# Energy Management in Microgrids with Renewable Energy Sources: A Literature Review
## 3 Microgrid Energy Management with Renewable Energy Generation
### Optimization Techniques
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Optimization Technique** & **Contributions** & **Constraints** & **Drawbacks** & **Single/Multi-objective** \\ \hline \multirow{4}{*}{[55]} & \multirow{4}{*}{Artificial fish swarm optimization} & An energy management planning of a MG & Power balance & & \\  & & including storage for a whole day is & Conventional power generation & Battery degradation cost is not & Mono-objective \\  & & including storage for a whole day is & Conventional power generates & considered. & \\  & & optimized, considering dynamic pricing & Energy storage & & \\  & & and demand side management. & Utility grid power & & \\ \hline \multirow{4}{*}{[57]} & \multirow{4}{*}{Particle swarm algorithm (PSO)} & Three different objectives are considered: & & & \\  & & Reliability, & & & \\  & (PSO) & cost of operation, and environmental & & & \\  & & impact. & & & \\ \hline \multirow{4}{*}{[58]} & \multirow{4}{*}{Racterial foraging algorithm} & Optimized the exchanging power with the grid and the generators and battery & Power balance & & \\  & & grid and the generators and battery & & Generation limits of distributed generators & Power loss not considered. & Multi-objective \\  & & Fast convergence. & Storage limits & & \\ \hline \multirow{4}{*}{[60]} & \multirow{4}{*}{Mixed-integer nonlinear programming (MINLP)} & Reduced dependency on forecast information. & Charge flow & & \\  & & Important battery models compared. & & Dispatch of generators & Battery lifetime prediction is ignored. & Multi-objective \\  & & Different battery models compared. & & Generator on/off programming & \\  & & Charge/Discharge of batteries & & & \\ \hline \multirow{4}{*}{[61]} & \multirow{4}{*}{Dynamic Rules} & MG management system uses different & Battery cost and degradation are & & \\  & & limits for the SOC of the batteries bank. & Power balance & not considered. & \\ \hline \multirow{4}{*}{[64]} & \multirow{4}{*}{Dynamic programming} & Energy management strategy for TV. & & & \\  & Batteries to stabilize and permit TV to run at & Charge/Discharge of batteries & & \\  & & at a constant and stable output power. & & & \\ \hline \multirow{4}{*}{[70]} & \multirow{4}{*}{Multi-agents} & Efficient strategy for real-time & Charge/Discharge of batteries & Prediction of battery ageing is not & Multi-objective \\  & & management of energy storage used to & Load Scheduling & & \\  & & compensate power mismatch optimally. & Power Balance & included. & \\ \hline \multirow{4}{*}{[72]} & \multirow{4}{*}{Multi-agents} & Control scheme composed of several levels & & & \\  & & with coordinated control. & & \\ \hline \multirow{4}{*}{[73]} & \multirow{4}{*}{Multi-agents} & Battery energy storage system, & & & \\  & & optimization problem based on distributed & & \\  & & intelligence, and a multi-agent system. & & & \\ \hline \multirow{4}{*}{[80]} & \multirow{4}{*}{Mixed integer programming} & Dual-stage optimization. First stage & & & \\  & & determines hourly unit commitment of the & & \\ \cline{1-1}  & & generates, the second stage performs & & \\ \cline{1-1}  & & economic dispatch of the generators and & & \\ \cline{1-1}  & & batteries. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Optimization Technique** & **Contributions** & **Constraints** & **Drawbacks** & **Single/Multi-objective** \\ \hline \multirow{3}{*}{[84]} & \multirow{3}{*}{Stochastic} & \multirow{3}{*}{A simple method to incorporate the impact of the scheduling in stand-alone mode on the grid-connected operation.} & Power balance & Battery ageing model is not considered. & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & & \\