and technology Oran, Algeria, 2004.

O. I. El gerd, Electrical energy systems theory, (Mc Graw. Hill Company 1971).

P. venkatesh, R. Gnanadass and Narayana Prasad Padhy, Comparison and application of evolutionary programming techniques to Combined economic emission dispatch with line flow constraints, IEEE Trans. Power Syst, vol 18, n 2, May 2003.

M. Benyahia, study a environmental economic dispatch by genetic algorithm and neural network, University of science and technology Oran, Algeria 2006.

Mitsuo Gen and Renwer Cheng, Genetic algorithms & Engineering optimization (Wiley service in engineering desing and automatisation, 2000).

D.E. Goldberg, Algorithm genetic exploration optimization (Adison Wesley, 191).

Masatoshi Sakawa, Genetic algorithm and fuzzy multiobjective optimization (Kluwer Academic, 2001).

Colin R. Reevers and Jonathan E. Row, Genetic algorithms Principe and perspectives A guide to GA theory (Kluwer Academic, 2003).

S. Baskar, P. Subbarj and M. V. C. Rao, Hybride Real coded genetic algorithm solution to economic dispatch problem, Elsevier Computer and electrical engineering, vol 29, May 2003, pp 407-419.

Kwan Woo Kim, Mitsuo Gen and Genji Yamazaki, Hybride genetic algorithm with fuzzy logic for resource-constrained projet scheduling, Elsevier applied soft computation, February 2003, pp 174-188.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize