Academic literature on the topic 'Energy-Constrained'

Dans cette thèse, nous étudions et concevons des algorithmes pour des agents mobiles se déplaçant dans un graphe avec une énergie limité, restreignant leurs mouvements. Chaque agent mobile est une entité, équipée d’une batterie, qui peut parcourir les arêtes du graphe et visiter les noeuds du graphe. A chaque mouvement, l’agent consomme une partie de son énergie. Contrairement à divers modèles bien étudiés pour les agents mobiles, très peu de recherches ont été menées pour le modèle compte tenu des limites d’énergie. Nous étudions les problèmes fondamentaux de l’exploration d’un graphe, du gathering et du collaborative delivery dans ce modèle<br>In this thesis we study and design algorithms for solving various well-known problems for mobile agents moving on a graph, with the additional constraint of limited energy which restricts the movement of the agents. Each mobile agent is an entity, equipped with a battery, that can traverse the edges of the graph and visit the nodes of the graph, consuming a part of its energy for movement. In contrast to various well-studied models for mobile agents, very little research has been conducted for the model considering the energy limitations. We study the fundamental problems of graph exploration, gathering and collaborative delivery in this model

Les contraintes en probabilité constituent un modèle pertinent pour gérer les incertitudes dans les problèmes de décision. En management d’énergie de nombreux problèmes d’optimisation ont des incertitudes sous-jacentes. En particulier c’est le cas des problèmes de gestion de la production au court-terme. Dans cette Thèse, nous investiguons les contraintes probabilistes sous l’angle théorique, algorithmique et applicative. Nous donnons quelques nouveaux résultats de différentiabilité des contraintes en probabilité et de convexité des ensembles admissibles. Des nouvelles variantes des méthodes de faisceaux « proximales » et « de niveaux » sont spécialement mises au point pour traiter des problèmes d’optimisation convexe sous contrainte en probabilité. Ces algorithmes gèrent en particulier, les erreurs d’évaluation de la contrainte en probabilité, ainsi que son gradient. La convergence vers une solution du problème est montrée. Enfin, nous examinons deux applications : l’optimisation d’une vallée hydraulique sous incertitude sur les apports et l’optimisation d’un planning de production sous incertitude sur la demande. Dans les deux cas nous utilisons une contrainte en probabilité pour gérer les incertitudes. Les résultats numériques présentés semblent montrer la faisabilité de résoudre des problèmes d’optimisation avec une contrainte en probabilité jointe portant sur un système de environ 200 contraintes. Il s’agit de l’ordre de grandeur nécessaire pour les applications. Les nouveaux résultats de différentiabilité concernent à la fois des contraintes en probabilité portant sur des systèmes linéaires et non-linéaires. Dans le deuxième cas, la convexité dans l’argument représentant le vecteur incertain est requise. Ce vecteur est supposé suivre une loi Gaussienne ou Student multi-variée. Les formules de gradient permettent l’application directe d’un schéma d’évaluation numérique efficient. Pour les contraintes en probabilité qui peuvent se réécrire à l’aide d’une Copule, nous donnons de nouveau résultats de convexité pour l’ensemble admissibles. Ces résultats requirent la concavité généralisée de la Copule, les distributions marginales sous-jacents et du système d’incertitude. Il est suffisant que ces propriétés de concavité généralisée tiennent sur un ensemble spécifique<br>In optimization problems involving uncertainty, probabilistic constraints are an important tool for defining safety of decisions. In Energy management, many optimization problems have some underlying uncertainty. In particular this is the case of unit commitment problems. In this Thesis, we will investigate probabilistic constraints from a theoretical, algorithmic and applicative point of view. We provide new insights on differentiability of probabilistic constraints and on convexity results of feasible sets. New variants of bundle methods, both of proximal and level type, specially tailored for convex optimization under probabilistic constraints, are given and convergence shown. Both methods explicitly deal with evaluation errors in both the gradient and value of the probabilistic constraint. We also look at two applications from energy management: cascaded reservoir management with uncertainty on inflows and unit commitment with uncertainty on customer load. In both applications uncertainty is dealt with through the use of probabilistic constraints. The presented numerical results seem to indicate the feasibility of solving an optimization problem with a joint probabilistic constraint on a system having up to 200 constraints. This is roughly the order of magnitude needed in the applications. The differentiability results involve probabilistic constraints on uncertain linear and nonlinear inequality systems. In the latter case a convexity structure in the underlying uncertainty vector is required. The uncertainty vector is assumed to have a multivariate Gaussian or Student law. The provided gradient formulae allow for efficient numerical sampling schemes. For probabilistic constraints that can be rewritten through the use of Copulae, we provide new insights on convexity of the feasible set. These results require a generalized concavity structure of the Copulae, the marginal distribution functions of the underlying random vector and of the underlying inequality system. These generalized concavity properties may hold only on specific sets

In optimization problems involving uncertainty, probabilistic constraints are an important tool for defining safety of decisions. In Energy management, many optimization problems have some underlying uncertainty. In particular this is the case of unit commitment problems. In this Thesis, we will investigate probabilistic constraints from a theoretical, algorithmic and applicative point of view. We provide new insights on differentiability of probabilistic constraints and on convexity results of feasible sets. New variants of bundle methods, both of proximal and level type, specially tailored for convex optimization under probabilistic constraints, are given and convergence shown. Both methods explicitly deal with evaluation errors in both the gradient and value of the probabilistic constraint. We also look at two applications from energy management: cascaded reservoir management with uncertainty on inflows and unit commitment with uncertainty on customer load. In both applications uncertainty is dealt with through the use of probabilistic constraints. The presented numerical results seem to indicate the feasibility of solving an optimization problem with a joint probabilistic constraint on a system having up to 200 constraints. This is roughly the order of magnitude needed in the applications. The differentiability results involve probabilistic constraints on uncertain linear and nonlinear inequality systems. In the latter case a convexity structure in the underlying uncertainty vector is required. The uncertainty vector is assumed to have a multivariate Gaussian or Student law. The provided gradient formulae allow for efficient numerical sampling schemes. For probabilistic constraints that can be rewritten through the use of Copulae, we provide new insights on convexity of the feasible set. These results require a generalized concavity structure of the Copulae, the marginal distribution functions of the underlying random vector and of the underlying inequality system. These generalized concavity properties may hold only on specific sets.

This thesis investigates the design and optimisation of constrained electromagnetic energy harvesters. It provides optimal design guidelines for constrained electromagnetic energy harvesters under harmonic and random vibrations. To find the characteristics of the vibration source, for instance vertical motion of a boat, the spectrum of the excitation amplitude should be obtained. Two Kalman filter based methods are proposed to overcome the difficulties of calculating displacement from measured acceleration. Analytical models describing the dynamics of linear and rotational electromagnetic energy harvesters are developed. These models are used to formulate a set of design rules for constrained linear and rotational energy harvesters subjected to a given sinusoidal excitation. For the sake of comparison and based on the electromechanical coupling coefficient of the systems, the maximum output power and the corresponding efficiency of linear and rotational harvesters are derived in a unified form. It is shown that under certain condition, rotational systems have greater capabilities in transferring energy to the load resistance and hence obtaining higher efficiency than linear systems. Also, the performance of a designed rotational harvester in response to broadband and band-limited random vibrations is evaluated and an optimum design process is presented for maximizing the output power under these conditions. It is furthermore shown that the profile of the spectral density of the measured acceleration signal of a typical boat can be approximated by a Cauchy distribution which is used to calculate the extracted power extracted by the proposed energy harvester in real conditions. In order to increase the operational bandwidth of rotational energy harvesters, subjected to time-varying frequency vibrations, a variable moment of inertia mechanism is proposed to adaptively tune the resonance frequency of harvester to match the excitation frequency. Also, the effects of combining the variable moment of inertia mechanism and adjusting the load resistance to increase the operational bandwidth of the system for constrained and unconstrained applications are studied. Finally, a ball screw based prototype is manufactured and the experimental results of its testing are presented which confirm the validity of the design and the derived dynamic equations of the system.

In wireless distributed networks, where multiple antennas can not be integrated in one node, cooperative Multi-Input Multi-Output (MIMO) techniques help to exploit the space time diversity gain in order to increase performances or to reduce the transmission energy consumption. In this thesis, strategies using cooperative MIMO techniques are proposed for Wireless Sensor Network (WSN) where the energy consumption is the most important design criterion. The performance and the energy consumption advantages of cooperative MIMO technique are investigated, in comparison with the SISO, multi-hop SISO and cooperative Relay techniques, and an optimal selection of transmit-receive antennas number in terms of energy consumption is also proposed as a function of transmission distances. Since the wireless nodes are physically separated in cooperative MIMO systems, the imperfect time synchronization between cooperative nodes clocks leads to an unsynchronized MIMO transmission. The performance degradation of this cooperative transmission synchronization error and the cooperative reception additional noise is evaluated by simulations. Two new cooperative reception techniques based on the relay principle and a new efficient space-time combination technique are proposed in order to increase the energy efficiency of cooperative MIMO systems. Finally, performance and energy consumption comparisons between cooperative MIMO and Relay techniques are performed and an association strategy is also proposed to exploit simultaneously the advantages of the two cooperative techniques<br>Dans les réseaux sans fil distribués où plusieurs antennes ne peuvent pas être intégrées dans un même nœud de communication, les techniques MIMO (Multiple Input Multiple Output) coopératives permettent d'exploiter le gain de la diversité spatio-temporelle pour augmenter les performances ou réduire l'énergie consommée pour les communications. Dans cette thèse, des stratégies MIMO coopératives sont proposées pour les réseaux de capteurs sans fil (RCS), où la consommation d'énergie est la contrainte la plus importante. Leur avantage en termes de taux d'erreur et de consommation d'énergie sur les techniques mono-antenne (SISO), même multi-étapes, et sur les techniques de relais, est clairement mis en évidence. Une sélection du nombre d'antennes d'émission et de réception, optimale en termes d'efficacité énergétique, est également proposée en fonction des distances de transmission. Les inconvénients du MIMO coopératif, comme les imperfections de synchronisation à l'émission ou les bruits additifs en réception, qui affectent leurs performances dans les réseaux sans fil distribués, sont abordés. Deux nouvelles techniques de réception coopérative basées sur le principe de relais, ainsi qu'une nouvelle technique de combinaison spatio-temporelle sont proposées afin d'augmenter l'efficacité énergique de ces systèmes MIMO coopératifs. Enfin, des comparaisons de performance et de consommation d'énergie entre les techniques MIMO coopératives et de relais montrent que leur utilisation dépend beaucoup de la topologie du réseau et de l'application. Une stratégie d'association est proposée pour exploiter simultanément les avantages des deux techniques de coopération