Dragonfly Algorithm for solving probabilistic Economic Load Dispatch problems

Abstract

Economic Load Dispatch problem in power system is solved by different methods to run the generating station in an economic way for different loading conditions. A combination of thermal power plant and renewable plant of wind and solar photovoltaic is considered for optimal solution at minimal cost using Dragonfly Algorithm. Swarming behaviour of dragonfly is used for optimization of the present Economic Load Dispatch problem. The modelling of solar and wind power plant is done using 2-m point estimation technique to consider the uncertainties in power generation from such renewable sources. The solution of objective function is found using the proposed method (Dragonfly Algorithm), and the same is compared with that obtained using other well-known algorithms such as Crow Search Algorithm, Ant Lion Optimizer, Oppositional Real-Coded Chemical Reaction Optimization, Biogeography-Based Optimization, Particle Swarm Optimization and Genetic Algorithm. It is found that the proposed method gives better solution in terms of execution time and cost effectiveness. To validate the performance of Dragonfly Algorithm, four test systems have been considered. It is observed that the total generation cost obtained by Dragonfly Algorithm for each test system is less (10,049.1948 $/h for test system I, 2018.0762 $/h for test system II, 15,268.8325 $/h for test system III and 32,310.2922 $/h for test system IV) as compared to other well-known optimization methods. It is also found that the time required to reach minimum solution is lesser in case of Dragonfly Algorithm (8 s for test system I, 12 s for test system II, 15 s for test system III and 20 s for test system IV) as compared to other optimization techniques.