# Energy Management in Microgrids with Renewable Energy Sources: A Literature Review
## 2 Microgrid Optimization Techniques  
Energy management of a microgrid involves a comprehensive automated system that is primarily aimed at achieving optimal resource scheduling [25; 26; 27]. It is based on advanced information technology and can optimize the management of distributed energy sources and energy storage system [28]. The microgrid optimization problem typically involves the following objectives:  
Maximize the output power of the generators at a particular time;  
Minimize the operating costs of the microgrid;  
Maximize the lifetime of energy storage systems;  
Minimize the environmental costs. Some of the classic optimization methods include mixed integer linear and non-linear programming. The objective function and constraints used in linear programming are linear functions \begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Reference** & **Contributions** \\ \hline
[21] & Authors presented a comparative analysis on decision making strategies for microgrid energy management systems. These methods are selected based on their suitability, practicability, and tractability, for optimal operation of microgrids. \\ \hline
[22] & Energy management integration methods, demand response, and storage systems are reviewed. Authors used more accurate models for storage including key factors such as the derating factors due temperature charge/discharge rate and ageing. \\ \hline
[23] & Authors presented a review on strategies and approaches used to implement energy management in stand-alone and grid-connected hybrid renewable energy systems. \\ \hline
[24] & Authors showed an extensive review on energy management methodologies applied in microgrids. EMS for real-time power regulation and short-/long-term energy management are reviewed. \\ \hline
[25] & Authors showed previous solutions approaches, optimization techniques, and tools used to solve energy management problem in microgrids. It includes heuristic, agent-based, MPC, evolutionary algorithms, and other methods. \\ \hline
[26] & Authors showed an overview of the latest research developments using optimization algorithms in microgrid planning and planning methodologies. \\ \hline
[27] & Authors presented an overview of current hybrid microgrids and optimization methods and applications. \\ \hline
[28] & Authors showed in detail the optimization of distributed energy microgrids in both the grid-connected and stand-alone mode. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Microgrids energy management review papers. with real-valued and whole-valued decision variables. Dynamic programming methods are used to solve more complex problems that can be discretized and sequenced. The problem is typically broken down into sub-problems that are optimally solved. Then, these solutions are superimposed to develop an optimal solution for the original problem. Metaheuristics is another important alternative in microgrid optimization. Heuristic techniques are combined to approximate the best solution using genetic algorithms, biological evolution, and statistical mechanisms for achieving optimal operation and control of microgrid energy. Predictive control techniques are used in applications where predicting the generation and loading is necessary to guarantee effective management of stored energy. This typically combines stochastic programming and control. The most remarkable among these techniques are the ones to predict the deterioration of elements of the grid, mainly storage systems. Optimization methods based on a multi-agent used on microgrids allow a decentralized management of the microgrid and consist of sections having autonomous behavior to execute the tasks with defined objectives. These agents, which include loads, distributed generators and storage systems, communicate with each other to achieve a minimal cost. Stochastic methods and robust programming are used to solve the optimization functions when the parameters have random variables, particularly in artificial neural networks, fuzzy logic, and game theory.