Thermal unit commitment solution using genetic algorithm combined with the principle of tabu search and priority list method

Abstract

Unit commitment (UC) is one of optimization problem which is important in electrical power systems as effort to minimize generation cost by applying an effective scheduling. However, the size of search space and many constraints in this problem are becoming the problems. This paper will present hybrid algorithm which integrates genetic algorithm (GA) combined with the principle of tabu search (TS) and priority list (PL) methods to solve the UC problem. PL will be used for solving the unit scheduled problem. GA and the principle of TS are used for solving the economic dispatch problem. To optimize GA parameters, design of experiment (DOE) method will be used. The proposed algorithm is tested on the IEEE 10 unit systems for a one day scheduling periods. The results are compared with methodological priority list, shuffled frog leaping algorithm, hybrid particle swarm optimization, standard GA, integer coded GA, and Lagrange relaxation GA methods. This proposed hybrid method shows that the total cost of the unit commitment problem is better than other compared methods and near-optimal solution.