Economic Dispatch Using Adapted Particle Swarm Optimization

Abstract

AbstractIn power system process Economic Dispatch (ED) foremost typical task. Minimize the total fuel cost of the generating units is the key objective of this problem subjected to satisfying inequality and equality constraints. Also, it converted into nonconvex and nonlinear complicated optimization problem, due to some practical constraints like prohibited operating zones, transmission losses and ramp rate limits. Recently, particle swarm optimization (PSO) is one of the well-known algorithm for solving nonconvex optimization problems. But, particularly for multimodal problem it may suffer to trap at local minima. To overcome the issues of PSO, this paper offering an adapted particle swarm optimization (viz. aPSO) for solving ED problems. In aPSO, new factors for inertia, cognitive and social components (i.e. acceleration coefficients and inertia weight) are familiarized. As per the concerts these factors are dynamically changed and properly maintained the diversity of the PSO. The projected aPSO is implemented to solve three altered unit test structures (3, 6 and 15) of ED problem. The experimental results of aPSO compared with related algorithms. It shows that the performance of aPSO are enhanced than the other algorithms in terms of searching ability and rate of convergence.KeywordsEconomic dispatchParticle Swarm OptimizationPosition updateVelocity