Operation and control of microgrid systems by using heuristic methods

Electrical energy is important to fulfill people's needs with developing technology. Electrical energy is reacquired to be uninterruptible and clean energy. Carbon gas emissions should be reduced since global warming increases. Instead of fossil fuels, renewable energy sources (RES) should be preferred to generate clean electrical energy. Solar energy, wind energy can be used. At this point, a grid-connected microgrid (MG) system with integrated RES is a useful choice for pollution. This thesis includes three parts which are optimization, control, and comparison with different situations in the MG system. Firstly, optimization techniques are searched for the applied MG which are analytical, heuristic, and metaheuristic methods. These are separated from each other from the point of the approach. Metaheuristic optimization techniques Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are preferred to find the optimal solutions since these methods are efficient methods to solve complex MG systems. Secondly, Control methods are investigated to apply in MG systems which are centralized, decentralized, and distributed control. A control strategy is applied for the designed MG system distributed control methodology which is a combination of centralized and decentralized control methodologies is used. Finally, a grid-connected MG system is solved by using two different optimization techniques (PSO-GA) and results are compared with each other. Results were demonstrated PSO is better than GA. Uninterruptible and clean energy has been aimed at the integration of RES.