A Modified Strength Pareto Evolutionary Algorithm 2 based Environmental /Economic Power Dispatch
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Abstract
A Strength Pareto Evolutionary Algorithm 2 (SPEA 2) approach for solving the multi-objective Environmental / Economic Power Dispatch (EEPD) problem is presented in this paper. In the past fuel cost consumption minimization was the aim (a single objective function) of economic power dispatch problem. Since the clean air act amendments have been applied to reduce SO2 and NOX emissions from power plants, the utilities change their strategies in order to reduce pollution and atmospheric emission as well, adding emission minimization as other objective function made economic power dispatch (EPD) a multi-objective problem having conflicting objectives. SPEA2 is the improved version of SPEA with better fitness assignment, density estimation, and modified archive truncation. In addition fuzzy set theory is employed to extract the best compromise solution. Several optimization run of the proposed method are carried out on 3-units system and 6-units standard IEEE 30-bus test system. The results demonstrate the capabilities of the proposed method to generate well-distributed Pareto-optimal non-dominated feasible solutions in single run. The comparison with other multi-objective methods demonstrates the superiority of the proposed method.
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Abido M. A, 2001, A New Multi-objective Evolutionary Algorithm for Environmental/Economic Power Dispatch, Power Engineering Society Summer Meeting, IEEE, Volume 2, Pages: 1263–1268.
Talaq J. H., El-Hawary F. and El-Hawary M. E., 1994, A Summary of Environmental/Economic Dispatch Algorithms, IEEE Transactions on Power Systems, Volume 9, No. 3, Pages: 1508–1516.
Perez-Guerrero R. E., Cedeno-Maldonado J. R. , 2005, Differential Evolution Based Economic Environmental Power Dispatch power Symposium, proceedings of the 37thAnnual North American, Pages: 191-197.
Abido M. A., 2003a, A Niched Pareto Genetic Algorithm for Multi-Objective Environmental/ Economic Dispatch, International Journal of Electrical Power & Energy Systems, Elsevier, Volume 25, No. 2, Pages: 79–105.
Abido M. A., 2003, Environmental/Economic Power Dispatch using Multi-Objective Evolutionary Algorithms, IEEE Transactions on Power Systems, Volume 18, No. 4, Pages:1529–1537.
Ah. King R. T. F., Rughooputh H. C. S. and Deb K., 2004, EvolutionaryMulti-Objective Environmental/Economic Dispatch: Stochastic vs. Deterministic Approaches, KanGAL Report number 2004019.
Agrawal S., Panigrahi B. K. and Tiwari M. K., 2008, Multiobjective Particle Swarm Algorithm with Fuzzy Clustering for Electrical Power Dispatch, Evolutionary Computation, IEEE Transactions on Evolutionary Computation, Volume 12, Issue 5, Pages:529 - 541.
Zitzler E., Laumanns M., and Thiele L., 2001, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, TIK-Rep.
Abraham A., Jain L. and Goldberg R., 2005, Evolutionary Multi-Objective Optimization: Theoretical Advances and Applications, Springer- Verlag, London.
Zitzler E. and Thiele L., 1999, Multi-Objective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach, IEEE Transactions on Evolutionary Computation Volume 3, no. 4, Pages: 257-271.
Michalewicz Z., 1996, Genetic Algorithms + Data Structures =Evolution Programs, 3rd ed., Springer-
Verlag, Berlin.
R.T.F.A King and Rughooputh H.C.S. , 2003, Elitist Multi-objective Evolutionary Algorithm for environmental/ Economic Dispatch, Evolutionary Computation, IEEE, Volume 2, pages: 1108-1114.
Wang L. F. and Singh C., 2008, Stochastic Economic Emission Load Dispatch Through a Modified Particle Swarm Optimization Algorithm Elsevier, Electric Power Systems Research, Volume 78, Issue 8,Pages: 1466-1476.