Application of Particle Swarm Optimization with Variable learning factors and inertia weight factor in a Deregulated Economic Dispatch

Abstract

This paper emphasizes the characteristics and effectiveness of PSO in attaining the optimal solution in economic dispatch of thermal units in a deregulated market. Economic dispatch plays a major role in the arena of complex power system achieving minimum fuel costs of thermal units while satisfying the equality and non-equality constraints. The merits of deregulation due to competitive bidding like market efficiency and cost minimization with faster time is exclusively anticipated in the competitive system at present. The robustness of the PSO is tested by altering the learning factors and inertia factors and the results are being assessed  with two test cases for 3 thermal units with population size of 1000 and 5000 using a demand of 850 MW and two test cases with 1000 MW for 3 and 8 thermal units with a similar population size. The outcome  show that PSO has the ability and effectiveness in reaching optima by selecting proper learning and inertia weight factors.