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Hajar, Khaled
Universite Grenoble Alpes, EEATS, Grenoble-inp, laboratoire Grenoble Images Parole Signal Automatique - Gipsa-Lab (France); Universite de Technologie et de Sciences Appliquees Libano-Francaise - ULF-Tripoli, Pole de Recherche Electronique Electrotechnique Automatique - Preaa (Lebanon)2017
Universite Grenoble Alpes, EEATS, Grenoble-inp, laboratoire Grenoble Images Parole Signal Automatique - Gipsa-Lab (France); Universite de Technologie et de Sciences Appliquees Libano-Francaise - ULF-Tripoli, Pole de Recherche Electronique Electrotechnique Automatique - Preaa (Lebanon)2017
AbstractAbstract
[en] Currently, energy management strategies in smart grids are mostly limited to the interest of a subsystem. As a general rule, each actor is autonomously managed regardless of whether it is integrated into a nearby power grid. For example, a building energy management system aims to provide the desired level of service to occupants and does not care about its impact on the system unless it has to meet certain constraints. This way of managing can of course lead to a given equilibrium but the resultant will be only a set of optimized subsystems that will rarely lead to an overall optimum in the pocket to which they belong. In view of what has been said above, and in view of a rapidly evolving distribution system architecture; the physical and algorithmic restructuring in physical or virtual sub networks will allow to answer efficiently the problems related to: - Security of supply - Massive integration of renewable energy - The quality of energy - The appearance of new unconventional loads - System services. In the literature, aspects of microgrid energy control and management are treated separately, and intelligent network interaction is simply proposed. To meet these challenges, the concept of smart grids has emerged over the last decade. It builds on the capabilities of modern communication systems that enable the continuous flow of data between the players in an intelligent network and the scalable computing capabilities to implement advanced large-scale energy management strategies ladder. This thesis proposes to carry out a systemic study of the control of microgrid which control aims at an optimized management of the energy in connection with a structure of what is commonly called 'intelligent network', while optimizing the local power under a model Predictive control (MPC). The MPC stands out among advanced network control strategies for several reasons. Firstly, it makes it possible to easily handle multi-variable systems which are subjected to multiple constraints. Secondly, it is able to anticipate future events by taking into account forecasts (for example, weather forecasts, forecast loads, etc.). For these reasons, part of this thesis is dedicated to MPC algorithms which aim to coordinate optimally a large number of actors in a microgrid (PV, Batteries, Wind, loads...). The idea is to have a local MPC controller for each microgrid and above it, an MPC management controller coordinator that influences the local controller in such a way that the overall optimality of the intelligent network is respected. The objective of maximizing local consumption of locally produced energy is considered. This objective is a step towards the energy independence of the local microgrids with respect to the main network, which however can intervene to buy the excess power of all microgrids of the cooperative. This thesis was prepared in co-supervision between the Gipsa-Lab of the Grenoble-Alpes University (UGA) and the PREEA of the Lebanese-French University of Technology and Applied Sciences in the application of the PARADISE project. This project aims, through its contributions, to optimize distribution networks that are portable in the presence of a high rate of intermittent production based on renewable energy; and this, by physical architectures and incremental algorithm. (author)
[fr]
Actuellement, les strategies de gestion de l'energie dans les reseaux intelligents sont pour la plupart limitees a l'interet d'un sous-systeme. En regle generale, chaque acteur est gere de facon autonome sans tenir compte du fait qu'il est integre dans un reseau electrique a proximite. Par exemple, un systeme de gestion de l'energie des batiments vise a fournir le niveau de service souhaite aux occupants et ne se soucie pas de son impact sur le reseau, sauf s'il doit en respecter certaines contraintes. Cette maniere de gerer peut conduire bien entendu a un equilibre donne mais la resultante ne sera qu'un ensemble de sous-systemes optimises qui ameneront rarement a un optimum global dans la poche a laquelle ils appartiennent. Compte tenu de ce qui est dit ci-dessus, et au vu d'une architecture de reseaux de distribution en evolution rapide; la restructuration physique et algorithmique en sous reseaux physiques ou virtuels permettra de repondre efficacement aux problematiques liees a: - La surete de la fourniture - L'integration massive de renouvelable - La qualite de l'energie - L'apparition de nouvelles charges non conventionnelles - Aux services systemes. Dans la litterature, les aspects du controle et de la gestion de l'energie de microreseaux sont traites separement, et l'interaction de reseau intelligent est simplement proposee. Pour relever ces defis, le concept de reseaux intelligents est apparu au cours de la derniere decennie. Il s'appuie sur les capacites des systemes de communication modernes qui permettent le flux continu de donnees entre les acteurs d'un reseau intelligent et sur les capacites de calcul evolutives permettant de mettre en oeuvre des strategies avancees de gestion de l'energie a grande echelle. Cette these se propose de mener une etude systemique du controle de microreseaux lequel controle vise une gestion optimisee de l'energie en lien avec une structure de ce qui est communement appele 'reseau intelligent' et ce, tout en optimisant la puissance locale sous un modele predictif de controle (MPC). Le MPC se distingue parmi les strategies avancees de controle de reseau pour plusieurs raisons. D'abord, il permet de traiter facilement des systemes multi variables qui sont soumis a de multiples contraintes. En second lieu, il est capable d'anticiper les evenements futurs en tenant compte des previsions (par exemple, previsions meteorologiques, previsions de charges,...). Pour ces raisons, une partie de cette these est dediee aux algorithmes MPC qui visent a coordonner de maniere optimale un grand nombre d'acteurs dans un microreseau (PV, Batteries, eolienne, charges,...). L'idee est d'avoir un controleur MPC local pour chaque microreseau et au-dessus, un coordinateur de controleur de gestion MPC qui influence le controleur local de telle maniere que l'optimalite globale du reseau intelligent soit respectee. L'objectif de maximiser la consommation locale d'energie produite localement est considere. Cet objectif est une etape vers l'independance energetique des microreseaux locaux vis-a-vis du reseau principal lequel toutefois peut intervenir pour acheter l'exces de puissance de l'ensemble des microreseaux de la cooperative. Cette these a ete preparee en co-tutelle entre le Gipsa-Lab de l'Universite Grenoble-Alpes (UGA) et le PREEA de l'universite de technologie et de sciences appliquees libano-francaise dans l'application du projet PARADISE. Ce dernier projet vise par ses contributions a optimiser des reseaux de distribution ilotables en presence d'un fort taux de production intermittente a base de renouvelable; et ce, par des architectures physiques et algorithmiques incrementales. (auteur)Original Title
Cooperative energetique intelligente
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Secondary Subject
Source
4 Jul 2017; 89 p; 63 refs.; Available from the INIS Liaison Officer for France, see the INIS website for current contact and E-mail addresses; These Docteur de la Communaute Universite Grenoble Alpes, Specialite: Automatique - Productique
Record Type
Miscellaneous
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Thesis/Dissertation
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AC SYSTEMS, ALGORITHMS, BATTERY CHARGE STATE, COMPUTERIZED SIMULATION, COOPERATIVES, DC SYSTEMS, ELECTRIC GENERATORS, ENERGY MANAGEMENT SYSTEMS, ENERGY STORAGE SYSTEMS, LEBANON, LOAD MANAGEMENT, ON-SITE POWER GENERATION, OPTIMIZATION, PHOTOVOLTAIC POWER PLANTS, PHOTOVOLTAIC POWER SUPPLIES, POWER DISTRIBUTION SYSTEMS, SMART GRIDS, WIND POWER PLANTS
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