Protecting critical infrastructure against cascading effects:The PRECINCT approach

Critical Infrastructures(CIs),which serve as the foundation of our modern society,are facing increasing risks from cyber threats,physical attacks,and natural disasters.Additionally,the interdependencies between CIs through-out their operational lifespan can also significantly impact their integrity and safety.As a result,enhancing the resilience of CIs has emerged as a top priority for many countries,including the European Union.This involves not only understanding the threats/attacks themselves but also gaining knowledge about the areas and infrastruc-tures that could potentially be affected.A European Union-funded project named PRECINCT(Preparedness and Resilience Enforcement for Critical INfrastructure Cascading Cyber-Physical Threats),under the Horizon 2020 program,tries to connect private and public stakeholders of CIs in a specific geographical area.The key objec-tive of this project is to establish a common cyber-physical security management approach that will ensure the protection of both citizens and infrastructures,creating a secure territory.This paper presents the components of PRECINCT,including a directory of PRECINCT Critical Infrastructure Protection(CIP)blueprints.These blueprints support CI communities in designing integrated ecosystems,operating and replicating PRECINCT components(or toolkits).The integration enables coordinated security and resilience management,incorporating improved’installation-specific’security solutions.Additionally,Serious Games(SG),and Digital Twins(DT)are a significant part of this project,serving as a novel vulnerability evaluation method for analysing complicated multi-system cascading effects in the PRECINCT Living Labs(LLs).The use of SG supports the concentrated advancement of innovative resilience enhancement services.

Smart energy management system framework for population dynamics modelling and suitable energy trajectories identification in islanded micro-grids

In an increasingly electrified and connected world,renewable energy production and robust distribution as well as sobriety paradigm,both for the individual and the society,will most likely play a central role regarding global systems stability.Consequently,while being able to conceive efficient storage systems coupled with robust energy management strategies present significant interests,a number of related studies often consider the human behaviour factor separately.While not decisive in large industrial factories,human demeanor impact cannot be overlooked in residential areas.As such,this work proposes an innovative and flexible dynamic population model,inspired from epidemiological methods,that allows simulation of a vast spectrum of social scenarios.By pairing this formalization with a smart energy management strategy,a complete framework is proposed.In particular,beyond the theoretical identification of sustainable parameters in a wide diversity of configurations,our experiments demonstrate the relevance of reinforcement learning agents as efficient energy management policies.Depending on the scenario,the trained agent enables an increase of the sustainability areas over baseline strategies up to 200%,thus hinting at ultimately softer societal impact.