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Nouredine Hadjsaid

Researcher at University of Grenoble

Publications -  53
Citations -  950

Nouredine Hadjsaid is an academic researcher from University of Grenoble. The author has contributed to research in topics: Smart grid & Electric power system. The author has an hindex of 11, co-authored 53 publications receiving 811 citations. Previous affiliations of Nouredine Hadjsaid include Grenoble Institute of Technology & Centre national de la recherche scientifique.

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Radial Network Reconfiguration Using Genetic Algorithm Based on the Matroid Theory

TL;DR: A theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem.
Journal ArticleDOI

Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools

TL;DR: In this article, the authors present a typology based on a literature review for both power sector models and long-term models of the energy system, and compare the power sector's components, such as electricity storage and grid.
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Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools

TL;DR: In this article, the authors present a typology based on a literature review for both power sector models and long-term models of the energy system, and compare the power sector's components, such as electricity storage and grid.
Journal ArticleDOI

Storage as a flexibility option in power systems with high shares of variable renewable energy sources: a POLES-based analysis

TL;DR: In this paper, the role of electricity storage for the integration of high shares of Variable Renewable Energy Sources (VRES 3) in the long-term evolution of the power system is demonstrated.
Proceedings ArticleDOI

Radial network reconfiguration using genetic algorithm based on the matroid theory

TL;DR: A theoretical approach based on the graph and matroid theories is considered in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem.