Publications

2020

• 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
• Machine learning application to priority scheduling in smart microgrids
  
  • Dridi Aicha
  • Moungla Hassine
  • Afifi Hossam
  • Badosa Jordi
  • Ossart Florence
  • Kamal Ahmed E.
  , 2020, pp.1695-1700. The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). Data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing. (10.1109/IWCMC48107.2020.9148096)
  DOI : 10.1109/IWCMC48107.2020.9148096
• 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 Sébastien
  • 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