Efficient energy management of renewable resources in microgrids

In the era of the fast-growing green energy technologies, microgrid (MG) systems with integrated renewable energy sources (RESs) such as solar and wind are becoming more attractive and ubiquitous. Furthermore, concerning the uncertain power production nature of the RESs, battery storage systems have turn out to be an inseparable part of MGs, while they improve the reliability, efficiency, and operating cost of the entire system. In such an intricate situation, optimal utilization of RESs is substantially dependent on the employed energy management system (EMS). Also, mitigation of fossil fuel generators that results in the reduction of multipollutant gasses hinges on the competency of EMS. This chapter presents an improved teaching- and learning-based optimization (ITLBO) method for the energy management of MGs in the islanding and grid-connected modes where the MG conducts a business trade-off with the main grid. To handle the uncertain behavior of the intermittent generation resources, the study has exploited a triple exponential smoothing (TES) method to predict their erratic power production level on a day-ahead basis. The proposed EMS is tested on several scenarios, and the simulation results are compared with other optimization algorithms. The obtained results demonstrate a noticeable improvement with the proposed method.