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José Horta, Eitan Altman, Mathieu Caujolle, Daniel Kofman, David Menga

Proc. of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), October 2018.
DOI: 10.1109/SmartGridComm.2018.8587495

Abstract: Future electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers’ local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected.

Ziad Ismaïl, Jean Leneutre, Alia Fourati

Proc. of the 14th European Dependable Computing Conference (EDCC), September 2018.
DOI: 10.1109/EDCC.2018.00030

Abstract: The management of security resources in a system always comes with a tradeoff. Given technical and budget constraints, the defender focuses on deploying the set of security countermeasures that offer the best level of system protection. However, optimizing the configuration and deployment of defense countermeasures for efficient attack detection and mitigation remains a challenging task. In this paper, we leverage the information present in an attack graph, representing the evolution of the state of the attacker in the system, to tackle the problem of finding the optimal security policy that offers the maximum level of system protection. Our solution can be used to assist asset owners to prioritize the deployment of security countermeasures and respond to intrusions efficiently. We validate our approach on an Advanced Metering Infrastructure (AMI) case study.