2012
DOI: 10.5120/4620-6629
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Optimization Generation Expansion Planning by HBMO

Abstract: The generation expansion planning (GEP) problem is a largescale mixed integer nonlinear programming (MINLP) problem cited as one of the most complex optimization problems. In this paper, an application of honey bee mating optimization for solving the generation expansion planning problem is presented. In the formulation, the objective is to minimize investment cost. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and val… Show more

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Cited by 8 publications
(6 citation statements)
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“…The most favorable type of generation technology to be installed and optimum level of generation from the units can be obtained from long term GEP problem [62]. The solution of a GEP problem framed with reliability as one of the constraint can be obtained using either mathematical methods [3][4][5][6] or Meta-heuristic optimization techniques [7][8][9] and is demonstrated in figure 2. Meza et al [3] presented an Analytic Hierarchy Approach (AHP) to describe and the solution of GEP represented as a long term multi objective model.…”
Section: B Methods To Obtain the Solution Of Gepmentioning
confidence: 99%
“…The most favorable type of generation technology to be installed and optimum level of generation from the units can be obtained from long term GEP problem [62]. The solution of a GEP problem framed with reliability as one of the constraint can be obtained using either mathematical methods [3][4][5][6] or Meta-heuristic optimization techniques [7][8][9] and is demonstrated in figure 2. Meza et al [3] presented an Analytic Hierarchy Approach (AHP) to describe and the solution of GEP represented as a long term multi objective model.…”
Section: B Methods To Obtain the Solution Of Gepmentioning
confidence: 99%
“…These include evolutionary programming [66], evolutionary strategy [66], ant colony optimisation [66], tabu search [66], simulated annealing [67], shuffled frog leaping algorithm [68], GA and game theory [69], GA and bender decomposition [65], GA [69, 70], honey bee algorithm [71], artificial immune system [72], inverse optimisation and bi‐level optimisation [73], PSO [74, 75] and expert system and fuzzy [76, 77].…”
Section: Solving Methodsmentioning
confidence: 99%
“…Some selection criteria that have been considered by researchers are overall investment cost, reliability, renewable fraction, etc. Examples of the meta‐heuristic techniques in GEP literature include genetic algorithm, PSO, simulated annealing, differential evolution, ant colony optimisation, artificial immune system, frog leaping algorithm, bee algorithm, fuzzy and expert system, and tabu search; methods such as harmony search, biogeography, and hill climbing have found application in TEP and offer promising applications in GEP problems.…”
Section: Optimisation Techniques In Gepmentioning
confidence: 99%