## 4 Review of Microgrid Control and Design
### Resilient Control Strategies for Microgrids  
Resilient control of microgrids refers to the design and implementation of control strategies that enable microgrids to withstand and recover from disturbances, uncertainties, and cyberattacks. Microgrids, as distributed energy systems, are inherently vulnerable to various disruptions, including natural disasters, equipment failures, and intentional cyberattacks. Resilient control aims to enhance the stability, reliability, and security of microgrids by incorporating mechanisms to detect, mitigate, and adapt to these challenges. Resilient control is an active area of research, with ongoing efforts to develop advanced control strategies and technologies that can further enhance the resilience of MGs in the face of increasing complexity and interconnectedness. Key characteristics of resilient control for MGs include:  
* Disturbance tolerance: The control system should be able to maintain stability and performance when perturbations are present, like sudden variations in load or generation, faults, and weather events, by using control techniques such as ADRC, droop control, H-infinity, and nonlinear controls [134; 135; 137; 138; 164]. * Adaptability: The control system should be able to adapt to changing operating conditions and uncertainties, such as variations in renewable energy generation and load patterns, where adaptive control algorithms are suitable because they are continuously adjusting their parameters based on real-time system data to maintain stability and performance despite changing conditions [132; 133]. * Cybersecurity: The control system must be designed to withstand cyberattacks, such as unauthorized access, data manipulation, and malware infections. Some researchers have proposed a cybersecurity protocol to protect the control system against cyberattacks by implementing measures such as encryption, access control, and intrusion detection [165; 166]. * Fault-tolerant and self-healing: The control system should have mechanisms to detect and isolate faults and to restore normal operation following disruptions. Various \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Technique** & **Level** & **Advantages** & **Drawbacks** & **References** \\ \hline H-infinity control & Primary and Secondary & Robust to disturbances and uncertainties & Complex to design, requires accurate system model & [134; 135] \\ \hline Decentralized economic dispatch & Secondary and Tertiary & Simple to implement, scalable & May not achieve optimal performance & [150; 151] \\ \hline Multi-agent systems & Tertiary & Can handle complex interactions among DERs & Complex to design and requires coordination between agents & [152; 153] \\ \hline Event-triggered control & Secondary and Tertiary & Reduces communication overhead & May not be suitable for systems with strict latency requirements & [154; 155] \\ \hline Game theory-based approaches & Tertiary & Model strategic interactions among DERs for resource allocation & May be challenging to implement in real-time & [156; 157] \\ \hline Demand response & Secondary and Tertiary & Coordinates energy consumption patterns to balance supply and demand & May require behavioral changes and incentives for consumers & [158] \\ \hline Deep reinforcement learning & Tertiary & Can learn optimal control strategies from data & Requires large amounts of training data and may be difficult to interpret & [159; 160] \\ \hline Decentralized consensus control & Secondary and Tertiary & Enables resilient operation in the presence of communication failures & May not achieve optimal performance due to limited information exchange & [161; 162] \\ \hline Distributed optimization & Secondary and Tertiary & Enables collaborative decision-making among DERs & May require extensive computational resources & [163] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._techniques were used in this context. For instance, in the research conducted by [PERSON] et al.