NDLI: Environmental/economic power dispatch using multiobjective evolutionary algorithms

Content Provider	IEEE Xplore Digital Library
Author	Abido, M.A.
Copyright Year	2003
Abstract	Summary form only given. This paper presents a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new strength Pareto evolutionary algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and non-commensurable objectives. The proposed approach employs a diversity-preserving mechanism to overcome the premature convergence and search bias problems. A hierarchical clustering algorithm is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise nondominated solution. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of the multiobjective EED problem in one single run. The comparison with the classical techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem. In addition, the extension of the proposed approach to include more objectives is a straightforward process.
Starting Page	920
Ending Page	925
File Size	455026
Page Count	6
File Format	PDF
ISBN	0780379896
DOI	10.1109/PES.2003.1270431
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power generation economics Environmental economics Evolutionary computation Costs Power system economics Power systems Genetic algorithms Fuel economy Power generation Constraint optimization
Content Type	Text
Resource Type	Article

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