## 4 Digitalization of MGs
### Artificial Intelligence (AI)  
In MGs, managing and optimizing power flow is a complex task that can benefit from advanced control and optimization strategies, as illustrated in Figure 8. AI algorithms, capable of analyzing large datasets and adapting to changing conditions, are well-suited for this purpose. By leveraging big data, AI tools have demonstrated their value in optimizing power systems, improving decision making, and overall efficiency. Additionally, they contribute to increased power processing, enhanced condition monitoring, and optimized supply chain management, ultimately leading to improved accuracy and efficiency in modern energy systems.. An important issue in microgrid operation is predicting the energy generation and demand patterns accurately [156; 157]. This is critical for managing the energy storage systems and ensuring reliable and cost-effective power supply. AI techniques, such as machine learning (ML) and deep learning (DL), have shown promising results in predicting energy demand and generation patterns accurately. ML algorithms can analyze historical data from the microgrid and identify patterns in energy demand and generation. These patterns can then be used to predict future energy demand and generation with a high degree of accuracy. DL algorithms, on the other hand, can analyze vast amounts of data and identify complex patterns that may not be apparent to human analysts. This allows for more accurate predictions of energy demand and generation, leading to improved microgrid operation [156; 158; 159]. Optimizing the operation of MGs is crucial for maximizing the use of RESs and minimizing energy costs. AI algorithms can optimize various aspects of microgrid operation, including energy storage, energy generation, and energy consumption [161; 162]. One of the most significant challenges in microgrid operation is managing the energy storage systems. These algorithms can optimize the operation of energy storage systems by predicting energy demand and generation patterns and adjusting the energy storage systems' charging and discharging schedules accordingly. This can lead to more efficient use of energy storage systems and reduced energy costs. In addition, the algorithms can enhance the energy generation side by forecasting renewable energy generation components and setting the power output of energy sources properly. This can maximize the use of RESs and reduce reliance on traditional energy sources, leading to reduced energy costs and increased energy independence. Furthermore, they can manage energy consumption in a similar manner. This can reduce energy costs and ensure that energy consumption is aligned with renewable energy generation. One of the AI applications in MGs is control, where they can handle various aspects of microgrid operation [159]. A significant challenge in microgrid control is managing the power flows among the various energy sources. AI algorithms can optimize power flows by foreseeing energy demand and generation plans and adjusting the power output of energy sources and storage systems accordingly. This can ensure a reliable and cost-effective power supply while minimizing the reliance on traditional energy sources. These approaches can also control the operation of energy storage systems by adjusting the charging and discharging schedules to ensure that the energy storage systems are operating efficiently. This can extend the lifespan of energy storage systems and reduce energy costs [162]. In addition, the algorithms can control energy consumption by adjusting energy consumption schedules based on energy demand and generation plans. This can ensure that energy consumption is aligned with renewable energy generation and reduces energy costs. Another critical application of AI in MGs is fault detection and diagnosis. MGs are vulnerable to faults, such as equipment failures or disturbances in the power supply. AI algorithms can monitor the system and detect these faults early, allowing for prompt diagnosis and repair.