# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 4 Discussion and Analysis
### NMGs' Configuration  
The challenges associated with the configuration of NMGs are multifaceted, covering formation, power distribution, and operational aspects. Formation involves defining boundaries by determining switch statuses, allocating DERs among MGs, specifying the type of interconnected MGs, and establishing the topology of these networks. In addition, power distribution within NMGs entails the analysis of power flow within the network. Furthermore, the operational dimension involves defining the behavior of NMGs over time, considering various conditions and scenarios. Formation in NMGs can be achieved through rapid methods like minimum spanning tree and k-means clustering. While these approaches are quick, they fall short in finding optimal solutions, especially when dealing with numerous DERs and components. To attain optimal solutions, researchers often turn to optimization methods such as MIP, heuristic approaches, game theory, or methods based on deep learning. Graph theory plays a crucial role in formulating the NMGs formation as a mathematical optimization problem by providing a mathematical abstraction of the distribution system. Most researchers in the field focus on defining the objective function as maximizing the restoration of all loads or critical loads to enhance resilience. However, it is noteworthy that enhancing resilience goes beyond simply maximizing the restored load; factors like the time of restoration and minimizing damages are integral components of resilience but are often overlooked in the literature. Constraints in the optimization problem typically revolve around operational factors, including power balance, power, current, voltage limits, the number of switch actions, and topology constraints, encompassing limitations on lines between components, the number of DGs in microgrids, radiality, and limits on interconnected MGs. Notably, frequency limits and transient switching protection are infrequently considered in the optimization problem, with only rare instances, such as in [22]. The primary objective of the formation process is to adjust the status of switches. However, this switching can lead to frequency deviations, causing imbalances that have severe consequences for the system. For example, opening switches on lines connecting large non-critical loads may trigger significant power imbalances, potentially damaging critical load equipment due to frequency deviation. Protective relays are designed to operate during frequency instability, leading to service interruptions or even a collapse of the microgrid. While many studies consider the radiality constraint, some overlook its importance. Maintaining a radial structure simplifies operational issues like synchronization and load sharing among microgrids. However, it is crucial to recognize that NMGs may not adhere strictly to a radial topology; they may include ring- and meshed-type topologies. Considering distribution systems with energy sources at consumer premises, where the flow direction is not predetermined, becomes essential. This non-radial network approach acknowledges the reversible relationship between parent-child nodes, allowing for the potential existence of multiple root nodes. Moreover, the stochasticity of unintentional islanding in networked microgrids' configuration must be considered. Given that a fundamental feature of microgrids is to seamlessly separate from the distribution system during outages and continue supplying its islanded portion, accounting for this stochastic element becomes critical in the configuration of networked microgrids. Many studies have focused on utilizing model-based optimization approaches to tackle uncertainties in the configuration of NMGs. However, the practical applications of these approaches face challenges due to the lack of operational models in the literature that consider technical parameters of MGs and are applied to real cases. In addressing these challenges, model-free methods such as DRL emerge as viable solutions, as indicated in [53; 54; 55; 56; 57].