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 (Department of Electrical Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India) and Madhumita Ghosh (Dr. B.C. Roy Engineering College, Durgapur, India)
Copyright: © 2017 |Pages: 17
DOI: 10.4018/IJEOE.2017010103
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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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1. Introduction

With sharp the rise in energy demand and resulting increased pollution, the issues of energy conservation and green power gained much attention to the researchers. Cogeneration or combined heat and power technology proves to be a promising alternative with its greater conversion efficiency than traditional generation method as it harnesses heat that would otherwise be wasted. Cogeneration is a sequential generation of two different forms of useful energy from a single primary energy source such as natural gas, typically electrical and thermal energy. The heat production capacity of most co-generation units depends on the power generated, and vice versa. The mutual dependency of multiple demand and heat–power capacity of those units introduces complexities in the integration of co-generation units into the economic dispatch (ED) problem.

Although a lot of works (Rooijers, & Amerongen, 1994; Guo, Henwood, & Ooijen, 1996; Song, Chou, & Stonham, 1999; Wong, & Algie, 2002; Su, & Chiang, 2004; Vasebi, Fesanghary, & Bathaee, 2007; Piperagkas, Anastasiadis, & Hatziargyriou, 2011; Sudhakaran, & Slochanal, 2003; Hosseini, Jafarnejad, Behrooz, & Gandomi, 2011) have been done in the field of cogeneration stating the objective of ED is to schedule the outputs of the online generating units so that the fuel cost of generation can be minimized, while simultaneously satisfying all system equality and inequality constraints.

Non-linear optimization methods, such as dual and quadratic programming (QP) (Rooijers, & Amerongen, 1994), lagrangian relaxation (Sashirekha, Pasupuleti, Moin, & Tan, 2013), gradient descent approaches, Lagrangian relaxation (LR) (Guo, Henwood, & Ooijen, 1996), were applied for solving combined heat power economic dispatch (CHPED) problem. However, these methods cannot handle non-convex fuel cost function of the generating units.

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