Potential Revenue from Electrical Energy Storage in ERCOT: The Impact of Location and Recent Trends.
Abstract not provided.
Byrne, Raymond H.↗
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Estimating Potential Revenue from Electrical Energy Storage in PJM.
Abstract not provided.
Byrne, Raymond H.↗
Potential Adoption and Benefits of Co-Optimized Multimode Engines and Fuels for U.S. Light-Duty Vehicles
Exploring a diverse portfolio of technologies for decarbonization is crucial to understanding the potential impacts of different technological solutions and their associated environmental implications. Using high-octane, high-sensitivity biofuel blends in co-optimized multimode engines can increase engine efficiency and reduce vehicle emissions. Here, the multimode engine research focuses on the benefits of light-duty vehicle engines, which can operate in multiple modes depending on the vehicle's load. Low-temperature combustion can improve efficiency and reduce emissions (such as those from oxides of nitrogen and particulate matter) during low-load operation, while spark ignition performance is maintained in high-load operation. These advanced engines can be optimized to run on blends of biobased fuels. This analysis models scenarios for potential market adoption of co-optimized multimode vehicles fueled by three different bioblendstocks: ethanol, isopropanol, and isobutanol. An integrated modeling approach is used to forecast the energy and environmental impacts of the deployment of co-optimized multimode vehicles and fuels in the light-duty sector over the 2020-to-2050 time horizon. The multidisciplinary approach combines vehicle sales modeling, system dynamics modeling of the biorefining industry, and life cycle assessment to estimate the emissions and energy benefits. The models consider market forces such as consumer preferences for vehicle attributes, biofuel supply and demand dynamics subject to biorefinery capacity build-out and bioresource constraints, and forecasted changes to the U.S. bulk energy system over time. Market adoption of co-optimized vehicles is evaluated across a wide parameter space for incremental vehicle cost and engine efficiency improvement. This analysis reveals that the deployment of co-optimized multimode fuels and vehicles results in up to a 5% reduction in annual sector-wide life cycle greenhouse gas (GHG) emissions by 2050, relative to a business-as-usual scenario, but is also indicates environmental trade-offs, such as higher life cycle water-use. Emission benefits could potentially increase beyond 2050, as the new technologies penetrate the market and gain a foothold. Results also show that, under certain circumstances, vehicles with engines co-optimized for use with high-octane, high-sensitivity biofuel blends can be cost-competitive with conventional gasoline, while reducing GHG emissions. Our modeling results indicate that co-optimized multimode fuels and engines can be strategically leveraged in tandem with electrification to decarbonize the light-duty sector. Co-optimized vehicles could play a role in the early years of the time horizon, while electric vehicles (EVs) could become more competitive in the later years, highlighting the complementary benefits of these technologies for GHG reductions.
Oke, Doris↗
Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data
The ever-increasing power of modern super-computers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.
Chan, Henry↗
A Comparison of Exact and Model Exchange–Correlation Potentials for Molecules
Accurate exchange–correlation (XC) potentials for three-dimensional systems–via solution of the inverse density functional theory (DFT) problem–are now available to test the quality of DFT approximations. Herein, the exact XC potential for seven molecules–dihydrogen at four different bond-lengths, lithium hydride, water, and ortho-benzyne–are computed from full configuration interaction reference densities. These are compared to model XC potentials from nonlocal (B3LYP, HSE06, SCAN0, and M08-HX) and semilocal/local (SCAN, PBE, and PW92) XC functionals. Whereas for most systems, relative errors in the ground-state densities are O(10 –3 –10 –2 ) , the model XC potentials have much higher errors of O(10 –1 –10 0 ) . Among the model XC functionals, SCAN0 offers the best agreement with the exact XC potential, underlining the significance of satisfying exact conditions as well as including nonlocal effects in XC functionals. This work indicates that tests against the exact XC potential will provide a promising new direction for building more accurate XC functionals for DFT.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Floating potential of emitting surfaces in plasmas with respect to the space potential
The potential difference between a floating emitting surface and the plasma surrounding it has been described by several sheath models, including the space-charge-limited sheath, the electron sheath with high emission current, and the inverse sheath produced by charge-exchange ion trapping. Our measurements reveal that each of these models has its own regime of validity. We determine the potential of an emissive filament relative to the plasma potential, emphasizing variations in emitted current density and neutral particle density. The potential of a filament in a diffuse plasma is first shown to vanish, consistent with the electron sheath model and increasing electron emission. In a denser plasma with ample neutral pressure, the floating filament potential is positive, as predicted by a derived ion trapping condition. In conclusion, the filament floated negatively in a third plasma, where flowing ions and electrons and nonnegligible electric fields may have disrupted ion trapping. Depending on the regime chosen, emitting surfaces can float positively or negatively with respect to the plasma potential.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Local chiral potentials with Δ -intermediate states and the structure of light nuclei
In this paper, we present fully local versions of the minimally nonlocal nucleon-nucleon potentials constructed in a previous paper [Piarulli et al., Phys. Rev. C 91, 024003 (2015)], and use them in hypersperical harmonics and quantum Monte Carlo calculations of ground and excited states of $^3$H, $^3$He, $^4$He, $^6$He, and $^6$Li nuclei. The long-range part of these local potentials includes one- and two-pion exchange contributions without and with $\Delta$-isobars in the intermediate states up to order $Q^3$ ($Q$ denotes generically the low momentum scale) in the chiral expansion, while the short-range part consists of contact interactions up to order $Q^4$. The low-energy constants multiplying these contact interactions are fitted to the 2013 Granada database in two different ranges of laboratory energies, either 0–125 MeV or 0–200 MeV, and to the deuteron binding energy and $nn$ singlet scattering length. Fits to these data are performed for three models characterized by long- and short-range cutoffs, $R_{\rm L}$ and $R_{\rm S}$, respectively, ranging from $(R_{\rm L},R_{\rm S})=(1.2,0.8)$ fm down to $(0.8,0.6)$ fm. Finally, the long-range (short-range) cutoff regularizes the one- and two-pion exchange (contact) part of the potential.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Microscopic optical potentials derived from ab initio translationally invariant nonlocal one-body densities
Not Available
Gennari, Michael↗
The structure of PccH from Geobactersulfurreducens-a novel low reduction potential monoheme cytochrome essential for accepting electrons from an electrode
The structure of cytochrome c (GSU3274) designated as PccH from Geobactersulfurreducens was determined at a resolution of 2.0 angstrom. PccH is a small (15kDa) cytochrome containing one c-type heme, found to be essential for the growth of G.sulfurreducens with respect to accepting electrons from graphite electrodes poised at -300mV versus standard hydrogen electrode. with fumarate as the terminal electron acceptor. The structure of PccH is unique among the monoheme cytochromes described to date. The structural fold of PccH can be described as forming two lobes with the heme sandwiched in a cleft between the two lobes. In addition, PccH has a low reduction potential of -24mV at pH7, which is unusual for monoheme cytochromes. Based on difference in structure, together with sequence phylogenetic analysis, we propose that PccH can be regarded as a first characterized example of a new subclass of class I monoheme cytochromes. The low reduction potential of PccH may enable the protein to be redox active at the typically negative potential ranges encountered by G. sulfurreducens. Because PccH is predicted to be located in the periplasm of this bacterium, it could not be involved in the first step of accepting electrons from the electrode but is very likely involved in the downstream electron transport events in the periplasm.
Dantas, Joana↗
The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling
The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.
13 HYDRO ENERGY↗
Multireference configuration interaction calculations of the first six ionization potentials of the uranium atom
The first 6 ionization potentials (IPs) of the uranium atom have been calculated using multireference configuration interaction (MRCI+Q) with extrapolations to the complete basis set limit using new all-electron correlation consistent basis sets. The latter was carried out with the third-order Douglas-Kroll-Hess Hamiltonian. Correlation down through the 5s5p5d electrons has been taken into account, as well as contributions to the IPs due to the Lamb shift. Spin-orbit coupling contributions calculated at the 4-component Kramers restricted configuration interaction level, as well as the Gaunt term computed at the Dirac-Hartree-Fock level, were added to the best scalar relativistic results. The final ionization potentials are expected to be accurate to at least 5 kcal/mol (0.2 eV) and thus more reliable than the current experimental values of IP{sub 3} through IP{sub 6}.
74 ATOMIC AND MOLECULAR PHYSICS↗
GPU-accelerated Tersoff potentials for massively parallel Molecular Dynamics simulations
The Tersoff potential is one of the empirical many-body potentials that has been widely used in simulation studies at atomic scales. Unlike pair-wise potentials, the Tersoff potential involves three-body terms, which require much more arithmetic operations and data dependency. In this contribution, we have implemented the GPU-accelerated version of several variants of the Tersoff potential for LAMMPS, an open-source massively parallel Molecular Dynamics code. Compared to the existing MPI implementation in LAMMPS, the GPU implementation exhibits a better scalability and offers a speedup of 2.2X when run on 1000 compute nodes on the Titan supercomputer. On a single node, the speedup ranges from 2.0 to 8.0 times, depending on the number of atoms per GPU and hardware configurations. The most notable features of our GPU-accelerated version include its design for MPI/accelerator heterogeneous parallelism, its compatibility with other functionalities in LAMMPS, its ability to give deterministic results and to support both NVIDIA CUDA- and OpenCL-enabled accelerators. Our implementation is now part of the GPU package in LAMMPS and accessible for public use.
Computer Science↗
Potential Economic Values of Low-Vapor-Pressure Gasoline-Range Bio-Blendstocks: Property Estimation, and Blending Optimization
Adding non-traditional blendstocks derived from biomass into gasoline has gained renewed interest since it will reduce greenhouse gas emissions and potentially enhance fuel properties and boost engine efficiencies. A competitive bio-blendstock candidate should have either lower prices or superior properties or both relative to conventional gasoline blending components. Gasoline is a mixture of hundreds compounds specified by properties rather than compositions. The addition of bio-blendstocks, most likely oxygenates, will modify the property mixing rules due to the non-ideal interactions between polar and nonpolar components. This paper presents an equation-of-state-based model for predicting Reid vapor pressure, reduced order non-linear property blending models for computing key properties of final gasoline products, and a blending optimization approach to identify key economic drivers. This paper also evaluates the economic values of bio-blendstocks to petroleum refiners using those models. In additional to ethanol, six low-vapor-pressure bio-blendstock candidates were evaluated: i-propanol, n-propanol, i-butanol, diisobutylene, cyclopentanone, and a mixture of furans. Reid vapor pressure, distillation temperatures, and octane numbers were identified as the key economic drivers of adding bio-blendstock. The economic values of those bio-blendstocks range from 2.22 to 4.00 US dollar per gasoline gallon equivalent in 2013 to 2017 5-year averaged price basis. The bio-blendstocks with higher octane numbers may have higher economic values. The uncertainties in property predictions may lead to roughly 15% deviation in the economic value evaluation.
Jiang, Yuan↗
Intersections of Disadvantaged Communities and Renewable Energy Potential: Data Set and Analysis to Inform Equitable Investment Prioritization in the United States
Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. We sought to identify areas where disadvantaged community indicators and high generation potential from cost-effective renewable energy opportunities intersect and deployment could lead to economic development and job creation. This presentation will highlight several of our findings. This research and the associated county-level data set are intended to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantaged communities.
community energy planning↗
Calculations of the pH-Dependent Onset Potential for CO Electrooxidation on Au(111)
CO electrooxidation on the Au(111) surface exhibits an onset potential that depends strongly on the pH of the electrolyte. In alkaline solution, the onset potential for CO electrooxidation is found, experimentally, to be 0.5 V lower than in acidic media on the reversible hydrogen electrode scale. This phenomenon is explained here with density functional theory which is used to calculate adsorbate binding energies including the electric double layer of the Au(111)/aqueous interface. Our model consists of a charged Au(111) slab and implicit solvation for the electrolyte. The double reference method is used to determine the potential-dependent CO electrooxidation reaction energetics. A microkinetic model, based upon the calculated reaction energetics, confi rms the lower onset potential in alkaline media. Our results show that there are three factors contributing to the lower onset potential in base: (1) stronger CO adsorption, (2) attraction between adsorbed CO and OH - , and (3) the high concentration of OH - in base.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Reduction Potentials of [FeFe]-Hydrogenase Accessory Iron–Sulfur Clusters Provide Insights into the Energetics of Proton Reduction Catalysis
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solar (fuels), biofuels (including algae and bioma↗