Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Smart DC Microgrid

Photovoltaic-wind hybrid systems are a particular type of energy source that can be used as a source of distributed generation to reduce power losses. There are two major limitations in designing these systems: availability of the electricity generated and the cost of the equipment. The purpose of this study is to determine the capacity and optimum design of the combined systems with energy sources and different strategies, as well as increase the amount of available index and reduce costs. In this design, the battery is used as a hybrid system backup to reduce the volatility of renewable energy sources. In this research, using a distributed generation source such as wind turbine, photovoltaic and battery, an intelligent optimization method based on multi-objective particle swarm optimization (MOPSO) algorithm is used. The used method is simulated in MATLAB and implemented on a 27.6 kW system installed on Texas A&M University campus and the outcomes have been evaluated. By comparing the results of the suggested method with multi-objective genetic algorithm, it is observed that multi-objective particle swarm optimization algorithm provides better responses.