A novel approach for electric load curve holistic modelling and simulation

International audienceThis paper presents a novel approach of an electric load curve simulator using a set of grey box models that results to an efficient trade-off between complete and complex physical models and fast simplified statistical models. The input parameters are macroscopic data coming from large databases such as national census, DSO’s client information and meteorological data such as temperature or irradiation data. The problem of matching between the different databases is investigated to assess comparable load curves. Validation is performed using load measurements at the medium voltage level. Once the model is calibrated it can be turned into a good prediction tool useful for planning studies since it permits easily to incorporate the evolution of usages, the characteristics of consumption devices, as well as the evolution of the building’s characteristics

Pantropical variability in tree crown allometry

Aim: Tree crowns determine light interception, carbon and water exchange. Thus, understanding the factors causing tree crown allometry to vary at the tree and stand level matters greatly for the development of future vegetation modelling and for the calibration of remote sensing products. Nevertheless, we know little about large‐scale variation and determinants in tropical tree crown allometry. In this study, we explored the continental variation in scaling exponents of site‐specific crown allometry and assessed their relationships with environmental and stand‐level variables in the tropics. / Location: Global tropics. / Time period: Early 21st century. / Major taxa studied: Woody plants. / Methods: Using a dataset of 87,737 trees distributed among 245 forest and savanna sites across the tropics, we fitted site‐specific allometric relationships between crown dimensions (crown depth, diameter and volume) and stem diameter using power‐law models. Stand‐level and environmental drivers of crown allometric relationships were assessed at pantropical and continental scales. / Results: The scaling exponents of allometric relationships between stem diameter and crown dimensions were higher in savannas than in forests. We identified that continental crown models were better than pantropical crown models and that continental differences in crown allometric relationships were driven by both stand‐level (wood density) and environmental (precipitation, cation exchange capacity and soil texture) variables for both tropical biomes. For a given diameter, forest trees from Asia and savanna trees from Australia had smaller crown dimensions than trees in Africa and America, with crown volumes for some Asian forest trees being smaller than those of trees in African forests. / Main conclusions: Our results provide new insight into geographical variability, with large continental differences in tropical tree crown allometry that were driven by stand‐level and environmental variables. They have implications for the assessment of ecosystem function and for the monitoring of woody biomass by remote sensing techniques in the global tropics

Pantropical variability in tree crown allometry

Aim Tree crowns determine light interception, carbon and water exchange. Thus, understanding the factors causing tree crown allometry to vary at the tree and stand level matters greatly for the development of future vegetation modelling and for the calibration of remote sensing products. Nevertheless, we know little about large‐scale variation and determinants in tropical tree crown allometry. In this study, we explored the continental variation in scaling exponents of site‐specific crown allometry and assessed their relationships with environmental and stand‐level variables in the tropics. Location Global tropics. Time period Early 21st century. Major taxa studied Woody plants. Methods Using a dataset of 87,737 trees distributed among 245 forest and savanna sites across the tropics, we fitted site‐specific allometric relationships between crown dimensions (crown depth, diameter and volume) and stem diameter using power‐law models. Stand‐level and environmental drivers of crown allometric relationships were assessed at pantropical and continental scales. Results The scaling exponents of allometric relationships between stem diameter and crown dimensions were higher in savannas than in forests. We identified that continental crown models were better than pantropical crown models and that continental differences in crown allometric relationships were driven by both stand‐level (wood density) and environmental (precipitation, cation exchange capacity and soil texture) variables for both tropical biomes. For a given diameter, forest trees from Asia and savanna trees from Australia had smaller crown dimensions than trees in Africa and America, with crown volumes for some Asian forest trees being smaller than those of trees in African forests. Main conclusions Our results provide new insight into geographical variability, with large continental differences in tropical tree crown allometry that were driven by stand‐level and environmental variables. They have implications for the assessment of ecosystem function and for the monitoring of woody biomass by remote sensing techniques in the global tropics

Electricity demand modeling using large scale databases to simulate different prospective scenarios

L’évolution de la consommation électrique est un point clé pour les choix à venir, tant pour les moyens de production d’électricité, que pour le dimensionnement du réseau à toutes ses échelles. Aujourd’hui, ce sont majoritairement des modèles statistiques basés sur les consommations passées et des tendances démographiques ou économétriques qui permettent de prédire cette consommation. Dans le contexte de la transition énergétique, des changements importants sont en cours et à venir, et la consommation future ne sera certainement pas une continuation des tendances passées. Modéliser ces changements nécessite une modélisation fine de type bottom-up de chaque contributeur de la consommation électrique. Ce type de modèle présente des challenges de modélisation, car il nécessite un grand nombre de paramètres d’entrée qui peuvent difficilement être renseignés de façon réaliste à grande échelle. En même temps, les données et informations de tout type n’ont jamais été autant disponibles. Cela représente à la fois un atout pour la modélisation, mais aussi une difficulté importante notamment à cause de l’hétérogénéité des données. Dans ce contexte, cette thèse présente une démarche de construction d’un simulateur de consommation électrique bottom-up capable de simuler différentes alternatives énergétiques à l’échelle de la France. Un travail de recensement, de classification et d’association des bases de données pour expliquer la consommation électrique a d’abord été mené. Ensuite, le modèle de consommation électrique a été présenté ; il a été validé et calibré sur une grande quantité de mesures de consommation électrique des départs HTA fournie par Enedis. Ce modèle a enfin pu être utilisé pour simuler différentes alternatives énergétiques afin d’aider au dimensionnement du réseau de distribution.Future trend of electricity demand is a key point for sizing both the electricity network and the power plants. In order to forecast future electricity demand, current models mostly use statistical approaches based on past demand measurements and on demographic and economic trends. Because of current context of energy transition which comes along with important changes, future electricity demand is not expected to be similar to past trends. Modeling these changes requires a bottom-up modeling of each contributor to electricity demand. This kind of model is challenging because of the large number of input data required. At the same time, data and information are more and more available. Such availability can be considered both as an asset for modeling and as an important issue because of data heterogeneity. In this context, this dissertation offers an approach to build a bottom-up load curve simulator which enables to simulate prospective scenarii at the scale of France country. Firstly, an assessment, classification, and matching of the large databases explaining the electricity demand have been performed. Then, the electricity demand model has been presented. It has been validated and calibrated on Enedis’ large volumes of electricity demand measurements of medium voltage feeders. Finally, this model has been used to simulate several prospective scenarii in order to improve the electricity distribution network sizing

Research of new anti-infective agents by design of inhibitors targeting the FtsZ protein in Staphylococcus aureus and the main protease (MPro) of SARS-CoV-2

Les travaux présentés dans ce manuscrit décrivent la recherche de molécules anti-infectieuses ciblant respectivement la protéine FtsZ de Staphylococcus aureus et la protéase majeure de SARS-CoV-2. Une stratégie pour lutter contre les pathogènes antibiorésistants est de développer des molécules ayant un nouveau mécanisme d’action. FtsZ, protéine essentielle de la division bactérienne, est donc une cible de choix. Deux approches ont été utilisées pour identifier de nouveaux inhibiteurs de cette protéine. D’une part, des composés portant un motif 2,6-difluorobenzamide, pharmacophore récurrent chez les inhibiteurs de FtsZ, ont été développés. D’autre part, le criblage virtuel par docking au sein de la poche allostérique de FtsZ de deux chimiothèques a été réalisé. Les hits potentiels ont été synthétisés et testés, permettant en particulier d’identifier des dérivés benzo[b]thiophènes présentant une activité antibactérienne. L’action sur le divisome et la cytotoxicité des meilleurs composés issus de chaque démarche ont été évaluées. La recherche de nouveaux antiviraux pour lutter contre la pandémie de COVID-19 causée par le SARS-CoV-2 a été au cœur de nombreuses études depuis deux ans. La protéase majeure (MPro) est une cible pertinente car elle est indispensable à la réplication virale. Deux études de criblage virtuel par docking ont été entreprises pour identifier de potentiels inhibiteurs de cette protéase. La première a été réalisée sur des chimiothèques d’électrophiles mous pouvant agir comme inhibiteurs covalents. La deuxième visait quant à elle à trouver des molécules potentiellement actives sur SARS-CoV-2 parmi une chimiothèque d’inhibiteurs connus de la protéase majeure du coronavirus aviaire. La stabilité des complexes entre les inhibiteurs non-covalents potentiels et la protéine a ensuite été étudiée par dynamique moléculaire.The work presented in this manuscript describes the research of anti-infective agents targeting respectively the protein FtsZ of Staphylococcus aureus and the main protease of SARS-CoV-2. A strategy to fight drug-resistant pathogens is to develop molecules with a new mode of action. FtsZ, an essential protein for bacterial division, is therefore a prime target. Two approaches were used to identify new inhibitors of this protein. On the one hand, compounds bearing a 2,6-difluorobenzamide moiety, a recurrent pharmacophore in FtsZ inhibitors, were developed. On the other hand, the virtual screening by docking in the allosteric pocket of FtsZ of two chemical libraries was performed. The identified potential hits were synthesized and tested, allowing in particular the identification of benzo[b]thiophene derivatives showing an antibacterial activity. The action on divisome and the cytotoxicity of the best compounds from each approach were assayed. Research of new antiviral agents to fight the COVID-19 pandemic caused by SARS-CoV-2 was at the heart of many studies over the past two years. The main protease (MPro) is a relevant target for it is essential for viral replication. Two virtual screening studies by docking were undertaken to identify potential inhibitors of this protease. The first one was performed on soft electrophiles chemical libraries, theoretically able to act as covalent inhibitors. The second one aimed at the discovery of compounds potentially active on SARS-CoV-2 amongst a library of avian coronavirus’ main protease known inhibitors. The stability of the complexes between the potential reversible inhibitors and the protein was studied by molecular dynamics studies

Recherche de nouveaux agents anti-infectieux par conception d'inhibiteurs ciblant la protéine FtsZ de Staphylococcus aureus et la protéase majeure (MPro) de SARS-CoV-2

The work presented in this manuscript describes the research of anti-infective agents targeting respectively the protein FtsZ of Staphylococcus aureus and the main protease of SARS-CoV-2. A strategy to fight drug-resistant pathogens is to develop molecules with a new mode of action. FtsZ, an essential protein for bacterial division, is therefore a prime target. Two approaches were used to identify new inhibitors of this protein. On the one hand, compounds bearing a 2,6-difluorobenzamide moiety, a recurrent pharmacophore in FtsZ inhibitors, were developed. On the other hand, the virtual screening by docking in the allosteric pocket of FtsZ of two chemical libraries was performed. The identified potential hits were synthesized and tested, allowing in particular the identification of benzo[b]thiophene derivatives showing an antibacterial activity. The action on divisome and the cytotoxicity of the best compounds from each approach were assayed. Research of new antiviral agents to fight the COVID-19 pandemic caused by SARS-CoV-2 was at the heart of many studies over the past two years. The main protease (MPro) is a relevant target for it is essential for viral replication. Two virtual screening studies by docking were undertaken to identify potential inhibitors of this protease. The first one was performed on soft electrophiles chemical libraries, theoretically able to act as covalent inhibitors. The second one aimed at the discovery of compounds potentially active on SARS-CoV-2 amongst a library of avian coronavirus’ main protease known inhibitors. The stability of the complexes between the potential reversible inhibitors and the protein was studied by molecular dynamics studies.Les travaux présentés dans ce manuscrit décrivent la recherche de molécules anti-infectieuses ciblant respectivement la protéine FtsZ de Staphylococcus aureus et la protéase majeure de SARS-CoV-2. Une stratégie pour lutter contre les pathogènes antibiorésistants est de développer des molécules ayant un nouveau mécanisme d’action. FtsZ, protéine essentielle de la division bactérienne, est donc une cible de choix. Deux approches ont été utilisées pour identifier de nouveaux inhibiteurs de cette protéine. D’une part, des composés portant un motif 2,6-difluorobenzamide, pharmacophore récurrent chez les inhibiteurs de FtsZ, ont été développés. D’autre part, le criblage virtuel par docking au sein de la poche allostérique de FtsZ de deux chimiothèques a été réalisé. Les hits potentiels ont été synthétisés et testés, permettant en particulier d’identifier des dérivés benzo[b]thiophènes présentant une activité antibactérienne. L’action sur le divisome et la cytotoxicité des meilleurs composés issus de chaque démarche ont été évaluées. La recherche de nouveaux antiviraux pour lutter contre la pandémie de COVID-19 causée par le SARS-CoV-2 a été au cœur de nombreuses études depuis deux ans. La protéase majeure (MPro) est une cible pertinente car elle est indispensable à la réplication virale. Deux études de criblage virtuel par docking ont été entreprises pour identifier de potentiels inhibiteurs de cette protéase. La première a été réalisée sur des chimiothèques d’électrophiles mous pouvant agir comme inhibiteurs covalents. La deuxième visait quant à elle à trouver des molécules potentiellement actives sur SARS-CoV-2 parmi une chimiothèque d’inhibiteurs connus de la protéase majeure du coronavirus aviaire. La stabilité des complexes entre les inhibiteurs non-covalents potentiels et la protéine a ensuite été étudiée par dynamique moléculaire

Modélisation de la consommation électrique à partir de grandes masses de données pour la simulation des alternatives énergétiques du futur

Future trend of electricity demand is a key point for sizing both the electricity network and the power plants. In order to forecast future electricity demand, current models mostly use statistical approaches based on past demand measurements and on demographic and economic trends. Because of current context of energy transition which comes along with important changes, future electricity demand is not expected to be similar to past trends. Modeling these changes requires a bottom-up modeling of each contributor to electricity demand. This kind of model is challenging because of the large number of input data required. At the same time, data and information are more and more available. Such availability can be considered both as an asset for modeling and as an important issue because of data heterogeneity. In this context, this dissertation offers an approach to build a bottom-up load curve simulator which enables to simulate prospective scenarii at the scale of France country. Firstly, an assessment, classification, and matching of the large databases explaining the electricity demand have been performed. Then, the electricity demand model has been presented. It has been validated and calibrated on Enedis’ large volumes of electricity demand measurements of medium voltage feeders. Finally, this model has been used to simulate several prospective scenarii in order to improve the electricity distribution network sizing.L’évolution de la consommation électrique est un point clé pour les choix à venir, tant pour les moyens de production d’électricité, que pour le dimensionnement du réseau à toutes ses échelles. Aujourd’hui, ce sont majoritairement des modèles statistiques basés sur les consommations passées et des tendances démographiques ou économétriques qui permettent de prédire cette consommation. Dans le contexte de la transition énergétique, des changements importants sont en cours et à venir, et la consommation future ne sera certainement pas une continuation des tendances passées. Modéliser ces changements nécessite une modélisation fine de type bottom-up de chaque contributeur de la consommation électrique. Ce type de modèle présente des challenges de modélisation, car il nécessite un grand nombre de paramètres d’entrée qui peuvent difficilement être renseignés de façon réaliste à grande échelle. En même temps, les données et informations de tout type n’ont jamais été autant disponibles. Cela représente à la fois un atout pour la modélisation, mais aussi une difficulté importante notamment à cause de l’hétérogénéité des données. Dans ce contexte, cette thèse présente une démarche de construction d’un simulateur de consommation électrique bottom-up capable de simuler différentes alternatives énergétiques à l’échelle de la France. Un travail de recensement, de classification et d’association des bases de données pour expliquer la consommation électrique a d’abord été mené. Ensuite, le modèle de consommation électrique a été présenté ; il a été validé et calibré sur une grande quantité de mesures de consommation électrique des départs HTA fournie par Enedis. Ce modèle a enfin pu être utilisé pour simuler différentes alternatives énergétiques afin d’aider au dimensionnement du réseau de distribution

Influence of speaker involvement and own personal actions in assimilation and appropriation of climate information by the audience

International audienceIn this presentation, two main speaker involvements are investigated. Firstly : the speaker is not deeply convinced by his message or is deeply convinced but not involved into personal actions yet. In the first case, he would communicate few emotions during his speech. In the second case, the gap between what the speaker wants to do and what he will really do, will lead to a cognitive dissonance. The information will be stressfully perceived. In any case, even if the talk is given in a way to be persuasive, the mirror neuron system, responsible among other of empathy, will capture the real involvement of the speaker in his message through his own actions. Among others, the unappropriate emotional data medium (nonexistent or stressful), involved in memorization process, lead to a possible partial or short term assimilation of the information, and so to a partial or short term changes in the audience's life. Finally, this presentation highlights the case where speaker combine both personal involvement and actions in accordance with the message. This leads to efficient communication thanks to good assimilation and appropriation. By reconciling theory, methodology and practice, the speaker can generate sustainable behavioral changes in the audience's personal life

Brain functions : the challenges to integrate and assimilate climate information

International audienceThe general public expects relevant, comprehensible and acceptable communication on climate change. Many efforts have been, and are still, being made to make the message clear and comprehensible. The key point is the acceptability of the message by the audience, which is seldom discussed in the literature. This presentation investigates the brain mechanisms, which allow it to understand, integrate and assimilate climate information. The brain faces challenges to integrate this information. We will tackle six of them here. Cognitive dissonance is one major factor. When receiving climate information, one understands that a change will occur. However, the consequences are so important that it is difficult to assimilate and project ourselves into this future reality. The future cannot be connected to something that is already known, and so it is challenging to construct meaning. In order to keep the system coherent, the brain distances itself from the information. The information that is heard will seem distant despite all efforts from the individual to assimilate it. A better understanding of cognitive biases in communication on climate change allows building acceptable talks, such as increasing the integration of the information by non-specialists, which, in turn, will help to transfer individuals knowledge into concrete action

Data-analysis and modelling of the effect of inter-row shading on the power production of photovoltaic plants

International audienceThe aim of this work is to propose a fast and easily implementable model allowing the consideration of inter-row shading in the simulation of the power production of PV plants. Our objective is to find the simplest model with the least number of input parameters. A formulation of the shaded fraction of the module area dependent on the PV configuration and the sun position is first proposed. An analysis of the measurements of two plants is then carried out to understand the effect of inter-row shading for a complete PV array. Based on this analysis, a model is proposed and validated. It is shown that the consideration of inter-row shading with the proposed model allows a relative improvement of the simulation accuracy (RMSE) ranging between 18 and 32 % for time steps affected by inter-row shading and between 7 and 12 % for a complete year