Centre Procédés, Energies Renouvelables et Systèmes Energétiques

In recent years, the penetration of photovoltaic (PV) generation in the energy mix of several countries has significantly increased thanks to policies favoring development of renewables and also to the significant cost reduction of this specific technology. The PV power production process is characterized by significant variability, as it depends on meteorological conditions, which brings new challenges to power system operators. To address these challenges, it is important to be able to observe and anticipate production levels. Accurate forecasting of the power output of PV plants is recognized today as a prerequisite for large-scale PV penetration on the grid. In this paper, we propose a statistical method to address the problem of stationarity of PV production data, and develop a model to forecast PV plant power output in the very short term (0-6 h). The proposed model uses distributed power plants as sensors and exploits their spatio-temporal dependencies to improve forecasts. The computational requirements of the method are low, making it appropriate for large-scale application and easy to use when online updating of the production data is possible. The improvement of the normalized root mean square error (nRMSE) can reach 20% or more in comparison with state-of-the-art forecasting techniques.

Silica aerogels are excellent thermal insulators, but their brittle nature has prevented widespread application. To overcome these mechanical limitations, silica-biopolymer hybrids are a promising alternative. A one-pot process to monolithic, superinsulating pectin-silica hybrid aerogels is presented. Their structural and physical properties can be tuned by adjusting the gelation pH and pectin concentration. Hybrid aerogels made at pH 1.5 exhibit minimal dust release and vastly improved mechanical properties while remaining excellent thermal insulators. The change in the mechanical properties is directly linked to the observed "neck-free" nanoscale network structure with thicker struts. Such a design is superior to "neck-limited", classical inorganic aerogels. This new class of materials opens up new perspectives for novel silica-biopolymer nanocomposite aerogels.

International audience

Determining the degradation mechanisms of oxygen evolution reaction (OER) catalysts is fundamental to design improved proton-exchange membrane water electrolyzer (PEMWE) devices but remains challenging under the demanding conditions of PEMWE anodes. To address this issue, we introduce a methodology combining identical-location transmission electron microscopy (IL-TEM), X-ray photoelectron spectroscopy (XPS), and electrochemical measurements, and apply it to iridium nanoparticles (NPs) covered by a thin oxide layer (IrOx) in OER conditions. The results show that, whatever the initial OER activity of the IrOx nanocatalysts, it gradually declines and reaches similar values after 30 000 potential cycles between 1.20 and 1.60 V versus the reversible hydrogen electrode (RHE). This drop in OER activity was ascribed to the progressive increase of the Ir oxidation state (fast change during electrochemical conditioning, milder change during accelerated stress testing) along with the increased concentrations of hydroxyl groups and water molecules. In contrast, no change in the mean oxidation state, no change in the hydroxyl/water coverage, and constant OER activity were noticed on the benchmark micrometer-sized IrO2 particles. In addition to chemical changes, Ir dissolution/redeposition and IrOx nanoparticle migration/agglomeration/detachment were made evident during the conditioning stage and in OER conditions, respectively. By combining the information derived from IL-TEM images and XPS measurements, we show that Ir(III) and Ir(V) are the best performing Ir valencies for the OER. These findings provide insights into the long-term OER activity of IrOx nanocatalysts as well as practical guidelines for the development of more active and more stable PEMWE anodes.

This paper presents an optimization model for Home Energy Management Systems from an aggregator's standpoint. The aggregator manages a set of resources such as PV, electrochemical batteries and thermal energy storage by means of electric water heaters. Resources are managed in order to participate in the day-ahead energy and local flexibility markets, also considering grid constraint support at the Point of Common Coupling. The resulting model is a Mixed-Integer Linear Programming problem in which the objective is to minimize day-ahead operation costs for the aggregator while complying with energy commitments in the day-ahead market and local flexibility requests. Three sources of uncertainty are considered: energy prices, PV production and load. Adjustable Robust Optimization is used to find a robust counterpart of the problem for including uncertainty. The results obtained show that using robust optimization allows strategic bidding to capture uncertainties while complying with obligations in the wholesale and local market. Data from a real-life energy community with 25 households is used to validate the proposed robust bidding methodology.

Purpose The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Design/methodology/approach The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation. Findings Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent. Research limitations/implications The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics. Practical implications This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency. Social implications The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping. Originality/value Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.

In this paper, a dynamic line-rating experiment is presented in which four machine-learning algorithms (generalized linear models, multivariate adaptive regression splines, random forests and quantile random forests) are used in conjunction with numerical weather predictions to model and predict the ampacity up to 27 h ahead in two conductor lines located in Northern Ireland. The results are evaluated against reference models and show a significant improvement in performance for point and probabilistic forecasts. The usefulness of probabilistic forecasts in this field is shown through the computation of a safety-margin forecast which can be used to avoid risk situations. With respect to the state of the art, the main contributions of this paper are an in depth look at explanatory variables and their relation to ampacity, the use of machine learning with numerical weather predictions to model ampacity, the development of a probabilistic forecast from standard point forecasts, and a favorable comparison to standard reference models. These results are directly applicable to protect and monitor transmission and distribution infrastructures, especially if renewable energy sources and/or distributed power generation systems are present.

The aim of this study was to assess long-term mortality and predictive factors of death after hospital admission for acute exacerbation of chronic obstructive pulmonary disease (COPD). 1824 patients (23.2% female; mean age 70.3±11.3 years) consecutively admitted for acute exacerbation of COPD in the respiratory medicine departments of 68 general hospitals between October 2006 and June 2007 were prospectively enrolled in a follow-up cohort. Their vital status was documented between October 2010 and April 2011. Vital status was available for 1750 patients (95.9%), among whom 787 (45%) died during follow-up. Multivariate analysis found that age (60-80 years and ≥80 years versus <60 years, relative risk 2.99, 95% CI 2.31-3.89), lower body mass index (25-30 kg·m(-2) versus ≤20 kg·m(-2), relative risk 0.80, 95% CI 0.66-0.97), lung cancer (relative risk 2.08, 95% CI 1.43-3.01), cardiovascular comorbidity (relative risk 1.35, 95% CI 1.16-1.58), previous hospital admissions for acute exacerbation of COPD (four or more versus none, relative risk 1.91, 95% CI 1.44-2.53), use of accessory respiratory muscles (relative risk 1.19, 95% CI 1.01-1.40) or lower-limb oedema (relative risk 1.74, 95% CI (1.44-2.12)) at admission and treatment by long-term oxygen therapy at discharge (relative risk 2.09, 95% CI 1.79-2.45) were independent risk factors of death. Mortality rate during the 4 years following hospital admission for acute exacerbation of COPD was high (45%). Simple clinical information relating to respiratory and general status can help in identifying high-risk patients and targeting more intensive follow-up and care. Interestingly, cardiovascular comorbidities and past hospitalisations for acute exacerbation of COPD, but not forced expiratory volume in 1 s, independently predicted the risk of death.

We demonstrate a 100 kHz optical parametric chirped-pulse amplifier delivering under 4-cycle (38 fs) pulses at ~3.2 µm with an average power of 15.2 W with a pulse-to-pulse energy stability <0.7% rms and a single-shot CEP noise of 65 mrad RMS over 8h. This source is continuously monitored, by using a fast 100 kHz data acquisition device, and presents an extreme stability, in the short and long terms.

Although parasitoids are used widely as a biological models for understanding the evolution of animal behaviour, most studies have been constrained to the laboratory. The dearth of field studies has been compounded by the almost complete ignorance of the physiological parameters involved in foraging and dispersal, in particular of the energetic constraints imposed by resource limitation. We estimated the dynamics of carbohydrates and lipids reserves of Venturia canescens (Gravenhorst) females by releasing individuals of known nutritional status in a natural environment and recapturing them using host-containing traps. The recapture rate was around 30%. These results were compared with the reserves of caged animals kept under different experimental conditions (freshly emerged, starved to death, fed ad libitum and partially starved). Wild animals were also sampled in order to estimate the resource levels of the local population. The results show that: (i) wasps are able to maintain a nearly constant level of energy over an extended foraging period; (ii) V. canescens takes sugars in the field; and (iii) the lipid reserves accumulated during the larval life may be limiting as lipogenesis does not take place in adults even under conditions of high sugar availability. These results demonstrate that wasps can forage for hosts and food and disperse in this habitat for hours and days without running into a severe risk of energy limitation.

Power systems with large shares of converter-interfaced renewables may be characterised by low grid inertia due to the lack of frequency containment provided by synchronous generators. Battery energy storage systems (BESSs), which can adjust their power output at much steeper ramping than conventional generation, are promising assets to restore suitable frequency regulation capacity levels. BESSs are typically connected to the grid with a power converter, which can be operated in either grid-forming or grid-following modes. This paper quantitatively assesses the impact of large-scale BESSs on the frequency containment of low inertia power grid and compares the performance of grid-forming and grid-following control modes. Numerical results are provided considering a detailed dynamic model of the IEEE 39-bus system where fully characterized models of stochastic demand and generation are taken into account. In order to assess the performance of the BESS control modes in a practical operative context, daily long simulations are considered where reserve levels for frequency containment and restoration are allocated considering the current practice of a transmission system operator in Europe. Numerical analyses on various metrics applied to grid frequency show that grid-forming outperforms grid-following converter control mode.

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast’s properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

Coal gasification and natural gas reforming are regarded as mature technologies for syngas production. These technologies are however highly polluting in terms of greenhouse gas emissions, mainly carbon dioxide. Natural gas reforming is considered cleaner than coal gasification but has some disadvantages in terms of higher plant maintenance and processing costs. It utilizes catalysts that are prone to poisoning, are costly, and require regular regeneration. In mitigation of these issues, plasma-based CO2 dissociation technologies could probably offer a new alternative for syngas production. The plasma-based technologies are more compact, have faster response and reaction times, and are relatively cheaper compared to conventional gasification and reforming. Assuming that electricity is produced by a low carbon emitting (renewable or nuclear) power plant, a comparative review of CO2 dissociation technology for syngas production shows that CO2 dissociation can be competitive from an environmental point of view but would face several challenges with the current plasma technologies available. Indeed, the results show that, for current plasma processes to be competitive with conventional processes for syngas production, the energy efficiency, conversion rate, and processing mass flow rates of the unit operations would have to be simultaneously increased. Syngas production would also be highly dependent on the specific energy input and characteristics of the plasma (technology, electric field, power, etc.). CO2 dissociation would give value to carbon dioxide as it consumes 0.33 mol of CO2 for each mole of syngas produced. Therefore, CO2 dissociation can be attractive as a possible option for the conversion of electrical energy to chemical energy, especially when the electrical energy is from a renewable and low cost electricity source.

The sensitivity of acetylcholinesterases (AChEs) from Musca domestica and from Drosophila melanogaster to the phosphatidylinositol-specific phospholipase C from Bacillus cereus and to the glycosylphosphatidylinositol-specific phospholipase C from Trypanosoma brucei was investigated. B. cereus phospholipase C solubilizes membrane-bound AChE, and both phospholipases convert amphiphilic AChEs into hydrophilic forms of the enzyme. The lipases uncover an immunological determinant that is found on other glycosylphosphatidylinositol-anchored membrane proteins after the same treatment. This immunological determinant is also present on the native hydrophilic form of AChE. The polypeptide bearing the active site of the membrane-bound enzyme migrates faster during sodium dodecyl sulfate-polyacrylamide gel electrophoresis than the same polypeptide from the soluble enzyme. We conclude that AChE from insect brain is attached to membranes via a glycophospholipid anchor. This anchor is covalently linked to the polypeptide bearing the active esterase site of the enzyme and can be cleaved by an endogenous lipase.

Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0-6 h). The method is based on quantile regression and a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-of-the-art probabilistic methods.

In the life cycle assessment (LCA) context, global sensitivity analysis (GSA) has been identified by several authors as a relevant practice to enhance the understanding of the model's structure and ensure reliability and credibility of the LCA results. GSA allows establishing a ranking among the input parameters, according to their influence on the variability of the output. Such feature is of high interest in particular when aiming at defining parameterized LCA models. When performing a GSA, the description of the variability of each input parameter may affect the results. This aspect is critical when studying new products or emerging technologies, where data regarding the model inputs are very uncertain and may cause misleading GSA outcomes, such as inappropriate input rankings. A systematic assessment of this sensitivity issue is now proposed. We develop a methodology to analyze the sensitivity of the GSA results (i.e. the stability of the ranking of the inputs) with respect to the description of such inputs of the model (i.e. the definition of their inherent variability). With this research, we aim at enriching the debate on the application of GSA to LCAs affected by high uncertainties. We illustrate its application with a case study, aiming at the elaboration of a simple model expressing the life cycle greenhouse gas emissions of enhanced geothermal systems (EGS) as a function of few key parameters. Our methodology allows identifying the key inputs of the LCA model, taking into account the uncertainty related to their description.

The use of high amounts of iridium in industrial proton exchange membrane water electrolyzers (PEMWE) could hinder their widespread use for the decarbonization of society with hydrogen. Nonthermally oxidized Ir nanoparticles supported on antimony-doped tin oxide (SnO2:Sb, ATO) aerogel allow decreasing the use of the precious metal by more than 70% while enhancing the electrocatalytic activity and stability. To date, the origin of these benefits remains unknown. Here, we present clear evidence of the mechanisms that lead to the enhancement of the electrochemical properties of the catalyst. Operando near-ambient pressure X-ray photoelectron spectroscopy on membrane electrode assemblies reveals a low degree of Ir oxidation, attributed to the oxygen spill-over from Ir to SnO2:Sb. Furthermore, the formation of highly unstable Ir(III) species is mitigated, while the decrease of Ir dissolution in Ir/SnO2:Sb is confirmed by inductively coupled plasma mass spectrometry. The mechanisms that lead to the high activity and stability of Ir catalysts supported on SnO2:Sb aerogel for PEMWE are thus unveiled.

Abstract. In the northwestern Mediterranean Sea, sperm whales, pilot whales and Risso's dolphins prey exclusively or preferentially on cephalopods. In order to evaluate their competition, we modelled their habitat suitability with the Ecological Niche Factor Analysis (ENFA) and compared their ecological niches using a discriminant analysis. We used a long term (1995–2005) small boat data set, with visual and acoustic (sperm whale) detections. Risso's dolphin had the shallowest and the more spatially restricted principal habitat, mainly located on the upper part of the continental slope (640 m mean depth). With a wider principal habitat, at 1750 m depth in average, the sperm whale used a deeper part of the slope as well as the closest offshore waters. Finally, the pilot whale has the most oceanic habitat (2500 m mean depth) mainly located in the central Ligurian Sea and Provençal basin. Therefore, potential competition for food between these species may be reduced by the differentiation of their habitats.

Distributed energy resources (DERs) installed in active distribution networks (ADNs) can be exploited to provide both active and reactive power reserves to the upper-layer grid (i.e., sub-transmission and transmission systems) at their connection point. This paper introduces a method to determine the capability area of an ADN for the provision of both active and reactive power reserves while considering the forecast errors of loads and stochastic generation, as well as the operational constraints of the grid and DERs. The method leverages a linearized load flow model and introduces a set of linear scenario-based robust optimization problems to estimate the reserve provision capability (RPC) area of the ADN. It is proved that, under certain assumptions, the RPC area is convex. The performance of the proposed method is tested on a modified version of the IEEE 33-bus distribution test system.

Implementing iridium oxide (IrOx) nanocatalysts can be a major breakthrough for oxygen evolution reaction (OER), the limiting reaction in polymer electrolyte membrane water electrolyzer devices. However, this strategy requires developing a support that is electronically conductive, is stable in OER conditions, features a large specific surface area and a porosity adapted to gas–liquid flows. To address these challenges, we synthesized IrOx nanoparticles, supported them on doped SnO2 aerogels (IrOx/doped SnO2), and assessed their electrocatalytic activity toward the OER and their resistance to corrosion in acidic media by means of a flow cell connected to an inductively coupled mass spectrometer (FC-ICP-MS). The FC-ICP-MS results show that the long-term OER activity of IrOx/doped SnO2 aerogels is controlled by the resistance to corrosion of the doping element, and by its concentration in the host SnO2 matrix. In particular, we provide quantitative evidence that Sb-doped SnO2 supports continuously dissolve while Ta-doped or Nb-doped SnO2 supports with appropriate doping concentrations are stable under acidic OER conditions. These results shed fundamental light on the complex equilibrium existing between SnO2 and the doping element oxide. They also open a reliable path to develop highly active and robust IrOx nanocatalysts for OER in acidic media.