ContRol methods for rELiable sensIng informAtion in interConnected Energy systems

The Internet of Things (IoT) is at the forefront of a transformation in electric power and energy systems to provide clean energy for sustainable global economic growth, by enabling novel capabilities, such as real-time monitoring and distributed control. The exciting opportunities given by smart meters, flexible demand, vehicles to grid technologies, and smart buildings all rely on having access to a large amount of real-time data. This is nowadays possible thanks to the advancements and affordability of sensing and communication technologies. However, the importance and effectiveness of these systems relies in the timeliness and accuracy of the data which is sensed, communicated and processed. What happens if the used information is not reliable, for example due to sensor faults, communication problems or cyber-attacks? In fact, affordable sensors could be prone to sensor faults, leading to missing or incorrect measurements; the need for always connected devices could be compromised by communication issues, such as delays or packet losses, resulting in outdated or missing information. Finally, novel sophisticated cyber-attacks, called cyber-physical attacks, targeting Industrial Control Systems, may intentionally modify some information to cause physical consequences on the systems. Recent attacks in Ukraine resulting in the disruption of power distribution have shown the feasibility and terrible effects of these attacks.
By taking measurements from monitoring sensors and devices, deriving information to take decisions and subsequently defining actions for the system, and repeating this cycle, IoT systems implement a so-called feedback control. The use of outdated or compromised data could lead to inefficient solutions or even dangerous operation conditions. The ability to appropriately deal with control systems within such frameworks is an imperative: reliable sensing information is fundamental for emerging energy systems, as well as reliable control systems.
The proposed programme provides answers to a key open research question: How to safely and efficiently control emerging energy systems applications based on the IoT, where it might be challenging to guarantee the reliability of the sensing information?
In fact, existing methods are not suitable for this novel interconnected and complex scenario.
The goal of this project is to design novel methods to monitor the reliability of sensing information, including sensors anomaly detection and localisation, and new control architectures resilient to possibly unreliable sensing information, specifically for interconnected IoT scenarios such as electric vehicles charging, demand and energy management in microgrids and smart buildings.
To achieve these objectives, the intuition is to enhance traditional control methods for distributed systems based on optimisation with innovative machine learning techniques on graphs. These methods well suit the considered energy systems that can be represented as a network of interconnected subsystems with loads, generators, storage, devices and sensors. Graph-based learning techniques will exploit the known network structure of the system to identify the relationships between the different elements of the network and to estimate and reconstruct the value of missing or compromised data. This idea represents a novelty in the research for systems control.
The developed methodologies will be adopted by systems operators, SMEs and ICT companies working in the sensing and IoT sectors for energy, to enhance the reliability of their systems, to protect operators and users, enabling the introduction of novel technologies for efficient and green energy systems, thus bringing a huge benefit to the society in terms of safety, resilience and sustainability.