L’exploitation de l’énergie solaire soulève des défis liés à la nature variable de la res- source concernée : le rayonnement solaire. Son comportement intermittent est un problème pour la gestion des centrales photovoltaïques et du réseau électrique. L'une des solutions largement envisagées est la prévision de la production photovoltaïque à différents horizons.L'objectif de cette thèse est d'explorer de nouvelles voies pour améliorer les prévisions existantes du rayonnement solaire, pour des horizons allant de quelques minutes à quelques heures, en exploitant les synergies possibles entre les mesures pyranométriques, les images hémisphériques du ciel prises depuis le sol et les images acquises par les satellites météorologiques géostationnaires. Ces deux types d’images ont des couvertures spatiales, des résolutions spatio-temporelles et des points de vue très différents.L’approche proposée dans cette thèse exploite cette différence de points de vue afin d’affiner la géolocalisation des nuages en 3D par stéréoscopie, dont l’évolution des ombres peut alors être estimée et prévue. Un simulateur géométrique de la méthode a été développé et permet d’en identifier certains avantages et limitations. La géolocalisation des nuages appliquée à des données réelles a permis d’élaborer des schémas d’estimations et de prévisions prometteurs du rayonnement solaire incident. Enfin, pour compléter l’analyse usuelle de ces performances de prévision, deux nouvelles métriques ont été proposées dans l’optique de quantifier deux notions essentielles : le respect du suivi des rampes et l’alignement temporel de la prévision par rapport à la mesure. / The exploitation of solar energy raises challenges related to the variable nature of the resources involved: the incident solar irradiance. Its intermittent behavior is an is- sue for photovoltaic power plants and grid management. One of the solutions that have been widely considered is the forecast of photovoltaic production at different time horizon.The aim of this thesis is to explore new ways for improving the existing solar irradiance forecasts, for horizons ranging from the present moment to few hours, by exploiting possible synergies between pyranometric measurements, hemispherical images of the sky taken from the ground and images acquired by geostationary meteorological satellites. These two types of images have completely different spatial coverage, spatio-temporal resolutions and are taken from two different locations.The proposed approach in this thesis exploits this difference in points of view in order to geolocate the clouds in 3D by stereoscopy, which shadows’ location and motion can then be estimated and forecasted. A geometric simulator of the method has been developed to identify some of the advantages and limitations of this approach. The geolocation of clouds applied to real data made it possible to develop promising estimates and forecasts of incident solar irradiance. Finally, to complete the usual analysis of forecasting performances, two new metrics have been proposed in order to quantify two essential notions: the ability to monitor the ramps and the temporal alignment of the forecast with the measurements.

In hot climate areas, the electrical load in a building spikes, but not by the same amount daily due to various conditions. In order to cover the hottest day of the year, large cooling systems are installed, but are not fully utilized during all hot summer days. As a result, the investments in these cooling systems cannot be fully justified. A solution for more optimal use of the building cooling system is presented in this dissertation using Cool Thermal Energy Storage (CTES) deployed at a district level. Such CTES systems are charged overnight and the cool charge is dispatched as cool air during the day. The integration of the CTES helps to downsize the otherwise large cooling systems designed for the hottest day of the year. This reduces the capital costs of installing large cooling systems. However, one important question remains - how much of the CTES should be charged during the night, such that the cooling load for the next day is fully met and at the same time the CTES charge is fully utilized during the day. The solution presented in this dissertation integrated the CTES with Photovoltaics (PV) power forecasting and building load forecasting at a district level for a more optimal charge/discharge management. A district comprises several buildings of different load profiles, all connected to the same cooling system with central CTES. The use of forecasting for both the PV and the building cooling load allows the building operator to more accurately determine how much of the CTES should be charged during the night, such that the cooling system and CTES can meet the cooling demand for the next day. Using this approach, the CTES would be optimally sized, and utilized more efficiently during the day. At the same time, peak load savings are achieved, thus benefiting an electric utility company. The district presented in this dissertation comprises PV panels and three types of buildings – a mosque, a clinic and an office building. In order to have a good estimation for the required CTES charge for the next day, reliable forecasts for the PV panel outputs and the electrical load of the three buildings are required. In the model developed for the current work, dust was introduced as a new input feature in all of the forecasting models to improve the models' accuracy. Dust levels play an important role in PV output forecasts in areas with high and variable dust values. The overall solution used both the PV panel forecasts and the building load forecasts to estimate the CTES charge for the next day. The presented method was tested against the baseline method with no forecasting system. Multiple scenarios were conducted with different cooling system sizes and different CTES capacities. Research findings indicated that the presented method utilized the CTES charge more efficiently than the baseline method. This led to more savings in the energy consumption at the district level. / Doctor of Philosophy / In hot weather areas around the world, the electrical load in a building spikes because of the cooling load, but not by the same amount daily due to various conditions. In order to meet the demand of the hottest day of the year, large cooling systems are installed. However, these large systems are not fully utilized during all hot summer days. As a result, the investments in these cooling systems cannot be fully justified. A solution for more optimal use of the building cooling system is presented in this dissertation using Cool Thermal Energy Storage (CTES) deployed at a district level. Such CTES systems are charged overnight and the cool charge is dispatched as cool air during the day. The integration of the CTES helps to downsize the otherwise large cooling systems designed for the hottest day of the year. This reduces the capital costs of installing large cooling systems. However, one important question remains - how much of the CTES should be charged during the night, such that the cooling load for the next day is fully met and at the same time the CTES charge is fully utilized during the day. The solution presented in this dissertation integrated the CTES with Photovoltaics (PV) power forecasting and building load forecasting at a district level for a more optimal charge/discharge management. A district comprises several buildings all connected to the same cooling system with central CTES. The use of the forecasting for both the PV and the building cooling load allows the building operator to more accurately determine how much of the CTES should be charged during the night, such that the cooling system and CTES can meet the cooling demand for the next day. Using this approach, the CTES would be optimally sized and utilized more efficiently. At the same time, peak load is lowered, thus benefiting an electric utility company.

This thesis deals with quantifying the merits of Distributed Maximum Power Point Tracking (DMPPT), as well as providing solutions to achieve DMPPT in PV systems. A general method based on 3D modeling is developed to determine the energy yield of PV installations exploiting different levels of DMPPT granularity. Sub-string-level DMPPT is shown to have up to 30% more annual energy yield than panel-level DMPPT. A Multi-Input-Single-Output (MISO) dc-dc converter is proposed to achieve DMPPT in parallel-connected applications. A digital current-mode controller is used to operate the MISO converter in pseudo-CCM mode. For series-connected applications, the virtualparallel concept is introduced to utilize the robustness of the parallel connection. This concept is demonstrated on a three-phase boost converter. The topology offers reduced output voltage ripple under shading which increases the life-time of the output capacitor. The prototypes yield output power benefits of up to 46% and 20% for the tested shading conditions.

This thesis deals with quantifying the merits of Distributed Maximum Power Point Tracking (DMPPT), as well as providing solutions to achieve DMPPT in PV systems. A general method based on 3D modeling is developed to determine the energy yield of PV installations exploiting different levels of DMPPT granularity. Sub-string-level DMPPT is shown to have up to 30% more annual energy yield than panel-level DMPPT. A Multi-Input-Single-Output (MISO) dc-dc converter is proposed to achieve DMPPT in parallel-connected applications. A digital current-mode controller is used to operate the MISO converter in pseudo-CCM mode. For series-connected applications, the virtualparallel concept is introduced to utilize the robustness of the parallel connection. This concept is demonstrated on a three-phase boost converter. The topology offers reduced output voltage ripple under shading which increases the life-time of the output capacitor. The prototypes yield output power benefits of up to 46% and 20% for the tested shading conditions.

Avec la mutation du paysage énergétique due au développement des énergies renouvelables, des véhicules électriques ou encore des systèmes de stockage, le réseau électrique actuel a besoin de se moderniser. Le concept de microgrid est une solution prometteuse basée sur les technologies de l'information et de la communication pour améliorer la gestion et l'efficacité de la production, du transport, de la distribution et de la consommation de l'électricité. Cependant, les défis technico-économiques associés à leur déploiement sont encore élevés. Ces travaux de thèse ont pour but d’apporter des contributions sur plusieurs points clés : prévision de la production et de la consommation, modélisation des équipements, et optimisation de la gestion du microgrid.Rivesaltes-grid est un démonstrateur de microgrid à l'échelle d'un bâtiment industriel composé d'un champ photovoltaïque de 60 kWc, de batteries lithium-ion de 85 kWh et d'un véhicule électrique. Il a permis de développer un système de gestion de l'énergie (EMS) innovant pour optimiser l'efficacité énergétique du microgrid. Cet EMS, basé sur une gestion par commande prédictive et la résolution d'un problème d'optimisation avec contraintes, permet de réduire de 6,2% le coût de fonctionnement. Cette gestion du microgrid nécessite comme entrées : (1) la prévision de production basée sur un algorithme de forêt aléatoire et une modélisation du champ PV par modèle 1-diode, (2) la prévision de la consommation à partir de l'algorithme de partitionnement k-means++ et (3) la modélisation dynamique du système de stockage avec ses contraintes. / With the transformation of the energy landscape due to the development of renewable energies, electric vehicles and storage systems, the current grid needs to be modernized. Microgrid concept is a promising solution based on information and communication technologies to improve the management and efficiency of electricity generation, transmission, distribution and consumption. However, the technical and economic challenges associated with their deployment are numerous. The thesis aims to provide contributions on several key points: production and consumption forecasting, equipment modeling, and microgrid management optimization.Rivesaltes-grid is a microgrid demonstrator on the scale of an industrial building consisting of 60 kWp photovoltaic array, 85 kWh lithium-ion batteries and an electric vehicle. It has enabled the development of an innovative energy management system (EMS) to optimize the microgrids energy efficiency. This EMS, based on predictive control management and the resolution of a constrained optimization problem, reduces operation cost by 6.2%. This microgrid management requires as input: (1) the production prediction based on a random forest algorithm and a modeling of the PV field by 1-diode model, (2) the consumption prediction from partitioning algorithm k-means++ and (3) dynamic modeling of the storage system with its constraints.