Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

نویسندگان

چکیده

Transmission expansion planning (TEP), the determination of new transmission lines to be added an existing power network, is a key element in system planning. Using classical optimization define most suitable reinforcements desirable alternative. However, extent under-study problems growing, because uncertainties introduced by renewable generation or electric vehicles (EVs) and larger sizes under consideration given trends for higher shares stronger market integration. This means that optimization, even using efficient techniques, such as stochastic decomposition, can have issues when solving large-sized problems. compounded fact that, many cases, it necessary solve large number instances problem order incorporate further considerations. Thus, interesting resort metaheuristics, which offer quick solutions at expense optimality guarantee. Metaheuristics combined with try extract best both worlds. There vast literature tests individual metaheuristics on specific case studies, but wide comparisons are missing. In this paper, genetic algorithm (GA), orthogonal crossover based differential evolution (OXDE), grey wolf optimizer (GWO), moth–flame (MFO), exchange (EMA), sine cosine (SCA) imperialistic competitive (ICA) tested compared. The algorithms applied standard test systems IEEE 24, 118 buses. Results indicate although all effective, they diverging profiles terms computational time finding optimal plans TEP.

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ژورنال

عنوان ژورنال: Energies

سال: 2021

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en14123618