Real-time energy management of an islanded microgrid using multi-objective particle swarm optimization

Abstract

The purpose of this thesis is to design an optimal combined heat and power islanded microgrid, through technology selection and unit sizing software, and perform optimal real-time energy management simulations using intelligent optimization techniques. Two software packages, HOMER® and WebOpt®, originally developed at the National Renewable Energy Laboratory (NREL) and Lawrence Berkley Laboratory (LBL), respectively, were utilized. Using these programs, different cases were created and compared to justify the selected technologies and their respective prices. The final microgrid design contains renewable and alternative energy generation, hydrogen as an energy carrier, and electric storage. Two intelligent optimization techniques, a modified Multi-objective Particle Swarm Optimization algorithm and a Multi-objective Genetic Algorithm in the Matlab optimization toolbox were used for energy management of the designed microgrid and their performance were compared. Simulation results show the modified Multi-objective Particle Swarm Optimization performs better. It is used to perform 24 hour energy management simulations. The simulation results show the benefits of the real-time optimization and the freedom of choice users have to meet their energy demands.