Optimal Costing of Power Generation in Microgrid Using Particle Swarm Optimization

Abstract

An essential method for assessing the effectiveness of microgrid (MG) operations and sizing is economic analysis. The most cost-effective operation and sizing of an MG necessitate the use of optimization techniques. MGs are optimized using a variety of methodologies, including gradient based and non-gradient-based algorithms. Particle swarm optimization (PSO) is a non-gradient-based evolutionary algorithm used for cost optimization due to its high performance and ease of implementation. Regarding microgrid operations and sizing, the effectiveness of several economic models based on PSO is discussed in this paper. A 10-kW generator system is a part of this system. It is divided into two generation units: one that produces PV power and the other that produces wind power. There are 5 kW in each generation system. These are coupled to the DC micro grid. The cost function and the constraints of the microgrid are described and its performance is analyzed using particle swarm optimization (PSO) algorithm. The convergence graph for the cost optimization of microgrid using PSO algorithm is presented in this paper. The simulation results of the system shows that the loss of load probability (LSPS) is around 1 % and the cost of energy (COE) is 6.03 $. The computation time required to run the algorithm is 0.30 seconds.