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Abstract:The solution accuracy of economic dispatch problems is associated with the accuracy of the fuel cost curve parameters. Therefore, updating of these parameters is a very important issue to further improve the final accuracy of economic dispatch problems. Estimating the parameters of the fuel cost curve may be the best solution for this issue. This paper presents an application of the artificial bee colony (ABC) algorithm to estimate the fuel cost curve parameters of thermal power plants. In the estimation problem, 1st-, 2nd-, and 3rd-order fuel cost functions are used, and the estimation problem is formulated as an optimization one. The ABC algorithm is used to solve this optimization problem by minimizing the total error in the estimated parameters. In this study, in order to evaluate the performance of the ABC algorithm, it is tested on 3 different cases that have 3 different fuel cost types, such as coal, oil, and gas. The results obtained from the proposed method are compared with the genetic algorithm, particle swarm optimization, and least square error methods reported previously in the literature. The results show that the ABC algorithm is stronger than the others at solving such a problem.

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In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods. ARTICLE HISTORY