# On Hybrid Nanogrids Energy Management Systems--An Insight into Embedded Systems
## 3 Nanogrid EMS
### Comparative Analyses of Nanogrid EMSs  
The case of a stand-alone network with the same performance and constraints as a building has not been extensively studied in the literature. In fact, EMS in buildings is considered a device connected to an electrical grid [16]. The combination of multiple sources and multiple loads is very similar to the constraints mentioned in the problem of this study. However, comfort level monitoring, long-term planning, uncertainties in weather data, and user behavior are not present in the context of an embedded system.  
[PERSON] et al. [75] proposed a comprehensive cyber-physical energy management system (CPEMS) that optimizes the operation of networked nanogrids with battery swapping stations. The main focus of the article is to optimize not only the reliability of the energy supply system but also its economy and resilience. The use of a mixed-integer linear programming (MILP) approach allows for robust decision-making that efficiently addresses the challenges of networked nanogrids. However, drawbacks include the complexity of implementing this system and the computational resources required for real-time optimization.  
[PERSON] et al. [76] proposed an EMS for islanded nanogrids, emphasizing the use of stochastic dynamic programming techniques for optimization. They focused on improving energy consumption and generation within islanded nanogrids, thereby increasing their overall efficiency and reliability. However, the computational requirements of stochastic dynamic programming can be resource-intensive.  
[PERSON] et al. [77] focused on optimizing power flow management in photovoltaic nanogrids with battery storage. Their work contributes to the efficient use of energy resources within the nanogrid, ensuring that surplus energy is stored and used effectively. However, real-world implementation requires compatible hardware and control systems.  
[PERSON] et al. [78] proposed an energy management strategy specifically tailored for grid-connected PV household nanogrid systems. Their contribution focuses on maximizing energy use while minimizing grid dependency, a critical aspect of sustainable and efficient energy management in residential contexts. However, the effectiveness of this strategy depends on regional solar conditions and household energy patterns.  
[PERSON] et al. [79] proposed a predictive control-based energy management system for hybrid AC-DC residential nanogrids. The main contribution is the implementation of predictive control strategies that enable real-time adjustments to energy flows, thereby improving energy efficiency and grid integration in residential nanogrids. However, the need for real-time data and control systems may not be readily available in all residential environments.  
[PERSON] et al. [80] proposed a decentralized mean field control approach to ensure the robustness and reliability of energy management in residential nanogrids. Their approach contributes to the resilience of these nanogrids by optimizing energy distribution and consumption. However, there is a need for an advanced control infrastructure.  
[PERSON] et al. [81] proposed a non-linear stochastic dynamic programming approach to the optimization of islanded nanogrids. Their contribution lies in providing advanced optimization techniques for islanded nanogrids, which offer effective solutions to energy management challenges. However, this strategy has potential complexity and computational requirements.  
[PERSON] et al. [82] proposed a compromise programming approach for coordinating energy resources in a network of grid-connected microgrids and nanogrids. Their contribution lies in achieving a balance in resource allocation, leading to efficient energy management in interconnected systems. However, it faces implementation challenges in interconnected systems.  
Table 2 summarizes the above contributions and limitations of the nanogrid EMSs with strategic, tactical, and economic objectives.  
\begin{table}
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