Interdependent Behavior of Critical Infrastructures

International audienc

Criticality Hierarchy Procedure Applied to Software Applications for Electrical Networks Management

International audienc

Modeling Cyber and Physical Interdependencies – Application in ICT and Power Grids

International audienc

Towards a Common Model for Studying Critical Infrastructure Interdependancies

International audienc

Modeling Cyber and Physical Interdependencies - Application in ICT and Power Grids

International audienc

ICT and Power Systems: An Integrated Approach

International audienc

Interdependencies of coupled heterogeneous infrastructures: the case of ICT and Energy

International audienceModern energy infrastructures and Information and Communication Technologies (ICT) are strongly coupled and as such heavily depend on each other. These infrastructures are essential for the proper functioning of our society and economies. Indeed, it is hard to imagine our present society without the services offered by these infrastructures. In addition, any failure of these infrastructures may cause significant economical losses. They are "critical infrastructures". Although, the energy and ICT infrastructures are heterogeneous in nature (different features, dynamics and communities), they need to be addressed in an integrated matter in the perspective of global infrastructures security. However, their interdependencies are not well known and significant efforts need to be dedicated for better understanding criticalities, vulnerabilities and cascading effects characterizing these strongly coupled heterogeneous infrastructures

Smart Scheduling of Electric Vehicles Based on Reinforcement Learning

As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs on a larger scale, starting from enhancing the network interoperability and accessibility and removing the uncertainty associated with the availability of charging stations. Another issue is of particular interest for EV drivers travelling longer distances and is related to scheduling a recharging operation at the estimated time of arrival, without long queuing times. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data