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This poster presents an overview of evaluating the impact of price-responsive load on power systems using integrated transmission and distribution simulation.
Abstract Bimodality in precipitation frequency distributions is often evident in atmospheric models, but rarely in observations. This study i) proposes a metric to objectively quantify the bimodality in precipitation distributions, ii) evaluates model simulations contributed to the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5), phase 6 (CMIP6), and the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project by comparing them to satellite-based and reanalysis precipitation products, and iii) investigates possible origins of bimodal precipitation distributions. Our results reveal that about 83% (20 out of 24) of CMIP5 and 70% (21 out of 30) of CMIP6 models used in this study exhibit bimodal distributions. The few DYAMOND models that use a deep convective parameterization also show bimodal distributions, while most DYAMOND models do not. Predictably, the bimodality originates from the separation of precipitation process between resolved grid-scale and parameterized subgrid-scale. However, in a larger number of models bimodality arises from the parameterized subgrid-scale convective precipitation alone.
An electron transport model based on the non-Maxwellian distribution f{sub 0}{proportional_to}e{sup -{nu}{sup m}} (NM model), caused by the inverse bremsstrahlung heating, is used in 1-D plane target and 2-D hohlraum simulations. In the NM model, the electron heat flux depends not only on the gradient of electron temperature T{sub e} but also on the gradients of electron number density and the index m. From 1-D simulations, the spatial distribution of T{sub e} is dune-like and T{sub e} decreases obviously in the flux-heated region, which is very different from the flat profile obtained by using the flux limit model (FL model) but similar to the experimental observations [Gregori et al., Phys. Rev. Lett. 92, 205006 (2004)] and the nonlocal results [Rosen et al., High Energy Density Phys. 7, 180 (2011)]. The reason which causes the dune-like profile of T{sub e} is discussed in the paper. From 2-D hohlraum simulations, the NM results of the plasma status, the emission peak and profile inside hohlraum are very different from the FL model results. Finally, it is hard to use an average flux limiter in the FL model to obtain the same hohlraum plasma status and emission with those under the NM model.
Energy management of transmission and distribution networks is becoming more challenged with the accelerated increasing of distributed energy resources (DERs) such as distributed photovoltaic (PV) generation and distributed energy storage. To better analyze the impacts of DERs on both transmission and distribution systems, a comprehensive transmission and distribution co-simulation platform should be developed. Furthermore, with DERs more actively participated in system operation such as providing real time grid services, their cyber vulnerability should be better understood to maintain system reliability. This paper discussed a cyber-physical events emulation based transmission & distribution co-simulation platform to perform different cyber events emulation and analyze the impacts of cyber physical events happened in distribution system on the T&D system operation. The case studies with both a transmission network and a synthetic distribution network data validate that the proposed T&D co-simulation platform can perform comprehensive cyber physical events emulation. Therefore, with extensive simulation using the proposed model, the system operator can accumulate adequate training data for the system situation awareness and grid anomaly detection purpose.
Purpose: While significant progress has been made in speeding up Monte Carlo (MC) dose calculation methods, they remain too time-consuming for the purpose of inverse planning. To achieve clinically usable calculation speeds, a precalculated Monte Carlo (PMC) algorithm for proton and electron transport was developed to run on graphics processing units (GPUs). The algorithm utilizes pregenerated particle track data from conventional MC codes for different materials such as water, bone, and lung to produce dose distributions in voxelized phantoms. While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from the limited number of unique tracks in the pregenerated track bank is missing from the paper. With a proper uncertainty analysis, an optimal number of tracks in the pregenerated track bank can be selected for a desired dose calculation uncertainty. Methods: Particle tracks were pregenerated for electrons and protons using EGSnrc and GEANT4 and saved in a database. The PMC algorithm for track selection, rotation, and transport was implemented on the Compute Unified Device Architecture (CUDA) 4.0 programming framework. PMC dose distributions were calculated in a variety of media and compared to benchmark dose distributions simulated from the corresponding general-purpose MC codes in the same conditions. A latent uncertainty metric was defined and analysis was performed by varying the pregenerated track bank size and the number of simulated primary particle histories and comparing dose values to a “ground truth” benchmark dose distribution calculated to 0.04% average uncertainty in voxels with dose greater than 20% of D{sub max}. Efficiency metrics were calculated against benchmark MC codes on a single CPU core with no variance reduction. Results: Dose distributions generated using PMC and benchmark MC codes were compared and found to be within 2% of each other in voxels with dose values greater than 20% of the maximum dose. In proton calculations, a small (≤1 mm) distance-to-agreement error was observed at the Bragg peak. Latent uncertainty was characterized for electrons and found to follow a Poisson distribution with the number of unique tracks per energy. A track bank of 12 energies and 60000 unique tracks per pregenerated energy in water had a size of 2.4 GB and achieved a latent uncertainty of approximately 1% at an optimal efficiency gain over DOSXYZnrc. Larger track banks produced a lower latent uncertainty at the cost of increased memory consumption. Using an NVIDIA GTX 590, efficiency analysis showed a 807 × efficiency increase over DOSXYZnrc for 16 MeV electrons in water and 508 × for 16 MeV electrons in bone. Conclusions: The PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty of 1% with a large efficiency gain over conventional MC codes. Before performing clinical dose calculations, models to calculate dose contributions from uncharged particles must be implemented. Following the successful implementation of these models, the PMC method will be evaluated as a candidate for inverse planning of modulated electron radiation therapy and scanned proton beams.
This presentation is intended to share the results of lab testing of five PV inverters with the Hawaiian Electric Companies and other stakeholders and interested parties. The tests included baseline testing of advanced inverter grid support functions, as well as distribution circuit-level tests to examine the impact of the PV inverters on simulated distribution feeders using power hardware-in-the-loop (PHIL) techniques. hardware-in-the-loop (PHIL) techniques.
Numerical algorithms to load relativistic Maxwell distributions in particle-in-cell (PIC) and Monte-Carlo simulations are presented. For stationary relativistic Maxwellian, the inverse transform method and the Sobol algorithm are reviewed. To boost particles to obtain relativistic shifted-Maxwellian, two rejection methods are proposed in a physically transparent manner. Their acceptance efficiencies are ≈50% for generic cases and 100% for symmetric distributions. They can be combined with arbitrary base algorithms.
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Natural and industrial granular flows often consist of several particle sizes, approximately forming a continuous particle size distribution (PSD). Continuous PSDs are ubiquitous, though existing kinetic-theory-based, hydrodynamic models for rapid granular flows are limited to a discrete number of species. The objective of this work is twofold: (i) to determine the number of discrete species required to accurately approximate a continuous PSD and (ii) to validate these results via a comparison with molecular dynamics (MD) simulations of continuous PSDs. With regard to the former, several analytic (Gaussian and lognormal) and experimental (coal and lunar soil simulants) distributions are investigated. Transport coefficients (pressure, shear viscosity, etc.) of the granular mixture given by the polydisperse theory of Garz´o et al. [“Enskog theory for polydisperse granular mixtures. I. Navier-Stokes order transport,” Phys. Rev. E 76, 031303 (2007);“Enskog theory for polydisperse granular mixtures. I. Navier-Stokes order transport,” 76, 031304 (2007)] are compared using an increasing number of species s to approximate the given PSD. These discrete approximations are determined by matching the first 2s moments of the approximation and the given continuous distribution. Relatively few species are required to approximate moderately wide distributions (Gaussian, lognormal), whereas even wider distributions (coal and lunar soil simulants) require a larger number of species. Regarding the second objective, a comparison between MD simulations and kinetictheory predictions for a simple shear flow of both Gaussian and lognormal PSDs reveal essentially no loss of accuracy stemming from the polydisperse theory itself (as compared to theories for monodisperse systems) or from the discrete approximations of continuous PSDs used in the polydisperse theory. C 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4744987]
Demand Response is playing an increasingly important role in smart grid control strategies. Modeling the behavior of populations of appliances under demand response is especially important to evaluate the effectiveness of these demand response programs. In this paper, an aggregated model is proposed for a class of Thermostatically Controlled Loads (TCLs). The model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. However, an accurate characterization of the collective dynamics however requires the aggregate model to have a high state space dimension. Most of the existing model reduction techniques require the stability of the underlying system which does not hold for the proposed aggregated model. In this work, a novel model reduction approach is developed for the proposed aggregated model, which can significantly reduce its complexity with small performance loss. The original and the reducedorder aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D, which is a realistic open source distribution simulation software. Index Terms – demand response, aggregated model, ancillary
The precipitation of energetic neutral atoms, produced through charge exchange collisions between solar wind ions and thermal atmospheric gases, is investigated for the Martian atmosphere. Connections between parameters of precipitating fast ions and resulting escape fluxes, altitude-dependent energy distributions of fast atoms and their coefficients of reflection from the Mars atmosphere, are established using accurate cross sections in Monte Carlo (MC) simulations. Distributions of secondary hot (SH) atoms and molecules, induced by precipitating particles, have been obtained and applied for computations of the non-thermal escape fluxes. A new collisional database on accurate energy-angular-dependent cross sections, required for description of the energy-momentum transfer in collisions of precipitating particles and production of non-thermal atmospheric atoms and molecules, is reported with analytic fitting equations. Three-dimensional MC simulations with accurate energy-angular-dependent cross sections have been carried out to track large ensembles of energetic atoms in a time-dependent manner as they propagate into the Martian atmosphere and transfer their energy to the ambient atoms and molecules. Results of the MC simulations on the energy-deposition altitude profiles, reflection coefficients, and time-dependent atmospheric heating, obtained for the isotropic hard sphere and anisotropic quantum cross sections, are compared. Atmospheric heating rates, thermalization depths, altitude profiles of production rates, energy distributions of SH atoms and molecules, and induced escape fluxes have been determined.
Purpose: Analyse the water-equivalence of MAGIC-f polymer gel for {sup 60}Co and {sup 192}Ir clinical brachytherapy sources, through dose distributions simulated with PENELOPE Monte Carlo code. Methods: The real geometry of {sup 60} (BEBIG, modelo Co0.A86) and {sup 192}192Ir (Varian, model GammaMed Plus) clinical brachytherapy sources were modelled on PENELOPE Monte Carlo simulation code. The most probable emission lines of photons were used for both sources: 17 emission lines for {sup 192}Ir and 12 lines for {sup 60}. The dose distributions were obtained in a cubic water or gel homogeneous phantom (30 × 30 × 30 cm{sup 3}), with the source positioned in the middle of the phantom. In all cases the number of simulation showers remained constant at 10{sup 9} particles. A specific material for gel was constructed in PENELOPE using weight fraction components of MAGIC-f: wH = 0,1062, wC = 0,0751, wN = 0,0139, wO = 0,8021, wS = 2,58×10{sup −6} e wCu = 5,08 × 10{sup −6}. The voxel size in the dose distributions was 0.6 mm. Dose distribution maps on the longitudinal and radial direction through the centre of the source were used to analyse the water-equivalence of MAGIC-f. Results: For the {sup 60} source, the maximum diferences in relative doses obtained in the gel and water were 0,65% and 1,90%, for radial and longitudinal direction, respectively. For {sup 192}Ir, the maximum difereces in relative doses were 0,30% and 1,05%, for radial and longitudinal direction, respectively. The materials equivalence can also be verified through the effective atomic number and density of each material: Zef-MAGIC-f = 7,07 e .MAGIC-f = 1,060 g/cm{sup 3} and Zef-water = 7,22. Conclusion: The results showed that MAGIC-f is water equivalent, consequently being suitable to simulate soft tissue, for Cobalt and Iridium energies. Hence, gel can be used as a dosimeter in clinical applications. Further investigation to its use in a clinical protocol is needed.
New particle formation (NPF) and growth govern cloud condensation nuclei (CCN) concentrations in many regions. The mechanisms governing the nucleation of molecular clusters vary substantially in different regions of the atmosphere. Additionally, the growth of these clusters from ~2 to 20 nm sizes is often governed by the availability of extremely low volatility organic vapours (ELVOCs). While the pathways to ELVOC formation from the oxidation of biogenic monoterpenes with ozone is better understood, the chemical and mechanistic pathways for ELVOC formation from oxidation of anthropogenic organics are not well understood. We integrate measurements and three-dimensional regional model simulations with the Weather Research and Forecasting Model coupled to chemistry (WRF-Chem) to understand the processes governing new particle formation and growth and secondary organic aerosol (SOA) formation during the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) field campaign at the Southern Great Plains (SGP) observatory in Oklahoma, and contrast it with a site within the Bankhead National Forest (BNF), Alabama in Southeast USA, where 5-year long measurements will begin in 2024. Simulations show that nucleation rates are at least an order of magnitude higher at SGP compared to BNF during the springtime days (April 28 and May 14, 2016), largely due to lower H2SO4 concentrations at BNF, which are needed for nucleation. In addition, the larger CS at BNF (compared to SGP) increase the loss of molecular clusters by coagulation to pre-existing particles. Among the 8 different nucleation mechanisms in WRF-Chem, we find that the amine+H2SO4 nucleation mechanism dominates at the SGP site, while the pure organic ion induced nucleation mechanism dominates over BNF. Through various WRF-Chem sensitivity simulations, we find that anthropogenic ELVOCs are critical for explaining the growth of newly formed particles and the resulting number size distribution observed near the surface at the SGP site during the daytime. In addition, we show that treating organic particles as semisolid, with strong diffusion-limited uptake of organic vapours, brings model predictions into closer agreement with the observed evolution of particle size distribution. Simulations also predict that anthropogenic SOA, formed by the oxidation of aromatic volatile organic compounds (VOCs), is the dominant organic aerosol component at SGP, while biogenic SOA dominates particle composition at the BNF site in Southeast USA on these days.
Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosol properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.
The reliability of climate simulations and projections, particularly in the regions with complex terrains, is greatly limited by the model resolution. Here, we evaluate the variable-resolution Community Earth System Model (VR-CESM) with a high-resolution (0.125°) refinement over the Rocky Mountain region. The VR-CESM results are compared with observations, as well as CESM simulation at a quasi-uniform 1° resolution (UNIF) and Canadian Regional Climate Model version 5 (CRCM5) simulation at a 0.11° resolution. We find that VR-CESM is effective at capturing the observed spatial patterns of temperature, precipitation, and snowpack in the Rocky Mountains with the performance comparable to CRCM5, while UNIF is unable to do so. VR-CESM and CRCM5 simulate better the seasonal variations of precipitation than UNIF, although VR-CESM still overestimates winter precipitation whereas CRCM5 and UNIF underestimate it. All simulations distribute more winter precipitation along the windward (west) flanks of mountain ridges with the greatest overestimation in VR-CESM. VR-CESM simulates much greater snow water equivalent peaks than CRCM5 and UNIF, although the peaks are still 10–40% less than observations. Moreover, the frequency of heavy precipitation events (daily precipitation ≥ 25 mm) in VR-CESM and CRCM5 is comparable to observations, whereas the same events in UNIF are an order of magnitude less frequent. In addition, VR-CESM captures the observed occurrence frequency and seasonal variation of rain-on-snow days and performs better than UNIF and CRCM5. Lastly, these results demonstrate the VR-CESM's capability in regional climate modeling over the mountainous regions and its promising applications for climate change studies.
NREL's advanced hosting capacity analysis and System Analysis Reports can help utilities, policymakers, and solar developers better understand the impact of adding new distributed photovoltaic (DPV) systems to the electrical distribution system. Advanced hosting capacity analysis considers the thresholds at which new DPV systems will trigger upgrades or changes to the electrical distribution system and evaluates the cost of different options for expanding the hosting capacity. It is one part of NREL's work to help stakeholders better understand the distribution system costs associated with integrating new DPV systems at different penetration levels. NREL's analysis uses a bottom-up methodology that involves simulating distribution systems. The analysis considers sequential increases in hosting capacity and corresponding upgrade costs from a baseline scenario with no DPV up to penetration levels greater than 100% of peak load.
We describe an emulator of a detailed cloud parcel model which has been trained to assess droplet nucleation from a complex, multimodal aerosol size distribution simulated by a global aerosol–climate model. The emulator is constructed using a sensitivity analysis approach (polynomial chaos expansion) which reproduces the behavior of the targeted parcel model across the full range of aerosol properties and meteorology simulated by the parent climate model. An iterative technique using aerosol fields sampled from a global model is used to identify the critical aerosol size distribution parameters necessary for accurately predicting activation. Across the large parameter space used to train them, the emulators estimate cloud droplet number concentration (CDNC) with a mean relative error of 9.2% for aerosol populations without giant cloud condensation nuclei (CCN) and 6.9% when including them. Versus a parcel model driven by those same aerosol fields, the best-performing emulator has a mean relative error of 4.6%, which is comparable with two commonly used activation schemes also evaluated here (which have mean relative errors of 2.9 and 6.7%, respectively). We identify the potential for regional biases in modeled CDNC, particularly in oceanic regimes, where our best-performing emulator tends to overpredict by 7%, whereas the reference activation schemes range in mean relative error from -3 to 7%. The emulators which include the effects of giant CCN are more accurate in continental regimes (mean relative error of 0.3%) but strongly overestimate CDNC in oceanic regimes by up to 22%, particularly in the Southern Ocean. Finally, the biases in CDNC resulting from the subjective choice of activation scheme could potentially influence the magnitude of the indirect effect diagnosed from the model incorporating it.
Purpose: Assessing the performance and uncertainty of a pre-calculated Monte Carlo (PMC) algorithm for proton and electron transport running on graphics processing units (GPU). While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from recycling a limited number of tracks in the pre-generated track bank is missing from the literature. With a proper uncertainty analysis, an optimal pre-generated track bank size can be selected for a desired dose calculation uncertainty. Methods: Particle tracks were pre-generated for electrons and protons using EGSnrc and GEANT4, respectively. The PMC algorithm for track transport was implemented on the CUDA programming framework. GPU-PMC dose distributions were compared to benchmark dose distributions simulated using general-purpose MC codes in the same conditions. A latent uncertainty analysis was performed by comparing GPUPMC dose values to a “ground truth” benchmark while varying the track bank size and primary particle histories. Results: GPU-PMC dose distributions and benchmark doses were within 1% of each other in voxels with dose greater than 50% of Dmax. In proton calculations, a submillimeter distance-to-agreement error was observed at the Bragg Peak. Latent uncertainty followed a Poisson distribution with the number of tracks per energy (TPE) and a track bank of 20,000 TPE produced a latent uncertainty of approximately 1%. Efficiency analysis showed a 937× and 508× gain over a single processor core running DOSXYZnrc for 16 MeV electrons in water and bone, respectively. Conclusion: The GPU-PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty below 1%. The track bank size necessary to achieve an optimal efficiency can be tuned based on the desired uncertainty. Coupled with a model to calculate dose contributions from uncharged particles, GPU-PMC is a candidate for inverse planning of modulated electron radiotherapy and scanned proton beams. This work was supported in part by FRSQ-MSSS (Grant No. 22090), NSERC RG (Grant No. 432290) and CIHR MOP (Grant No. MOP-211360)