Particle Swarm Optimisation for Economic Dispatch with Cubic Fuel Cost Function

This paper presents an efficient and reliable particle swarm optimisation (PSO) algorithm for solving the economic dispatch (ED) problems with smooth cost functions as well as cubic fuel cost functions. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. For such cases, the PSO is applied to the ED problems with real power of generator in a system as state variables. However when the incremental cost of each unit is assumed to be equal, the complexity involved in this may be reduced by using the incremental cost as state variables. To show its efficiency and effectiveness, the proposed PSO is applied to test one with smooth cost functions and then with cubic fuel cost function. The proposed PSO algorithm has been tested on 3 generator systems with smooth cost functions and 3 generator systems, 5 generator systems and 26 generator systems with cubic fuel cost function. The results are compared with genetic algorithm (GA) and shown better results and computation efficiency than genetic algorithm