Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 55, Issue -, Pages 542-553Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2013.10.013
Keywords
Multi-objectives optimization; Artificial bee colony; Constrained handling; Swarm intelligence; Hydrothermal system; Short-term scheduling
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Funding
- National Natural Science Foundation of China [51239004]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20100142110012]
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In this paper, we present a multi-objective artificial bee colony (MOABC) algorithm and compare its efficiency with other existing algorithms for short-term scheduling of hydrothermal systems. We formulate the short-term combined economic and emission dispatch of hydrothermal systems as a complicated nonlinear optimization problem with a group of complex constraints. We modify the select operator of artificial bee colony algorithm to adapt the multi-objective problem optimization, and change the employed bee phase and probability calculation of onlooker bee phase to avoid local maxima. Furthermore, we utilize a progressive optimality algorithm based method to enhance the local search ability of the MOABC. Moreover, the constraint handling method has been proposed to resolve the complex constraints. We demonstrate the performance of the MOABC algorithm and compare it with other existing algorithms using the data from a hydrothermal power system in three different cases. The results show that the MOABC can obtain better schedule results with less fuel cost and environment pollution, more close to the true Pareto front and better diversification of non-dominated solutions compared to other existing methods. (C) 2013 Elsevier Ltd. All rights reserved.
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