# Review of Energy Management Systems in Microgrids
## 3 Energy Management Systems Applications in Microgrids
An energy management system for a connected microgrid utilizing fuzzy logic based on the Mamdani algorithm was introduced by [71]. Making decisions regarding the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Proposed Method** & **Energy Management Application** \\ \hline
[61] & Model predictive energy management & By managing the microgrid, it is aimed at reducing expenses and limitations related to energy demand and production. \\ \hline
[62] & TCP/IP-based control and communication & It provides communication between photovoltaic system generation and storage systems to solve the energy management problem. \\ \hline
[63] & Model-based energy management & In a hybrid system, an energy management system that provides supply/demand balance was implemented with an energy storage system. \\ \hline
[64] & Model-based energy management & The application was developed to maximize the daily performance of distributed generation. \\ \hline
[65] & Model predictive energy management & This system ensures power flow distribution and optimum energy distribution at the lowest possible cost while extending the life of the energy storage system. Analyzing the power provided by each production unit, the operating cost and the charge state of the battery allows the evaluation of the fulfillment of the power balance. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model predictive control-based energy management systems applications. management of the energy flow in the microgrid model--which is made up of energy storage components and renewable energy sources--is the primary goal. A scheme that combines genetic algorithms and fuzzy logic was used to realize the optimization. A challenge for microgrid energy management systems is managing uncertainty. This issue was resolved by using oversized batteries, which is not the best solution. Load and renewable energy resources, like wind turbines and PV modules, can be predicted using techniques like combining multiple artificial neural networks with other techniques to manage uncertainties in the energy management system. Research has attempted to reduce production costs, improve the utilization of distributed energy sources, and reduce emissions by employing various kinds of artificial neural networks in studies based on energy management systems [72]. Online energy management systems have an advantage over offline ones because they can manage uncertainties by looking at real-time data, which is particularly useful given the intermittent nature of renewable energy resources and the highly stochastic nature of market prices and loads. Every distributed energy source and customer can now benefit from the application of an energy and load management model based on reinforcement learning [73]. A program based on incentive-based demand response was proposed by [PERSON] and [PERSON] [74] for the operations of grid-connected microgrids. The grid-connected operational mode of a microgrid was examined using the game theory-based demand response program. The findings indicate that when the grid operator's distributed generation benefit is maximized at the price of minimizing fuel/transaction costs, lower costs could be obtained in the microgrid. In Ref. [75], a new deep learning-based prediction model for microgrid operation is proposed, considering renewable energy resources, load, and day-ahead price uncertainties. To overcome demand-side uncertainties, a program was developed to provide participants with optimal incentive rate strategies, as different customers have different attitudes toward paid incentives. In this program, reasonable incentive rates are determined according to customers' bid/offer data by using ranking points to determine the clustering structure. A summary of artificial intelligence methods-based energy management systems applications in microgrids is given in Table 5.  
In Ref. [76], energy management during a grid outage in microgrids--each with two photovoltaic and wind generators in addition to local load--was examined.