The rapid expansion of Electric Vehicle (EV) charging capacities, driven by the surge in transportation electrification, has a direct impact on the electric power system, particularly the Low Voltage (LV) grid. Consequently, there is a pressing need to redesign energy management systems, placing a greater emphasis on mitigating the effects of EV charging. In this article, we crafted an online grid-level Model Predictive Control (MPC) incorporating Machine Learning (ML) prediction enhanced with Singular Spectrum Analysis (SSA). ML models predicts the future EV charging demands, which are the key factors for the MPC for managing efficiently a large-scale charging infrastructure, aiming to mitigate EV charging impacts, specifically phase unbalance and overloading. The MPC results, especially on phase unbalance metrics, mirrored in a predictive manner those of offline/no forecast optimization from our previous work. A week-long MPC simulation comparison with uncontrolled case showcased a 63% reduction in peak demand duration, a 68% decrease in Phasing Unbalance Index (PUI), and a 45% reduction in Voltage Unbalance Factor (VUF). The minimum voltage unbalance improved from 0.897 pu to 0.913 pu, reducing out-of-standard drops duration by 21%. Lastly, we observed a 20% cut in lines’ losses, equating to a 50 kgCO ₂ reduction within a week.

With the growth of electric vehicles’ (EVs) deployment as a substitute for internal combustion engine vehicles, the impact of this kind of load on the distribution grid cannot be neglected. An in-depth study needs to be performed on a regional basis to investigate the impacts of electric vehicle (EV) charging on the grid for each country’s grid configuration and specifications, in order to be able to reduce them. In this work, we built a case study of a charging infrastructure of a Tunisian workplace parking lot, by combining different measured data and simulations using OpenDSS and Matlab. The first objective was to analyze the integration impacts on the Tunisian low-voltage (LV) grid including phase unbalance, voltage drop, harmonics, and power losses. We found that 10 metric tons of carbon dioxide (${\mathrm{MtCO}}_2$) in yearly emissions were caused by power losses, and 50% of these emissions came from harmonic losses, which can be avoided by active and passive filtering. The second objective was to decrease phase unbalance by formulating an optimization problem and solving it by combining a genetic algorithm (GA) and a pattern search (PS) in the Matlab environment. The GA returned interesting results by balancing the phases, and the addition of PS as a hybrid function reduced the convergence speed by 38%. Moreover, the optimization led to a reduction of 83% in the neutral current maximum value, a reduction of 67% in the violation period of the voltage drop, a minimum voltage drop of 0.94 pu. and kept the total current consumption within a fixed limit. The developed model can be adapted to any similar workplace parking facility in Tunisia that is equipped with an EV charging infrastructure.

Pour fixer en parallèle des milliers de biomolécules (ADN, ARN, protéines, etc.) à la surface des puces à ADN ou àprotéines, il faudrait choisir parmi une multitude de méthodes, selon la biomolécule à fixer et la nature du support, elle-mêmeconditionnée par la méthode de détection utilisée. Jusque-là, l’immobilisation de biomolécules sur une puce en utilisantla microfluidique continue présentait l’avantage d’un greffage en mode dynamique. Dans ce mode, les biomolécules sontcontinuellement transportées, via des microcanaux, à proximité de la puce. La volonté continue d’augmenter le nombredes zones immobilisées nécessite l’emploi de la microfluidique discrète pour cette fonction biologique. En effet, en modespotting (simple dépôt de gouttes contenant les biomolécules) le nombre de zones immobilisées est uniquement limité parle volume de liquide minimum pouvant être déposé. Cependant, en choisissant cette méthode fluidique, il fallait, jusque là,renoncer à une réaction en mode dynamique : le greffage s’effectue ainsi en mode passif. Ces travaux de thèse présententune méthode pour créer une dynamique à l’intérieure des gouttes porteuses des biomolécules durant l’opération de greffagemoléculaire. Nous avons choisi d’utiliser un générateur de vibration de basse fréquence permettant d’induire simultanémentdes écoulements dans plusieurs gouttes. De la conception, la réalisation et la caractérisation des générateurs de vibration àla caractérisation et la modélisation de l’écoulement induit par ce dispositif dans le liquide, ou encore à la caractérisation del’opération biologique visée, nous démontrons l’efficacité des vibrations basses fréquences dans ce domaine.