# Review of Energy Management Systems in Microgrids
## 3 Energy Management Systems Applications in Microgrids
The effectiveness of the microgrid is examined and contrasted under two conditions: one in which it has no energy management system and the other in which it has one in response to variations in irradiation and electricity demand. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Proposed Method** & **Energy Management Application** \\ \hline
[56] & Stochastic programming & Considering the uncertainties in solar and wind energy production and energy demand, an energy management model for a microgrid that takes into account supply and demand uncertainty \\  & & is presented. \\ \hline
[57] & Multi-objective stochastic & The objective function applied at various stages of the microgrid reduces the operating costs of renewable resources by minimizing system losses. \\ \hline
[58] & Stochastic coordination framework & An application was developed to reduce the system cost, pumped storage unit, and energy trading costs with the main grid, and wholesale electricity price and wind energy uncertainties were modeled using the estimation method. \\ \hline
[59] & Lyapunov optimization method & Using hourly marketing techniques, it was possible to dispatch conventional and distributed generation economically while also managing the uncertainty associated with renewable generation. \\ \hline
[60] & Stochastic optimization & In a multi-energy microgrid, the strategies developed in the day-ahead and intraday energy markets are coordinated using an adaptive stochastic optimization approach. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stochastic and powerful programming-based energy management systems applications. Analyzing the power and operating costs provided by each production unit allows to evaluate whether the battery's state of charge and power balance are maintained. A summary of model predictive control-based energy management systems applications in microgrids is given in Table 4.  
A two-stage AI-based energy management in an isolated microgrid is proposed in [66] to find the optimal day-ahead distribution. With the efficient management of microgrid power sources, including diesel generators, battery energy storage systems, and intermittent renewable energy resources, the deployment aims to minimize expected operating costs, reactive power costs, spinning reserve, and load-shedding. To model the uncertainties in the output power of renewable energy resources to be used in the formulation of stochastic programming, generative adversarial networks were utilized to generate scenarios based on data. Ref. [67] used models with two different recurrent neural network architectures (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) to predict wind speed and solar irradiance at a designated location for the establishment of microgrids. The models improved the prediction accuracy of the models. While data on extreme weather events such as wind were taken into account to increase the data, it was trained using meteorological data obtained from meteorological stations and energy management was carried out in the system. Fuzzy logic controllers are used to streamline system control, especially in microgrids that have several different operating modes and components. Because fuzzy logic controllers do not require complex mathematical modeling or rely on the nonlinearity of the microgrid's parts, the system specifically favors them. This leads to the creation of an all-encompassing energy management system based on simple linguistic concepts. Ref. [68] describes a fuzzy logic control-based energy management technique for electric cars and hybrid energy storage systems that use fuel cells, batteries, and supercapacitors. This study was carried out on a test microgrid. For optimal control of the energy storage system in a residential microgrid, Ref. [69] proposes an energy management system based on fuzzy logic. Research on the design of energy management systems ought to consider low complexity, encompassing both input and rule numbers [70].