application of swarm-based optimization algorithms for solving dynamic economic load dispatch problem

dynamic economic load dispatch is one of the most important roles of power generation’s operation and control. it determines the optimal controls of production of generator units with predicted load demand over a certain period of time. economic dispatch at minimum production cost is one of the most important subjects in the power network’s operation, which is a complicated nonlinear constrained optimization problem. since dynamic economic load dispatch was introduced, several intelligent methods have been used to solve this problem. in this paper, an improved particle swarm optimizer (ipso) and water cycle optimizer (wco), as swarm-based optimization algorithms, have been proposed to solve dynamic economic load dispatch problem and their results compare with each other. these algorithms are applied to a dynamic economic dispatch problem for 6-unit power systems with a 24-h load demand at each one hour time intervals. the goal of the research is categorized in two parts; first of all, introduction of application of new heuristic method for solving economic load dispatch problem and second, comparison between two swarm-based algorithms. obtained results show that wco is very fast and also reach to better results and minimum.