Multi-timescale Reinforcement Security on Cyber-Physical Smart Grids: Design, Monitoring, and Operation

Descripción del proyecto

Garantizar el futuro de los sistemas energéticos inteligentes

Las redes inteligentes modernas se están convirtiendo en sistemas complejos al integrar las energías renovables, pero esta transición aumenta la vulnerabilidad ante apagones a gran escala y ciberataques selectivos. Con el apoyo de las acciones Marie Skłodowska-Curie, el proyecto MRSSG tiene como objetivo hacer frente a estos riesgos urgentes, garantizando la seguridad de las infraestructuras eléctricas mediante un novedoso marco de tres niveles. Mediante el uso de técnicas avanzadas de aprendizaje profundo para detectar amenazas cibernéticas y de leyes de control distribuido para mitigar las perturbaciones en tiempo real, el proyecto garantizará un funcionamiento fiable de la red eléctrica. Además, incorporará controladores autónomos para mantener la estabilidad física. El objetivo del proyecto es proporcionar algoritmos de código abierto y directrices de implementación, con el fin último de garantizar la seguridad de los servicios esenciales y apoyar el avance de Europa hacia un futuro energético sostenible, seguro y digitalmente integrado.

Objetivo

The rapid integration of distributed energy resources (DERs), renewable generation, and advanced communication technologies is transforming modern smart grids into complex cyber-physical systems (CPS). While these developments enhance efficiency and flexibility, they also expose power infrastructures to escalating cyber and physical threats, as highlighted by recent large-scale blackouts and targeted cyberattacks in Europe. Ensuring the resilience of CPS-based smart grids is therefore of urgent societal, economic, and scientific importance. This project, Multi-timescale Reinforcement Security on Cyber-Physical Smart Grids (MRSSG), proposes a novel three-tier security framework spanning long-, mid-, and short-term timescales. At the cyber level, the project will design spatiotemporal deep learning algorithms to detect heterogeneous attacks—including stealth, denial-of-service, replay, and false data injection—by capturing both dynamic power flow variations and network topology features. At the operational level, distributed attack-resilient control with adaptive laws will be developed to mitigate real-time disturbances and preserve reliable grid operations without reliance on centralized structures. At the physical level, a canonical self-disciplined stabilization controller will be established to guarantee large-signal stability across diverse DER converters, enabling autonomous plug-and-play resilience without disclosing sensitive system parameters. By addressing critical knowledge gaps in multi-timescale detection, mitigation, and stabilization, MRSSG will strengthen the security and reliability of European smart grids under extensive cyber-physical risks. The outcomes will contribute new theoretical foundations, open-source algorithms, and practical implementation guidelines, ensuring robust protection for essential services and supporting Europe's transition toward a sustainable, secure, and digitally integrated energy future.