# Review of Energy Management Systems in Microgrids
## 3 Energy Management Systems Applications in Microgrids
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Proposed Method** & **Energy Management Application** \\ \hline \multirow{3}{*}{[49]} & \multirow{3}{*}{Genetic algorithms} & Genetic algorithms were used to provide energy supply options through the use of diesel generators and demonstrated reduced operating costs of the microgrid. \\ \cline{1-1}  & & \\ \hline \multirow{2}{*}{[50]} & \multirow{2}{*}{Genetic algorithms} & Power management is implemented to reduce the operating costs of distributed energy resources. \\ \hline \multirow{2}{*}{[51]} & \multirow{2}{*}{Particle swarm algorithm} & It takes into account changes in the microgrid’s load demands and renewable resource supply and provides appropriate advance (24 h) forecasts to mitigate these changes. \\ \hline \multirow{2}{*}{[52]} & \multirow{2}{*}{Grey wolf optimization} & The algorithm is used to optimize the timing and operation of the microgrid system. Thanks to the system, general operating costs are reduced. \\ \hline \multirow{2}{*}{[53]} & \multirow{2}{*}{Fuzzy self-adaptive particle swarm algorithm} & The algorithm, developed to reduce microgrid operating costs, realizes the multi-purpose energy management system of a grid-connected microgrid. \\ \hline \multirow{2}{*}{[54]} & \multirow{2}{*}{Grey wolf optimization} & With a new approach to optimizing an interconnected microgrid, both the costs of the power plants and emissions from fossil fuel sources were kept to a minimum. \\ \hline \multirow{2}{*}{[55]} & \multirow{2}{*}{Genetic algorithms} & It takes into account changes in wind and radiation sources and extracts data from a load profile to optimize the microgrid to save energy and reduce operating expenses. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Meta-heuristic approach-based energy management systems applications. gous single-level mixed integer linear program reformulation is then derived. The day-ahead and intraday energy market strategies are coordinated using an adaptive stochastic optimization approach. A summary of stochastic and powerful programming-based energy management systems applications in microgrids is given in Table 3.  
A model predictive control approach was introduced by [61] to manage a mixed-generation microgrid that combines distributed and renewable sources. The goal of the model is to lower the expenses and limitations associated with energy demand and generation. Ref. [62] introduced an energy management system based on a control algorithm to manage distributed generation, energy storage systems, and microgrids made up of supply grids and various loads. TCP/IP-based control and communication was introduced as a solution to the transition problem between solar system generation and storage systems. In Ref. [63], an application to operate a hybrid system with solar energy and battery storage was presented. Batteries were used to store grid power during off-peak hours and provide power to customers during peak demand hours. Ref. [64] used model predictive control to maximize the daily performance of the diesel-wind-PV hybrid system. A method was defined that combines the system with forecast data on temperature, wind speed, solar irradiance, and daily load. Microgrids are now a viable option for integrating distributed generation to provide remote communities with energy, so it is critical to control and manage them well. The three control levels of a DC microgrid operating in isolated mode are designed and simulated in [65]. A model predictive control-based energy management system with real-time measurement feedback is also suggested. This system ensures power flow distribution and optimal energy dispatch at the lowest possible cost while prolonging the life of the energy storage system. Disturbances generated in the lower control levels can be responded to by the energy management system.