Articles

Indazole versus indole-based cationic merocyanines with red shifted in-cellulo emission for selective mitochondria imaging
- Boujut Margot
- Chevalier Arnaud
- Schapman Damien
- Bénard Magalie
- Galas Ludovic
- Gallavardin Thibault
- Franck Xavier
Dyes and Pigments, Elsevier , 2022, 198, pp.109988 . Selective fluorophores are seminal for the development of fluorescent cell imaging allowing the simultaneous monitoring of several biological parameters. Mitochondria are one of the main targets of optical microscopy as their dynamics are related to many biological events. One of the best strategy to target them, is to design cationic dyes which accumulate preferentially in their membranes due to their high electrochemical potential. In this work, indazole scaffold was explored to build new cationic merocyanine dyes and compared with indole scaffold. This nitrogen heteroaromatic structure is still very uncommon in dyes chemistry; therefore, a lot has to be discovered about the effects of pH, solvent polarity and methylation in cyclic nitrogen on their optical properties. Finally, fluorescent imaging revealed that a red shift occurred in the emission of these molecules inside the mitochondria. (10.1016/j.dyepig.2021.109988)
DOI : 10.1016/j.dyepig.2021.109988
Molecular dynamics between amorphous and crystalline phases of e-beam irradiated piezoelectric PVDF thin films employing solid-state NMR spectroscopy
- Potrzebowska Natalia
- Cavani Olivier
- Kazmierski Slawomir
- Wegrowe Jean-Eric
- Potrzebowski Marek
- Clochard Marie-Claude
Polymer Degradation and Stability, Elsevier , 2022, 195, pp.109786 . (10.1016/j.polymdegradstab.2021.109786)
DOI : 10.1016/j.polymdegradstab.2021.109786
Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data
- Le Guenedal Théo
- Drobinski Philippe
- Tankov Peter
Geoscientific Model Development, European Geosciences Union , 2022, 15 (21), pp.8001-8039 . Tropical cyclones are responsible for a large share of global damage resulting from natural disasters, and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA), presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with the data from CMIP models offering a reliable solution to resolve tropical cyclones in climate projections. We illustrate this by producing damage projections in representative concentration pathways (RCPs) at the global level and for individual countries. The algorithm contains a module to correct biases in climate models based on the distributions of the climate variables in the reanalyses. This model was primary developed to provide the economic and financial community with reliable signals allowing for a better quantification of physical risks in the long term, to estimate, for example, the impact on sovereign debt. (10.5194/gmd-15-8001-2022)
DOI : 10.5194/gmd-15-8001-2022
Generating natural adversarial Remote Sensing Images
- Burnel Jean-Christophe
- Fatras Kilian
- Flamary Rémi
- Courty Nicolas
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers , 2022, 60, pp.1-14 . Over the last years, Remote Sensing Images (RSI) analysis have started resorting to using deep neural networks to solve most of the commonly faced problems, such as detection, land cover classification or segmentation. As far as critical decision making can be based upon the results of RSI analysis, it is important to clearly identify and understand potential security threats occurring in those machine learning algorithms. Notably, it has recently been found that neural networks are particularly sensitive to carefully designed attacks, generally crafted given the full knowledge of the considered deep network. In this paper, we consider the more realistic but challenging case where one wants to generate such attacks in the case of a black-box neural network. In this case, only the prediction score of the network is accessible, given a specific input. Examples that lure away the network's prediction, while being perceptually similar to real images, are called natural or unrestricted adversarial examples. We present an original method to generate such examples, based on a variant of the Wasserstein Generative Adversarial Network. We demonstrate its effectiveness on natural adversarial hyper-spectral image generation and image modification for fooling a state-of-the-art detector. Among others, we also conduct a perceptual evaluation with human annotators to better assess the effectiveness of the proposed method. (10.1109/TGRS.2021.3110601)
DOI : 10.1109/TGRS.2021.3110601
Estimation of the Uncertainty due to Each Step of Simulating the Photovoltaic Conversion under Real Operating Conditions
- Migan-Dubois Anne
- Badosa Jordi
- Bourdin Vincent
- Torres Aguilar Moira
- Bonnassieux Yvan
International Journal of Photoenergy, Hindawi Publishing Corporation , 2021, 2021, pp.4228658 . The simulation of photovoltaic installations is a major issue for their sizing, their smart grid operation, and their fault detection and diagnosis. In this article, we study in detail every step of the simulation chain, either from the global horizontal irradiance and the ambient temperature (i.e., 4 steps of simulation) or considering the global in-plane irradiance and the module operating temperature (i.e., 1 step of simulation). The average quality estimation of the models is made through the calculations of average annual error between estimations and measurements, from 2016 to 2020. We have shown that the most uncertain step is the conversion of the global irradiance in its diffuse and direct components (17.2%, 2 models tested). If the model goes up to the in-plane irradiance, the average annual error decreases to 5.3% (6 models tested). The photovoltaic module temperature calculation induces an error of less than 2Â°C (4 models tested with 2 configurations). Meanwhile, the photoelectrical conversion shows a 3.5% error, similar to the measurement uncertainties, considering as input, the modules temperature, and the in-plane irradiance. If the simulation goes from the global irradiance and the ambient temperature measured locally, the estimation leads to a 6.7% average annual error. If the local measurements are not available, we can use the closest meteorological station’s records (13 for our study), and the error becomes 12.1%. Finally, we can also use the satellite images that lead to a 15.2% error, for average per year. The impact of available input shows that modeling the DC photovoltaic production, using global horizontal irradiance and ambient temperature, gives rise to an error of 6.6% for local measurements, 12.1% for weather station measurements, and 15.2% for satellite images estimations. This article thus draws up a review of the existing models, allowing to calculate the DC production of a photovoltaic module, depending on the atmospheric conditions, and highlights the most precise or most critical steps, considering in situ and weather station ground-based measurements, and also estimation from satellite images. (10.1155/2021/4228658)
DOI : 10.1155/2021/4228658
Electrochemical Active Surface Area Determination of Iridium‐Based Mixed Oxides by Mercury Underpotential Deposition
- Duran Silvia
- Elmaalouf Marine
- Odziomek Mateusz
- Piquemal Jean‐yves
- Faustini Marco
- Giraud Marion
- Peron Jennifer
- Tard Cédric
ChemElectroChem, Weinheim : Wiley-VCH , 2021, 8 (18), pp.3519-3524 . The electrochemical surface area (ECSA) is a critical property to describe, analyze and compare electrocatalysts. The determination of the mass activity of a given catalyst is associated with this parameter which can thus lead to materials benchmarking. Reliable and robust methods to measure ECSA are needed, and those have to accommodate different structures, morphologies and compositions. In this study, we investigate mercury underpotential deposition (HgUPD) as a way to estimate ECSA for ultraporous electrocatalysts based on iridium and iridium-molybdenum electrocatalysts for the oxygen evolution reaction. Results reveal a clear agreement between physisorption measurements and HgUPD with excellent reproducibility. The method shows also that pre- and post-catalysis surface area measurements are not affected by the catalytic process on short timescale, opening the possibility of electrocalyst stability and degradation monitoring. (10.1002/celc.202100649)
DOI : 10.1002/celc.202100649
Mixing nanostructured Ni/piezoPVDF composite thin films with e-beam irradiation: A beneficial synergy to piezoelectric response
- Potrzebowska Natalia
- Cavani Olivier
- Oral Ozlem
- Doaré Olivier
- Melilli Giuseppe
- Wegrowe Jean-Eric
- Clochard Marie-Claude.
Materials Today Communications, Elsevier , 2021, 28, pp.102528 . (10.1016/j.mtcomm.2021.102528)
DOI : 10.1016/j.mtcomm.2021.102528
Integration of climate variability and climate change in renewable energy planning
- Drobinski Philippe
- Tantet Alexis
Physics-Uspekhi, Turpion , 2021 . The trajectory outlined in the Paris Agreement to keep global warming below 2°C dictates not only the timing but also the speed at which the transformation of our energy system must take place to decarbonize energy production. Complying with the Paris Agreement requires reducing the carbon content of energy by about 75% and therefore making a rapid transition from fossil production to production based on low-carbon technologies. Among these technologies are those based on renewable energies. The variability of the climate itself induces a fluctuating or even an intermittent production of variable renewable energy (solar, wind, marine), challenging the balance of the electricity grid. In this context, to speak of energy transition is to face the problem of increasing the penetration of low-carbon energy production while limiting the variability while ensuring socio-technical feasibility and economic viability. The problem is not simple and the delicate balance between urgency (drastic reduction in emissions) and utopia (what strategy for low carbon energies, opportunities and obstacles) needs to be clearly defined. (10.3367/UFNe.2021.07.039080)
DOI : 10.3367/UFNe.2021.07.039080
Chiral Radical Cation Salts of Me-EDT-TTF and DM-EDT-TTF with Octahedral, Linear and Tetrahedral Monoanions
- Mroweh Nabil
- Bogdan Alexandra
- Pop Flavia
- Auban-Senzier Pascale
- Vanthuyne Nicolas
- Lopes Elsa B
- Almeida Manuel
- Avarvari Narcis
Magnetochemistry, MDPI , 2021, 7 (6), pp.87 . Methyl-ethylenedithio-tetrathiafulvalene (Me-EDT-TTF (1) and dimethyl-ethylenedithio-tetrathiafulvalene (DM-EDT-TTF (2) are valuable precursors for chiral molecular conductors, which are generally obtained by electrocrystallization in the presence of various counter-ions. The number of the stereogenic centers, their relative location on the molecule, the nature of the counter-ion and the electrocrystallization conditions play a paramount role in the crystal structures and conducting properties of the resulting materials. Here, we report the preparation and detailed structural characterization of the following series of radical cation salts: (i) mixed valence (1)2AsF6 as racemic, and (S) and (R) enantiomers; (ii) [(S)-1]AsF6·C4H8O and [(R)-1]AsF6·C4H8O where a strong dimerization of the donors is observed; (iii) (1)I3 and (2)I3 as racemic and enantiopure forms and (iv) [(meso)-2]PF6 and [(meso)-2]XO4 (X = Cl, Re), based on the new donor (meso)-2. In the latter, the two methyl substituents necessarily adopt axial and equatorial conformations, thus leading to a completely different packing of the donors when compared to the chiral form (S,S)/(R,R) of 2 in its radical cation salts. Single crystal resistivity measurements, complemented by thermoelectric power measurements in the case of (1)2AsF6, suggest quasi-metallic conductivity for the latter in the high temperature regime, with σRT ≈ 1–10 S cm–1, while semiconducting behavior is observed for the (meso)-2 based salts. (10.3390/magnetochemistry7060087)
DOI : 10.3390/magnetochemistry7060087
The Tuning Strategy of IPSL‐CM6A‐LR
- Mignot Juliette
- Hourdin Frédéric
- Deshayes Julie
- Boucher Olivier
- Gastineau Guillaume
- Musat Ionela
- Vancoppenolle Martin
- Servonnat Jérôme
- Caubel Arnaud
- Cheruy Frédérique
- Denvil Sebastien
- Dufresne Jean-Louis
- Éthé Christian
- Fairhead Laurent
- Foujols Marie-Alice
- Grandpeix Jean-Yves
- Levavasseur Guillaume
- Marti Olivier
- Menary Matthew B
- Rio Catherine
- rousset clement
- Silvy Yona
Journal of Advances in Modeling Earth Systems, American Geophysical Union , 2021, 13 (5), pp.e2020MS002340 . Climate change is a serious issue for humanity with important ramifications for policy and decision making. Robust and cost-efficient policies on mitigation and adaptation require assessments of current and future risks for natural and human systems under a range of socioeconomic scenarios. Those assessments rely on numerical simulations performed with state-of-the-art climate models. Simulations are coordinated at an international level within the Coupled Model Intercomparison Project (CMIP) which provides the bedrock for a substantial part of the publications synthesized in the Intergovernmental Panel on Climate Change (IPCC) reports. Such projects are fundamental in order to document the robust features as well as the relatively large uncertainties in the future climate projections. Among others, these uncertainties come from the various assumptions made by the ∼30 teams that develop CMIP-class models. In particular, because of (10.1029/2020ms002340)
DOI : 10.1029/2020ms002340
Charge-Transfer Chemical Reactions in Nanofluidic Fabry-Pérot Cavities
- Mauro L
- Caicedo K
- Jonusauskas G
- Avriller Rémi
Physical Review B: Condensed Matter and Materials Physics (1998-2015), American Physical Society , 2021 . We investigate the chemical reactivity of molecular populations confined inside a nanofluidic Fabry-Pérot cavity. Due to strong light-matter interactions developing between a resonant electromagnetic cavity-mode and the electric dipole moment of the confined molecules, a polariton is formed. The former gets dressed by environmental vibrational and rotational degrees of freedom of the solvent. We call the resulting polariton dressed by its cloud of environmental excitation a "reacton", since it further undergoes chemical reactions. We characterize how the reacton formation modifies the kinetics of a photoisomerization chemical reaction involving an elementary charge-transfer process. We show that the reaction driving-force and reorganization energy are both modulated optically by the reactant concentration, the vacuum Rabi splitting and the de-tuning between the Fabry-Pérot cavity frequency and targeted electronic transition. Finally, we compute the ultrafast picosecond dynamics of the whole photochemical reaction. We predict that despite optical cavity losses and solvent-mediated non-radiative relaxation, measurable signatures of the reacton formation can be found in state-of-the-art pump-probe experiments. (10.1103/PhysRevB.103.165412)
DOI : 10.1103/PhysRevB.103.165412
POT : Python Optimal Transport
- Flamary Rémi
- Courty Nicolas
- Gramfort Alexandre
- Alaya Mokhtar Zahdi
- Boisbunon Aurélie
- Chambon Stanislas
- Chapel Laetitia
- Corenflos Adrien
- Fatras Kilian
- Fournier Nemo
- Gautheron Léo
- Gayraud Nathalie T H
- Janati Hicham
- Rakotomamonjy Alain
- Redko Ievgen
- Rolet Antoine
- Schutz Antony
- Seguy Vivien
- Sutherland Danica J
- Tavenard Romain
- Tong Alexander
- Vayer Titouan
Journal of Machine Learning Research, Microtome Publishing , 2021 . Optimal transport has recently been reintroduced to the machine learning community thanks in part to novel efficient optimization procedures allowing for medium to large scale applications. We propose a Python toolbox that implements several key optimal transport ideas for the machine learning community. The toolbox contains implementations of a number of founding works of OT for machine learning such as Sinkhorn algorithm and Wasserstein barycenters, but also provides generic solvers that can be used for conducting novel fundamental research. This toolbox, named POT for Python Optimal Transport, is open source with an MIT license.
A Minimal System Cost Minimization Model for Variable Renewable Energy Integration: Application to France and Comparison to Mean-Variance Analysis
- Tantet Alexis
- Drobinski Philippe
Energies, MDPI , 2021, 14 (16), pp.5143 . The viability of Variable Renewable Energy (VRE)-investment strategies depends on the response of dispatchable producers to satisfy the net load. We lack a simple research tool with sufficient complexity to represent major phenomena associated with the response of dispatchable producers to the integration of high shares of VRE and their impact on system costs. We develop a minimization of the system cost allowing one to quantify and decompose the system value of VRE depending on an aggregate dispatchable production. Defining the variable cost of the dispatchable generation as quadratic with a coefficient depending on macroeconomic factors such as the cost of greenhouse gas emissions leads to the simplest version of the model. In the absence of curtailment, and for particular parameter values, this version is equivalent to a mean-variance problem. We apply this model to France with solar and wind capacities distributed over the administrative regions of metropolitan France. In this case, ignoring the wholesale price effect and variability has a relatively small impact on optimal investments, but leads to largely underestimating the system total cost and overestimating the system marginal cost. (10.3390/en14165143)
DOI : 10.3390/en14165143
Impact of natural disasters on consumer behavior: case of the 2017 El Niño phenomenon in Peru
- Alatrista-Salas Hugo
- Gauthier Vincent
- Nunez-Del-Prado Miguel
- Becker Monique
PLoS ONE, Public Library of Science , 2021, 16 (1), pp.e0244409:1-e0244409:23 . El Niño is an extreme weather event featuring unusual warming of surface waters in the eastern equatorial Pacific Ocean. This phenomenon is characterized by heavy rains and floods that negatively affect the economic activities of the impacted areas. Understanding how this phenomenon influences consumption behavior at different granularity levels is essential for recommending strategies to normalize the situation. With this aim, we performed a multi-scale analysis of data associated with bank transactions involving credit and debit cards. Our findings can be summarized into two main results: Coarse-grained analysis reveals the presence of the El Niño phenomenon and the recovery time in a given territory, while fine-grained analysis demonstrates a change in individuals’ purchasing patterns and in merchant relevance as a consequence of the climatic event. The results also indicate that society successfully withstood the natural disaster owing to the economic structure built over time. In this study, we present a new method that may be useful for better characterizing future extreme events. (10.1371/journal.pone.0244409)
DOI : 10.1371/journal.pone.0244409
Utility-Scale PV-Battery versus CSP-Thermal Storage in Morocco: Storage and Cost Effect under Penetration Scenarios
- Bouramdane Ayat-Allah
- Tantet Alexis
- Drobinski Philippe
Energies, MDPI , 2021, 14 (15), pp.4675 . In this study, we examine how Battery Storage (BES) and Thermal Storage (TES) combined with solar Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies with an increased storage duration and rental cost together with diversification would influence the Moroccan mix and to what extent the variability (i.e., adequacy risk) can be reduced; this is done using recent (2013) cost data and under various penetration scenarios. To do this, we use MERRA-2 climate reanalysis to simulate hourly demand and capacity factors (CFs) of wind, solar PV and CSP without and with increasing storage capabilities—as defined by the CSP Solar Multiple (SM) and PV Inverter Loading Ratio (ILR). We adjust these time series to observations for the four Moroccan electrical zones over the year 2018. Our objective is to maximize the renewable (RE) penetration and minimize the imbalances between RE production and consumption considering three optimization strategies. We analyze mixes along Pareto fronts using the Mean-Variance Portfolio approach—implemented in the E4CLIM model—in which we add a maximum-cost constraint to take into account the different rental costs of wind, PV and CSP. We propose a method to calculate the rental cost of storage and production technologies taking into account the constraints on storage associated with the increase of SM and ILR in the added PV-BES and CSP-TES modules, keeping the mean solar CFs fixed. We perform some load bands-reduction diagnostics to assess the reliability benefits provided by each RE technology. We find that, at low penetrations, the maximum-cost budget is not reached because a small capacity is needed. The higher the ILR for PV, the larger the share of PV in the mix compared to wind and CSP without storage is removed completely. Between PV-BES and CSP-TES, the latter is preferred as it has larger storage capacity and thus stronger impact in reducing the adequacy risk. As additional BES are installed, more than TES, PV-BES is favored. At high penetrations, optimal mixes are impacted by cost, the more so as CSP (resp., PV) with high SM (resp., ILR) are installed. Wind is preferably installed due to its high mean CF compared to cost, followed by either PV-BES or CSP/CSP-TES. Scenarios without or with medium storage capacity favor CSP/CSP-TES, while high storage duration scenarios are dominated by low-cost PV-BES. However, scenarios ignoring the storage cost and constraints provide more weight to PV-BES whatever the penetration level. We also show that significant reduction of RE variability can only be achieved through geographical diversification. Technological complementarity may only help to reduce the variance when PV and CSP are both installed without or with a small amount of storage. However, the diversification effect is slightly smaller when the SM and ILR are increased and the covariances are reduced as well since mixes become less diversified. (10.3390/en14154675)
DOI : 10.3390/en14154675
Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts
- Le Gal La Salle Josselin
- Badosa Jordi
- David Mathieu
- Pinson Pierre
- Lauret Philippe
Renewable Energy, Elsevier , 2020, 162, pp.1321-1339 . (10.1016/j.renene.2020.07.042)
DOI : 10.1016/j.renene.2020.07.042
A Two-Step Energy Management Method Guided by Day-Ahead Quantile Solar Forecasts: Cross-Impacts on Four Services for Smart-Buildings
- Calderon-Obaldia Fausto
- Badosa Jordi
- Migan-Dubois Anne
- Bourdin Vincent
Energies, MDPI , 2020, 13 (22), pp.5882 . The research work hereby presented, emerges from the urge to answer the well-known question of how the uncertainty of intermittent renewable sources affects the performance of a microgrid and how could we deal with it. More specifically, we want to evaluate what could be the impact in performance of a microgrid that is intended to serve a smart-building (powered by photovoltaic panels and with battery energy storage), when the uncertainty of the photovoltaic-production forecasts is considered in the energy management process through the use of quantile forecasts. For this, several objectives (or services) are targeted based in a two-step (double-objective) energy management framework, which combines optimization-based and rule-based algorithms. The performance is evaluated based on some particular services, namely: energy cost, carbon footprint, grid peak power, and grid commitment; with the latter being a novel service proposed in the domain of microgrids. Simulations are performed whlie using data of a study-case microgrid (Drahi-Xnovation center, Ecole Polytechnique, France). The use of quantile forecasts (obtained with an analog-ensemble method) is tested as a mean to deal with (i.e., decrease) the uncertainty of the solar PV production. The proposed energy management framework is compared with basic reference strategies and the results show the superior performance of the former in almost all of the proposed services and forecasting scenarios. The fact of how optimizing for some services during the scheduling (i.e., grid commitment) can be highly detrimental for the performance of the non-targeted services, is an interesting finding of this work. The differences regarding the optimal forecasting eccentricity (i.e., the forecasting quantile) required when optimizing for the different services and seasons of the year is also considered an important conclusion of the study. This fact highlights the usefulness of the quantile forecasting approach in an energy management system, as a tool to deal with the intrinsic uncertainty of PV power production. (10.3390/en13225882)
DOI : 10.3390/en13225882
Reliability Predictors for Solar Irradiance Satellite-Based Forecast
- Cros Sylvain
- Badosa Jordi
- Szantaï André
- Haeffelin Martial
Energies, MDPI , 2020, 13 (21), pp.5566 . The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index (Kc), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and Kc. Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed Kc in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods. View Full-Text (10.3390/en13215566)
DOI : 10.3390/en13215566
The Economic Value of Wind Energy Nowcasting
- Dupré Aurore
- Drobinski Philippe
- Badosa Jordi
- Briard Christian
- Tankov Peter
Energies, MDPI , 2020, 13 (20), pp.5266 . In recent years, environmental concerns resulted in an increase in the use of renewable resources such as wind energy. However, high penetration of the wind power is a challenge due to the intermittency of this resource. In this context, the wind energy forecasting has become a major issue. In particular, for the end users of wind energy forecasts, a critical but often neglected issue is the economic value of the forecast. In this work, we investigate the economic value of forecasting from 30 min to 3 h ahead, also known as nowcasting. Nowcasting is mainly used to inform trading decisions in the intraday market. Two sources of uncertainty affecting wind farm revenues are investigated, namely forecasting errors and price variations. The impact of these uncertainties is assessed for six wind farms and several balancing strategies using market data. Results are compared with the baseline case of no nowcasting and with the idealized case of perfect nowcast. The three settings show significant differences while the impact of the choice of a specific balancing strategy appears minor. (10.3390/en13205266)
DOI : 10.3390/en13205266
Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain
- Maimó-Far Aina
- Tantet Alexis
- Homar Víctor
- Drobinski Philippe
Energies, MDPI , 2020 . We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction. (10.3390/en13195132)
DOI : 10.3390/en13195132
Adequacy of Renewable Energy Mixes with Concentrated Solar Power and Photovoltaic in Morocco: Impact of Thermal Storage and Cost
- Bouramdane Ayat-Allah
- Tantet Alexis
- Drobinski Philippe
Energies, MDPI , 2020 . In this paper, we analyze the sensitivity of the optimal mixes to cost and variability associated with solar technologies and examine the role of Thermal Energy Storage (TES) combined to Concentrated Solar Power (CSP) together with time-space complementarity in reducing the adequacy risk-imposed by variable Renewable Energies (RE)-on the Moroccan electricity system. To do that, we model the optimal recommissioning of RE mixes including Photovoltaic (PV), wind energy and CSP without or with increasing levels of TES. Our objective is to maximize the RE production at a given cost, but also to limit the variance of the RE production stemming from meteorological fluctuations. This mean-variance analysis is a bi-objective optimization problem that is implemented in the E4CLIM modeling platform-which allows us to use climate data to simulate hourly Capacity Factors (CFs) and demand profiles adjusted to observations. We adapt this software to Morocco and its four electrical zones for the year 2018, add new CSP and TES simulation modules, perform some load reduction diagnostics, and account for the different rental costs of the three RE technologies by adding a maximum-cost constraint. We find that the risk decreases with the addition of TES to CSP, the more so as storage is increased keeping the mean capacity factor fixed. On the other hand, due to the higher cost of CSP compared to PV and wind, the maximum-cost constraint prevents the increase of the RE penetration without reducing the share of CSP compared to PV and wind and letting the risk increase in return. Thus, if small level of risk and higher penetrations are targeted, investment must be increased to install more CSP with TES. We also show that regional diversification is key to reduce the risk and that technological diversification is relevant when installing both PV and CSP without storage, but less so as the surplus of energy available for TES is increased and the CSP profiles flatten. Finally, we find that, thanks to TES, CSP is more suited than PV and wind to meet peak loads. This can be measured by the capacity credit, but not by the variance-based risk, suggesting that the latter is only a crude representation of the adequacy risk. (10.3390/en13195087)
DOI : 10.3390/en13195087
Defining and Quantifying Intermittency in the Power Sector
- Suchet Daniel
- Jeantet Adrien
- Elghozi Thomas
- Jehl Zacharie
Energies, MDPI , 2020, 13 (13), pp.3366 . The lack of a systematic definition of intermittency in the power sector blurs the use of this term in the public debate: the same power source can be described as stable or intermittent, depending on the standpoint of the authors. This work tackles a quantitative definition of intermittency adapted to the power sector, linked to the nature of the source, and not to the current state of the energy mix or the production predictive capacity. A quantitative indicator is devised, discussed and graphically depicted. A case study is illustrated by the analysis of the 2018 production data in France and then developed further to evaluate the impact of two methods often considered to reduce intermittency: aggregation and complementarity between wind and solar productions. (10.3390/en13133366)
DOI : 10.3390/en13133366
Advances in reconstructing the AMOC using sea surface observations of salinity
- Estella-Perez Victor
- Mignot Juliette
- Guilyardi Éric
- Swingedouw Didier
- Reverdin Gilles
Climate Dynamics, Springer Verlag , 2020, 1 . The Atlantic meridional overturning circulation (AMOC) is one of the main drivers of climate variability at decadal and longer time scales. As there are no direct multi-decadal observations of this key circulation, the reconstruction of past AMOC variations is essential. This work presents a step forward in reconstructing the AMOC using climate models and time-varying surface nudging of salinity and temperature data, for which independent multi-decadal observed series are available. A number of nudging protocols are explored in a perfect model framework to best reproduce the AMOC variability accommodating to the characteristics of SST and SSS available products. As reference SST products with sufficient space and time coverage are available, we here choose to focus on the limitations associated to SSS products with the goal of providing protocols using independent salinity products. We consider a global gridded dataset and, additionally, a coarser SSS dataset restricted to the Atlantic and with a quite low spatial resolution (order of 10 degrees vs. 2 for the model grid). We show how, using the latter, we can improve the efficiency of the nudging on the AMOC reconstruction by adding a high-resolution annual cycle to the coarse resolution SSS product as well as a spatial downscaling to account for SSS gradient. The final protocol retained for the coarse SSS data is able to reconstruct a 100-year long AMOC period (average of 10.18 Sv and a standard deviation of 1.39 Sv), with a correlation of 0.76 to the target and a RMSE of 0.99 Sv. These values can be respectively compared to 0.85 and 0.75 Sv when using the global salinity surface observations. This work provides a first step towards understanding the limitations and prospects of historical AMOC reconstructions using different sea surface salinity datasets for the surface nudging. (10.1007/s00382-020-05304-4)
DOI : 10.1007/s00382-020-05304-4
Presentation and evaluation of the IPSL‐CM6A‐LR climate model
- Boucher Olivier
- Servonnat Jérôme
- Albright Anna Lea
- Aumont Olivier
- Balkanski Yves
- Bastrikov Vladislav
- Bekki Slimane
- Bonnet Rémy
- Bony Sandrine
- Bopp Laurent
- Braconnot Pascale
- Brockmann Patrick
- Cadule Patricia
- Caubel Arnaud
- Cheruy Frédérique
- Codron Francis
- Cozic Anne
- Cugnet David
- d'Andrea Fabio
- Davini Paolo
- de Lavergne Casimir
- Denvil Sebastien
- Deshayes Julie
- Devilliers Marion
- Ducharne Agnès
- Dufresne Jean-Louis
- Dupont Eliott
- Éthé Christian
- Fairhead Laurent
- Falletti Lola
- Flavoni Simona
- Foujols Marie-Alice
- Gardoll Sébastien
- Gastineau Guillaume
- Ghattas Josefine
- Grandpeix Jean-Yves
- Guenet Bertrand
- Guez Lionel
- Guilyardi Éric
- Guimberteau Matthieu
- Hauglustaine Didier
- Hourdin Frédéric
- Idelkadi Abderrahmane
- Joussaume Sylvie
- Kageyama Masa
- Khodri Myriam
- Krinner Gerhard
- Lebas Nicolas
- Levavasseur Guillaume
- Lévy Claire
- Li Laurent
- Lott François
- Lurton Thibaut
- Luyssaert Sebastiaan
- Madec Gurvan
- Madeleine Jean-Baptiste
- Maignan Fabienne
- Marchand Marion
- Marti Olivier
- Mellul Lidia
- Meurdesoif Yann
- Mignot Juliette
- Musat Ionela
- Ottle Catherine
- Peylin Philippe
- Planton Yann
- Polcher Jan
- Rio Catherine
- Rochetin Nicolas
- rousset clement
- Sepulchre Pierre
- Sima Adriana
- Swingedouw Didier
- Thiéblemont Rémi
- Traore Abdoul Khadre
- Vancoppenolle Martin
- Vial Jessica
- Vialard Jérôme
- Viovy Nicolas
- Vuichard Nicolas
Journal of Advances in Modeling Earth Systems, American Geophysical Union , 2020, 12 (7), pp.e2019MS002010 . This study presents the global climate model IPSL-CM6A-LR developed at Institut Pierre-Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL-CM5A-LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed. (10.1029/2019MS002010)
DOI : 10.1029/2019MS002010
Sub-hourly forecasting of wind speed and wind energy
- Dupré Aurore
- Drobinski Philippe
- Alonzo Bastien
- Badosa Jordi
- Briard Christian
- Plougonven Riwal
Renewable Energy, Elsevier , 2020, 145, pp.2373 - 2379 . (10.1016/j.renene.2019.07.161)
DOI : 10.1016/j.renene.2019.07.161