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At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Probabilistic Forecast of All-Sky Solar Radiation Using Enhanced WRF-Solar: Preprint

This study presents enhancements of the Weather Research and Forecasting model with solar extensions (WRF-Solar) to provide probabilistic forecasts of solar radiation. Our approach builds ensemble WRF-Solar runs by introducing stochastic perturbations of variables that produce the largest uncertainties in predicting surface irradiance and clouds. The key variables are identified using tangent linear sensitivity analysis of six physics packages responsible for all-sky irradiance variability. An optimal strategy to stochastically perturb the selected variables is developed and applied to WRF-Solar to generate ensemble members for day-ahead solar prediction. The National Solar Radiation Database (NSRDB) is used to validate the ensemble forecast at arbitrary locations on the model grid. Preliminary results indicate that the proposed technique can potentially produce WRF-Solar ensembles providing reliable information of solar prediction uncertainty. This study describes the implemented methodology and initial results as well as future research to improve ensemble-based probabilistic forecasts with WRF-Solar.

all-sky↗

DOE OSTI2018

Author Correction: Carbon budgets for 1.5 and 2 °C targets lowered by natural wetland and permafrost feedbacks

In the version of this Article originally published in July 2018, a parallelization coding problem, which meant that a subset of model grid cells were subjected to erroneous updating of atmospheric gas concentrations, resulted in incorrect calculation of atmospheric CO 2 for these grid cells, and therefore underestimation of the carbon uptake by land through vegetation growth and eventual increases to soil carbon stocks. Having re-run the simulations using the corrected code, the authors found that the original estimates of the impact of the natural wetland methane feedback were overestimated. The permafrost and natural wetland methane feedback requires lower permissible emissions of 9–15% to achieve climate stabilization at 1.5 °C, compared with the original published estimate of 17–23%. The Article text, Table 1 and Fig. 3 have been updated online to reflect the revised numerical estimates. The Supplementary Information file has also been amended, with Supplementary Figs 6, 7, 8 and 9 replaced with revised versions produced using the corrected model output. As the strength of feedbacks remain significant, still require inclusion in climate policy and are nonlinear with global warming, the overall conclusions of the Article remain unchanged.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2008

Forecasting Lightning Threat using Cloud-Resolving Model Simulations

Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models,the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of forecasts become available.

McCaul, Eugene W., Jr.↗

DOE OSTI2022

The High Fraction of Thin Disk Galaxies Continues to Challenge ΛCDM Cosmology

Any viable cosmological framework has to match the observed proportion of early- and late-type galaxies. In this contribution, we focus on the distribution of galaxy morphological types in the standard model of cosmology (Lambda cold dark matter, ΛCDM). Using the latest state-of-the-art cosmological ΛCDM simulations known as Illustris, IllustrisTNG, and EAGLE, we calculate the intrinsic and sky-projected aspect ratio distribution of the stars in subhalos with stellar mass M * > 10 10 M ⊙ at redshift z = 0. There is a significant deficit of intrinsically thin disk galaxies, which however comprise most of the locally observed galaxy population. Consequently, the sky-projected aspect ratio distribution produced by these ΛCDM simulations disagrees with the Galaxy And Mass Assembly (GAMA) survey and Sloan Digital Sky Survey at ≥12.52σ (TNG50-1) and ≥14.82σ (EAGLE50) confidence. The deficit of intrinsically thin galaxies could be due to a much less hierarchical merger-driven build-up of observed galaxies than is given by the ΛCDM framework. It might also arise from the implemented sub-grid models, or from the limited resolution of the above-mentioned hydrodynamical simulations. We estimate that an 8 5 times better mass resolution realization than TNG50-1 would reduce the tension with GAMA to the 5.58σ level. Finally, we show that galaxies with fewer major mergers have a somewhat thinner aspect ratio distribution. Given also the high expected frequency of minor mergers in ΛCDM, the problem may be due to minor mergers. In this case, the angular momentum problem could be alleviated in Milgromian dynamics because of a reduced merger frequency arising from the absence of dynamical friction between extended dark matter halos.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Understanding Inter-Annual Variability of PV Energy Production in the Contiguous United States

Year-to-year variability of photovoltaic (PV) generation is an important factor for project financing as well as for modeling the reliability and resource adequacy of power systems. In this work, we analyze inter-annual variability of PV generation across the contiguous United States using the National Solar Radiation Database (NSRDB) from 1998 to 2014. We compare the estimated PV generation from the typical meteorological year (TMY) against the long-term mean, and find that on average the TMY tends to overpredict estimated PV production. However, we also found significant regional bias and spatial clustering in that comparison, such that there are many regional pockets of underprediction as well. Finally, we show that there is not a single year of data that most closely approximates a representative 'resource year' for all regions of the United States. These results point to the need to understand inter-annual variability at individual sites and to be aware of the shortcomings of using TMY or a single year of data for project or grid modeling.

14 SOLAR ENERGY↗

NASA NTRS2000

Operational Assimilation of GOES Data into a Mesoscale Model

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite- observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The technique has been employed on a semi-operational basis at the Global Hydrology and Climate Center (GHCC) within the Penn State/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) since 1 November 1998. We performed the assimilation on a model grid centered over the Southeastern US. In addition, a control run without assimilation was performed to provide insight into the performance of the assimilation technique. Bulk verification statistics (BIAS and RMSE) of surface air temperature and relative humidity of more than 250 case days has been performed to date. Results show that assimilation of the satellite data results reduces both the bias and RMSE for simulations of surface air temperature and relative humidity. We are working with forecasters at the National Weather Service Forecast Office located in Birmingham, AL to evaluate the impact of the assimilation on precipitation forecasts. In addition, work is currently underway to determine the ability of both the assimilation technique and Early Eta to sense the significant changes that occur in both leaf expansion and soil moisture conditions during the Spring-Summer of 1999. This will be accomplished via examination of the simulated surface energy budgets from both modeling systems.

Lapenta, William↗

DOE OSTI2017

Regime dependence of ice cloud heterogeneity – a convective life‐cycle effect?

Cloud condensate varies on scales smaller than those typically resolved by global weather and climate models. In order to accurately predict the radiative and microphysical process rates representative of the entire model grid box, the effect of the subgrid‐scale heterogeneity of cloud must be taken into account. In this study, observed ice water content retrieved from A‐Train satellite observations is used to explore how spatial ice condensate variability, characterized by the fractional standard deviation (FSD, the standard deviation divided by the mean), varies with cloud regime. FSD is generally lower for overcast cloud scenes, but an additional predictor based on convective activity is needed to capture the high FSD associated with more turbulent clouds and reproduce the observed latitudinal and height variations of FSD. Convective clouds that extend only a few kilometres above the freezing level are likely to be smaller at an earlier stage in their life cycle, and actively growing with a higher FSD. In contrast, more mature clouds reaching the tropopause are larger and generally have a lower variability and smaller FSD. To capture this life‐cycle effect, a new parametrization is tested which uses the ratio of a model's convectively detrained condensate to the existing cloud condensate mass as a proxy for the cloud's convective life stage to highlight areas with enhanced condensate variability. The parametrization is scale‐adaptive, situation‐dependent and captures seasonally varying global patterns and the zonal mean vertical structure of observed ice condensate variability well. Ground‐based observations obtained from five Atmospheric Radiation Measurement sites provide independent confirmation that the parametrization satisfactorily captures condensate variability in high‐altitude ice clouds.

Ahlgrimm, Maike↗

DOE OSTI2014

DR Resources for Energy and Ancillary Services in the West (Presentation)

Demand response (DR) resources present a potentially important source of grid flexibility however, DR in grid models is limited by data availability and modeling complexity. This presentation focuses on the co-optimization of DR resources to provide energy and ancillary services in a production cost model of the Colorado "test system". We assume each DR resource can provide energy services by either shedding load or shifting its use between different times, as well as operating reserves: frequency regulation, contingency reserve, and flexibility (or ramping) reserve. There are significant variations in the availabilities of different types of DR resources, which affect both the operational savings as well as the revenue for each DR resource. The results presented include the system-wide avoided fuel and generator start-up costs as well as the composite revenue for each DR resource by energy and operating reserves.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

FY10 Report on Multi-scale Simulation of Solvent Extraction Processes: Molecular-scale and Continuum-scale Studies

This task is aimed at predictive modeling of solvent extraction processes in typical extraction equipment through multiple simulation methods at various scales of resolution. We have conducted detailed continuum fluid dynamics simulation on the process unit level as well as simulations of the molecular-level physical interactions which govern extraction chemistry. Through combination of information gained through simulations at each of these two tiers along with advanced techniques such as the Lattice Boltzmann Method (LBM) which can bridge these two scales, we can develop the tools to work towards predictive simulation for solvent extraction on the equipment scale (Figure 1). The goal of such a tool-along with enabling optimized design and operation of extraction units-would be to allow prediction of stage extraction effrciency under specified conditions. Simulation efforts on each of the two scales will be described below. As the initial application of FELBM in the work performed during FYl0 has been on annular mixing it will be discussed in context of the continuum-scale. In the future, however, it is anticipated that the real value of FELBM will be in its use as a tool for sub-grid model development through highly refined DNS-like multiphase simulations facilitating exploration and development of droplet models including breakup and coalescence which will be needed for the large-scale simulations where droplet level physics cannot be resolved. In this area, it can have a significant advantage over traditional CFD methods as its high computational efficiency allows exploration of significantly greater physical detail especially as computational resources increase in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2007

Generating a Long-Term Land Data Record from the AVHRR and MODIS Instruments

The goal of NASA's Land Long Term Iiata Record (LTDR) project is to produce a consistent long term data set from the AVHRR and MODIS instruments for land climate studies. The project will create daily surface reflectance and normalized difference vegetation index (NDVI) products at a resolution of 0.05 deg., which is identical to the Climate Modeling Grid (CMG) used for MODIS products from EOS Terra and Aqua. Higher order products such as burned area, land surface temperature, albedo, bidirectional reflectance distribution function (BRDF) correction, leaf area index (LAI), and fraction of photosyntheticalIy active radiation absorbed by vegetation (fPAR), will be created. The LTDR project will reprocess Global Area Coverage (GAC) data from AVHRR sensors onboard NOAA satellites by applying the preprocessing improvements identified in the AVHRR Pathfinder Il project and atmospheric and BRDF corrections used in MODIS processing. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and near infrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view (IFOV). Atmospheric corrections for Rayleigh scattering, ozone, and water vapor are undertaken, with aerosol correction being implemented. The LTDR also produces a surface reflectance product for channel 3 (3.75 micrometers). Quality assessment (QA) is an integral part of the LTDR production system, which is monitoring temporal trands in the AVHRR products using time-series approaches developed for MODIS land product quality assessment. The land surface reflectance products have been evaluated at AERONET sites. The AVHRR data record from LTDR is also being compared to products from the PAL (Pathfinder AVHRR Land) and GIMMS (Global Inventory Modeling and Mapping Studies) systems to assess the relative merits of this reprocessing vis-a-vis these existing data products. The LTDR products and associated information can be found at http://ltdr.nascom.nasa.gov/ltdr/ltdr.html.

Pedelty, Jeffrey↗

DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration: Preprint

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions, i.e., the reserve requirement, renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained off-line using historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine and battery. Case studies demonstrated that the proposed method outperforms other operating reserve determination methods.

distribution system↗

DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained offline using a historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery. Case studies demonstrated that the proposed method outperforms other policies with static operating reserves.

distribution system↗

DOE OSTI2020

Growth modeling to evaluate alternative cultivation strategies to enhance national microalgal biomass production

In this paper, we present a model-based assessment of alternative cultivation strategies on algal biomass production within the conterminous United States. Our assessment focuses on two basic cultivation strategies: 1) seasonal rotation of three representative freshwater algal strains that are well suited for warm weather, cold weather, and all-season diverse weather conditions, respectively; and 2) variation between three open pond water depths (15cm, 20cm, and 25cm). The enhanced Biomass Assessment Tool (BAT) is applied on a site-specific, hourly basis at 5,832 North American Land Data Assimilation System Phase 2 1/8° meteorological model grid cells over a 30-year period (1980-2009) to evaluate the operational strategies. Recognizing that resource management decisions may also consider scales beyond the individual site, we also conduct a regional assessment of focused on 7 representative climate zones in the conterminous United States. Results demonstrate that the spatial variability of algal productivity is largely affected by the strain-specific growth response to light and temperature that vary significantly by climate zone and latitude. Therefore, with a limited set of alternative cultivation strategies, the BAT identified the best performing combination of cultivation strategies on a site-specific basis that considerably enhances national annual biomass productivity. Specifically, the appropriate choice of seasonal strain rotation is able to significantly dampen climate-driven seasonal and spatial variability.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Empowering Rural Electrification in Honduras: An Integrated Assessment of PV/BESS and Productive Uses of Electricity in Gracias a Dios

Honduras faces significant challenges in its energy sector, particularly in rural areas where access to reliable and affordable electricity remains limited. The flagship rural electrification initiative for Honduras Secretary of Energy (SEN) is the Politica de Acceso Universal a la Electricidad (PAUEH - Universal Electricity Access Policy), a key solution for addressing this energy poverty is the deployment of more than 1700 distributed solar and hybrid mini-grid solutions. In late 2023 as a first step towards supporting SEN's electrification efforts, the National Renewable Energy Laboratory (NREL) developed a literature review of SEN electrification policy documents and conducted a series of technical capacity-building workshops with SEN and other energy sector stakeholders in Honduras focused on using NREL's open-source REopt tool to conduct techno-economic assessments and develop least-cost optimizations for potential solar + storage mini-grid systems. Building on some initial capacity building on mini-grid modeling, the National Renewable Energy Laboratory (NREL) worked with SEN to develop a detailed techno-economic assessment for electrifying two schools, a healthcare clinic, and a hospital in a hypothetical community within the department of Gracias a Dios. The analysis also evaluates the business case for cold storage productive use of energy (PUE) applications for the fisheries value chain and how the incorporation of these PUE loads potentially impacts both the viability of the PV+BESS solutions as well as local economic development. By assessing the potential for deployment of integrated PV/BESS systems to both support critical community services like education and healthcare, as well as potential for downstream enterprise and economic development, this analysis represents a first step that can help to inform specific strategies for development of pilot PV+BESS projects aligned with national priorities and sector level planning under PAUEH.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems

The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, has the potential to fundamentally transform how we manage power systems, especially in scenarios with a higher proportion of renewable energy sources. One critical aspect is solving linear systems of equations, crucial for power system applications like power flow analysis, for which the Harrow-Hassidim-Lloyd (HHL) algorithm is a well-known quantum solution. However, HHL quantum circuits often exhibit excessive depth, making them impractical for current Noisy-Intermediate-Scale-Quantum (NISQ) devices. In this paper, we introduce a versatile framework, powered by NWQSim, that bridges the gap between power system applications and quantum linear solvers available in Qiskit. This framework empowers researchers to efficiently explore power system applications using quantum linear solvers. Through innovative gate fusion strategies, reduced circuit depth, and GPU acceleration, our simulator significantly enhances resource efficiency. Power flow case studies have demonstrated up to a eight-fold speedup compared to Qiskit Aer, all while maintaining comparable levels of accuracy.

quantum computing, Harrow-Hassidim-Lloyd, high-per↗

DOE OSTI2024

Fortalecimiento de la Electrificación Rural en Honduras: Una Evaluación Integrada de PV+BESS y Usos Productivos de la Electricidad en Gracias a Dios, Honduras [Empowering Rural Electrification in Honduras: An Integrated Assessment of PV/BESS and Productive Uses of Electricity in Gracias a Dios] (Spanish Translation)

14 SOLAR ENERGY↗

DOE OSTI2024

Equivalent-Circuit Models of Grid-Forming IBRs for Electromagnetic-Transient Simulations

We derive equivalent-circuit models of control- and physical-layer subsystems of grid-forming (GFM) inverter-based resources (IBRs) for electromagnetic-transient (EMT) simulations. Three different primary controllers are considered: Droop, Virtual Synchronous Machine (VSM), and dispatchable Virtual Oscillator Control (dVOC). In addition, the models include cascaded voltage- and current-control loops, and LCL output filters. Simulations for a single-inverter setup and for a network of five inverters in a modified IEEE 14-bus topology are presented. The equivalent-circuit models simulated with analog electronic circuit-simulator software (in our case, LTspice) offer the same accuracy, and with no explicit tuning of the solver, lower computational burden compared to block-diagram-based implementations in commercial off-the-shelf EMT software (in our case, MATLAB-Simulink).

electromagnetic transient simulation↗

NASA NTRS2013

Photoionization Modeling of Oxygen K Absorption in the Interstellar Medium:

We present detailed analyses of oxygen K absorption in the interstellar medium (ISM) using four high-resolution Chandra spectra toward the X-ray low-mass binary XTE J1817-330. The 11-25 Angstrom broadband is described with a simple absorption model that takes into account the pile-up effect and results in an estimate of the hydrogen column density. The oxygen K-edge region (21-25 Angstroms) is fitted with the physical warmabs model, which is based on a photoionization model grid generated with the xstar code with the most up-to-date atomic database. This approach allows a benchmark of the atomic data which involves wavelength shifts of both the K lines and photoionization cross sections in order to fit the observed spectra accurately. As a result we obtain a column density of N(sub H) = 1.38 +/- 0.01 × 10(exp 21) cm(exp −2); an ionization parameter of log xi = −2.70 +/- 0.023; an oxygen abundance of A(sub O) = 0.689 (+0.015/−0.010); and ionization fractions of O(sub I)/O = 0.911, O(sub II)/O = 0.077, and O(sub III)/O = 0.012 that are in good agreement with results from previous studies. Since the oxygen abundance in warmabs is given relative to the solar standard of Grevesse & Sauval, a rescaling with the revision by Asplund et al. yields A(sub O) = 0.952(+0.020/−0.013), a value close to solar that reinforces the new standard.We identify several atomic absorption lines-K(alpha), K(beta), and K(gamma) in O(sub I) and O(sub II) and K(alpha) in O(sub III), O(sub VI), and O(sub VII)-the last two probably residing in the neighborhood of the source rather than in the ISM. This is the first firm detection of oxygen K resonances with principal quantum numbers n greater than 2 associated with ISM cold absorption.

oxygen↗