**1. Introduction**

Proper management of energy flow in MG (Micro-grid) systems must be carried out in order to improve the global performance of the system, to minimize the cost of the electrical bill, and to extend the lifetime of its components (e.g., converters, batteries, fuel cells). In general, energy management (EM) approaches involve an objective function, which could be used to maximize the efficiency of the hybrid RES system and to minimize energy consumption while improving the consumers' quality of services. For instance, an EM control strategy that considers only the availability of the electricity can be developed to switch, at each time, from RESs (renewable energy sources) to storage devices or to the utility grid without considering the electricity price or the profitability of the system. In other cases, control strategies can interact with the generators by limiting the power generation. The aim is to ensure the electrical quality of services and, consequently, minimize the profitability of the installation. However, despite the ability of these strategies to reach the defined objective, they might decrease the performance of other criteria, such as the batteries' lifetime, the system's installation cost, and profitability.

Actual commercial inverters provide high-performance energy balance by interconnecting RESs, energy storage systems, and the utility grid, taking into consideration only a single-objective function. This later is mainly implemented in order to increase the availability of the electricity for building's loads. With a limited configuration, the inverter

**Citation:** Elmouatamid, A.; Ouladsine, R.; Bakhouya, M.; El Kamoun, N.; Khaidar, M.; Zine-Dine, K. Review of Control and Energy Management Approaches in Micro-Grid Systems. *Energies* **2021**, *14*, 168. https://dx.doi.org/10.3390/ en14010168

Received: 3 November 2020 Accepted: 11 December 2020 Published: 31 December 2020

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can use batteries or the TEG at any moment without taking into account other constraints, such as the electricity cost and the C/D (charge/discharge) cycle of the batteries. For instance, high and frequent cycles of the C/D cycle of batteries could decrease their performance while reducing the system's profitability. EM strategies that are deployed in the actual inverters use "if-else" statements to perform real-time decisions. For instance, the defined setpoint values (i.e., control inputs) cannot be adjusted according to predictive variations of RESs production, load demand, and battery SoC (state of charge). Such EM strategies are considered as "passive strategy" in their decisions and actions [1]. Control strategies incorporating multiple-objective functions are therefore required for efficient energy management (i.e., ensuring electricity availability) while taking into consideration operational constraints (e.g., costs, reliability, and flexibility). In fact, "active strategies" for EM should be developed in order to adapt the setpoint values accordingly. These strategies could use intelligent and predictive control techniques together with recent IoT/Big-data technologies (e.g., data monitoring, data analysis, data mining, machine learning) for efficient EM in hybrid RES systems. In this work, control structures and strategies from the literature are presented by highlighting their advantages and drawbacks in the context of MG for smart buildings.
