Modeling and optimization of a solar-wind hybrid microgrid with statcom

With the widespread use of micro-grids and the installed power of solar-wind energy systems, their negative effects on the grid increase, and studies to take various precautions are gaining importance. Due to the natural intermittent characteristics of solar radiation and wind, renewable energy systems such as off-grid PV/wind are not completely safe to meet the ever-changing load demand, so power instabilities are experienced and it is a reasonable solution to create a hybrid system by reactive power compensation. In this thesis, first of all, doubly-fed asynchronous generator (ÇBAG) based wind system and static synchronous compensator (STATCOM) are modeled. The simulation of the whole system, STATCOM reactive power analysis and performance analysis of the solar-wind hybrid microgrid were performed in Matlab/Simulink. Four proportional-integral (PI) controller parameters in the control circuit of STATCOM are optimized using heuristic algorithm techniques, and satisfactory results are obtained, results are compared in regard to the voltage stability and reactive power control. Real-time studies are carried out by incorporating STATCOM to the doubly-fed asynchronous generator based wind energy system and physically implemented by means of Opal-RT technology. Various performance criteria based on monitoring errors have been used to evaluate the effectiveness of the optimization method. In addition, the conventional tuning method and genetic algorithm optimization method are performed and compared with nature-insprired optimization algorithms based on the natural water cycle and the bacteria foraging optimization methods, respectively. The optimized control parameters are evaluated in a doubly-fed asynchronous generator wind energy experimental setup. Experimental results are given to verify the effectiveness of each optimization method. Optimized control parameters have been tested under constant wind speed and variable wind speed in an experimental setup with Opal-RT technology. Experimental results for different optimization methods and conditions are presented to confirm the effectiveness of the water cycle optimization method and bacteria foraging optimization method.