# On Hybrid Nanogrids Energy Management Systems--An Insight into Embedded Systems
## 1 Introduction
Moreover, the integration of ESSs brings  
Figure 1: Number of published papers on EMS for microgrids and nanogrids. additional economic benefits by storing excess power during periods of low demand or low generation costs, which can be utilized during peak periods with higher electricity prices [24]. This facilitates the transition towards a more flexible, cost-effective, and electrified future [7; 8; 9; 10]. Over the past few decades, various types of ESSs have been developed and classified based on their specific storage mechanisms. This paper addresses the case of hybrid nanogrid energy management systems used in an embedded context. The system contains a variety of energy production sources, including wind turbines, solar panels, and tidal turbines, as well as fossil fuel-based sources. The production sources must generate enough energy to ensure the power needed for all the loads. Additionally, the system is equipped with energy storage systems such as batteries and fuel cells. An embedded system is a specialized computer system designed to perform specific functions within a larger device or system. It is distinct from general-purpose computers and is typically integrated into a host device to execute dedicated tasks or control functions. Embedded systems are characterized by their compact size, efficiency, and real-time processing capabilities. They often interact with the physical world through sensors, actuators, and communication interfaces, enabling them to monitor and control devices, machinery, appliances, vehicles, and various other applications [25]. An ongoing evolution in embedded systems is to achieve optimal system performance in response to changing factors such as environmental conditions and user requirements. In printing systems, for example, customers are increasingly looking to achieve higher productivity while also aiming to reduce the energy consumed. One approach to achieving these desired outcomes involves two key steps: first, ensuring system adaptability to enable the adjustment of system characteristics; and second, using this adaptability to effectively manage the operation of the system within the existing conditions in order to ultimately optimize its performance [26]. The appropriate value for the control parameters (decision variables) must be chosen to optimize the system's characteristics (objectives). The power applied to a component and the speed of the system are two examples of parameters. The range of possible values is often constrained. For example, a component requires a certain amount of power at a given speed. This is an optimization problem known as multiobjective optimization. In this context, this paper provides a review of the EMS used for a hybrid nanogrid in an embedded context. Several review studies have covered various microgrid topics, including interconnecting multiple microgrids [27], a survey of experimental microgrid systems implemented in Europe, North America, and Asia [28], microgrid protection and control schemes [29], and microgrid reactive power compensation methods [30]. They also discussed droop control methods [31], control strategies for voltage and frequency regulation of microgrids [32], and control methods for inverter-based microgrids [33]. They considered the modeling, design, planning, and architectures of hybrid renewable microgrids and nanogrids [34], control strategies for distributed energy resources in microgrids [35], and an analysis of AC and DC microgrids [36]. Reviews of homeostatic control-based energy-efficient microgeneration systems [37], microgrid uncertainty quantification techniques [38], and a review of network-based energy efficiency in buildings and microgrids [39] were also included in the literature. The state of the art in the field of EMS is large, but it has been developed for applications that do not directly address the problem considered in this study.