Because of the increasing number of distributed energy sources integrated with the distribution network various optimization methods are very useful in determining the optimal placement, type and size of the distributed generators. One of the most commonly used algorithms are so called metaheuristics, like simulated annealing, tabu search or genetic algorithms. The aim of this paper is to present the advantages and benefits of metaheuristicss in optimization of DG allocation in the distribution network.
Particle Swarm Optimization Technique in Optimizing Size of Distributed Generation
he size of Distributed Generation (DG) is very important in order to reduce the impact of installing a DG in the distribution Network. Without proper connection and sizing of DG, it will cause the power loss to increase and also might cause the voltage in the network to operate beyond the acceptable limit. Therefore, most researchers have concentrated on the optimization technique to regulate the DG’s output to compute its optimal size. In this paper, the concept of Evolutionary Particle Swarm Optimization (EPSO) method is implemented in sizing the DG units. By substituting the concept of Evolutionary Programming (EP) in some part of Particle Swarm Optimization (PSO) algorithm process, it will make the process of convergence become faster. The algorithm has been tested in 33bus distribution system with 3 units of DG that operate in PV mode. Its performance was compared with the performance when using the traditional PSO and without using any optimization method. In terms of power loss reduction and voltage profile, the EPSO can give similar performance as PSO. Moreover, the EPSO requires less number of iteration and computing time to converge. Thus, it can be said that the EPSO is superior in term of speed, while maintaining the same performance.