Abstract
A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems
Highlights
Economic dispatch is known as the requirement of the generation units to meet the demand load in a power system at a minimum cost [1]
Some of the intuitive metaheuristic optimization methods used in economic dispatch problem (EDP) solutions are the genetic algorithm (GA) [11], particle swarm optimization (PSO) [2], artificial bee colony (ABC) [12,13], gravitation search algorithm (GSA) [14], and teaching-learning-based optimization (TLBO) [15]
In this study, the GSA-TLBO method, which is a hybrid of the standard GSA algorithm in the global search space and TLBO in the local search space, has been proposed
Summary
Economic dispatch is known as the requirement of the generation units to meet the demand load in a power system at a minimum cost [1]. Some of the intuitive metaheuristic optimization methods used in EDP solutions are the genetic algorithm (GA) [11], particle swarm optimization (PSO) [2], artificial bee colony (ABC) [12,13], gravitation search algorithm (GSA) [14], and teaching-learning-based optimization (TLBO) [15] Even if these methods solve the EDP, basically, they do not recover from the long computation time and the problem of sticking to the local optimum [3,7]. Hybrid methods are obtained by combining the points where the heuristic algorithms are strong and the EDP is applied In this way, effective solutions can be found to reach the minimum fuel cost quickly without getting to the local optimum. Obtained bus test results were compared with the results of other methods in the literature
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