# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Operation  
There are two primary types of networked microgrids based on their operational characteristics: predetermined networked microgrids (PNMGs) and dynamic networked microgrids (DNMGs). A predefined networked microgrid maintains a consistent switching status and network configuration regardless of the system's operating conditions and customer priorities. The boundaries of the microgrid are determined based on factors such as supply adequacy, reliability indices, and maximum coverage. These predetermined networked microgrids operate according to established rules and agreements. For example, grid-tied microgrids are connected to the main grid and coordinate their operation with the utility grid, following predetermined agreements and regulations for power sharing and exchange. Virtual power plants integrate various distributed energy resources and function as a single controllable entity, with power generation and sharing predetermined based on the capabilities and capacities of the distributed energy resources [88]. Community microgrids, designed to serve specific communities or areas, also fall into the category of predetermined networked microgrids [89]. They have predefined connections, power sharing arrangements, and operational strategies tailored to meet the specific needs of the community.  
On the other hand, DNMGs, an evolved form of networked microgrids, have gained popularity due to their advanced structure. As per [90; 91; 92], dynamic microgrids can be described as microgrids with adaptable boundaries that dynamically adjust to maintain a balance between generation and load. This flexibility enables dynamic microgrids to optimize their operations in real time, ensuring efficient utilization of resources and meeting the evolving demands of the system. DNMGs exhibit real-time adaptability and flexibility, utilizing advanced control algorithms, communication technologies, and intelligent decision-making capabilities to optimize resource utilization and ensure reliable operation. DNMGs are capable of self-healing, automatically detecting and responding to faults or disruptions, and reconfiguring their operations to restore power supply [91; 93; 94]. Additionally, demand-responsive microgrids dynamically adjust power consumption and load profiles based on grid conditions and user preferences, enabling efficient utilization of energy resources. Multi-agent systems are also a type of dynamic networked microgridthat facilitate real-time coordination and cooperation among interconnected components, optimizing power sharing and load balancing [95; 96].  
Dynamic networked microgrids offer distinct advantages when compared to predetermined networked microgrids. Their flexible boundaries, which expand or shrink based on the real-time generation and load conditions, enable superior adaptability to changing energy demands and resource availability. This flexibility enhances the overall resilience of the system, as dynamic networked microgrids can reconfigure themselves in response to disruptions or faults, isolating affected sections and ensuring uninterrupted operation. Moreover, dynamic networked microgrids optimize the utilization of distributed energy resources by dynamically adjusting connections and allocations, leading to improved energy efficiency and cost-effectiveness [97]. The scalability of dynamic networked microgrids allows for seamless integration of new microgrids and DERs, accommodating the growing demand for renewable energy sources. Additionally, their ability to balance loads and manage voltage and frequency fluctuations enhances grid stability. Overall, dynamic networked microgrids offer increased flexibility, resilience, optimal resource utilization, scalability, and grid stability, making them a promising solution for efficient and sustainable power distribution in the evolving energy landscape. While the benefits of DNMGs are evidently greater than those of PDNMGs, Table 3 indicates that over 40 percent of studies focus on configuring PNMGs.