# Review of Energy Management Systems in Microgrids
## 3 Energy Management Systems Applications in Microgrids
To optimize the microgrid, the optimization takes into account variations in wind and solar irradiation and extracts data from a load profile. A summary of meta-heuristic approach-based energy management systems applications in microgrids is given in Table 2. \begin{table}
\begin{tabular}{c c c} \hline
**Ref.** & **Proposed Method** & **Energy Management Application** \\ \hline
[42] & Quadratic programming & Quadratic programming was used to achieve the best possible economic distribution. \\ \hline
[43] & Linear programming & The problem is resolved by applying a mixed-integer linear programming solution technique, which optimizes the microgrid’s performance concerning the fuel cell’s on/off connection, energy storage system, and main grid conditions. \\ \hline
[44] & Non-linear programming & Mixed-integer nonlinear programming forms the basis of the objective function of the suggested optimization algorithm, which minimizes daily operating costs. \\ \hline
[45] & Mixed-integer nonlinear programming & Designed with mixed-integer non-linear base unit commitment and optimal power flow models, the system reduces fuel consumption, system expenses, and reactive power penalty costs. \\ \hline
[46] & Mixed-integer linear programming & Optimizes the system with operational decisions that take into account market price variation and storage system layout. \\ \hline
[47] & Mixed-integer quadratic programming & Optimum load flow was achieved by considering real-time dynamic balancing and daily static scheduling. \\ \hline
[48] & Mixed-integer linear programming & It is aimed at distributed energy management for modern distribution systems embedded in networked microgrids. By iteratively adjusting the node price signals, the alternative direction method of the multiplier-based distributed framework is developed. \\ \hline \end{tabular}
\end{table} 
Table 1: Classical method-based energy management systems applications. An energy management model for a microgrid that takes into account supply and demand uncertainty is presented [56]. The study also takes into account uncertainties in solar and wind energy production and energy demand. The Nuclear Energy Research Center in Taiwan tested it on a real grid with stochastic programming. In the first stage, battery capacity was optimized. In the second, an ideal microgrid operating strategy was evaluated. A multi-objective stochastic technique was employed by the author to present a hybrid microgrid optimization system in [57]. The objective function of this study was applied at various microgrid stages to minimize system losses and lower the operating costs of renewable resources. The feeding systems' overall operating costs and losses were weighted and used to formulate the problem. Mixed-integer linear programming was utilized to solve the problem, and the IEEE 37 node distribution system was used to test the proposed approach. A hybrid grid-connected community microgrid architecture is also advised for agricultural purposes [58]. To reduce the cost of the irrigation system, pumped storage unit, and energy trading costs with the main grid, the author suggested a stochastic coordination framework. The wholesale electricity price and the uncertainties associated with wind power are modeled using the point estimated method. An optimization technique for a two-stage interconnected grid was presented by [59]. The first stage uses a conventional generator, and the second stage uses hourly marketing to ensure that the conventional and distributed generation is dispatched economically. This combination enables the Lyapunov optimization method to be used to manage uncertainty in renewable generation. In Ref. [60], a novel approach to energy management for a thermal and electrical multi-energy microgrid is proposed. Industrial, commercial, and residential agencies optimize their energy trading strategies at the bottom level, while energy planning and pricing strategies are optimized at the top level. For computational tractability, an analo