# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 3 Networked Microgrids' Control
### Control
#### 3.2.1 Control Architecture
Within centralized control architecture, all decision-making processes and control actions are orchestrated and overseen by a central processing unit or controller [119]. The central entity gathers data and measurements from various microgrid components and coordinates their operation. It takes a comprehensive view of the entire networked microgrid and implements actions to optimize overall performance, as highlighted in [109; 110]. This control architecture is applied in the management of energy for both grid-connected [111] and islanded NMGs [112; 113; 114]. Centralized control is effective in situations requiring precise coordination and centralized optimization, but it often faces criticism for the potential single-point communication failure problem and reliability issues [115; 116; 117]. Additionally, it struggles to effectively manage a system with a large number of agents [118]. Conversely, decentralized control hinges on local decision-making processes at the level of individual microgrid components. Every component within the microgrid operates autonomously, utilizing predefined algorithms grounded in local measurements to optimize its local operation, whether functioning as a distributed energy resource or a load. This methodology reduces complexity and improves privacy protection by requiring only a restricted exchange of information, as highlighted in [120]. Decentralized control assumes a more distributed character, potentially enhancing the microgrid's resilience by diminishing reliance on a central controller [121; 122; 123; 124]. Decentralized controls are used for networked MGs systems and grid-connected MGs with a mix of fast-changing distributed generators owned by different parties [125; 126]. Distributed control represents a hybrid approach amalgamating elements from both centralized and decentralized control paradigms. In this architecture, each local controller communicates and cooperates with neighboring controllers within the microgrid. This intercommunication allows for a degree of centralized decision making while preserving a measure of autonomy at the local level. Distributed control aims to combine the benefits of centralized coordination with the resilience and adaptability afforded by local decision making. This method ensures the regular operation of NMGs by adjusting voltage and frequency, even without communication with master controllers [127; 128; 129; 130; 131]. While information sharing among neighboring agents is crucial for certain control functions in distributed control, it raises privacy concerns due to the potential exposure of sensitive data. To mitigate this issue and uphold privacy in NMGs, various methods are proposed in [132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142]. The distributed control of networked microgrids involves a sophisticated information network, where each DG incorporates remote sensing and control actuation with its microgrid center controller. However, this complexity introduces susceptibility to cyberattacks on communication links for inter-microgrid data sharing. Addressing this concern, refs. [143; 144; 145; 146; 147; 148] introduce a cyber-resilient distributed control framework.