**5. State of the Art Synthesis and Our Contribution**

Control strategies generally use single-objective function procedures (e.g., maximizing the quality of the services). Without considering different operating constraints, these procedures are easier to implement and to deploy in real-sitting scenarios. Moreover, control strategies, which take into consideration only the energy availability within MG components (e.g., energy sources, storage devices, traditional electric grid), could be implemented by simple algorithms. These algorithms implement procedures that switch, at each time, from RES either to storage devices or to the TEG. For instance, actual commercial inverters are able to efficiently manage the interconnection between RESs, energy storage systems, and the utility grid by incorporating a single-objective function. In particular, the MG system's EM takes into consideration only the availability of the electricity for being supplied to buildings loads. The inverter can use either batteries or the utility grid once without taking into account other parameters, such as the actual electricity cost as well as battery C/D cycles. However, in a limited time, high battery C/D cycles could decrease their performance, which impact on the profitability of the system. In other cases, controllers can interact with energy sources generators (e.g., solar, wind) in real-time in order to limit the power generation (LPPT). The aim is to ensure the quality of the electrical services (e.g., frequency, voltage), and consequently, to minimize the profitability of MG system's components. Despite their advantages, they could have negative impacts on the batteries' lifecycle and system's profitability. Therefore, context-awareness principles and predictive analytics could be exploited for developing context-driven control approaches.

The current state of knowledge aims to develop context-driven control approaches for the energy management of MG systems in the context of smart buildings. Mainly, a predictive control approach, named MAPCASTE (Measure, Analyze, Predict, foreCAST, and Execute) [37], is developed and deployed in real-sitting scenarios for energy management in MG systems (see Figure 9). Unlike the control approaches from literature, MAPCASTE considers multiple-objective functions, which take into consideration battery C/D cycles as well as electricity price forecasting [37]. The main aim is to ensure, in an optimal way, the continuous electricity supply from different installed sources (e.g., RESs, batteries, TEG) to building's services. The proposed approach is based on predictive control models, which are able to generate a sequence of future control actions over a prediction horizon.

**Figure 9.** The proposed control approach schemes with operation process.

However, in order to carry out the MAPCASTE, several forecasted inputs values are required, mainly the power production/consumption and batteries SoC. This requires an advanced metering infrastructure, which makes it possible to measure and predict all inputs values. Therefore, an MG was deployed together with an IoT/Big data platform in order to conduct experiments and validate developed models. The deployed MG system contains RESs and battery storage systems, which are connected together with the TEG in order to supply the electrical energy to the building's loads (e.g., lighting, ventilation). The IoT/Big data platform was developed and deployed in order to allow

measuring and forecasting RESs power generation, loads consumption, and batteries SoC. Sensing/actuating components with a control card are installed in order to monitor and manage the whole MG system, offering the possibility to test the developed control techniques in real context [37,152]. Moreover, based on this review, ongoing works focus on the development of smart converters. In fact, the actual commercial inverters offer the possibility to manage the power flows between different power sources, loads, energy storage systems, and utility grids with high performance. However, these inverters are limited generally to a single-objective function, the satisfaction of the load demand, without considering other operating constraints, such as the electricity price and the battery state of health. Moreover, the integration of new IoT/Big-data technologies to the actual inverter has improved the performance of the system to control and predict the suitable actions for EM and control. Mainly, the integration of machine-learning algorithms is required to analyze the data and to predict the actions for EM in MG systems. In this way, the development of smart inverter has enhanced the possibility to integrate multiple-objective functions and operating constraints that can be integrated in the EM approaches. Therefore, the deployment of predictive control strategies in real scenarios requires the use of openaccess power converter. For that, we are deploying our proper power inverter in order to have the ability to conduct real testing of predictive control strategies with specific constraints and multiple-objective functions. The deployment of smart inverter offers the possibility to create MG networks using IoT/Big-data technologies. In this context, a platform for MG2MG energy and data exchange will be developed based on the predictive control deployed in the smart inverters.

#### **6. Conclusions**

The energy management and optimization control in MG systems are becoming a multiple-objective "management/optimization" function to be satisfied by solving simultaneously technical, economic, and environmental problems. Therefore, several approaches (e.g., exact, stochastic, and predictive) have been proposed for energy management. These approaches were chosen based on their practicality, reliability, and resource availability in MG environment. This work reviewed recent research work related to EM in MG systems. In particular, we focused on different control approaches that have been proposed to efficiently operate MG systems, including centralized, decentralized, and hierarchical management structures. A comprehensive description of control and optimization methods was highlighted, particularly to identify the most common and effective method for EM in MG systems. Predictive control was a good candidate, since it integrates optimal control and multivariable processes and is a flexible control scheme that allows the easy inclusion of system constraints and optimization functions. It is robust against uncertainty and powersmoothing problems. Thus, multiple control objective and constraint functions can be implemented for the same control strategy. However, despite the power of these predictive control techniques, their deployment in real-sitting scenarios requires a holistic platform that integrates MG components together with all equipment for measuring and predicting important input data. With recent technological advances in microprocessors, data analysis, and machine learning, predictive control can be seen as a promising alternative for energy management in MG systems.

**Author Contributions:** Conceptualization, A.E., M.B. and R.O.; Data curation, A.E.; Methodology, A.E., R.O. and M.B.; Software, A.E. and R.O.; Supervision, R.O., M.B., N.E.K., M.K. and K.Z.-D.; Validation, R.O., M.K. and M.B.; Writing—original draft, A.E.; Writing—review & editing, A.E., M.B., N.E.K., M.K. and K.Z.-D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by USAID under the PEER program, grant number 5-398. It is also partially supported by HOLSYS project, which is funded by IRESEN (iresen-inno-projet-2020–2022).

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