## 3 The F-Channel Platform  
By its definition and the basic idea behind it, the F-channel platform represents an application of a web-based client-server for TSO-DSO coordination, utilizing high-resolution weather forecasting [34], artificial intelligence methods [35; 36; 37; 38], a GUI based on the georeferenced projection of the grid on the map, and cloud calculation engines. The F-channel platform should encompass and implement a set of common functionalities for the SOs regarding the enhancement of forecasting capability and efficiency in various time frames, limiting the volume of flexibility needs [39; 40; 41] in the process, as well as the identification and prequalification of various flexibility resources willing to purchase grid services [42; 43; 44] through the \"market-to-network\" coupling of the alternative solutions. In a simplified way, this type of relationship between the under-investigation process participants is demonstrated in Figure 2, and it involves a Forecasting and a Coordination module. The integration of AI and cloud computing can lead to complex implementation processes. These were taken into consideration, and the allocated computing resources were split into  
Figure 1: Representation of the deep network modeling of all voltage levels. two groups: continuously allocated ones and ones allocated per computation/transaction. This made the implementation processes simpler and the computation time shorter. The F-channel platform is envisaged as the web-based client server application that will contribute to enhancing active power management for TSO-DSO coordination by using artificial intelligence methods and cloud calculation engines. In order to achieve this, however, certain preconditions needed to be met, with the first of them being the availability of the detailed climatic data models. The luxury of having these models at hand opened the possibility of predicting the various parameters relevant from the SO point of view. For instance, predictions of wind speeds and insolation patterns with a rather high resolution gave the chance to adapt the AI algorithms to connect those predictions with the behavior of the generation units in the system in the way that has never been seen before. Along with that, the forecasts of the demand power and the occurrences of conductor ting and storms that could harm the reliability of the system operation are also noteworthy. All of these can be seen as the ideas behind the enhancements and advancements that were introduced in the Flexibility channel. Additionally, implementing a project can be difficult when it comes to navigating the regulations and policies in force for new technologies [45]. This project focused on giving the user the option of manually defining the domain of the DSO/microgrid or TSO voltage level region of interest for which the simulations of the power production, flows, and consumption are conducted to avoid the unnecessary calculations and sub-optimal usage of the available resources. These calculations were set to include contingency analyses, among other functionalities. Once the capability described in this work as a case study (performing the analyses solely for the part of the system determined by the user) comes to life, it will guarantee the shorter duration of the calculations without putting their reliability at any kind of risk. The objectives of this can be seen in the list provided below:  
* Frequency stability. * Load flow, contingency monitoring, and predictions. * Predictive congestion management for maintaining secure and stable power system operation. * Cost-effective operation of the system. * Early warning on the hazardous power system regimes. * Better FSP planning and managing flexibility resources. * Better predictions for the energy and power system's state. * Improved identification of the available flexibility resources on all power system levels. Figure 2: The F-channel platform’s expected role in the flexibility market. * Improved prediction of the system flexibility needs.