Most of the solutions used to monitor and control the electrical system tend to be centralized, meaning that data is sent to a central system to be stored and processed. In recent years, many applications have been moved to the Cloud to improve their availability, reliability, scalability and replicability. One limitation to migrating applications to the Cloud is the time constraints imposed by high communication latencies. Fog/Edge computing solutions bring the benefits of the Cloud to the field level by providing processing and decision capabilities in peripheral nodes. Processing takes place close to the process, reducing latency and the amount of data transferred to the Cloud.

The research program on this topic starts with the identification of use cases in the energy sector that benefit most from Cloud and Fog/Edge computing architectures and ends with the preparation of a real demonstrator. This will make it possible to evaluate and compare the performance of this new paradigm with traditional solutions.

By also enabling continuous data streaming to the Cloud, Fog/Edge architectures will enable online analytics processes. To this end, the elements of the architecture will be equipped with the ability to continuously send a flow of information about their status and processes, enabling real-time analysis, even automatically with machine learning algorithms. Finally, new architectural solutions are evaluated for the creation of Supervisory Control And Data Acquisition (SCADA) applications, electrical system protection applications and end-user applications. On the basis of the cases considered, an initial architecture was defined and its basic components were identified. This was followed by some preliminary testing of the individual components. The tests were carried out by implementing energy consumption monitoring and electric vehicle charging solutions using IoT devices (based on low-cost single board computers) that transmit data to applications installed in a private Cloud and collect data in Fog devices.