Enhancement of the Performance of Micro Grids

Abstract

With the growth of the demand, the large centralized generating stations keep struggling to catch that growth in demands, so the generation trend now is looking forward to the smaller distributed generation (DG) systems which are located near to the load centers. This reduces the transmission losses and decreases the need to expand the transmission network. The DG systems importance increased due to their help in enhancing the reliability, power quality, voltage profile, and economic and environmental profits. The main goal of this thesis is to show the effectiveness of using new metaheuristic optimization algorithms; namely the Cuttlefish Algorithm (CFA) and the Sun Flower Optimization (SFO) algorithm in selecting the parameters of the PI controller. The main target is to improve the DGs and Micro Grid performance. The vector cascaded control method is used to control the inverter which relies on the proportional-integral (PI) controller. The multi-objective function for this research is deduced from the Response Surface Methodology (RSM). The MINITAB program is used to run the RSM model. The simulation results are tested under three different operating states which are: 1) The system conversion from grid-connected to islanded mode, 2) Changing the load during islanded mode and 3) Symmetrical and Asymmetrical fault during islanded mode. The validity of the system is proved by the simulation results which are achieved using PSCAD/EMTDC. The MATLAB software is used for the optimization process. The results verify the flexibility, justification, and applicability of the presented Cuttlefish Algorithm (CFA) and the Sun Flower Optimization (SFO) algorithms versus the particle swarm optimization (PSO) algorithm.