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Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization

Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization

Provas Kumar Roy, Madhumita Ghosh
Copyright: © 2017 |Volume: 6 |Issue: 1 |Pages: 17
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781522515210|DOI: 10.4018/IJEOE.2017010103
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MLA

Roy, Provas Kumar, and Madhumita Ghosh. "Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization." IJEOE vol.6, no.1 2017: pp.49-65. http://doi.org/10.4018/IJEOE.2017010103

APA

Roy, P. K. & Ghosh, M. (2017). Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization. International Journal of Energy Optimization and Engineering (IJEOE), 6(1), 49-65. http://doi.org/10.4018/IJEOE.2017010103

Chicago

Roy, Provas Kumar, and Madhumita Ghosh. "Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization," International Journal of Energy Optimization and Engineering (IJEOE) 6, no.1: 49-65. http://doi.org/10.4018/IJEOE.2017010103

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Abstract

This paper explores the performance of biogeography-based optimization (BBO) algorithm for solving combined heat and power dispatch (CHPD) or cogeneration problem of power system. BBO is a type of evolutionary algorithm which is based on the theory of biogeography and is inspired by the two concepts, namely migration of species between “islands” via flotsam, wind, flying, swimming, etc. and mutation. To improve the convergence property and solution quality, blended crossover strategy of genetic algorithm (GA) is integrated with conventional BBO algorithm in this study. The effect of valve-point in cost function is considered by adding an absolute sinusoidal term with the conventional polynomial cost function. The potential of the proposed BBO and GA based BBO (GABBO) algorithms are assessed by means of an extensive comparative study of the solutions obtained for small and medium CHPD problems of power systems. To show the priority of the proposed algorithm, comparative studies are carried out to examine the effectiveness of the proposed BBO and GABBO approaches over evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO) and time varying acceleration coefficients PSO (TVAC-PSO reported in the literature. The experimental results and comparison with other algorithms demonstrate that the proposed GABBO algorithm can be a proficient substitute lying on solving combined heat and power dispatch problems.

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