Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 80, Issue 9, Pages 1171-1181Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2010.03.010
Keywords
Environmental/economic power dispatch; Evolutionary algorithms; Differential evolution algorithm; Multi-objective optimization
Categories
Funding
- National Natural Science Foundation of P.R. China [60835004, 60775047, 60974048]
- National High Technology Research and Development Program of China [2007AA04Z244, 2008AA04Z214]
- Hunan Provincial Education Department [08C337, 06C305]
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This paper presents a multi-objective differential evolution (MODE) algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost, emission and system loss. The proposed MODE approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach have been carried out on the IEEE 30- and 118-bus test system. The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem. The comparison with reported results of other MOEAs reveals the superiority of the proposed MODE approach and confirms its potential for solving other power systems multi-objective optimization problems. (C) 2010 Elsevier B.V. All rights reserved.
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