SIGnal-based control for Resilient, Energy-Efficient, Net-zero systems

Descrizione del progetto

Reti elettriche più intelligenti per un’Europa a zero emissioni nette

La rete elettrica europea deve affrontare le sfide dovute alla variabilità delle fonti rinnovabili e alle condizioni meteorologiche avverse. L’approccio attuale alla gestione di un sistema di questo tipo porta a un uso inefficiente delle risorse e un rischio elevato di guasti su larga scala, come dimostrato dal blackout verificatosi in Spagna nel 2025. Con il sostegno del programma di azioni Marie Skłodowska-Curie, il progetto SIGREEN si propone di collaudare una strategia di controllo innovativa basata sui segnali. Attraverso l’applicazione di tecniche avanzate di matematica, apprendimento automatico e megadati, SIGREEN si propone di sviluppare soluzioni per affrontare problemi che coinvolgono più scale temporali. Gli algoritmi sviluppati saranno collaudati in contesti reali e, secondo le attese, contribuiranno a ridurre al minimo le perdite e a evitare i guasti. Le innovazioni che ne deriveranno apporteranno un contributo significativo al raggiungimento degli obiettivi climatici definiti dall’UE.

Obiettivo

The transition to net-zero energy systems requires electricity grids that are not only efficient but also resilient to growing uncertainty from renewable generation and extreme events. As renewable penetration rises, unmanaged variability already causes inefficiencies through curtailment (wasting available green electricity) and increases the risk of large-scale outages such as the Iberian blackout of April 2025. Current control methods are not well suited to manage this complexity. SIGREEN (SIGnal-based control for Resilient, Energy-Efficient, Net-zero systems) tackles this challenge by developing a new signal-based control framework that leverages advanced mathematics, statistics, machine learning, and large datasets, with further exploitation guidance from the UK-based company Sunsynk.

The project has three goals: (1) to create new mathematical tools for describing systems under uncertainty, (2) to design stochastic control methods for efficiency and resilience under rare and multi-time-scale disturbances, and (3) to exploit these methods to improve efficiency and resilience of distributed energy networks with validations in simulations and Controller Hardware-In-the-Loop (C-HIL) experiments that mimic real-world conditions. Results will be shared through leading scientific conferences, workshops, international research visits, and industry-facing events such as Enlit Europe 2027.

The project will deliver new algorithms, datasets, and experimental demonstrations that advance clean energy research and support Europe’s climate and energy goals. For the researcher, the fellowship provides training in advanced research, teaching, and leadership skills, making it an ideal springboard towards future European research grants (such as the ERC Starting Grant) and a competitive academic career.