# Review of Energy Management Systems in Microgrids
## 4 Conclusions  
In this article, a review of energy management systems and energy management system applications in developed microgrids is presented. A comprehensive and critical analysis of energy management strategies and solution approaches has been carried out. Optimizing system reliability, energy planning, and operation in microgrids that can operate both on the island and on the grid is the main goal of the energy management system for sustainable development. Therefore, a microgrid energy management system is a multi-purpose issue that addresses financial, environmental, and technical concerns. Although there are many methods to perform energy management in microgrids, artificial intelligence technologies have become popular recently and have great potential in the future. Artificial intelligence technologies hold great promise for transforming microgrid operations and facilitating the broad integration of renewable energy resources. In addition to enabling real-time decision-making and better resource utilization, the application of machine learning, deep learning, and other artificial intelligence algorithms \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Proposed Method** & **Energy Management Application** \\ \hline \multirow{3}{*}{[76]} & Multi-agent management with & \multirow{3}{*}{\begin{tabular}{c} Production costs were decreased by using a \\ multi-agent management system based on JADE’s \\ differential evolution algorithm. \\ \end{tabular} } \\  & & \\ \hline \multirow{3}{*}{[77]} & Multi-agent-based energy & \multirow{3}{*}{\begin{tabular}{c} By applying energy management system \\ optimization between distributed generation \\ management and coordination of demand response, \\ it aimed at reducing operating expenses. \\ \end{tabular} } \\  & & \\ \hline \multirow{3}{*}{[78]} & Multi-agent with MATLAB & \multirow{3}{*}{\begin{tabular}{c} Multi-agent systems are used in the designed DC \\ microgrid to guarantee power quality and \\ supply-demand balance. \\ \end{tabular} } \\  & & \\ \hline \multirow{3}{*}{[79]} & Multi-agent with JADE & \multirow{3}{*}{\begin{tabular}{c} It is based on optimizing the energy obtained from \\ renewable sources using MPPT algorithms. JADE \\ was used to design communication and information \\ shared between the created agents. \\ \end{tabular} } \\  & & \\ \hline \multirow{3}{*}{[80]} & Multi-agent with JADE & \multirow{3}{*}{\begin{tabular}{c} Taking into account various load models and energy \\ from distributed energy resources, the application \\ provides customers with an attractive benefit-cost \\ ratio and reduces peak consumption. \\ \end{tabular} } \\  & & \\ \hline \multirow{3}{*}{[81]} & Multi-agent and based on the & \multirow{3}{*}{\
\begin{tabular}{c} The microgrid’s economic performance was \\ enhanced by developing a client-server \\ architecture-based simulation platform for energy \\ management systems. \\ \end{tabular} } \\  & & \\ \hline \multirow{3}{*}{[82]} & Multi-agent with MATLAB & \multirow{3}{*}{
\begin{tabular}{c} The application, which maximizes load flexibility \\ and the use of renewable resources, was developed \\ using MATLAB and JADE platforms. \\ \end{tabular} } \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Multi-agent system-based energy management systems. can enhance predictive analytics, optimization, control, and monitoring of microgrids. Although artificial intelligence technologies have many advantages, they also present many difficulties, including interpretability, privacy, and data quality. To facilitate the broad adoption of artificial intelligence technologies in microgrids, future research and development should address these issues and provide fresh strategies and solutions. Energy management techniques are chosen for the best possible operation of microgrids due to their applicability and traceability.