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5. Energy Not Exchanged: A Metric to Quantify Energy Resilience in Smart Grids.

Author

Soualah, Hassen, Jodin, Gurvan, Le Goff Latimier, Roman, and Ben Ahmed, Hamid

Abstract

In high-impact, low-probability (HILP) events, resilience is defined as the ability of a system to return to a normal operating state after a failure. The generalization of information technologies and distributed renewable production is transforming the power grid into the so-called smart grid, thus allowing for new mitigation methods to address failures. After illustrating the limits of currently existing metrics, this paper proposes a method to quantify the resilience of smart grids during physical line faults while identifying the most impactful failures. For this purpose, a new resilience metric is defined in order to quantify Energy Not Exchanged (ENE). The calculation of this metric in a power grid via the optimal power flow (OPF) serves, therefore, to quantify the extreme resilience of the grid. In addition, various mitigation strategies, which enable maintaining a high level of resilience, despite the presence of failure, are simulated and then compared to one another (tie switch and microgrid formation). [ABSTRACT FROM AUTHOR]

Published
2023
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6. Modeling of the wind power forecast errors and associated optimal storage strategy

Author

Le Goff Latimier, Roman, Le Bouëdec, Enzo, Monbet, Valérie, Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Rennes (ENS Rennes), Institut de Recherche Mathématique de Rennes (IRMAR), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-AGROCAMPUS OUEST, Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), IRMAR-STAT, Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Inria Rennes – Bretagne Atlantique, and Monbet, Valérie

Subjects
Markov Switching Auto Regressive, Stochastic Dynamic Programming, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [SPI.NRJ]Engineering Sciences [physics]/Electric power, wind energy, forecast errors, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], [SPI.NRJ] Engineering Sciences [physics]/Electric power
Abstract

Production forecast errors are the main hurdle to integrate variable renewable energies into electrical power systems. Regardless of the technique, these errors are inherent in the forecast exercise, although their magnitude significantly vary depending on the method and the horizon. As power systems have to balance out these errors, their dynamic and stochastic modeling is valuable for the real time operation. This study proposes a Markov Switching Auto Regressive-MS-AR-approach. The strength of such a model is to be able to identify weather types according to the reliability of the forecast. These types are captured with a hidden state whose evolution is controlled by a transition matrix. The autocorrelation and variance parameters of the AR models are then different from one state to another. After having validated its statistical relevance, this model is used to solve the problem of the optimal management of a storage associated with a wind power plant when this virtual power plant must respect a production commitment. The resolution is carried out by stochastic dynamic programming while comparing the proposed MS-AR with several other models of forecast errors. This illustrative problem highlights the improvements made by a fine modeling of forecast errors.

Published
2019

7. Computation Time Analysis of the Magnetic Gear Analytical Model.

Author

Desvaux, Melaine, Traulle, Benjamin, Le Goff Latimier, Roman, Sire, Stephane, Multon, Bernard, and Ben Ahmed, Hamid

Subjects
MAGNETIC fields, PERMEABILITY, LAPLACE transformation, POISSON regression, ELECTRICAL harmonics
Abstract

This paper focuses on the computation time and precision of a linear 2-D magnetic gear analytical model. Two main models of magnetic gears are studied: 1) the first with an infinite relative permeability of yokes and 2) the second with a finite relative permeability of yokes. These models are based on the subdomain resolution of Laplace and Poisson equations. To accurately compute the magnetic field distribution, it is necessary to take into account certain harmonics of the various rings and other system harmonics due to modulation. Global system harmonics, which increase the value of computation time, must also be taken into account. If the magnetic gear has a high pole number, then computation time increases even more and no longer allows for system optimization. This paper proposes to compute magnetic field distribution using different harmonic selection methods in order to significantly reduce the computation time for the magnetic torque without any loss of accuracy. [ABSTRACT FROM AUTHOR]

Published
2017
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