Improved Particle Swarm Optimization Based Dynamic Economic Dispatch of Power System

To realize the sustainable development, the manufactories/plants must have efficient management/schedule plan and control methods for systems from the aspects of economy, environment and social community. The optimization algorithms are necessary to obtain the optimal or sub-optimal solutions for the manufactories/plants. As one of the most important systems, the power systems play very important role in world. Most of the generators consume the natural resources such as coal, gas, petrol and nuclear materials, and it is very important that the generators do not waste the natural resources and meet the needs of human beings. Hence it is very important to get an optimal or acceptable sub-optimal dynamical economic dispatch, which determines the optimal generation scheme to meet the predicted load demand over a time horizon satisfying the constraint such as ramp-rate limits of generators between time intervals, for a power system which may involve conventional generators and some other alternate sources (solar, wind) of energy. Since power systems are very complicated and there are many constraints, it is difficult to find the optimal solution(s) based on the classical mathematical methods. In the past several decades, there are many kinds of evolutionary optimization algorithms which have been proposed. As one of the nature-inspired meta-heuristic algorithms, Particle Swarm Optimization (PSO) and its variants have shown its advantages in many applications. In this paper we propose a random dimension velocity updated PSO. Simulations are used to test the proposed PSO based several benchmark functions and then the proposed PSO is applied to the dynamic economic dispatch problems of power system. All the simulation results prove the efficiency of the proposed method.