Operational Strategy Optimization in an Optimal Sized Smart Microgrid

Recently, microgrids (MGs) have attracted considerable attention as a high-quality and reliable source of electricity. In this paper, energy management in MGs is addressed in the light of economic efficiency, environmental restrictions, and reliability improvement via: 1) optimizing the type and capacity of distributed generation (DG) sources, as well as the capacity of storage devices (SD); and 2) developing an operational strategy (OS) for energy management in MGs. A master-slave objective function based on net present value (as an economic indicator) is proposed. Such objective function is solved using a hybrid optimization method. This method includes two steps. In the first step, 2-D slave object functions (SOFs), operating costs, and consumer outage cost (as a reliability index) are minimized by quadratic programming and particle swarm optimization (PSO) algorithms, respectively. Then Pareto curve is drawn for SOF and fuzzy logic is employed to select the best SOF solution, OS, from Pareto curve. In the second step, using the best OS from step one for any iteration, PSO algorithms employed to solve master objective function, and to determine the optimum capacity and type of DGs and SDs. The results show that the proposed framework can be considered as an efficient tool in planning and energy management of MGs.