## 2 Smart Grids System
### Power Supply
#### 2.2.2 Renewable Energy (RE)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tools/Algorithm** & **Achievement** & \multicolumn{3}{c}{**Implementation**} & \multirow{2}{*}{**Ref.**} \\ \cline{3-4}  & & **BM** & **DC-Device** & & **RE** & **CC** \\ \hline Constant current & \multicolumn{3}{c}{Reduce the number of battery chargers to} \\ (CC)/constant & \multicolumn{3}{c}{Improvements battery charging and management.} & ✓ & \(\times\) & \(\times\) & [99] \\ voltage (CV) & \multicolumn{3}{c}{Improvements battery charging and management.} & & & \\ \hline GA & \multicolumn{3}{c}{Propose a new charging algorithm to reducing the} \\  & \multicolumn{3}{c}{charge energy and loss.} & ✓ & \(\times\) & \(\times\) & [72] \\ \hline MEET algorithm & \multicolumn{3}{c}{Three types of battery energy storage systems} \\ MEET algorithm & \multicolumn{3}{c}{(BESSs) were used to improve the system’s} & ✓ & \(\times\) & \(\times\) & [102] \\  & \multicolumn{3}{c}{availability and energy efficiency.} & & & \\ \hline Orthogonal least squares algorithm & \multicolumn{3}{c}{Provide a feature stemming from (GPR) for deduces} & ✓ & \(\times\) & \(\times\) & [86] \\  & the unknown SoC value’s probability allocation & & & & \\ \hline Scheduling algorithm & \multicolumn{3}{c}{The numerical analysis illustrates adaptive resonant} \\  & \multicolumn{3}{c}{beam charging (ARBC) led to 61\% battery charging} & ✓ & \(\times\) & \(\times\) & [22] \\  & \multicolumn{3}{c}{energy and 53–60\% supplied power.} & & & \\ \hline GA & \multicolumn{3}{c}{used optimum charging methods are reduced} \\  & \multicolumn{3}{c}{charge times, performance improved, and extended} & ✓ & \(\times\) & \(\times\) & [83] \\  & \multicolumn{3}{c}{battery life} & & & \\ \hline MATLAB algorithm & \multicolumn{3}{c}{The sorting and cumulative voltage summation} \\  & \multicolumn{3}{c}{(SCVS) was shown to perform the best through the} & ✓ & \(\times\) & \(\times\) & [105] \\  & \multicolumn{3}{c}{solar energy option of charging the battery.} & & & \\ \hline PSO developed based & \multicolumn{3}{c}{A hybrid approach uses to manage the electric} \\  & \multicolumn{3}{c}{vehicle charging station (EVCS) to peak shaving and} \\  & \multicolumn{3}{c}{the most efficient charging/discharging of EVs} \\  & \multicolumn{3}{c}{applied to a standard network (IEEE 69 buses).} & & & \\ \hline PSO & \multicolumn{3}{c}{Scheduling controllers can reduce the power} \\  & \multicolumn{3}{c}{consumption and costs of grids.} & & & \\ \hline Arbitrage optimization & \multicolumn{3}{c}{A battery energy storage system (BESS) capable of} \\  & \multicolumn{3}{c}{discharging for 1.5–2 h at maximum power and} & ✓ & \(\times\) & \(\times\) & [115] \\  & \multicolumn{3}{c}{provides quick response and energy arbitrage.} & & & \\ \hline CubeSat battery & \multicolumn{3}{c}{Choosing electric power system (EPS) architectural} \\  & \multicolumn{3}{c}{converters for solar panels and unregulated dc-bus} & ✓ & \(\times\) & \(\times\) & [101] \\  & \multicolumn{3}{c}{have the maximum efficiency.} & & & \\ \hline MOA & \multicolumn{3}{c}{A double-layer metaheuristic optimizer provides a} \\  & \multicolumn{3}{c}{novel stochastic technique for optimizing solar} & ✓ & \(\times\) & \(\times\) & [27] \\  & \multicolumn{3}{c}{hosting capacity in distribution networks.} & & & \\ \hline Stochastic algorithm & \multicolumn{3}{c}{Propose a simple statistical model to breaking a} \\  & \multicolumn{3}{c}{battery energy storage system up into minor} & ✓ & \(\times\) & \(\times\) & [107] \\  & \multicolumn{3}{c}{segments that lead to significant increases.} & & & \\ \hline JAYA algorithm & \multicolumn{3}{c}{Compact and optimized SOC estimating model for} \\  & \multicolumn{3}{c}{statistical error values such as SOC error used to} & ✓ & \(\times\) & \(\times\) & [104] \\  & \multicolumn{3}{c}{validate the model’s performance.} & & & \\ \hline NA & \multicolumn{3}{c}{Reduce the amount of data sent by extracting} & ✓ & \(\times\) & \(\times\) & [116] \\  & \multicolumn{3}{c}{features voltage descriptive.} & & & \\ \hline NA & \multicolumn{3}{c}{Clean electric power using information and} \\  & \multicolumn{3}{c}{communication technology (ICT), the user can} & ✓ & ✓ & \(\times\) & [117] \\  & \multicolumn{3}{c}{monitor the load, battery, and panel current.} & & & \\ \hline GA & \multicolumn{3}{c}{The household load control system that included} \\  & \multicolumn{3}{c}{(RESs) led to lowered cost of electricity from (228 to} \\  & \multicolumn{3}{c}{51) USD and the peak-to-average ratio (PAR) from} & \(\times\) & ✓ & \(\times\) & [108] \\  & \multicolumn{3}{c}{2.68 to 1.12.} & & & \\ \hline LMA, Gaussian & \multicolumn{3}{c}{Established microgrid system for testing and} \\  & \multicolumn{3}{c}{Trust-Region} \\  & \multicolumn{3}{c}{simulation, focusing on dimensioning and control} \\  & \multicolumn{3}{c}{techniques, the residue discovered less than 5\%.} & & & \\ \hline \hline \end{tabular}
\end{table}