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Wind power is inherently intermittent and poses challenges to the stable integration of wind turbines (WTs) into a power system. Battery energy storage systems (BESSs) are often used to mitigate wind power fluctuations, charging and discharging batteries depending on wind conditions. Like every multisource system, the WT-BESS hybrid system requires power electronic converters to control the dc and ac power flows. Multiple dc-dc and dc-ac power conversion stages enable high control flexibility, but have a low energy conversion efficiency and require several bulky passive filters. Single-stage multiport inverters (MPIs) that connect multiple sources to the ac grid in a single conversion stage have been increasingly investigated in recent years as a potential alternative. This paper examines the performance of the NPC-type Dual-Port Inverter (DPI) for a wind energy system based on a permanent magnet synchronous generator and battery storage device. The modulation of the DPI and the integrated BESS and WT maximum power point tracking control are presented and discussed. As proved by numerical simulations, the single-stage DPI can inject smooth electrical power into the grid at a unity power factor under highly variable speed conditions while always extracting the maximum power from the turbine.

Abstract Species concentration (e.g. CO) and temperature measurements in the combustion field require fast-response technique without interfering species. In the last decade, tunable diode lasers have been established as strong technique to measure species such as CO, CO2, and H2O as well as temperature with high sensitivity. The drawback is the degree of interference that might hamper the robustness of the technique. In this work simultaneous measurements of temperature and CO concentration were carried out using an interference-free mid-infrared laser-based absorption technique behind reflected shock waves. Two transition lines of CO (P(v″ = 0, J″ = 21) and P(v″ = 1, J″ = 21)) in the fundamental vibrational band near 4.87 and 4.93 μm, respectively, were selected. Absorbance interferences from CO2 and H2O at room and high temperatures were evaluated. Spectroscopic parameters for the development of the system were measured: line strengths and collisional broadening coefficients (in Ar) of both lines were obtained at 1020–1950 K by using the scanned-wavelength direct-absorption method. The technique was demonstrated for non-reactive and reactive mixtures. For the non-reactive case, temperature and CO concentration were measured at 1030–1910 K and 1.0–3.7 bar. For the reactive case, oxidation of i-C8H18/O2/Ar and i-C8H18/C2H5OH/O2/Ar mixtures were investigated at three equivalence ratios of 2.0, 1.0, and 0.5. The two newly adopted lines exhibited good performance in the detection of CO concentration and are immune to interferences from CO2 and H2O. In addition, the simulated data from the state-of-the-art isooctane/ethanol mechanisms in literature were compared with the measured data, showing overall good agreement.

Reducing ecological impact is a major challenge for today’s industry, particularly the rail industry, which is one of the most energy-intensive industries. Indeed, this industry faces two paradoxical needs: on the first hand, it must decrease its energy consumption, meeting both an environmental goal and a financial objective, and on the other hand, it must not only maintain but increase the circulation of train, thus allowing a larger part of the population to use the most ecological means of land transport. Reducing consumption requires a prediction model. The formalization of this model is complex, particularly when driver control is taken into account. The complexity of the model must be chosen carefully. The SNCF has a very sophisticated model with a large number of parameters that need to be evaluated. In this work, we consider a train dynamics model simplified to a non-linear differential longitudinal dynamics equation coupled to a power balance [1]. In this model it is possible to distinguish two types of inputs:the model parameters which will be calibrated and the environment variables (wind, driver’s control, etc.) which will change from one journey to another. In order to calibrate the model, we rely on a priori knowledge from SNCF experts who provide information on the values of the model parameters. We also have a set of measurements (time, speed, power consumption, control) taken on the same train, for different journeys on different tracks. The used methodology is Bayesian calibration [2], which makes it possible to use the two types of information available to us while injecting model errors to take account of our imperfect knowledge of the system. These errors are parameterized by hyper-parameters that should also be calibrated. It is then necessary to formalize the experts’ knowledge mathematically in order to create our prior distributions, and then to write a likelihood function that will allow us to take into account both the measurements and the model errors. Another difficulty is the lack of knowledge about driver control as the control isn’t measured. The control linked to each measurement must be determined and appears in our problem asfunctional hyper-parameters.REFERENCES:[1] Julien Nespoulous, Christian Soize, Christine Funfschilling, and Guillaume Perrin. Optimisation of train speed to limit energy consumption. Vehicle System Dynamics, 60(10):3540–3557, October 2022.[2] Christian Soize. Uncertainty quantification: An accelerated course with advanced applications in computational engineering. Springer International Publishing, Cham, Switzerland, 2018.

Today, the use of renewable energies in buildings represents one of the main ways to reach a sustainable world. Whilst present buildings are still often energivorous systems, in the near future they will have to be converted to (or replaced by) zero energy buildings, also capable to export green energy (produced on-site by renewables) towards other buildings and/or users. This review article focuses on a selection of research papers, presented at the 16th International Conference on Building Simulation (BS 2019), regarding renewable energy applications, energy saving and comfort techniques for buildings. BS 2019 conference was organized in collaboration with the International Building Performance Simulation Association (IBPSA) and it was held at the Angelicum Congress Centre (San Tommaso d’Aquino Pontifex University) in Rome, Italy, during September 2-4, 2019. The conference was attended by 912 researchers and experts, with 660 presented research papers. The above-mentioned selection of papers is included in a dedicated Special Issue of the Renewable Energy - An International Journal (RENE), titled “Renewable energies: simulation tools and applications”. Reported studies are mostly dedicated to models, simulations, and optimization procedures of renewable energy devices. Specifically, photovoltaic systems, building integrated photovoltaic collectors, hybrid photovoltaic/thermal systems, solar thermal collectors as well as other energy efficiency tools are analysed through different simulation approaches and suitable optimization procedures. Attention is also paid to specific case studies related to innovative combinations of renewable energy devices and innovative envelope materials in different building typologies and weather zones. In some papers, solar energy is exploited for space heating and cooling purposes, while in other articles renewables or other energy tools are studied to achieve comfort targets, low grid dependencies, smart building/communities, and mainly the zero energy building goal.

La place centrale des savoirs dans les choix d’action publique est remise en cause par la « mise en risque » de phénomènes climatiques et hydrologiques qui s’avèrent imprévisibles et irréversibles. C’est le cas du recul du trait de côte des falaises en Picardie. Longtemps basé sur une approche scientifique et technique dite de « recul moyen », l’État doit recomposer ses moyens d’action face à l’occurrence de risques extrêmes et brusques. Or, il ne s’agit pas seulement de changer de modalités dans l’approche technique, ni de promouvoir de nouveaux outils de prévention pour construire des stratégies de recomposition territoriale des littoraux. Le cas de l’effondrement de la falaise d’Ault illustre le besoin urgent d’intégrer dans l’action publique une pluralité de connaissances des effets du changement climatique, tant en termes de savoirs qui peuvent être alternatifs et scientifiques que de connaissances tirées de l’expérience sensible, personnelle ou esthétique. The central place of knowledge in public policy choices is being challenged by climate and hydrological phenomena that turn out to be unpredictable and irreversible. This is the case with the retreat of the coastline of the cliffs in Picardy. For a long time based on a scientific and technical approach known as “average retreat”, the national government now has to reconstitute its methods of action in the face of extreme and sudden risks. But it’s not just a question of changing the technical approach, or promoting new prevention tools to build strategies for reorganising coastal areas. The case of the collapse of the cliffs at Ault illustrates the urgent need to integrate multiple forms of knowledge about the effects of climate change into public action, both in terms of alternative and scientific knowledge and in terms of knowledge rooted in sensitive, personal, or aesthetic experience. International audience

Predicting the variance of oil-price returns is of paramount importance for policymakers and investors. Recent research has focused on whether disaggregate measures of economic-policy uncertainty provide better forecasts. Given that the United States (U.S.) is a major player in the international oil market, we extend this line of research by exploring by means of machine-learning techniques whether accounting for U.S. state-level measures of economic-policy uncertainty results in more accurate forecasts. We find improvements in forecast accuracy, especially when we study intermediate and long forecast horizons. This finding is robust to various changes in the model configuration (realized variance vs. realized volatility, sample period, recursive vs. rolling-estimation window, loss function of forecast consumers). Understandably, our findings have important implications for oil traders and policy authorities.

Climate change is expected to alter the population dynamics of pioneer tree species and their planned use in sustainable forest management, but we have a limited understanding of how their demographic rates change in response to climate changes during ecological restoration. Based on 12 years of demographic data for a pioneer tree species (Pinus massoniana) censused in three plots that correspond to three stages of ecological restoration in southeastern China. We built integral projection models (IPMs) to assess vital rates (survival, growth, reproduction) and population growth in each plot, then evaluated demographic changes to simulated changes in seasonal mean temperature and precipitation in the current and previous census period. The plot representing the medium restoration stage had the highest population growth rate (λ = 0.983). Mean population survival probability increased with ecological restoration, and reproduction probability was significantly suppressed at the high restoration stage. Survival is always the most important vital rate for λ, and climate affects λ primarily via survival at each restoration stage. The current spring temperature was the most critical climate variable for λ in the low and medium restoration stages, and previous summer temperature was most critical in the high restoration stage. Simulated warming leads to a decrease in the stochastic population growth rate (λs) of P. massoniana in every stage. These findings suggest that during ecological restoration, P. massoniana responds to habitat change via modified demographic performance, thus altering its response to climate change. Despite diverse responses to climate change, the persistence of P. massoniana populations is facing a widespread threat of warming states at each restoration stages.

<div class="section abstract"><div class="htmlview paragraph">Although in the latest years the use of compression ignition engines has been a thread of discussion in the automotive field, it is possible to affirm that it still will be a fundamental producer of mechanical power in other sectors, such as naval and off-road applications. However, the necessity of reducing emissions requires to keep on studying new solutions for this kind of engine. Dual fuel combustion concept with methane has demonstrated to be effective in preserving the performance of the original engine and reducing soot, but issues related to the low flame speed forced researcher to find an alternative fuel at low impact of CO₂. Hydrogen, thanks to its chemical and physical properties, can be a perfect candidate to ensure a good level of combustion efficiency; however, this is possible only with a proper management of the in-cylinder mixture ignition by means of a pilot injection, preventing uncontrolled autoignition events as well. Moreover, an effective injection strategy can be beneficial for a further reduction of carbonous pollutants from the diesel fuel pilot. Therefore, this work is aimed to numerically analyze the sensitivity of the combustion development in a diesel engine converted to operate in dual fuel mode, where hydrogen is injected in the intake manifold and diesel pilot is directly injected in the cylinder. Starting from a test case at a constant engine speed of 2000 rpm experimentally validated, numerical simulations are carried out with the software ANSYS Forte, using a Turbulence-Kinetics interaction model and the Autoinduced Ignition Flame Propagation model for diesel and hydrogen, respectively. Lookup tables were specifically implemented for the evaluation of the laminar flame speed through H₂/air mixtures.</div></div>

Gaussian process regression (GPR) has shown superiority in terms of state estimation for its nonparametric characteristic and uncertainty prediction ability. Due to its heavy computational complexity, GPR is generally used for small datasets. To efficiently deal with the big data, the sparse spectrum approximation method has been successfully applied to GPR to decrease the computational complexity. However, the stationarity of this method is a strict assumption for data and usually mismatches the industrial processes. In this article, we proposed a sparse nonstationary GPR, which can deal with the nonstationary relationship among samples and make the model more flexible, to settle the aforementioned problems. Furthermore, the performance of the proposed method is evaluated using three public datasets and a sampled diesel engine dataset, and the results show the superiority of our proposed method in terms of accuracy.