# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Formation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Categories** & **Features** & **Limits** & **Ref.** \\ \hline \multirow{4}{*}{Clustering} & \multirow{4}{*}{Partitional, Hierarchical, and Density-Based} & Create a straightforward approach with minimal mathematical complexity to support large-scale NMG by focusing on specific MGs. & \(\bullet\) & Designed for the formation of uncoupled multi-microgrids. & [8; 9; 10; 11; 12; 13; 14; 15; 16; 17] \\  & & Create a straightforward approach with minimal mathematical complexity to support large-scale NMG by focusing on specific MGs. & \(\bullet\) & No assurance of finding optimal solutions. & [8; 9; 10; 11; 12; 13; 14; 15; 16; 17] \\ \hline \multirow{4}{*}{Graph theory} & \multirow{4}{*}{MST, and BFS} & Facilitate visualization of distributed-grid problems to find optimal solution rapidly. & \(\bullet\) & Designed for the formation of uncoupled multi-microgrids. & [18; 9; 10; 11; 12; 13; 14; 15; 16; 17] \\  & & Facilitate visualization of distributed-grid problems to find optimal solution rapidly. & \(\bullet\) & Lacks consideration for transient response. & [18; 19; 20; 21; 22; 23; 24; 25; 26] \\  & & & Fails to address protection concerns. & \\ \hline \multirow{4}{*}{MIP} & \multirow{4}{*}{MINLP, MILP, and MISOCP} & \multirow{4}{*}{Capable of finding the optimal solution for problems in which decision variables can take on both continuous and discrete values.} & \(\bullet\) & Computationally expensive. Practically infeasible when the size of the system is large or for real-time decision making. & [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41] \\  & & & & Less consideration for transient response. & \\ \cline{1-1}  & & & & Need for a thorough and accurate mathematical model of the environment & \\ \cline{1-1}  & & & & Non-convex characteristics of power flow constraints. & \\ \hline \multirow{4}{*}{Heuristic} & \multirow{4}{*}{BFS, BSO, Tabu, ABS, and PSO} & \multirow{4}{*}{Discover close-to-optimal solutions within a reasonable timeframe.} & \(\bullet\) & Less consideration for transient response of NMGs & \\ \cline{1-1}  & & & & No assurance of an optimal NMGs’ formation. & [42; 43; 44; 45; 46] \\ \cline{1-1}  & & & & Lacks consideration for transient protection and frequency deviation. & [47; 48; 49; 50; 51; 52] \\ \hline \multirow{4}{*}{Game theory} & \multirow{4}{*}{Cooperative, and Dart Game} & \multirow{4}{*}{Modeling interactions and strategic interdependence among microgrids.} & \(\bullet\) & Computationally expensive. & \\ \cline{1-1}  & & & Lacks consideration for transient protection and frequency deviation. & [47; 48; 49; 50; 51; 52] \\ \cline{1-1}  & & & & Practically infeasible when the size of the NMG is large or for real-time decision making. & [47; 48; 53; 54; 55; 56; 57; 58; 59; 60; 61] \\ \hline \multirow{4}{*}{DRL} & \multirow{4}{*}{DQN and multi agent DQN} & \multirow{4}{*}{DQN} & \multirow{4}{*}{Advanced machine learning techniques with a model-free nature enable dynamic configuration, allowing for their application in an online mode.} & \(\bullet\) & Lack of maturity and reliability in power system applications. & \\ \cline{1-1}  & & & Lacks consideration for transient protection and frequency deviation. & \\ \cline{1-1}  & & & & Complex implementation poses challenges in deployment. & [53; 54; 55; 56; 57; 58; 59; 60; 61] \\ \cline{1-1}  & & & & Dependence on online and historical data of the network for effective functioning. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categorization of approaches for forming NMGs.