**4. Conclusions and Future Research**

The literature review highlighted two approaches for microgrid energy management: The centralized and decentralized approaches. The first incorporates optimization using the available information in the absence of a coordination strategy between the actors in a microgrid. A computer centre transmits the optimal settings to each participant. The second approach implements optimization using partial information and a strategy for coordinating the microgrid participants; each participant evaluates its own optimal settings. Centralized managemen<sup>t</sup> is mostly implemented in metaheuristic methods, and decentralized managemen<sup>t</sup> is frequently implemented in methods based on multi-agents. Many publications have proposed centralized managemen<sup>t</sup> for microgrids. However, the incursion of distributed energy resources (DER) may cause this type of managemen<sup>t</sup> to face issues when implemented in a centralized information system because there might be a demand for high computational cost due to the large quantity of data. Distributed energy managemen<sup>t</sup> may be an alternative solution to this problem. It solves the problem of data processing and reduces processing needs by using distributed controllers that manage the data in real time and require communication equipment that might result in additional costs (for e.g., Bluetooth, Wi-Fi, wireless networks, and IoT).

An energy managemen<sup>t</sup> model for a microgrid includes data acquisition systems, supervised control, human machine interface (HMI), and the monitoring and data analysis of meteorological variables.

The literature review mainly presented managemen<sup>t</sup> methods based on foresight and short-term management. The choice of centralized or decentralized managemen<sup>t</sup> ensures that the microgrid designer and operator realize a cost–benefit balance. This enables one to determine the managemen<sup>t</sup> model that is most convenient for the microgrid. Though decentralized managemen<sup>t</sup> o ffers more flexibility, an integral analysis is necessary to ensure reliable and safe system operation.

The energy managemen<sup>t</sup> problem or optimization control in a microgrid becomes a mono-objective management/optimization model when a single cost function is presented. This function typically corresponds to the operating cost of the microgrids. The problem becomes a multi-objective management/optimization model when it simultaneously presents a solution to the technical, economic, and environmental problems. Based on the literature, di fferent authors have addressed the problem and provided solutions using methods such as the classic ones with linear and nonlinear programming, heuristic methods, predictive control, dynamic programming, agent-based methods, and artificial intelligence. These methods are chosen based on their practicality, reliability, and resource availability in the microgrid environment.

With regard to storage systems in microgrids, lithium batteries can be an important alternative to lead-acid batteries in the future. The advantages of Li-ion batteries compared to lead-acid batteries are a long cycle life, fast charging, high energy density, and low maintenance. Currently, lead acid batteries are economically better than Li-ion batteries when used in microgrids, but a decrease in the acquisition cost of lithium batteries is expected in the coming years that will cause them to be competitive with those of lead-acid. Thus, further research on the optimal energy managemen<sup>t</sup> of energy systems and the managemen<sup>t</sup> of lithium batteries is required while considering more accurate degradation models to accurately predict the battery lifetime in real operating conditions.

**Author Contributions:** Conceptualization, Y.E.G.V. and R.D.-L.; methodology, Y.E.G.V. and R.D.-L.; formal analysis, J.L.B.-A.; investigation, Y.E.G.V.; resources, Y.E.G.V. and R.D.-L.; data curation, R.D.-L.; writing—original draft preparation, Y.E.G.V.; writing—review and editing, R.D.-L. and J.L.B.-A.; visualization, Y.E.G.V. and R.D.-L.; supervision, R.D.-L. and J.L.B.-A.; project administration, R.D.-L.; funding acquisition, R.D.-L. and J.L.B.-A.

**Funding:** This research was funded by Government of Aragon "Gobierno de Aragón. Grupo de referencia Gestión estratégica de la energía eléctrica", gran<sup>t</sup> number 28850.

**Conflicts of Interest:** The authors declare no conflict of interest.
