Real-time energy management of an islanded microgrid using multi-objective Particle Swarm Optimization

While minimizing cost has always been a primary objective in energy management, because of increasing concerns over emissions, minimization of this objective has been brought to the forefront of energy management as well. Minimization of cost and emission are two conflicting objectives. Moreover, the optimization problem becomes more complex with the addition of renewable technologies that have varying power generation energy storage. This paper presents a multi-objective, multi-constraint energy management optimization problem for an islanded microgrid solved in real time using a modified Multi-objective Particle Swarm Optimization (MOPSO) algorithm. Simulation results show the benefits of real-time optimization and the freedom of choice users make to meet their energy demands. Furthermore, the simulation results from the MOPSO-based algorithm are compared with those from the Multi-objective Genetic Algorithm (MOGA)-based optimization package available in the Matlab optimization toolbox. The results show that the proposed MOPSO-based algorithm used for a 24-hour period energy management simulation performs much faster than the MOGA-based optimization package.