Sample records for nattional energy modeling

  1. Sandia Energy - Systems Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Simulation Model Energy, Power & Water Simulation Model SunCity Model Water, Energy and Carbon Sequestration Model Gila Basin-Az Water Settlement Model Electrical Grid Storage...

  2. Sandia Energy - Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Research and Innovation (CIRI), Computational Modeling & Simulation, Energy, Energy Storage, Energy Storage Systems, Facilities, HITEC, Infrastructure Security,...

  3. Colorado: Energy Modeling Products Support Energy Efficiency...

    Energy Savers [EERE]

    Colorado: Energy Modeling Products Support Energy Efficiency Projects Colorado: Energy Modeling Products Support Energy Efficiency Projects May 1, 2014 - 11:04am Addthis Xcel...

  4. Energy-consumption modelling

    SciTech Connect (OSTI)

    Reiter, E.R.

    1980-01-01T23:59:59.000Z

    A highly sophisticated and accurate approach is described to compute on an hourly or daily basis the energy consumption for space heating by individual buildings, urban sectors, and whole cities. The need for models and specifically weather-sensitive models, composite models, and space-heating models are discussed. Development of the Colorado State University Model, based on heat-transfer equations and on a heuristic, adaptive, self-organizing computation learning approach, is described. Results of modeling energy consumption by the city of Minneapolis and Cheyenne are given. Some data on energy consumption in individual buildings are included.

  5. Sandia Energy - Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sandia Will Host PV Bankability Workshop at Solar Power International (SPI) 2013 Computational Modeling & Simulation, Distribution Grid Integration, Energy, Facilities, Grid...

  6. Hoechst Celanese Energy Model 

    E-Print Network [OSTI]

    Fitzpatrick, B. A.; Gangadhar, K.

    1992-01-01T23:59:59.000Z

    operating areas or "units" in HOCEM, though some of the units are as simple as cooling towers and others as complex as production areas. A VAX-based spreadsheet software program, Graphic Outlook, from stone Mountain Computing was selected as the model... an energy model which is modular in structure and granular in function. It was also our intention to develop the model on a VAX based software platform so that an on-line plant information system could be linked to the model. Graphic Outlook, a VAX...

  7. Sandia Energy - Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMary CrawfordMesaModeling

  8. Model Building Energy Code

    Broader source: Energy.gov [DOE]

    ''Much of the information presented in this summary is drawn from the U.S. Department of Energy’s (DOE) Building Energy Codes Program and the Building Codes Assistance Project (BCAP). For more...

  9. Inventory of state energy models

    SciTech Connect (OSTI)

    Melcher, A.G.; Gist, R.L.; Underwood, R.G.; Weber, J.C.

    1980-03-31T23:59:59.000Z

    These models address a variety of purposes, such as supply or demand of energy or of certain types of energy, emergency management of energy, conservation in end uses of energy, and economic factors. Fifty-one models are briefly described as to: purpose; energy system; applications;status; validation; outputs by sector, energy type, economic and physical units, geographic area, and time frame; structure and modeling techniques; submodels; working assumptions; inputs; data sources; related models; costs; references; and contacts. Discussions in the report include: project purposes and methods of research, state energy modeling in general, model types and terminology, and Federal legislation to which state modeling is relevant. Also, a state-by-state listing of modeling efforts is provided and other model inventories are identified. The report includes a brief encylopedia of terms used in energy models. It is assumed that many readers of the report will not be experienced in the technical aspects of modeling. The project was accomplished by telephone conversations and document review by a team from the Colorado School of Mines Research Institute and the faculty of the Colorado School of Mines. A Technical Committee (listed in the report) provided advice during the course of the project.

  10. Sandia Energy - Sandia, NREL Release Wave Energy Converter Modeling...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sandia, NREL Release Wave Energy Converter Modeling and Simulation Code: WEC-Sim Home Renewable Energy Energy Water Power Partnership News News & Events Computational Modeling &...

  11. Sandia Energy - Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Simulations Guide Nanowire Research Read More Permalink Gallery Sandia Study Shows Large LNG Fires Hotter but Smaller Than Expected Analysis, Energy Assurance, Infrastructure...

  12. Regions in Energy Market Models

    SciTech Connect (OSTI)

    Short, W.

    2007-02-01T23:59:59.000Z

    This report explores the different options for spatial resolution of an energy market model--and the advantages and disadvantages of models with fine spatial resolution. It examines different options for capturing spatial variations, considers the tradeoffs between them, and presents a few examples from one particular model that has been run at different levels of spatial resolution.

  13. Autotune Building Energy Models

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny: The FutureComments fromof Energy Automation WorldofAutotune Building Energy

  14. Modelling dark energy 

    E-Print Network [OSTI]

    Jackson, Brendan Marc

    2011-11-23T23:59:59.000Z

    One of the most pressing, modern cosmological mysteries is the cause of the accelerated expansion of the universe. The energy density required to cause this large scale opposition to gravity is known to be both far in ...

  15. Sandia Energy - Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMary CrawfordMesa

  16. Sandia Energy » Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementingnpitche Home About npitche This

  17. National Energy Modeling System (NEMS)

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    The National Energy Modeling System (NEMS) is a computer-based, energy-economy modeling system of U.S. through 2030. NEMS projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomic and financial factors, world energy markets, resource availability and costs, behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics. NEMS was designed and implemented by the Energy Information Administration (EIA) of the U.S. Department of Energy (DOE). NEMS can be used to analyze the effects of existing and proposed government laws and regulations related to energy production and use; the potential impact of new and advanced energy production, conversion, and consumption technologies; the impact and cost of greenhouse gas control; the impact of increased use of renewable energy sources; and the potential savings from increased efficiency of energy use; and the impact of regulations on the use of alternative or reformulated fuels. NEMS has also been used for a number of special analyses at the request of the Administration, U.S. Congress, other offices of DOE and other government agencies, who specify the scenarios and assumptions for the analysis. Modules allow analyses to be conducted in energy topic areas such as residential demand, industrial demand, electricity market, oil and gas supply, renewable fuels, etc.

  18. Building Energy Modeling Library

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny: Theof Energy Future ofHydronicBuildingDepartmentDavidDepartment ofAmir

  19. Sandia Energy - Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMary CrawfordMesa delMikeClimate

  20. Sandia Energy - Phenomenological Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution Grid IntegrationOffshore WindPartnershipPhenomenological

  1. Sandia Energy - Systems Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution GridDocumentsInstitute ofSitingStaffSunshine toSystems

  2. Sandia Energy - Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution Grid Integration Permalink Gallery Mesa delMission

  3. Hoechst Celanese Energy Model

    E-Print Network [OSTI]

    Fitzpatrick, B. A.; Gangadhar, K.

    operating areas or "units" in HOCEM, though some of the units are as simple as cooling towers and others as complex as production areas. A VAX-based spreadsheet software program, Graphic Outlook, from stone Mountain Computing was selected as the model... heuristic guidelines. Finally, HOCEM optimizes on the actual costs of operation. STANDARDS DEVELOPMENT As was mentioned above, each of -50 operating areas forecasts its utilities demand. The general format for these "unit spreadsheets...

  4. COMMUTER Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual Siteof EnergyInnovation inOpen Energy InformationSeries Jump to:CMRCOMMUTER Model Jump

  5. 100% DD Energy Model Update

    SciTech Connect (OSTI)

    None

    2011-06-30T23:59:59.000Z

    The Miami Science Museum energy model has been used during DD to test the buildingâ??s potential for energy savings as measured by ASHRAE 90.1-2007 Appendix G. This standard compares the designed buildingâ??s yearly energy cost with that of a code-compliant building. The building is currently on track show 20% or better improvement over the ASHRAE 90.1-2007 Appendix G baseline; this performance would ensure minimum compliance with both LEED 2.2 and current Florida Energy Code, which both reference a less strict version of ASHRAE 90.1. In addition to being an exercise in energy code compliance, the energy model has been used as a design tool to show the relative performance benefit of individual energy conservation measures (ECMs). These ECMs are areas where the design team has improved upon code-minimum design paths to improve the energy performance of the building. By adding ECMs one a time to a code-compliant baseline building, the current analysis identifies which ECMs are most effective in helping the building meet its energy performance goals.

  6. EnergyPlus Model Appendix G -EnergyPlus Model

    E-Print Network [OSTI]

    Home B) C_ela 55.66 51.51 ELA (in.2) 38.83 35.93 The heating, ventilation, and air conditioning (HVAC) system is modeled as a single-speed heat pump with a Seasonal Energy Efficiency Ratio (SEER) of 13 where internal gains, heat pump operation mode and zone thermostat set-points are varied. Two sets

  7. Staffing Model | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directed offOCHCO Overview OCHCO OverviewRepositoryManagement |SolarSpecialStaffing Model Staffing Model

  8. Autotune E+ Building Energy Models

    SciTech Connect (OSTI)

    New, Joshua Ryan [ORNL; Sanyal, Jibonananda [ORNL; Bhandari, Mahabir S [ORNL; Shrestha, Som S [ORNL

    2012-01-01T23:59:59.000Z

    This paper introduces a novel Autotune methodology under development for calibrating building energy models (BEM). It is aimed at developing an automated BEM tuning methodology that enables models to reproduce measured data such as utility bills, sub-meter, and/or sensor data accurately and robustly by selecting best-match E+ input parameters in a systematic, automated, and repeatable fashion. The approach is applicable to a building retrofit scenario and aims to quantify the trade-offs between tuning accuracy and the minimal amount of ground truth data required to calibrate the model. Autotune will use a suite of machine-learning algorithms developed and run on supercomputers to generate calibration functions. Specifically, the project will begin with a de-tuned model and then perform Monte Carlo simulations on the model by perturbing the uncertain parameters within permitted ranges. Machine learning algorithms will then extract minimal perturbation combinations that result in modeled results that most closely track sensor data. A large database of parametric EnergyPlus (E+) simulations has been made publicly available. Autotune is currently being applied to a heavily instrumented residential building as well as three light commercial buildings in which a de-tuned model is autotuned using faux sensor data from the corresponding target E+ model.

  9. Material models of dark energy

    E-Print Network [OSTI]

    Jonathan A. Pearson

    2014-09-16T23:59:59.000Z

    We review and develop a new class of "dark energy" models, in which the relativistic theory of solids is used to construct material models of dark energy. These are models which include the effects of a continuous medium with well defined physical properties at the level of linearized perturbations. The formalism is constructed for a medium with arbitrary symmetry, and then specialised to isotropic media (which will be the case of interest for the majority of cosmological applications). We develop the theory of relativistic isotropic viscoelastic media whilst keeping in mind that we ultimately want to observationally constrain the allowed properties of the material model. We do this by obtaining the viscoelastic equations of state for perturbations (the entropy and anisotropic stress), as well as identifying the consistent corner of the theory which has constant equation of state parameter $\\dot{w}=0$. We also connect to the non-relativistic theory of solids, by identifying the two quadratic invariants that are needed to construct the energy-momentum tensor, namely the Rayleigh dissipation function and Lagrangian for perturbations. Finally, we develop the notion that the viscoelastic behavior of the medium can be thought of as a non-minimally coupled massive gravity theory. This also provides a tool-kit for constructing consistent generalizations of coupled dark energy theories.

  10. Sandia Energy - Models & Tools

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItemResearch > TheNuclear PressLaboratory Fellows Jerry SimmonsModels & Tools

  11. Sandia Energy - Global Climate Models

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Scienceand RequirementsCoatingsUltra-High-Voltage SiliconEnergyFailureGlobal Climate Models Home

  12. Conceptual Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:PowerCER.png ElColumbia,2005) |Use ofInformationConceptual Model

  13. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMary CrawfordMesaModelingClimate

  14. Renewable Energy and Efficiency Modeling Analysis Partnership: An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    E-Print Network [OSTI]

    Blair, N.

    2010-01-01T23:59:59.000Z

    photovoltaics renewable energy renewable energy certificate Regional Energy Deployment System model Renewable Energy and Efficiency

  15. State Energy Program: Kentucky Implementation Model Resources

    Broader source: Energy.gov [DOE]

    Below are resources associated with the U.S. Department of Energy's Weatherization and Intergovernmental Programs Office State Energy Program Kentucky Implementation Model.

  16. Agegraphic Chaplygin gas model of dark energy

    E-Print Network [OSTI]

    Ahmad Sheykhi

    2010-02-07T23:59:59.000Z

    We establish a connection between the agegraphic models of dark energy and Chaplygin gas energy density in non-flat universe. We reconstruct the potential of the agegraphic scalar field as well as the dynamics of the scalar field according to the evolution of the agegraphic dark energy. We also extend our study to the interacting agegraphic generalized Chaplygin gas dark energy model.

  17. Evaluating Energy Efficiency Policies with Energy-Economy Models

    SciTech Connect (OSTI)

    Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A.

    2010-08-01T23:59:59.000Z

    The growing complexities of energy systems, environmental problems and technology markets are driving and testing most energy-economy models to their limits. To further advance bottom-up models from a multidisciplinary energy efficiency policy evaluation perspective, we review and critically analyse bottom-up energy-economy models and corresponding evaluation studies on energy efficiency policies to induce technological change. We use the household sector as a case study. Our analysis focuses on decision frameworks for technology choice, type of evaluation being carried out, treatment of market and behavioural failures, evaluated policy instruments, and key determinants used to mimic policy instruments. Although the review confirms criticism related to energy-economy models (e.g. unrealistic representation of decision-making by consumers when choosing technologies), they provide valuable guidance for policy evaluation related to energy efficiency. Different areas to further advance models remain open, particularly related to modelling issues, techno-economic and environmental aspects, behavioural determinants, and policy considerations.

  18. Energy Transition Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluating A PotentialJump to:Emminol JumpEnergy SystemSystems Network ESN

  19. Building Energy Modeling | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directed offOCHCO OverviewAttachments EnergyFebruary 29 - MarchCodes Resources Building CodesofDepartment

  20. Webcast of the Renewable Energy Competency Model | Department...

    Broader source: Energy.gov (indexed) [DOE]

    Renewable Energy Competency Model Webcast of the Renewable Energy Competency Model Addthis Description The Department of Energy held a webcast titled ""Renewable Energy Competency...

  1. Models and Tools for Evaluating Energy Efficiency and Renewable...

    Energy Savers [EERE]

    Models and Tools for Evaluating Energy Efficiency and Renewable Energy Programs Webinar Models and Tools for Evaluating Energy Efficiency and Renewable Energy Programs Webinar May...

  2. Multiscale modeling of spatially variable water and energy balance processes

    E-Print Network [OSTI]

    Famiglietti, J. S; Wood, E. F

    1994-01-01T23:59:59.000Z

    MULTISCALE WATER AND ENERGY BALANCE MODELING Wood, E. F. ,spatially variable water and energy balance processes J. S.modeling. Water and energy balance models are developed at

  3. Energy Modeling Software | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    and Specification for Parking Lots Lighten Energy Load The GE GeoSpring(tm) Electric Heat Pump Water Heater is readily integrated into new and existing home designs. Taking up the...

  4. Modeling the Energy Efficiency of Heterogeneous Clusters

    E-Print Network [OSTI]

    Teo, Yong-Meng

    are an alternative for energy-efficient clusters [18], [20], [23]. On the contrary, other researchersModeling the Energy Efficiency of Heterogeneous Clusters Lavanya Ramapantulu, Bogdan Marius Tudor analyze the energy efficiency of mixing high-performance and low-power nodes in a cluster. Using a model

  5. Decision Models for Bulk Energy Transportation Networks

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    ... ... Primary Energy Supplies Gas Coal Railroad, Barge ... ... Storage & Transportation Systems Energy Transportation Networks #12;Structural Model: Energy Flows GAS COAL ELECTRIC Case A: 2002, and the amount of electricity generated #12;Structural Model: Effects of Katrina Average natural gas nodal price

  6. Renewable Energy and Efficiency Modeling Analysis Partnership: An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    E-Print Network [OSTI]

    Blair, N.

    2010-01-01T23:59:59.000Z

    and Renewable Energy (Office of) Energy Information Administration Energy Modeling Forum Environmental Protection Agency Federal

  7. Comparing holographic dark energy models with statefinder

    E-Print Network [OSTI]

    Jing-Lei Cui; Jing-Fei Zhang

    2014-04-20T23:59:59.000Z

    We apply the statefinder diagnostic to the holographic dark energy models, including the original holographic dark energy (HDE) model, the new holographic dark energy model, the new agegraphic dark energy (NADE) model, and the Ricci dark energy model. In the low-redshift region the holographic dark energy models are degenerate with each other and with the $\\Lambda$CDM model in the $H(z)$ and $q(z)$ evolutions. In particular, the HDE model is highly degenerate with the $\\Lambda$CDM model, and in the HDE model the cases with different parameter values are also in strong degeneracy. Since the observational data are mainly within the low-redshift region, it is very important to break this low-redshift degeneracy in the $H(z)$ and $q(z)$ diagnostics by using some quantities with higher order derivatives of the scale factor. It is shown that the statefinder diagnostic $r(z)$ is very useful in breaking the low-redshift degeneracies. By employing the statefinder diagnostic the holographic dark energy models can be differentiated efficiently in the low-redshift region. The degeneracy between the holographic dark energy models and the $\\Lambda$CDM model can also be broken by this method. Especially for the HDE model, all the previous strong degeneracies appearing in the $H(z)$ and $q(z)$ diagnostics are broken effectively. But for the NADE model, the degeneracy between the cases with different parameter values cannot be broken, even though the statefinder diagnostic is used. A direct comparison of the holographic dark energy models in the $r$--$s$ plane is also made, in which the separations between the models (including the $\\Lambda$CDM model) can be directly measured in the light of the current values $\\{r_0,s_0\\}$ of the models.

  8. Energy Modeling Community Resources | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page onYouTube YouTube Note: Since the YouTube|6721 FederalTexas Energy Incentive Programs,EnergyAugust 10, 2011 2:30Commercial

  9. Building Energy Modeling Projects | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33Frequently20,000 RussianBy:WhetherNovember 13,National RenewableEnergyView the Building

  10. Energy Flow Models for the Steel Industry

    E-Print Network [OSTI]

    Hyman, B.; Andersen, J. P.

    Energy patterns in the U. S. steel industry are examined using several models. First is an end-use model based on data in the 1994 Manufacturing Energy Consumption Survey (MECS). Then a seven-step process model is presented and material flow through...

  11. Energy Flow Models for the Steel Industry 

    E-Print Network [OSTI]

    Hyman, B.; Andersen, J. P.

    1998-01-01T23:59:59.000Z

    each step is calibrated against Commerce Dept. data. Third, a detailed energy flow model is presented for coke ovens and blast furnaces, two very energy-intensive steps in our seven step model of steelmaking. This process-step model is calibrated...

  12. The Quintom Model of Dark Energy

    E-Print Network [OSTI]

    Bo Feng

    2006-02-07T23:59:59.000Z

    In this paper I give a brief review on the recently proposed new scenario of dark energy model dubbed $Quintom$. Quintom describes the dynamical dark energy models where the equation of state getting across the cosmological constant boundary during evolutions. I discuss some aspects on the quintom model buildings and the observational consequences.

  13. NUCLEAR ENERGY SYSTEM COST MODELING

    SciTech Connect (OSTI)

    Francesco Ganda; Brent Dixon

    2012-09-01T23:59:59.000Z

    The U.S. Department of Energy’s Fuel Cycle Technologies (FCT) Program is preparing to perform an evaluation of the full range of possible Nuclear Energy Systems (NES) in 2013. These include all practical combinations of fuels and transmuters (reactors and sub-critical systems) in single and multi-tier combinations of burners and breeders with no, partial, and full recycle. As part of this evaluation, Levelized Cost of Electricity at Equilibrium (LCAE) ranges for each representative system will be calculated. To facilitate the cost analyses, the 2009 Advanced Fuel Cycle Cost Basis Report is being amended to provide up-to-date cost data for each step in the fuel cycle, and a new analysis tool, NE-COST, has been developed. This paper explains the innovative “Island” approach used by NE-COST to streamline and simplify the economic analysis effort and provides examples of LCAE costs generated. The Island approach treats each transmuter (or target burner) and the associated fuel cycle facilities as a separate analysis module, allowing reuse of modules that appear frequently in the NES options list. For example, a number of options to be screened will include a once-through uranium oxide (UOX) fueled light water reactor (LWR). The UOX LWR may be standalone, or may be the first stage in a multi-stage system. Using the Island approach, the UOX LWR only needs to be modeled once and the module can then be reused on subsequent fuel cycles. NE-COST models the unit operations and life cycle costs associated with each step of the fuel cycle on each island. This includes three front-end options for supplying feedstock to fuel fabrication (mining/enrichment, reprocessing of used fuel from another island, and/or reprocessing of this island’s used fuel), along with the transmuter and back-end storage/disposal. Results of each island are combined based on the fractional energy generated by each islands in an equilibrium system. The cost analyses use the probability distributions of key parameters and employs Monte Carlo sampling to arrive at an island’s cost probability density function (PDF). When comparing two NES to determine delta cost, strongly correlated parameters can be cancelled out so that only the differences in the systems contribute to the relative cost PDFs. For example, one comparative analysis presented in the paper is a single stage LWR-UOX system versus a two-stage LWR-UOX to LWR-MOX system. In this case, the first stage of both systems is the same (but with different fractional energy generation), while the second stage of the UOX to MOX system uses the same type transmuter but the fuel type and feedstock sources are different. In this case, the cost difference between systems is driven by only the fuel cycle differences of the MOX stage.

  14. Modeling Energy Demand Aggregators for Residential Consumers

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Modeling Energy Demand Aggregators for Residential Consumers G. Di Bella, L. Giarr`e, M. Ippolito, A. Jean-Marie, G. Neglia and I. Tinnirello § January 2, 2014 Abstract Energy demand aggregators are new actors in the energy scenario: they gather a group of energy consumers and implement a demand

  15. Directory of Energy Information Administration models 1996

    SciTech Connect (OSTI)

    NONE

    1996-07-01T23:59:59.000Z

    This directory revises and updates the Directory of Energy Information Administration Models 1995, DOE/EIA-0293(95), Energy Information Administration (EIA), U.S. Department of Energy, July 1995. Four models have been deleted in this directory as they are no longer being used: (1) Market Penetration Model for Ground-Water Heat Pump Systems (MPGWHP); (2) Market Penetration Model for Residential Rooftop PV Systems (MPRESPV-PC); (3) Market Penetration Model for Active and Passive Solar Technologies (MPSOLARPC); and (4) Revenue Requirements Modeling System (RRMS).

  16. Directory of Energy Information Administration Models 1994

    SciTech Connect (OSTI)

    Not Available

    1994-07-01T23:59:59.000Z

    This directory revises and updates the 1993 directory and includes 15 models of the National Energy Modeling System (NEMS). Three other new models in use by the Energy Information Administration (EIA) have also been included: the Motor Gasoline Market Model (MGMM), Distillate Market Model (DMM), and the Propane Market Model (PPMM). This directory contains descriptions about each model, including title, acronym, purpose, followed by more detailed information on characteristics, uses and requirements. Sources for additional information are identified. Included in this directory are 37 EIA models active as of February 1, 1994.

  17. Interacting agegraphic tachyon model of dark energy

    E-Print Network [OSTI]

    A. Sheykhi

    2009-11-16T23:59:59.000Z

    Scalar-field dark energy models like tachyon are often regarded as an effective description of an underlying theory of dark energy. In this Letter, we implement the interacting agegraphic dark energy models with tachyon field. We demonstrate that the interacting agegraphic evolution of the universe can be described completely by a single tachyon scalar field. We thus reconstruct the potential as well as the dynamics of the tachyon field according to the evolutionary behavior of interacting agegraphic dark energy.

  18. Hybrid Energy System Modeling in Modelica

    SciTech Connect (OSTI)

    William R. Binder; Christiaan J. J. Paredis; Humberto E. Garcia

    2014-03-01T23:59:59.000Z

    In this paper, a Hybrid Energy System (HES) configuration is modeled in Modelica. Hybrid Energy Systems (HES) have as their defining characteristic the use of one or more energy inputs, combined with the potential for multiple energy outputs. Compared to traditional energy systems, HES provide additional operational flexibility so that high variability in both energy production and consumption levels can be absorbed more effectively. This is particularly important when including renewable energy sources, whose output levels are inherently variable, determined by nature. The specific HES configuration modeled in this paper include two energy inputs: a nuclear plant, and a series of wind turbines. In addition, the system produces two energy outputs: electricity and synthetic fuel. The models are verified through simulations of the individual components, and the system as a whole. The simulations are performed for a range of component sizes, operating conditions, and control schemes.

  19. Dark Energy and Dark Matter Models

    E-Print Network [OSTI]

    Burra G. Sidharth

    2015-01-07T23:59:59.000Z

    We revisit the problems of dark energy and dark matter and several models designed to explain them, in the light of some latest findings.

  20. Renewable Energy and Efficiency Modeling Analysis Partnership: An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    E-Print Network [OSTI]

    Blair, N.

    2010-01-01T23:59:59.000Z

    COVERED (From - To) Renewable Energy and Efficiency Modelingphotovoltaics renewable energy renewable energy certificatecoordinated by the Renewable Energy and Efficiency Modeling

  1. About Building Energy Modeling | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33Frequently20,000 RussianBy:Whether you're a16-17, 201529, 2015 8:00AM EDT to September

  2. Energy Modeling Community Resources | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny:RevisedAdvisoryStandard |in STEMEnergyI.ofTrack 1should theJulyMayCommercial

  3. Modeling Renewable Energy Readiness: The UAE Context

    E-Print Network [OSTI]

    Choucri, Nazli

    Modeling technology policy is becoming an increasingly important capability to steer states and societies toward sustainability. This paper presents a simulation-modeling approach to evaluate renewable energy readiness, ...

  4. The China-in-Global Energy Model

    E-Print Network [OSTI]

    Qi, T.

    The China-in-Global Energy Model (C-GEM) is a global Computable General Equilibrium (CGE) model that captures the interaction of production, consumption and trade among multiple global regions and sectors – including five ...

  5. Energy modeling IV--planning for energy disruptions

    SciTech Connect (OSTI)

    Feingold, B.W.; Courtney, L. (eds.)

    1982-01-01T23:59:59.000Z

    On May 10-12, 1982, the Institute of Gas Technology held the symposium ''Energy Modeling IV: Planning for Energy Disruptions,'' the fourth in a series of energy modeling symposia. Although all four of the energy modeling symposia presented by IGT emphasized new modeling techniques, each had a specific theme. This symposium addressed the role of modeling in dealing with the problems of disruptions in the supply and price of energy. The symposium brought together modelers and planners from federal and state governmental agencies, utilities, management and consulting organizations, and academic institutions. The participants discussed the complex planning problems presented by both gradual and sudden fluctuations in energy supply or price, whether caused by political, physical, economic, or natural events, and the resultant threats to the stability of businesses and the security of nations. A separate abstract was pepared for each paper for the Energy Data Base (EDB); on paper is included in Energy Research Abstracts (ERA) and 22 for Energy Abstracts for Policy Analysis (EAPA).

  6. Energy Band Model Based on Effective Mass

    E-Print Network [OSTI]

    Viktor Ariel

    2012-09-06T23:59:59.000Z

    In this work, we demonstrate an alternative method of deriving an isotropic energy band model using a one-dimensional definition of the effective mass and experimentally observed dependence of mass on energy. We extend the effective mass definition to anti-particles and particles with zero rest mass. We assume an often observed linear dependence of mass on energy and derive a generalized non-parabolic energy-momentum relation. The resulting non-parabolicity leads to velocity saturation at high particle energies. We apply the energy band model to free relativistic particles and carriers in solid state materials and obtain commonly used dispersion relations and experimentally confirmed effective masses. We apply the model to zero rest mass particles in graphene and propose using the effective mass for photons. Therefore, it appears that the new energy band model based on the effective mass can be applied to relativistic particles and carriers in solid state materials.

  7. Modelling energy efficiency for computation

    E-Print Network [OSTI]

    Reams, Charles

    2012-11-13T23:59:59.000Z

    In the last decade, efficient use of energy has become a topic of global significance, touching almost every area of modern life, including computing. From mobile to desktop to server, energy efficiency concerns are now ubiquitous. However...

  8. Model Acquisition Language for Energy-Efficient Product Contracts...

    Broader source: Energy.gov (indexed) [DOE]

    Technologies Energy-Efficient Products Model Acquisition Language for Energy-Efficient Product Contracts Model Acquisition Language for Energy-Efficient Product Contracts...

  9. Scripted Building Energy Modeling and Analysis (Presentation)

    SciTech Connect (OSTI)

    Macumber, D.

    2012-10-01T23:59:59.000Z

    Building energy analysis is often time-intensive, error-prone, and non-reproducible. Entire energy analyses can be scripted end-to-end using the OpenStudio Ruby API. Common tasks within an analysis can be automated using OpenStudio Measures. Graphical user interfaces (GUI's) and component libraries reduce time, decrease errors, and improve repeatability in energy modeling.

  10. Towards increased policy relevance in energy modeling

    SciTech Connect (OSTI)

    Worrell, Ernst; Ramesohl, Stephan; Boyd, Gale

    2003-07-29T23:59:59.000Z

    Historically, most energy models were reasonably equipped to assess the impact of a subsidy or change in taxation, but are often insufficient to assess the impact of more innovative policy instruments. We evaluate the models used to assess future energy use, focusing on industrial energy use. We explore approaches to engineering-economic analysis that could help improve the realism and policy relevance of engineering-economic modeling frameworks. We also explore solutions to strengthen the policy usefulness of engineering-economic analysis that can be built from a framework of multi-disciplinary cooperation. We focus on the so-called ''engineering-economic'' (or ''bottom-up'') models, as they include the amount of detail that is commonly needed to model policy scenarios. We identify research priorities for the modeling framework, technology representation in models, policy evaluation and modeling of decision-making behavior.

  11. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMaryEnergy Permalink Gallery

  12. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMaryEnergy Permalink

  13. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMaryEnergy

  14. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMaryEnergyCapabilities Permalink

  15. Sandia Energy - Reference Model Documents

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    (2011). The Contribution of Environmental Siting and Permitting Requirements to the Cost of Energy for Marine and Hydrokinetic Devices. M. Previsic (2011). Economic Methodology...

  16. Analytical Modeling | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia: Energy Resources JumpAnaconda, Montana: Energy Jump to: navigation,

  17. Modeling Techniques | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville,Missoula, Montana: EnergyAnalysis of Energy Demand (MAED-2)

  18. Modeling of Customer Adoption of Distributed Energy Resources

    E-Print Network [OSTI]

    Modeling of Customer Adoption of Distributed Energy Resources CALIFORNIA ENERGY COMMISSION Reliability Technology Solutions Modeling of Customer Adoption of Distributed Energy Resources Prepared the consequences. #12;#12;Modeling of Customer Adoption of Distributed Energy Resources iii Table of Contents

  19. Energy Modeling for the Artisan Food Center

    SciTech Connect (OSTI)

    Goel, Supriya

    2013-05-01T23:59:59.000Z

    The Artisan Food Center is a 6912 sq.ft food processing plant located in Dayton, Washington. PNNL was contacted by Strecker Engineering to assist with the building’s energy analysis as a part of the project’s U.S. Green Building Council’s Leadership in Energy and Environmental Design (LEED) submittal requirements. The project is aiming for LEED Silver certification, one of the prerequisites to which is a whole building energy model to demonstrate compliance with American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) 90.1 2007 Appendix G, Performance Rating Method. The building incorporates a number of energy efficiency measures as part of its design and the energy analysis aimed at providing Strecker Engineering with the know-how of developing an energy model for the project as well as an estimate of energy savings of the proposed design over the baseline design, which could be used to document points in the LEED documentation. This report documents the ASHRAE 90.1 2007 baseline model design, the proposed model design, the modeling assumptions and procedures as well as the energy savings results in order to inform the Strecker Engineering team on a possible whole building energy model.

  20. Integrated energy and water conservation modeling

    SciTech Connect (OSTI)

    Monsabert, S. de; Liner, B.L. [George Mason Univ., Fairfax, VA (United States)

    1998-04-01T23:59:59.000Z

    Under the Energy Policy Act of 1992, the Federal Energy Management Program (FEMP) is required to provide federal facility managers with a clear determination of the impact of water conservation practices on energy consumption. This paper introduces the WATERGY model, which is a spreadsheet model to analyze total energy savings associated with water conservation efforts. The contribution of this effort is the development of a synergistic model based on engineering algorithms as opposed to lumped parameter estimates. The model explicitly details the relationships between direct and indirect water and energy savings. Irrigation, plumbing fixture, appliance, and boiler blowdown savings comprise the direct water component of the model. Reduction in leakage and unaccounted-for water in the distribution system are calculated as indirect water savings. Direct energy savings are calculated for hot water production. Indirect energy savings associated with distribution and collection, electric line losses, and unaccounted-for gas are determined by the model. Data sources, algorithms, and engineering assumptions used in the development of the model are detailed. The model capabilities are demonstrated for a hypothetical federal facility.

  1. DOD low energy model installation program

    SciTech Connect (OSTI)

    Fournier, D.F. Jr.

    1994-12-31T23:59:59.000Z

    The Model Low Energy Installation Program is a demonstration of an installation-wide, comprehensive energy conservation program that meets the Department of Defense (DOD) energy management goals of reducing energy usage and costs by at least 20%. It employs the required strategies for meeting these goals, quantifies the environmental compliance benefits resulting from energy conservation and serves as a prototype for DOD wide application. This project will develop both analysis tools and implementation procedures as well as demonstrate the effectiveness of a comprehensive, coordinated energy conservation program based on state-of-the-art technologies.

  2. Directory of Energy Information Administration Models 1993

    SciTech Connect (OSTI)

    Not Available

    1993-07-06T23:59:59.000Z

    This directory contains descriptions about each model, including the title, acronym, purpose, followed by more detailed information on characteristics, uses, and requirements. Sources for additional information are identified. Included in this directory are 35 EIA models active as of May 1, 1993. Models that run on personal computers are identified by ``PC`` as part of the acronym. EIA is developing new models, a National Energy Modeling System (NEMS), and is making changes to existing models to include new technologies, environmental issues, conservation, and renewables, as well as extend forecast horizon. Other parts of the Department are involved in this modeling effort. A fully operational model is planned which will integrate completed segments of NEMS for its first official application--preparation of EIA`s Annual Energy Outlook 1994. Abstracts for the new models will be included in next year`s version of this directory.

  3. Directory of energy information administration models 1995

    SciTech Connect (OSTI)

    NONE

    1995-07-13T23:59:59.000Z

    This updated directory has been published annually; after this issue, it will be published only biennially. The Disruption Impact Simulator Model in use by EIA is included. Model descriptions have been updated according to revised documentation approved during the past year. This directory contains descriptions about each model, including title, acronym, purpose, followed by more detailed information on characteristics, uses, and requirements. Sources for additional information are identified. Included are 37 EIA models active as of February 1, 1995. The first group is the National Energy Modeling System (NEMS) models. The second group is all other EIA models that are not part of NEMS. Appendix A identifies major EIA modeling systems and the models within these systems. Appendix B is a summary of the `Annual Energy Outlook` Forecasting System.

  4. Modeling of Uncertainty in Wind Energy Forecast

    E-Print Network [OSTI]

    regression and splines are combined to model the prediction error from Tunø Knob wind power plant. This data of the thesis is quantile regression and splines in the context of wind power modeling. Lyngby, February 2006Modeling of Uncertainty in Wind Energy Forecast Jan Kloppenborg Møller Kongens Lyngby 2006 IMM-2006

  5. Dark Energy: Observational Evidence and Theoretical Models

    E-Print Network [OSTI]

    Novosyadlyj, B; Shtanov, Yu; Zhuk, A

    2015-01-01T23:59:59.000Z

    The book elucidates the current state of the dark energy problem and presents the results of the authors, who work in this area. It describes the observational evidence for the existence of dark energy, the methods and results of constraining of its parameters, modeling of dark energy by scalar fields, the space-times with extra spatial dimensions, especially Kaluza---Klein models, the braneworld models with a single extra dimension as well as the problems of positive definition of gravitational energy in General Relativity, energy conditions and consequences of their violation in the presence of dark energy. This monograph is intended for science professionals, educators and graduate students, specializing in general relativity, cosmology, field theory and particle physics.

  6. Sandia Energy - Global Climate Models

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItemResearch > TheNuclear Press ReleasesInAppliedEnergyGeothermal Home

  7. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMaryEnergy PermalinkClimate

  8. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757Kelley Ruehl Home KelleyMary

  9. Sandia Energy - Wind Generator Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementing Nonlinear757KelleyEffectsonSandia'sEventNotECWillie

  10. Building Energy Modeling (BEM) Overview

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny: Theof Energy Future ofHydronicBuildingDepartmentDavidDepartment of

  11. Sandia Energy - Reference Model Documents

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution GridDocuments Home Stationary Power Energy Conversion

  12. JEDI Models | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOf Kilauea Volcano, Hawaii | Wind Farm Jump to: navigation, search7JEDI

  13. Numerical Modeling | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRoseConcerns Jumpsource HistoryFractures below a Borehole Floor, A Jump

  14. Model Wind Energy Facility Ordinance

    Broader source: Energy.gov [DOE]

    Note: This model ordinance was designed to provide guidance to local governments that wish to develop their own siting rules for wind turbines. While it was developed as part of a cooperative...

  15. Model Ordinance for Renewable Energy Projects

    Broader source: Energy.gov [DOE]

    '''''NOTE: This model ordinance was designed to provide guidance to local governments that wish to develop their own siting rules for renewable energy projects. While it was developed by the Oregon...

  16. Decision Models for Bulk Energy Transportation Networks

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    emissions prices? How would CO2 regulations impact coal, gas, electricity, & SO2 markets? 3. Disruptions1 Decision Models for Bulk Energy Transportation Networks Electrical Engineering Professor Jim Mc: · integrated fuel, electricity networks · environmental impacts · electricity commodity markets · behavior

  17. Equipment Energy Models Using Spreadsheet Programs

    E-Print Network [OSTI]

    Gilbert, J. S.

    EQUIPMENT ENERGY MODELS USING SPREADSHEET PROGRAMS Joel S. Gilbert, Dames & Moore, Bethesda, Maryland Engineering calculations on PC's are undergoing a revolution with the advent of spreadsheet programs. The author has found that virtually all...

  18. Policy modeling for industrial energy use

    SciTech Connect (OSTI)

    Worrell, Ernst; Park, Hi-Chun; Lee, Sang-Gon; Jung, Yonghun; Kato, Hiroyuki; Ramesohl, Stephan; Boyd, Gale; Eichhammer, Wolfgang; Nyboer, John; Jaccard, Mark; Nordqvist, Joakim; Boyd, Christopher; Klee, Howard; Anglani, Norma; Biermans, Gijs

    2003-03-01T23:59:59.000Z

    The international workshop on Policy Modeling for Industrial Energy Use was jointly organized by EETA (Professional Network for Engineering Economic Technology Analysis) and INEDIS (International Network for Energy Demand Analysis in the Industrial Sector). The workshop has helped to layout the needs and challenges to include policy more explicitly in energy-efficiency modeling. The current state-of-the-art models have a proven track record in forecasting future trends under conditions similar to those faced in the recent past. However, the future of energy policy in a climate-restrained world is likely to demand different and additional services to be provided by energy modelers. In this workshop some of the international models used to make energy consumption forecasts have been discussed as well as innovations to enable the modeling of policy scenarios. This was followed by the discussion of future challenges, new insights in the data needed to determine the inputs into energy model s, and methods to incorporate decision making and policy in the models. Based on the discussion the workshop participants came to the following conclusions and recommendations: Current energy models are already complex, and it is already difficult to collect the model inputs. Hence, new approaches should be transparent and not lead to extremely complex models that try to ''do everything''. The model structure will be determined by the questions that need to be answered. A good understanding of the decision making framework of policy makers and clear communication on the needs are essential to make any future energy modeling effort successful. There is a need to better understand the effects of policy on future energy use, emissions and the economy. To allow the inclusion of policy instruments in models, evaluation of programs and instruments is essential, and need to be included in the policy instrument design. Increased efforts are needed to better understand the effects of innovative (no n-monetary) policy instruments through evaluation and to develop approaches to model both conventional and innovative policies. The explicit modeling of barriers and decision making in the models seems a promising way to enable modeling of conventional and innovative policies. A modular modeling approach is essential to not only provide transparency, but also to use the available resources most effectively and efficiently. Many large models have been developed in the past, but have been abandoned after only brief periods of use. A development path based on modular building blocks needs the establishment of a flexible but uniform modeling framework. The leadership of international agencies and organizations is essential in the establishment of such a framework. A preference is given for ''softlinks'' between different modules and models, to increase transparency and reduce complexity. There is a strong need to improve the efficiency of data collection and interpretation efforts to produce reliable model inputs. The workshop participants support the need for the establishment of an (in-)formal exchanges of information, as well as modeling approaches. The development of an informal network of research institutes and universities to help build a common dataset and exchange ideas on specific areas is proposed. Starting with an exchange of students would be a relative low-cost way to start such collaboration. It would be essential to focus on specific topics. It is also essential to maintain means of regular exchange of ideas between researchers in the different focus points.

  19. QCD parton model at collider energies

    SciTech Connect (OSTI)

    Ellis, R.K.

    1984-09-01T23:59:59.000Z

    Using the example of vector boson production, the application of the QCD improved parton model at collider energies is reviewed. The reliability of the extrapolation to SSC energies is assessed. Predictions at ..sqrt..S = 0.54 TeV are compared with data. 21 references.

  20. World Energy Projection System model documentation

    SciTech Connect (OSTI)

    Hutzler, M.J.; Anderson, A.T.

    1997-09-01T23:59:59.000Z

    The World Energy Projection System (WEPS) was developed by the Office of Integrated Analysis and Forecasting within the Energy Information Administration (EIA), the independent statistical and analytical agency of the US Department of Energy. WEPS is an integrated set of personal computer based spreadsheets containing data compilations, assumption specifications, descriptive analysis procedures, and projection models. The WEPS accounting framework incorporates projections from independently documented models and assumptions about the future energy intensity of economic activity (ratios of total energy consumption divided by gross domestic product GDP), and about the rate of incremental energy requirements met by natural gas, coal, and renewable energy sources (hydroelectricity, geothermal, solar, wind, biomass, and other renewable resources). Projections produced by WEPS are published in the annual report, International Energy Outlook. This report documents the structure and procedures incorporated in the 1998 version of the WEPS model. It has been written to provide an overview of the structure of the system and technical details about the operation of each component of the model for persons who wish to know how WEPS projections are produced by EIA.

  1. Synthesised Constraint Models for Distributed Energy Management

    E-Print Network [OSTI]

    Reif, Wolfgang

    generation [1], demand-side manage- ment, or building control software. In a producer-based view, supplySynthesised Constraint Models for Distributed Energy Management Alexander Schiendorfer, Jan frequently encountered in energy management systems such as the coordination of power generators in a virtual

  2. Coastal Structures Modeling Complex | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, clickInformationNew York: Energy ResourcesCoastal Structures Modeling

  3. Stringy Model of Cosmological Dark Energy

    E-Print Network [OSTI]

    Irina Ya. Aref'eva

    2007-10-16T23:59:59.000Z

    A string field theory(SFT) nonlocal model of the cosmological dark energy providing w<-1 is briefly surveyed. We summarize recent developments and open problems, as well as point out some theoretical issues related with others applications of the SFT nonlocal models in cosmology, in particular, in inflation and cosmological singularity.

  4. Sandia Energy - Severe Accident Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution GridDocumentsInstitute of AdvancedSecuritySensors &Severe

  5. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution Grid Integration Permalink Gallery Mesa delMission

  6. Sandia Energy - Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution Grid Integration Permalink Gallery Mesa delMission

  7. Improved diagnostic model for estimating wind energy

    SciTech Connect (OSTI)

    Endlich, R.M.; Lee, J.D.

    1983-03-01T23:59:59.000Z

    Because wind data are available only at scattered locations, a quantitative method is needed to estimate the wind resource at specific sites where wind energy generation may be economically feasible. This report describes a computer model that makes such estimates. The model uses standard weather reports and terrain heights in deriving wind estimates; the method of computation has been changed from what has been used previously. The performance of the current model is compared with that of the earlier version at three sites; estimates of wind energy at four new sites are also presented.

  8. Uncalibrated Building Energy Simulation Modeling Results

    E-Print Network [OSTI]

    Ahmad, M.; Culp, C.H.

    for the Level 1 and Level 2 models with measured data for WERC (2004 post-commissioning data). ESL-PA-06-10-01 VOLUME 12, NUMBER 4, OCTOBER 2006 1151 Figure 6. Comparison of simulated daily total energy consumption for the Level 1 and Level 2 models with 1999...,450 m2]), the simulation using 1999 data underestimates the energy use in all categories except the whole building electrical usage. Table 3 identifies the magnitude of these discrepancies for a full year’s consumption. The Level 1 model actually per...

  9. Category:Conceptual Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual Model Add.png Add a new Conceptual Model

  10. Modeling of Energy Production Decisions: An Alaska Oil Case Study

    E-Print Network [OSTI]

    Leighty, Wayne

    2008-01-01T23:59:59.000Z

    RR-08-26 Modeling of Energy Production Decisions: An Alaskarapid or gradual energy production in the future? • Doesnet social benefit from energy production and achieving a

  11. Building Energy Model Development for Retrofit Homes

    SciTech Connect (OSTI)

    Chasar, David; McIlvaine, Janet; Blanchard, Jeremy; Widder, Sarah H.; Baechler, Michael C.

    2012-09-30T23:59:59.000Z

    Based on previous research conducted by Pacific Northwest National Laboratory and Florida Solar Energy Center providing technical assistance to implement 22 deep energy retrofits across the nation, 6 homes were selected in Florida and Texas for detailed post-retrofit energy modeling to assess realized energy savings (Chandra et al, 2012). However, assessing realized savings can be difficult for some homes where pre-retrofit occupancy and energy performance are unknown. Initially, savings had been estimated using a HERS Index comparison for these homes. However, this does not account for confounding factors such as occupancy and weather. This research addresses a method to more reliably assess energy savings achieved in deep energy retrofits for which pre-retrofit utility bills or occupancy information in not available. A metered home, Riverdale, was selected as a test case for development of a modeling procedure to account occupancy and weather factors, potentially creating more accurate estimates of energy savings. This “true up” procedure was developed using Energy Gauge USA software and post-retrofit homeowner information and utility bills. The 12 step process adjusts the post-retrofit modeling results to correlate with post-retrofit utility bills and known occupancy information. The “trued” post retrofit model is then used to estimate pre-retrofit energy consumption by changing the building efficiency characteristics to reflect the pre-retrofit condition, but keeping all weather and occupancy-related factors the same. This creates a pre-retrofit model that is more comparable to the post-retrofit energy use profile and can improve energy savings estimates. For this test case, a home for which pre- and post- retrofit utility bills were available was selected for comparison and assessment of the accuracy of the “true up” procedure. Based on the current method, this procedure is quite time intensive. However, streamlined processing spreadsheets or incorporation into existing software tools would improve the efficiency of the process. Retrofit activity appears to be gaining market share, and this would be a potentially valuable capability with relevance to marketing, program management, and retrofit success metrics.

  12. Energy policy modeling: United States and Canadian experiences. Volume I. Specialized energy policy models

    SciTech Connect (OSTI)

    Ziemba, W.T.; Schwartz, S.L.; Koenigsberg, E. (eds.)

    1980-01-01T23:59:59.000Z

    The Canadian Energy Policy Modeling Conference held in North Vancouver, May 18-20, 1978, was organized to assess the state of the art in energy modeling in North America. A major aim of the conference was to determine the extent to which energy modeling had and could make a contribution to the energy-policy decision-making process. Two volumes contain revised and updated versions of the major papers presented at the conference plus edited transcripts of the panel discussions and several additional papers aimed at particular topics deemed worthy of further study. This volume, Vol. I, is concerned with specialized models and contains the following sections: (a) Energy Demand Modeling (7 papers); (b) Energy Supply Modeling (5 papers); (c) Coal and Transportation Modeling (6 papers); and (d) Problems and Interactions of Energy, Environment, and Conservation (4 papers). A separate abstract was prepared for each of the 22 papers for Energy Abstracts for Policy Analysis (EAPA); 5 abstracts will appear in Energy Research Abstracts (ERA).

  13. Characterizing emerging industrial technologies in energy models

    SciTech Connect (OSTI)

    Laitner, John A. (Skip); Worrell, Ernst; Galitsky, Christina; Hanson, Donald A.

    2003-07-29T23:59:59.000Z

    Conservation supply curves are a common tool in economic analysis. As such, they provide an important opportunity to include a non-linear representation of technology and technological change in economy-wide models. Because supply curves are closely related to production isoquants, we explore the possibility of using bottom-up technology assessments to inform top-down representations of energy models of the U.S. economy. Based on a recent report by LBNL and ACEEE on emerging industrial technologies within the United States, we have constructed a supply curve for 54 such technologies for the year 2015. Each of the selected technologies has been assessed with respect to energy efficiency characteristics, likely energy savings by 2015, economics, and environmental performance, as well as needs for further development or implementation of the technology. The technical potential for primary energy savings of the 54 identified technologies is equal to 3.54 Quads, or 8.4 percent of the assume d2015 industrial energy consumption. Based on the supply curve, assuming a discount rate of 15 percent and 2015 prices as forecasted in the Annual Energy Outlook2002, we estimate the economic potential to be 2.66 Quads - or 6.3 percent of the assumed forecast consumption for 2015. In addition, we further estimate how much these industrial technologies might contribute to standard reference case projections, and how much additional energy savings might be available assuming a different mix of policies and incentives. Finally, we review the prospects for integrating the findings of this and similar studies into standard economic models. Although further work needs to be completed to provide the necessary link between supply curves and production isoquants, it is hoped that this link will be a useful starting point for discussion with developers of energy-economic models.

  14. Uncalibrated Building Energy Simulation Modeling Results 

    E-Print Network [OSTI]

    Ahmad, M.; Culp, C.H.

    2006-01-01T23:59:59.000Z

    VOLUME 12, NUMBER 4 HVAC&R RESEARCH OCTOBER 2006 1141 Uncalibrated Building Energy Simulation Modeling Results Mushtaq Ahmad Charles H. Culp, PhD, PE Associate Member ASHRAE Fellow ASHRAE Received June 23, 2005; accepted April 17, 2006... the uncalibrated simulations were completed. The dis- crepancies between the simulated and measured total yearly building energy use varied over ±30% with one outlier. The results show that discrepancies ranged over ±90% between the sim- ulations and the measured...

  15. Alternative Dark Energy Models: An Overview

    E-Print Network [OSTI]

    J. A. S. Lima

    2004-02-04T23:59:59.000Z

    A large number of recent observational data strongly suggest that we live in a flat, accelerating Universe composed of $\\sim$ 1/3 of matter (baryonic + dark) and $\\sim$ 2/3 of an exotic component with large negative pressure, usually named {\\bf Dark Energy} or {\\bf Quintessence}. The basic set of experiments includes: observations from SNe Ia, CMB anisotropies, large scale structure, X-ray data from galaxy clusters, age estimates of globular clusters and old high redshift galaxies (OHRG's). Such results seem to provide the remaining piece of information connecting the inflationary flatness prediction ($\\Omega_{\\rm{T}} = 1$) with astronomical observations. Theoretically, they have also stimulated the current interest for more general models containing an extra component describing this unknown dark energy, and simultaneously accounting for the present accelerating stage of the Universe. An overlook in the literature shows that at least five dark energy candidates have been proposed in the context of general relativistic models. Since the cosmological constant and rolling scalar field models have already been extensively discussed, in this short review we focus our attention to the three remaining candidates, namely: a decaying vacuum energy density (or ${\\bf \\Lambda(t)}$ {\\bf models}), the {\\bf X-matter}, and the so-called {\\bf Chaplygin-type gas}. A summary of their main results is given and some difficulties underlying the emerging dark energy paradigm are also briefly examined.

  16. Category:Modeling Techniques | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual ModelLists forMercury Vapor page? ForTechniques

  17. Category:Numerical Modeling | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual ModelLists forMercury VaporTemplatespage? For

  18. Property:Buildings/Models | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar GroupInformationInformationYearConstruction1ModelNameModelYear

  19. Conformal Higgs model: predicted dark energy density

    E-Print Network [OSTI]

    R. K. Nesbet

    2014-11-03T23:59:59.000Z

    Postulated universal Weyl conformal scaling symmetry provides an alternative to the $\\Lambda$CDM paradigm for cosmology. Recent applications to galactic rotation velocities, Hubble expansion, and a model of dark galactic halos explain qualitative phenomena and fit observed data without invoking dark matter. Significant revision of theory relevant to galactic collisions and clusters is implied, but not yet tested. Dark energy is found to be a consequence of conformal symmetry for the Higgs scalar field of electroweak physics. The present paper tests this implication. The conformal Higgs model acquires a gravitational effect described by a modified Friedmann cosmic evolution equation, shown to fit cosmological data going back to the cosmic microwave background epoch. The tachyonic mass parameter of the Higgs model becomes dark energy in the Friedmann equation. A dynamical model of this parameter, analogous to the Higgs mechanism for gauge boson mass, is derived and tested here. An approximate calculation yields a result consistent with the empirical magnitude inferred from Hubble expansion.

  20. Gauss Bonnet dark energy Chaplygin Gas Model

    E-Print Network [OSTI]

    Elahe Karimkhani; Asma Alaii; Abdolhossein Khodam-Mohammadi

    2015-02-27T23:59:59.000Z

    In this work we incorporate GB dark energy density and its modification, MGB, with Chaplygin gas component. We show that, presence of Chaplygin gas provides us a feature to obtain an exact solution for scalar field and potential of scalar field. Investigation on squared of sound speed provides a lower limit for constant parameters of MGB model. Also, we could find some bounds for free parameters of model.

  1. Gauss Bonnet dark energy Chaplygin Gas Model

    E-Print Network [OSTI]

    Karimkhani, Elahe; Khodam-Mohammadi, Abdolhossein

    2015-01-01T23:59:59.000Z

    In this work we incorporate GB dark energy density and its modification, MGB, with Chaplygin gas component. We show that, presence of Chaplygin gas provides us a feature to obtain an exact solution for scalar field and potential of scalar field. Investigation on squared of sound speed provides a lower limit for constant parameters of MGB model. Also, we could find some bounds for free parameters of model.

  2. A MINIMAL MODEL OF ENERGY MANAGEMENT IN THE BRAIN

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    a minimal model of energy management inside a single cortical area, featuring local energy storage a simple model for energy man- agement within a single cortical area. By energy management, we mean a setA MINIMAL MODEL OF ENERGY MANAGEMENT IN THE BRAIN Florian A. Dehmelt, Christian K. Machens Group

  3. Business Models for Energy Access | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:Power LP Biomass Facility JumpBurleigh County,Busch RanchModels for

  4. Industrial Sector Energy Efficiency Modeling (ISEEM) Framework Documentation

    E-Print Network [OSTI]

    Karali, Nihan

    2014-01-01T23:59:59.000Z

    Energy Supply Modeling Package EFOM-12C Mark 1 MathematicalEnergy Supply Modeling Package EFOM-12C Mark 1 User’s Guide,the Economy EU European Union EFOM Energy Flow Optimization

  5. Modelling the impact of user behaviour on heat energy consumption

    E-Print Network [OSTI]

    Combe, Nicola Miss; Harrison, David Professor; Way, Celia Miss

    2011-01-01T23:59:59.000Z

    USA MODELLING THE IMPACT OF USER BEHAVIOUR ON HEAT ENERGY CONSUMPTIONUSA The second point of interest to research was modelling the excess energy consumptionUSA Figure 3. Actual heating and hot water energy consumption

  6. Sandia National Laboratories: Analysis, Modeling, Cost of Energy...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ProgramsAnalysis, Modeling, Cost of Energy, and Policy Impact: Wind Vision 2014 Analysis, Modeling, Cost of Energy, and Policy Impact: Wind Vision 2014 The "20% Wind Energy by...

  7. Determinant Formulas for Matrix Model Free Energy

    E-Print Network [OSTI]

    D. Vasiliev

    2005-07-11T23:59:59.000Z

    The paper contains a new non-perturbative representation for subleading contribution to the free energy of multicut solution for hermitian matrix model. This representation is a generalisation of the formula, proposed by Klemm, Marino and Theisen for two cut solution, which was obtained by comparing the cubic matrix model with the topological B-model on the local Calabi-Yau geometry $\\hat {II}$ and was checked perturbatively. In this paper we give a direct proof of their formula and generalise it to the general multicut solution.

  8. Energy policy modeling: United States and Canadian experiences. Volume II. Integrative energy policy models

    SciTech Connect (OSTI)

    Ziemba, W.T.; Schwartz, S.L. (eds.)

    1980-01-01T23:59:59.000Z

    The Canadian Energy Policy Modeling Conference, held in North Vancouver, May 18-20, 1978, was organized to assess the state of the art in energy modeling in North America. A major aim of the conference was to determine the extent to which energy modeling had and could make a contribution to the energy-policy decision-making process. Two volumes contain revised and updated versions of the major papers presented at the conference plus edited transcripts of the panel discussions and several additional papers aimed at particular topics deemed worthy of further study. For this volume, Vol. II, a separate abstract was prepared for each of 17 papers, 2 panel discussions, and three session-introduction commentaries for Energy Abstracts for Policy Analysis (EAPA); 7 abstracts will appear in Energy Research Abstracts (ERA).

  9. Energy-oriented models for WDM networks Abstract--A realistic energy-oriented model is necessary to

    E-Print Network [OSTI]

    Politčcnica de Catalunya, Universitat

    Energy-oriented models for WDM networks Abstract-- A realistic energy-oriented model is necessary to formally characterize the energy consumption and the consequent carbon footprint of actual and future high-capacity WDM networks. The energy model describes the energy consumption of the various network elements (NE

  10. Modeling and Implementation of Energy Neutral Sensing Systems

    E-Print Network [OSTI]

    Carloni, Luca

    and sensing applications. The net- work energy-management is modeled as a feedback control systemModeling and Implementation of Energy Neutral Sensing Systems Marcin K. Szczodrak Columbia]: Organization and Design-- Distributed Systems General Terms Design, Modeling, Experimentation, Measurement

  11. Category:Building Models | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:EzfeedflagBiomassSustainableCSLInformationMissouri:Catalyst2-Mpage?Brophy OccurrenceModel.

  12. Steam System Modeler | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page onYouTube YouTube Note: Since the.pdfBreakingMayDepartment of Staffing Model5ThomasEnergyReceives Energy Assessment |Steam

  13. Stimulation Prediction Models | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt LtdShawangunk,SoutheastSt.SteepStimulation Prediction Models Jump to:

  14. Vehicle Modeling and Simulation | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your DensityEnergy U.S.-China Electric Vehicle and03/02 TUEValidation of& Systems Simulation|Modeling and

  15. EPA NONROAD Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOE FacilityDimondale,South, NewDyer County,ECO2LtdLegalClass VHandbook

  16. Data and Modeling Techniques | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOE Facility Database Data andDarnestown, Maryland:(Blackwell, Et2007)and

  17. Threshold 21 Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries PvtStratosolarTharaldson Ethanol LLCEnergyoThornwood, NewThreshold 21 Model Jump

  18. Biomass Scenario Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:EzfeedflagBiomass ConversionsSouthby 2022 | OpenEI Community Biomass PowerScenario Model

  19. Observing and modeling Earths energy flows

    SciTech Connect (OSTI)

    Stevens B.; Schwartz S.

    2012-05-11T23:59:59.000Z

    This article reviews, from the authors perspective, progress in observing and modeling energy flows in Earth's climate system. Emphasis is placed on the state of understanding of Earth's energy flows and their susceptibility to perturbations, with particular emphasis on the roles of clouds and aerosols. More accurate measurements of the total solar irradiance and the rate of change of ocean enthalpy help constrain individual components of the energy budget at the top of the atmosphere to within {+-}2 W m{sup -2}. The measurements demonstrate that Earth reflects substantially less solar radiation and emits more terrestrial radiation than was believed even a decade ago. Active remote sensing is helping to constrain the surface energy budget, but new estimates of downwelling surface irradiance that benefit from such methods are proving difficult to reconcile with existing precipitation climatologies. Overall, the energy budget at the surface is much more uncertain than at the top of the atmosphere. A decade of high-precision measurements of the energy budget at the top of the atmosphere is providing new opportunities to track Earth's energy flows on timescales ranging from days to years, and at very high spatial resolution. The measurements show that the principal limitation in the estimate of secular trends now lies in the natural variability of the Earth system itself. The forcing-feedback-response framework, which has developed to understand how changes in Earth's energy flows affect surface temperature, is reviewed in light of recent work that shows fast responses (adjustments) of the system are central to the definition of the effective forcing that results from a change in atmospheric composition. In many cases, the adjustment, rather than the characterization of the compositional perturbation (associated, for instance, with changing greenhouse gas concentrations, or aerosol burdens), limits accurate determination of the radiative forcing. Changes in clouds contribute importantly to this adjustment and thus contribute both to uncertainty in estimates of radiative forcing and to uncertainty in the response. Models are indispensable to calculation of the adjustment of the system to a compositional change but are known to be flawed in their representation of clouds. Advances in tracking Earth's energy flows and compositional changes on daily through decadal timescales are shown to provide both a critical and constructive framework for advancing model development and evaluation.

  20. Comparison of Building Energy Modeling Programs: Building Loads

    E-Print Network [OSTI]

    LBNL-6034E Comparison of Building Energy Modeling Programs: Building Loads Dandan Zhu1 , Tianzhen Energy, the U.S.-China Clean Energy Research Center for Building Energy Efficiency, of the U;Comparison of Building Energy Modeling Programs: Building Loads A joint effort between Lawrence Berkeley

  1. Modelling and geometry optimisation of wave energy converters

    E-Print Network [OSTI]

    Nřrvĺg, Kjetil

    Modelling and geometry optimisation of wave energy converters Adi Kurniawan Supervisors: Prof;Research questions Modelling How to develop more realistic wave energy converter (WEC) models while wave energy converter (WEC) models while at the same time reduce their simulation time? Optimisation

  2. Scripted Building Energy Modeling and Analysis: Preprint

    SciTech Connect (OSTI)

    Hale, E.; Macumber, D.; Benne, K.; Goldwasser, D.

    2012-08-01T23:59:59.000Z

    Building energy modeling and analysis is currently a time-intensive, error-prone, and nonreproducible process. This paper describes the scripting platform of the OpenStudio tool suite (http://openstudio.nrel.gov) and demonstrates its use in several contexts. Two classes of scripts are described and demonstrated: measures and free-form scripts. Measures are small, single-purpose scripts that conform to a predefined interface. Because measures are fairly simple, they can be written or modified by inexperienced programmers.

  3. Panel 2, Modeling the Financial and System Benefits of Energy...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Modeling the Financial and System Benefits of Energy Storage Applications in Distribution Systems Patrick Balducci, Senior Economist, Pacific NW National Laboratory Hydrogen Energy...

  4. Model for Energy Supply System Alternatives and their General...

    Open Energy Info (EERE)

    for Energy Supply System Alternatives and their General Environmental Impacts (MESSAGE) Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Model for Energy Supply System...

  5. Webcast of the Renewable Energy Competency Model: An Aid to Build a Renewable Energy Skilled Workforce

    Broader source: Energy.gov [DOE]

    The Department of Energy held a webcast titled "Renewable Energy Competency Model: An Aid to Build a Renewable Energy Skilled Workforce" on Monday, October 22, 2012. The Renewable Energy Competency...

  6. Characterizing emerging industrial technologies in energy models

    E-Print Network [OSTI]

    Laitner, John A. Skip; Worrell, Ernst; Galitsky, Christina; Hanson, Donald A.

    2003-01-01T23:59:59.000Z

    EIA), 2001. “Annual Energy Outlook 2002,” Energy Informationas forecasted in the Annual Energy Outlook 2002, we estimateQuads based on the Annual Energy Outlook 2002 (AEO 2002) (

  7. NREL's System Advisor Model Simplifies Complex Energy Analysis...

    Office of Scientific and Technical Information (OSTI)

    NREL's System Advisor Model Simplifies Complex Energy Analysis (Fact Sheet) Re-direct Destination: NREL has developed a tool -- the System Advisor Model (SAM) -- that can help...

  8. Estimating home energy decision parameters for a hybrid energyYeconomy policy model

    E-Print Network [OSTI]

    Estimating home energy decision parameters for a hybrid energyYeconomy policy model Mark Jaccard, Canada E-mail: jaccard@sfu.ca Hybrid energyYeconomy models combine the advantages of a technologically parameters translate into the behavioral parameters of a hybrid model. We then simulate household energy

  9. Model documentation report: Transportation sector model of the National Energy Modeling System

    SciTech Connect (OSTI)

    Not Available

    1994-03-01T23:59:59.000Z

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. This document serves three purposes. First, it is a reference document providing a detailed description of TRAN for model analysts, users, and the public. Second, this report meets the legal requirements of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports (Public Law 93-275, 57(b)(1)). Third, it permits continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

  10. Rotational and divergent kinetic energy in the mesoscale model ALADIN

    E-Print Network [OSTI]

    Zagar, Nedjeljka

    energy, divergent energy, ALADIN, limited-area modelling 1. Introduction Horizontal divergenceRotational and divergent kinetic energy in the mesoscale model ALADIN By V. BLAZ ICA1 *, N. Z AGAR1 received 7 June 2012; in final form 7 March 2013) ABSTRACT Kinetic energy spectra from the mesoscale

  11. Parameter estimation for energy balance models with memory

    E-Print Network [OSTI]

    Parameter estimation for energy balance models with memory By Lionel Roques1,*, Micka¨el D parameter estimation for one-dimensional energy balance models with mem- ory (EBMMs) given localized estimate is still possible in certain cases. Keywords: age dating; Bayesian inference; energy balance model

  12. Canopy radiation transmission for an energy balance snowmelt model

    E-Print Network [OSTI]

    Tarboton, David

    Canopy radiation transmission for an energy balance snowmelt model Vinod Mahat1 and David G deep canopy solution. This solution enhances capability for modeling energy balance processes in a distributed energy balance snowmelt model and results compared with observations made in three different

  13. Modelling spot and forward prices for energy companies

    E-Print Network [OSTI]

    Bhulai, Sandjai

    Modelling spot and forward prices for energy companies Dafydd Steele MSc Stochastics and Financial forward and spot prices for energy com- panies. The two main ways of modelling power prices are stochastic Mathematics dafydd.steele@edf-energy.com August 5, 2010 #12;Abstract The focus of this thesis is on modelling

  14. Distributed Energy Resources Market Diffusion Model

    SciTech Connect (OSTI)

    Maribu, Karl Magnus; Firestone, Ryan; Marnay, Chris; Siddiqui,Afzal S.

    2006-06-16T23:59:59.000Z

    Distributed generation (DG) technologies, such as gas-fired reciprocating engines and microturbines, have been found to be economically beneficial in meeting commercial-sector electrical, heating, and cooling loads. Even though the electric-only efficiency of DG is lower than that offered by traditional central stations, combined heat and power (CHP) applications using recovered heat can make the overall system energy efficiency of distributed energy resources (DER) greater. From a policy perspective, however, it would be useful to have good estimates of penetration rates of DER under various economic and regulatory scenarios. In order to examine the extent to which DER systems may be adopted at a national level, we model the diffusion of DER in the US commercial building sector under different technical research and technology outreach scenarios. In this context, technology market diffusion is assumed to depend on the system's economic attractiveness and the developer's knowledge about the technology. The latter can be spread both by word-of-mouth and by public outreach programs. To account for regional differences in energy markets and climates, as well as the economic potential for different building types, optimal DER systems are found for several building types and regions. Technology diffusion is then predicted via two scenarios: a baseline scenario and a program scenario, in which more research improves DER performance and stronger technology outreach programs increase DER knowledge. The results depict a large and diverse market where both optimal installed capacity and profitability vary significantly across regions and building types. According to the technology diffusion model, the West region will take the lead in DER installations mainly due to high electricity prices, followed by a later adoption in the Northeast and Midwest regions. Since the DER market is in an early stage, both technology research and outreach programs have the potential to increase DER adoption, and thus, shift building energy consumption to a more efficient alternative.

  15. Sandia Energy - Marine Hydrokinetics Technology: Reference Model

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Scienceand RequirementsCoatingsUltra-High-VoltagePowerUpdatesDevelopment Reference Model

  16. Sandia Energy - PV Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution Grid IntegrationOffshore Wind RD&D:PV Modeling &

  17. Advanced Modeling & Simulation | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergyDepartment ofATVM LoanActiveMission »Advanced Modeling &

  18. Model documentation Natural Gas Transmission and Distribution Model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    NONE

    1996-02-26T23:59:59.000Z

    The Natural Gas Transmission and Distribution Model (NGTDM) of the National Energy Modeling System is developed and maintained by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting. This report documents the archived version of the NGTDM that was used to produce the natural gas forecasts presented in the Annual Energy Outlook 1996, (DOE/EIA-0383(96)). The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic approach, and provides detail on the methodology employed. Previously this report represented Volume I of a two-volume set. Volume II reported on model performance, detailing convergence criteria and properties, results of sensitivity testing, comparison of model outputs with the literature and/or other model results, and major unresolved issues.

  19. Characterizing emerging industrial technologies in energy models

    E-Print Network [OSTI]

    Laitner, John A. Skip; Worrell, Ernst; Galitsky, Christina; Hanson, Donald A.

    2003-01-01T23:59:59.000Z

    Efficient and Clean Energy Technologies, 2000. Scenarios ofEmerging Energy-Efficient Industrial Technologies,” Lawrenceinformation about energy efficiency technologies, their

  20. Model Predictive Control for Energy Efficient Buildings

    E-Print Network [OSTI]

    Ma, Yudong

    2012-01-01T23:59:59.000Z

    Learning Control for Thermal Energy Storage Systems”. In:Predictive Control of Thermal Energy Storage in Buildingmaking use of building thermal energy storage, and this work

  1. Model Predictive Control for Energy Efficient Buildings

    E-Print Network [OSTI]

    Ma, Yudong

    2012-01-01T23:59:59.000Z

    chillers/cooling towers for energy conversion, an electricalconsuming energy are chillers, cooling towers, and pumps. Atconsuming energy are chillers, cooling towers, and pumps. It

  2. Model documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System; Volume 1

    SciTech Connect (OSTI)

    NONE

    1994-02-24T23:59:59.000Z

    The Natural Gas Transmission and Distribution Model (NGTDM) is a component of the National Energy Modeling System (NEMS) used to represent the domestic natural gas transmission and distribution system. NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the Energy Information Administration (EIA) and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. This report documents the archived version of NGTDM that was used to produce the natural gas forecasts used in support of the Annual Energy Outlook 1994, DOE/EIA-0383(94). The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic design, provides detail on the methodology employed, and describes the model inputs, outputs, and key assumptions. It is intended to fulfill the legal obligation of the EIA to provide adequate documentation in support of its models (Public Law 94-385, Section 57.b.2). This report represents Volume 1 of a two-volume set. (Volume 2 will report on model performance, detailing convergence criteria and properties, results of sensitivity testing, comparison of model outputs with the literature and/or other model results, and major unresolved issues.) Subsequent chapters of this report provide: (1) an overview of the NGTDM (Chapter 2); (2) a description of the interface between the National Energy Modeling System (NEMS) and the NGTDM (Chapter 3); (3) an overview of the solution methodology of the NGTDM (Chapter 4); (4) the solution methodology for the Annual Flow Module (Chapter 5); (5) the solution methodology for the Distributor Tariff Module (Chapter 6); (6) the solution methodology for the Capacity Expansion Module (Chapter 7); (7) the solution methodology for the Pipeline Tariff Module (Chapter 8); and (8) a description of model assumptions, inputs, and outputs (Chapter 9).

  3. An Energy-Aware Simulation Model and Transaction Protocol

    E-Print Network [OSTI]

    Pedram, Massoud

    functioning in a real world environment ! We study the effects of redistributing energy on the two network density function ! Host energy model ! Computation energy: power consumption of SP, EPIC, LPIC ! Communication energy: power consumption for SR transmission and reception ! Data transmission energy

  4. Draft Report Development of Model Curriculum in Renewable Energy

    E-Print Network [OSTI]

    Banerjee, Rangan

    Draft Report On Development of Model Curriculum in Renewable Energy Energy Systems Engineering, Indian Institute of Technology Bombay Powai, Mumbai ­ 400 076 August 2003 #12;Draft Renewable Energy energy systems for the future, it is necessary to incorporate renewable energy in the traditional

  5. Business Model Resources | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    Run a Program Getting Started Business Model Resources Business Model Resources Business Models Guide Business Model Planning Resources - Working with Partners Sample Program...

  6. Framework for Coupling Room Air Models to Heat Balance Model Load and Energy Calculations (RP-1222)

    E-Print Network [OSTI]

    Chen, Qingyan "Yan"

    1 Framework for Coupling Room Air Models to Heat Balance Model Load and Energy Calculations (RP in a program for hourly load calculations of a single thermal zone. The heat balance model for load and energy to heat balance model load and energy calculations," HVAC&R Research, 10(2), 91-111. #12;2 · Mixed

  7. Modelling Business Energy Consumption using Agent-based Simulation Modelling Jason Wong and Kay Cao1

    E-Print Network [OSTI]

    to develop a prototype agent based simulation model for business energy consumption, using data from the 2008 presents a framework of the model for estimating business energy consumption. Section V discusses the dataModelling Business Energy Consumption using Agent-based Simulation Modelling Jason Wong and Kay Cao

  8. Carrots and Sticks: A Comprehensive Business Model for the Successful Achievement of Energy Efficiency Resource Standards

    E-Print Network [OSTI]

    Satchwell, Andrew

    2013-01-01T23:59:59.000Z

    a comprehensive energy efficiency business model on utilitya comprehensive energy efficiency business model on utilityframework of the energy efficiency business model. The

  9. Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building

    E-Print Network [OSTI]

    Dudley, Junqiao Han

    2010-01-01T23:59:59.000Z

    We have studied a low energy building on a campus of theEnergyPlus Model in a Low Energy Campus Building Junqiao HanEnergyPlus Model in a Low Energy Campus Building Junqiao Han

  10. Methodology for Modeling Building Energy Performance across the Commercial Sector

    SciTech Connect (OSTI)

    Griffith, B.; Long, N.; Torcellini, P.; Judkoff, R.; Crawley, D.; Ryan, J.

    2008-03-01T23:59:59.000Z

    This report uses EnergyPlus simulations of each building in the 2003 Commercial Buildings Energy Consumption Survey (CBECS) to document and demonstrate bottom-up methods of modeling the entire U.S. commercial buildings sector (EIA 2006). The ability to use a whole-building simulation tool to model the entire sector is of interest because the energy models enable us to answer subsequent 'what-if' questions that involve technologies and practices related to energy. This report documents how the whole-building models were generated from the building characteristics in 2003 CBECS and compares the simulation results to the survey data for energy use.

  11. Modeling and analysis of energy conversion systems

    SciTech Connect (OSTI)

    Den Braven, K.R. (Idaho Univ., Moscow, ID (USA). Dept. of Mechanical Engineering); Stanger, S. (EG and G Idaho, Inc., Idaho Falls, ID (USA))

    1990-10-01T23:59:59.000Z

    An investigation was conducted to assess the need for and the feasibility of developing a computer code that could model thermodynamic systems and predict the performance of energy conversion systems. To assess the market need for this code, representatives of a few industrial organizations were contacted, including manufacturers, system and component designers, and research personnel. Researchers and small manufacturers, designers, and installers were very interested in the possibility of using the proposed code. However, large companies were satisfied with the existing codes that they have developed for their own use. Also, a survey was conduced of available codes that could be used or possibly modified for the desired purpose. The codes were evaluated with respect to a list of desirable features, which was prepared as a result of the survey. A few publicly available codes were found that might be suitable. The development, verification, and maintenance of such a code would require a substantial, ongoing effort. 21 refs.

  12. Modeling of Field Distribution and Energy Storage in Diphasic Dielectrics

    E-Print Network [OSTI]

    Koledintseva, Marina Y.

    Modeling of Field Distribution and Energy Storage in Diphasic Dielectrics S. K. Patil, M. Y, USA Modeling of electrostatic field distribution and energy storage in diphasic dielectrics containing to the increased energy storage density. For composites with lower volume fractions of high-permittivity inclusions

  13. MODELING OF HYDRO-PNEUMATIC ENERGY STORAGE USING PUMP TURBINES

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    MODELING OF HYDRO-PNEUMATIC ENERGY STORAGE USING PUMP TURBINES E. Ortego, A. Dazin, G. Caignaert, F. Colas, O. Coutier-Delgosha Abstract: Modelling of a hydro-pneumatic energy storage system is the main demand response strategy. 1 Introduction Energy storage is one of the most exciting solutions considered

  14. Multi-Factor Energy Price Models Exotic Derivatives Pricing

    E-Print Network [OSTI]

    Jaimungal, Sebastian

    Multi-Factor Energy Price Models and Exotic Derivatives Pricing by Samuel Hikspoors A thesis of Statistics University of Toronto c Copyright by Samuel Hikspoors 2008 #12;Multi-Factor Energy Price Models and practitioners alike recently started to develop the tools of energy derivatives pricing

  15. MODELS AND METRICS FOR ENERGY-EFFICIENT COMPUTER SYSTEMS

    E-Print Network [OSTI]

    Kozyrakis, Christos

    MODELS AND METRICS FOR ENERGY-EFFICIENT COMPUTER SYSTEMS A DISSERTATION SUBMITTED TO THE DEPARTMENT promising energy-efficient technolo- gies, and models to understand the effects of resource utilization decisions on power con- sumption. To facilitate energy-efficiency improvements, this dissertation presents

  16. Energy-aware scheduling: models and complexity results

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Energy-aware scheduling: models and complexity results Guillaume Aupy (1st year PhD student) LIP several energy-aware scheduling algorithms whose design is optimized for different speed models. Dynamic plans. I. INTRODUCTION Energy-aware scheduling has proven an important issue in the past decade, both

  17. Challenges for Long-Term Energy Models: Modeling Energy Use and Energy Efficiency

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade Year-0E (2001)gasoline prices4Consumption The StateLong-Term Energy

  18. Modeling of thermal energy storage in groundwater aquifers

    E-Print Network [OSTI]

    Reed, David Bryan

    1979-01-01T23:59:59.000Z

    , nuclear fission, fusion, geo- thermal energy, and solar energy as potential alternate energy sources to replace natural gas and oil. Of these, soIar energy is one of the most promisino alternate energy sources for space heating and cooling. Solar...MODELING OF THERMAL ENERGY STORAGE IN GROUNDWATER AQUIFERS A Thesis by DAVID BRYAN REED Submitted to the Graduate College of Texas A&M University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE December 1979...

  19. World oil futures: results from the OILTANK model presented at the energy modeling forum

    SciTech Connect (OSTI)

    Ervik, L.K.; Johannessen, O.; Nunn, D.W.

    1980-09-01T23:59:59.000Z

    This report gives results from the OILTANK simulation model presented at the Energy Modeling Forum on future world oil price. 12 scenarios are presented.

  20. Model Predictive Control for Energy Efficient Buildings

    E-Print Network [OSTI]

    Ma, Yudong

    2012-01-01T23:59:59.000Z

    solution”. In: Energy and Buildings 52.0 (2012), pp. 39–49.with GenOpt”. In: Energy and Buildings 42.7 (2010), pp.lation Program”. In: Energy and Buildings 33.4 (2001), pp.

  1. Modelling the UK perennial energy crop market 

    E-Print Network [OSTI]

    Alexander, Peter Mark William

    2014-11-27T23:59:59.000Z

    Biomass produced from perennial energy crops, Miscanthus and willow or poplar grown as short-rotation coppice, is expected to contribute to UK renewable energy targets and reduce the carbon intensity of energy production. ...

  2. Line--energy Ginzburg--Landau models: zero--energy states

    E-Print Network [OSTI]

    Otto, Felix

    Line--energy Ginzburg--Landau models: zero--energy states Pierre­Emmanuel Jabin*, email: jabin of vanishing energy. We classify these zero--energy states in the whole space: They are either con­ stant or a vortex. A bounded domain can sustain a zero--energy state only if the domain is a disk and the state

  3. Hybrid Simulation Modeling to Estimate U.S. Energy Elasticities

    E-Print Network [OSTI]

    Hybrid Simulation Modeling to Estimate U.S. Energy Elasticities by Adam C. Baylin-Stern B.A. & Sc in the estimation of ESUBs from CIMS. Keywords: Elasticity of substitution; hybrid energy-economy model; translog-Stern Degree: Project No.: Master of Resource Management 535 Title of Thesis: Hybrid Simulation Modeling

  4. Strategies for Energy Efficient Resource Management of Hybrid Programming Models

    E-Print Network [OSTI]

    1 Strategies for Energy Efficient Resource Management of Hybrid Programming Models Dong Li Member, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid hybrid programming models that use both message-passing and shared- memory, due to the increasing

  5. Distributed Energy Resources Market Diffusion Model

    E-Print Network [OSTI]

    Maribu, Karl Magnus; Firestone, Ryan; Marnay, Chris; Siddiqui, Afzal S.

    2006-01-01T23:59:59.000Z

    where both optimal installed capacity and profitability varyParameters DER-MaDiM Installed Capacity Energy Consumptiondifferent results, installed capacities, changes in energy

  6. Energy Demand Modelling Introduction to the PhD project

    E-Print Network [OSTI]

    Energy Demand Modelling Introduction to the PhD project Erika Zvingilaite Risø DTU System Analysis for optimization of energy systems Environmental effects Global externalities cost of CO2 Future scenarios for the Nordic energy systems 2010, 2020, 2030, 2040, 2050 (energy-production, consumption, emissions, net costs

  7. Modeling energy consumption in cellular networks L. Decreusefond

    E-Print Network [OSTI]

    Boyer, Edmond

    Modeling energy consumption in cellular networks L. Decreusefond Telecom Paristech, LTCI Paris Abstract--In this paper we present a new analysis of energy consumption in cellular networks. We focus on the distribution of energy consumed by a base station for one isolated cell. We first define the energy consumption

  8. The National Energy Modeling System: An overview 1998

    SciTech Connect (OSTI)

    NONE

    1998-02-01T23:59:59.000Z

    The National Energy Modeling System (NEMS) is a computer-based, energy-economy modeling system of US energy markets for the midterm period through 2020. NEMS projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomic and financial factors world energy markets, resource availability and costs, behavior and technological choice criteria, cost and performance characteristics of energy technologies, and demographics. This report presents an overview of the structure and methodology of NEMS and each of its components. The first chapter provides a description of the design and objectives of the system, followed by a chapter on the overall modeling structure and solution algorithm. The remainder of the report summarizes the methodology and scope of the component modules of NEMS. The model descriptions are intended for readers familiar with terminology from economics, operations research, and energy modeling. 21 figs.

  9. Modeling Interregional Transmission Congestion in the National Energy Modeling System

    E-Print Network [OSTI]

    Gumerman, Etan; Chan, Peter; Lesieutre, Bernard; Marnay, Chris; Wang, Juan

    2006-01-01T23:59:59.000Z

    Administration. 2005a. Annual Energy Outlook 2005. EIA/DOE.RON SERC TWh WECC Annual Energy Outlook U.S. Department ofAccording to the Annual Energy Outlook (AEO) 2004 Reference

  10. Energy Storage R&D - Thermal Management Studies and Modeling

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Merit Review Energy Storage R&D Thermal Management Studies and Modeling Ahmad A. Pesaran, Ph. D. National Renewable Energy Laboratory Golden, Colorado February 25-28, 2008 DOE...

  11. Implementation of a Corporate Energy Accounting and Forecasting Model

    E-Print Network [OSTI]

    Kympton, H. W.; Bowman, B. M.

    1981-01-01T23:59:59.000Z

    The development and implementation of a Frito-Lay computer based energy consumption reporting and modeling program is discussed. The system has been designed to relate actual plant energy consumption to a standard consumption which incorporates...

  12. Free energy and complexity of spherical bipartite models

    E-Print Network [OSTI]

    Antonio Auffinger; Wei-Kuo Chen

    2014-05-09T23:59:59.000Z

    We investigate both free energy and complexity of the spherical bipartite spin glass model. We first prove a variational formula in high temperature for the limiting free energy based on the well-known Crisanti-Sommers representation of the mixed p-spin spherical model. Next, we show that the mean number of local minima at low levels of energy is exponentially large in the size of the system and we derive a bound on the location of the ground state energy.

  13. The driven overdamped mean field model Non-eq. free energies for the mean field model

    E-Print Network [OSTI]

    Dauxois, Thierry

    The driven overdamped mean field model Non-eq. free energies for the mean field model Large deviations for turbulent flows Non-Equilibrium Free Energies for Particle Systems and Turbulent Flows F Treilles. F. Bouchet ENSL-CNRS Non-Equilibrium Free Energies #12;The driven overdamped mean field model Non

  14. Model documentation report: Industrial sector demand module of the national energy modeling system

    SciTech Connect (OSTI)

    NONE

    1998-01-01T23:59:59.000Z

    This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description of the NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirements of the Energy Information Administration (EIA) to provide adequate documentation in support of its model. Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

  15. Renewable Energy and Efficiency Modeling Analysis Partnership: An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    E-Print Network [OSTI]

    Blair, N.

    2010-01-01T23:59:59.000Z

    power and renewable energy sources. o If the model includedmodels that contain other renewable sources (solar, geothermal, bio- power,

  16. Energy transfers in shell models for MHD turbulence

    E-Print Network [OSTI]

    T. Lessinnes; M. K. Verma; D. Carati

    2008-07-31T23:59:59.000Z

    A systematic procedure to derive shell models for MHD turbulence is proposed. It takes into account the conservation of ideal quadratic invariants such as the total energy, the cross-helicity and the magnetic helicity as well as the conservation of the magnetic energy by the advection term in the induction equation. This approach also leads to simple expressions for the energy exchanges as well as to unambiguous definitions for the energy fluxes. When applied to the existing shell models with nonlinear interactions limited to the nearest neighbour shells, this procedure reproduces well known models but suggests a reinterpretation of the energy fluxes.

  17. Nuclear Energy Advanced Modeling and Simulation (NEAMS) Software...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Software Verification and Validation (V&V) Plan Requirements Nuclear Energy Advanced Modeling and Simulation (NEAMS) Software Verification and Validation (V&V) Plan Requirements...

  18. Force Field Modeling of Conformational Energies: Importance of Multipole

    E-Print Network [OSTI]

    Ponder, Jay

    Force Field Modeling of Conformational Energies: Importance of Multipole Moments and Intramolecular as the molecules become more polar. Inclusion of multipole moments and intramolecular polarization can improve

  19. Model for Energy Supply System Alternatives and their General...

    Open Energy Info (EERE)

    System Alternatives and their General Environmental Impacts (MESSAGE) (Redirected from Model for Energy Supply System Alternatives and their General Environmental Impacts) Jump to:...

  20. US Energy Service Company Industry: History and Business Models

    Broader source: Energy.gov (indexed) [DOE]

    Energy Service Company Industry: History and Business Models Don Gilligan President, NAESCO May 6, 2011 Overview of Presentation * US ESCO industry evolution: Five phases *...

  1. Macro-System Model Overview | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Presentation on Macro-System Model Overview given by Mark Ruth of the National Renewable Energy Laboratory during the DOE Hydrogen Transition Analysis Workshop on January 26,...

  2. Comparison of Real World Energy Consumption to Models and DOE...

    Broader source: Energy.gov (indexed) [DOE]

    energy performance of appliances and equipment as it compares with models and test procedures. The study looked to determine whether DOE and industry test procedures...

  3. Model Examines Cumulative Impacts of Wind Energy Development...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    an area that currently supports important populations of greater sage-grouse and has high wind energy development potential. This early model prototype demonstrated the utility of...

  4. Modeling of Energy Production Decisions: An Alaska Oil Case Study

    E-Print Network [OSTI]

    Leighty, Wayne

    2008-01-01T23:59:59.000Z

    Cartelisation in the Oil Market,” Energy Policy, 25(13),1991) “Models of the Oil Market,” in Fundamentals of Pureis warranted. In a review of oil market models, Salehi-

  5. Energy Systems Modeling Symposium Co-Sponsored by

    E-Print Network [OSTI]

    Knowlton School of Architecture, OSU Natural Gas Infrastructure Modeling: From Local Distribution to Transboundary Networks Bhavik Bakshi Chemical and Biomolecular Engineering, OSU The Role of Natural Capital Industrial and Systems Engineering, OSU Integrating Energy Modeling with the Environment, Economy & Society

  6. Dark energy in some integrable and nonintegrable FRW cosmological models

    E-Print Network [OSTI]

    Kuralay Esmakhanova; Nurgissa Myrzakulov; Gulgasyl Nugmanova; Yerlan Myrzakulov; Leonid Chechin; Ratbay Myrzakulov

    2011-09-14T23:59:59.000Z

    One of the greatest challenges in cosmology today is to determine the nature of dark energy, the sourse of the observed present acceleration of the Universe. Besides the vacuum energy, various dark energy models have been suggested. The Friedmann - Robertson - Walker (FRW) spacetime plays an important role in modern cosmology. In particular, the most popular models of dark energy work in the FRW spacetime. In this work, a new class of integrable FRW cosmological models is presented. These models induced by the well-known Painlev$\\acute{e}$ equations. Some nonintegrable FRW models are also considered. These last models are constructed with the help of Pinney, Schr$\\ddot{o}$dinger and hypergeometric equations. Scalar field description and two-dimensional generalizations of some cosmological models are presented. Finally some integrable and nonintegrable $F(R)$ and $F(G)$ gravity models are constructed.

  7. Modeling and simulation of HVAC Results in EnergyPlus

    E-Print Network [OSTI]

    LBNL-5564E Modeling and simulation of HVAC Results in EnergyPlus Mangesh Basarkar, Xiufeng Pang;MODELING AND SIMULATION OF HVAC FAULTS IN ENERGYPLUS Mangesh Basarkar, Xiufeng Pang, Liping Wang, Philip

  8. An indoorâ??outdoor building energy simulator to study urban modification effects on building energy use â?? Model description and validation

    E-Print Network [OSTI]

    Yaghoobian, Neda; Kleissl, Jan

    2012-01-01T23:59:59.000Z

    T.   Williamson, Urban surface energy balance modelsmodel of the international urban energy balance model comparison, The International Urban Energy Balance Models  Comparison 

  9. A Supply-Demand Model Based Scalable Energy Management System for Improved Energy

    E-Print Network [OSTI]

    Bhunia, Swarup

    energy generation and consumption parameters. The system uses economics inspired supply-demand modelA Supply-Demand Model Based Scalable Energy Management System for Improved Energy Utilization Western Reserve University, *Cleveland State University, +Rockwell Automation, Cleveland, OR, USA Email

  10. Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP): An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    SciTech Connect (OSTI)

    Blair, N.; Jenkin, T.; Milford, J.; Short, W.; Sullivan, P.; Evans, D.; Lieberman, E.; Goldstein, G.; Wright, E.; Jayaraman, K. R.; Venkatesh, B.; Kleiman, G.; Namovicz, C.; Smith, B.; Palmer, K.; Wiser, R.; Wood, F.

    2009-09-01T23:59:59.000Z

    Energy system modeling can be intentionally or unintentionally misused by decision-makers. This report describes how both can be minimized through careful use of models and thorough understanding of their underlying approaches and assumptions. The analysis summarized here assesses the impact that model and data choices have on forecasting energy systems by comparing seven different electric-sector models. This analysis was coordinated by the Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP), a collaboration among governmental, academic, and nongovernmental participants.

  11. Modeling new approaches for electric energy efficiency

    SciTech Connect (OSTI)

    Munns, Diane

    2008-03-15T23:59:59.000Z

    To align utilities and consumers' interests, three incentive methods have emerged to foster efficiency: shared savings, bonus return on equity, and energy service company. A fourth incentive method, virtual power plant, is being proposed by Duke Energy. (author)

  12. Holographic tachyon model of dark energy

    E-Print Network [OSTI]

    M R Setare

    2007-09-11T23:59:59.000Z

    In this paper we consider a correspondence between the holographic dark energy density and tachyon energy density in FRW universe. Then we reconstruct the potential and the dynamics of the tachyon field which describe tachyon cosmology.

  13. Interacting holographic dark energy models: A general approach

    E-Print Network [OSTI]

    S. Som; A. Sil

    2014-12-01T23:59:59.000Z

    Dark energy models inspired by the cosmological holographic principle are studied in homogeneous isotropic spacetime with a general choice for the dark energy density $\\rho_d=3(\\alpha H^2+\\beta\\dot{H})$. Special choices of the parameters enable us to obtain three different holographic models, including the holographic Ricci dark energy(RDE) model. Effect of interaction between dark matter and dark energy on the dynamics of those models are investigated for different popular forms of interaction. It is found that crossing of phantom divide can be avoided in RDE models for $\\beta>0.5$ irrespective of the presence of interaction. A choice of $\\alpha=1$ and $\\beta=2/3$ leads to a varying $\\Lambda$-like model introducing an IR cutoff length $\\Lambda^{-1/2}$. It is concluded that among the popular choices an interaction of the form $Q\\propto H\\rho_m$ suits the best in avoiding the coincidence problem in this model.

  14. Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems

    E-Print Network [OSTI]

    Qiu, Qinru

    Binghamton University, State University of New York Binghamton, New York, USA {jlu5, sliu5, qwu, qqiuAccurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded}@binghamton.edu Abstract -- Energy availability is the primary subject that drives the research innovations in energy

  15. Energy Infrastructure Modeling and Analysis (EIMA) | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33Frequently20,000 Russian NuclearandJunetrackEllen| DepartmentTracking Database, INL EnergyEnergy

  16. Building Energy Simulation & Modeling | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directed offOCHCO OverviewAttachments EnergyFebruary 29 - MarchCodes Resources BuildingInnovation |Building

  17. Renewable Energy Technologies Financial Model (RET Finance) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ | RoadmapRenewable Energy RFPs HomeResources,(RESLtd

  18. National Energy Modeling System (NEMS) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup |JilinLuOpen Energy Information National AllianceDayNEMS) Jump to:

  19. Energy Analysis Models, Tools and Software Technologies - Energy Innovation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContractElectron-StateEnergyHeavy Duty Vehicle Efficiency Energy 101:

  20. Model documentation: Natural gas transmission and distribution model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    NONE

    1995-02-17T23:59:59.000Z

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. NEMS was developed in the Office of integrated Analysis and Forecasting of the Energy information Administration (EIA). NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the EIA and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. The NGTDM is the model within the NEMS that represents the transmission, distribution, and pricing of natural gas. The model also includes representations of the end-use demand for natural gas, the production of domestic natural gas, and the availability of natural gas traded on the international market based on information received from other NEMS models. The NGTDM determines the flow of natural gas in an aggregate, domestic pipeline network, connecting domestic and foreign supply regions with 12 demand regions. The methodology employed allows the analysis of impacts of regional capacity constraints in the interstate natural gas pipeline network and the identification of pipeline capacity expansion requirements. There is an explicit representation of core and noncore markets for natural gas transmission and distribution services, and the key components of pipeline tariffs are represented in a pricing algorithm. Natural gas pricing and flow patterns are derived by obtaining a market equilibrium across the three main elements of the natural gas market: the supply element, the demand element, and the transmission and distribution network that links them. The NGTDM consists of four modules: the Annual Flow Module, the Capacity F-expansion Module, the Pipeline Tariff Module, and the Distributor Tariff Module. A model abstract is provided in Appendix A.

  1. Statefinder diagnosis and the interacting ghost model of dark energy

    E-Print Network [OSTI]

    M. Malekjani; A. Khodam-Mohammadi

    2012-02-19T23:59:59.000Z

    A new model of dark energy namely "ghost dark energy model" has recently been suggested to interpret the positive acceleration of cosmic expansion. The energy density of ghost dark energy is proportional to the hubble parameter. In this paper we perform the statefinder diagnostic tool for this model both in flat and non-flat universe. We discuss the dependency of the evolutionary trajectories in $s-r$ and $q-r$ planes on the interaction parameter between dark matter and dark energy as well as the spatial curvature parameter of the universe. Eventually, in the light of SNe+BAO+OHD+CMB observational data, we plot the evolutionary trajectories in $s-r$ and $q-r$ planes for the best fit values of the cosmological parameters and compare the interacting ghost model with other dynamical dark energy models. We show that the evolutionary trajectory of ghost dark energy in statefinder diagram is similar to holographic dark energy model. It has been shown that the statefinder location of $\\Lambda$CDM is in good agreement with observation and therefore the dark energy models whose current statefinder values are far from the $\\Lambda$CDM point can be ruled out.

  2. Estimation of Building Parameters Using Simplified Energy Balance Model and Metered Whole Building Energy Use

    E-Print Network [OSTI]

    Masuda, H.; Claridge, D.

    2012-01-01T23:59:59.000Z

    , cooling and heating and weather data using multiple linear regression models based on the simplified steady-state energy balance for a whole building. Two approaches using different response variables: the energy balance load (EBL) and the building thermal...

  3. Estimation of Building Parameters Using Simplified Energy Balance Model and Metered Whole Building Energy Use 

    E-Print Network [OSTI]

    Masuda, H.; Claridge, D.

    2012-01-01T23:59:59.000Z

    , cooling and heating and weather data using multiple linear regression models based on the simplified steady-state energy balance for a whole building. Two approaches using different response variables: the energy balance load (EBL) and the building thermal...

  4. Energy Modeling II: interface between model builder and decision maker

    SciTech Connect (OSTI)

    Not Available

    1980-01-01T23:59:59.000Z

    A separate abstract was prepared for each of the 44 papers and the keynote address for the Energy Data Base (EDB) and Energy Abstracts for Policy Analysis (EAPA); 21 of the abstracts will appear in Energy Research Abstracts (ERA). Two papers were processed earlier.

  5. Net Balanced Floorplanning Based on Elastic Energy Model

    E-Print Network [OSTI]

    Nannarelli, Alberto

    Net Balanced Floorplanning Based on Elastic Energy Model Wei Liu and Alberto Nannarelli Dept variations can introduce extra signal skew, it is desirable to have floorplans with balanced net delays based on the elastic energy model. The B*-tree, which is based on an ordered binary tree, is used

  6. Wind Energy Applications of Unified and Dynamic Turbulence Models

    E-Print Network [OSTI]

    Heinz, Stefan

    Wind Energy Applications of Unified and Dynamic Turbulence Models Stefan Heinz and Harish Gopalan applicable as a low cost alternative. 1 Introduction There is a growing interest in using wind energy suggests the possibility of providing 20% of the electricity in the U.S. by wind energy in 2030

  7. MESOSCALE MODELLING OF WIND ENERGY OVER NON-HOMOGENEOUS TERRAIN

    E-Print Network [OSTI]

    Pielke, Roger A.

    MESOSCALE MODELLING OF WIND ENERGY OVER NON-HOMOGENEOUS TERRAIN (ReviewArticle) Y. MAHRER.1. OBSERVATIONALAPPROACHES Evaluations of wind energy based on wind observations (usually surface winds) at well, the resolution of the wind energy pattern throughout an extended area by this methodology requires a large number

  8. Non resonant transmission modelling with Statistical modal Energy distribution Analysis

    E-Print Network [OSTI]

    Boyer, Edmond

    be used as an alternative to Statistical Energy Analysis for describing subsystems with low modal overlap1 Non resonant transmission modelling with Statistical modal Energy distribution Analysis L. Maxit Capelle, F-69621 Villeurbanne Cedex, France Statistical modal Energy distribution Analysis (SmEdA) can

  9. Electrostatic Free Energy and its Variations in Implicit Solvent Models

    E-Print Network [OSTI]

    Li, Bo

    Electrostatic Free Energy and its Variations in Implicit Solvent Models Jianwei Che , Joachim. The unique set of such concentrations that minimize this free energy are given by the usual Boltzmann. The variation of the electrostatic free energy with respect to the location change of solute-solvent interfaces

  10. A Walking Model with No Energy Cost M. W. Gomes

    E-Print Network [OSTI]

    Ruina, Andy L.

    on a frictional surface. Can legged transport over level ground be similarly energy-cost free? NatureA Walking Model with No Energy Cost M. W. Gomes Mechanics, Cornell University; now at Mechanical these minor friction losses, is a zero- energy-cost walking mechanism possible? Consider walking mechanisms

  11. ORQA: Modeling Energy and Quality of Service within AUTOSAR Models

    E-Print Network [OSTI]

    Boyer, Edmond

    Systems]: Consumer Products--electric vehicle General Terms Design, Management Keywords Autosar, model the vehicle autonomy. A vehicle management is achieved by the em- bedded systems, modeled following-oriented Quality of Ser- vice models. This paper presents Orqa, a framework to model and manage the electric

  12. A Dynamic Energy Budget (DEB) model for the energy usage and reproduction of the Icelandic capelin (Mallotus villosus)

    E-Print Network [OSTI]

    Einarsson, Baldvin; Birnir, Bjorn; Sigurđsson, Sven Ţ.

    2010-01-01T23:59:59.000Z

    S.A.L.M. , 2010. Dynamic Energy Budget Theory For Metabolicthe use of dynamic energy budget theory. Biological Reviewsthrough dynamic energy budget models. Jour- nal of Animal

  13. Structure formation in inhomogeneous Early Dark Energy models

    SciTech Connect (OSTI)

    Batista, R.C. [Escola de Cięncias e Tecnologia, Universidade Federal do Rio Grande do Norte, Caixa Postal 1524, 59072-970, Natal, Rio Grande do Norte (Brazil); Pace, F., E-mail: rbatista@ect.ufrn.br, E-mail: francesco.pace@port.ac.uk [Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth, PO1 3FX (United Kingdom)

    2013-06-01T23:59:59.000Z

    We study the impact of Early Dark Energy fluctuations in the linear and non-linear regimes of structure formation. In these models the energy density of dark energy is non-negligible at high redshifts and the fluctuations in the dark energy component can have the same order of magnitude of dark matter fluctuations. Since two basic approximations usually taken in the standard scenario of quintessence models, that both dark energy density during the matter dominated period and dark energy fluctuations on small scales are negligible, are not valid in such models, we first study approximate analytical solutions for dark matter and dark energy perturbations in the linear regime. This study is helpful to find consistent initial conditions for the system of equations and to analytically understand the effects of Early Dark Energy and its fluctuations, which are also verified numerically. In the linear regime we compute the matter growth and variation of the gravitational potential associated with the Integrated Sachs-Wolf effect, showing that these observables present important modifications due to Early Dark Energy fluctuations, though making them more similar to the ?CDM model. We also make use of the Spherical Collapse model to study the influence of Early Dark Energy fluctuations in the nonlinear regime of structure formation, especially on ?{sub c} parameter, and their contribution to the halo mass, which we show can be of the order of 10%. We finally compute how the number density of halos is modified in comparison to the ?CDM model and address the problem of how to correct the mass function in order to take into account the contribution of clustered dark energy. We conclude that the inhomogeneous Early Dark Energy models are more similar to the ?CDM model than its homogeneous counterparts.

  14. Spatial Statistical Procedures to Validate Input Data in Energy Models

    SciTech Connect (OSTI)

    Johannesson, G.; Stewart, J.; Barr, C.; Brady Sabeff, L.; George, R.; Heimiller, D.; Milbrandt, A.

    2006-01-01T23:59:59.000Z

    Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, economic trends, and other primarily non-energy related uses. Systematic collection of empirical data solely for regional, national, and global energy modeling has not been established as in the abovementioned fields. Empirical and modeled data relevant to energy modeling is reported and available at various spatial and temporal scales that might or might not be those needed and used by the energy modeling community. The incorrect representation of spatial and temporal components of these data sets can result in energy models producing misleading conclusions, especially in cases of newly evolving technologies with spatial and temporal operating characteristics different from the dominant fossil and nuclear technologies that powered the energy economy over the last two hundred years. Increased private and government research and development and public interest in alternative technologies that have a benign effect on the climate and the environment have spurred interest in wind, solar, hydrogen, and other alternative energy sources and energy carriers. Many of these technologies require much finer spatial and temporal detail to determine optimal engineering designs, resource availability, and market potential. This paper presents exploratory and modeling techniques in spatial statistics that can improve the usefulness of empirical and modeled data sets that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) predicting missing data, and (3) merging spatial data sets. In addition, we introduce relevant statistical software models commonly used in the field for various sizes and types of data sets.

  15. Model Predictive Control for Energy Efficient Buildings

    E-Print Network [OSTI]

    Ma, Yudong

    2012-01-01T23:59:59.000Z

    control logic for building energy systems. Most moderncontrol actuators. Modern digital building automation systemssystem in the lab. The lab is equipped with a modern digital control

  16. Global Transportation Roadmap Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAddInformationEnergyEnergy JumpGlobal

  17. ICCT Roadmap Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetecGtel JumpCounty, Texas: EnergyHy9Moat ofEnergy52 -IBIS LLC JumpOpenEIICCT

  18. NETL - CARBEN Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRose BendMiasoleTremor(Question) |Renewable Energy | Open EnergyCARBEN

  19. Carrots and Sticks: A Comprehensive Business Model for the Successful Achievement of Energy Efficiency Resource Standards

    E-Print Network [OSTI]

    Satchwell, Andrew

    2013-01-01T23:59:59.000Z

    energy efficiency business model on utility earnings EES w/energy efficiency business model on utility ROE EES w/RPCSticks: A Comprehensive Business Model for the Successful

  20. Colorado: Energy Modeling Products Support Energy Efficiency Projects |

    Office of Environmental Management (EM)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 1112011AT&T, Inc.'sEnergyTexas1. FeedstockCLEANSprings Gets an

  1. Building Energy Simulation & Modeling | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742Energy China U.S. Department ofJune 2,The BigSiding RetrofitforCamberlyDepartment BEopt

  2. Wave Energy Converter Extreme Conditions Modeling Workshop | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro IndustriesTown ofNationwideWTED Jump to: navigation,AreaWatson, NewWauseon,Wave Dragon

  3. Retrofit Energy Savings Estimation Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty Edit with form History FacebookRegenesysRenewableStrategies (EC-LEDS)

  4. Sandia Energy - Decision Models for Integrating Energy/Water

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItemResearch > TheNuclear Press ReleasesInApplied &ClimateContact Us Home

  5. Model Energy Efficiency Program Impact Evaluation Guide | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRose BendMiasole IncMinuteman WindMoana Geothermal Area

  6. National Energy Modeling System (United States) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRose BendMiasoleTremor(Question)8/14/2007NCPV Jump to: navigation,United

  7. Models and Tools for Evaluating Energy Efficiency and Renewable Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742Energy China 2015of 2005 atthe DistrictIndependentDepartment4.docfromImpactProducts |Programs

  8. I Found My Energy Role Model | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directed offOCHCO2: FinalOffers3.pdf0-45.pdf0 BudgetGoalsHealthHow toHydrothermal Photo LibrarySuccessGC2/06I

  9. Energy Efficient Equipment Product Model Listings | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489 No revision|LLC Place: Ketchum, Idaho(1)

  10. Glauber model for heavy ion collisions from low energies to high energies

    E-Print Network [OSTI]

    P. Shukla

    2001-12-13T23:59:59.000Z

    The Glauber model is extensively applied to heavy ion collision for describing a number of interaction processes over a wide range of energies from near the Coulomb barrier to higher energies. The model gives the nucleus-nucleus interaction in terms of interaction between the constituent nucleons with a given density distribution. The model is a semiclassical model picturing the nuclear collision in the impact parameter representation where the nuclei move along the collision direction in a straight path. In these lectures we derive this model and discuss its applications in variety of problems in nuclear and high energy physics.

  11. Model documentation Renewable Fuels Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1996-01-01T23:59:59.000Z

    This report documents the objectives, analaytical approach and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the 1996 Annual Energy Outlook forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described.

  12. Developing an Energy Performance Modeling Startup Kit

    SciTech Connect (OSTI)

    Wood, A.

    2012-10-01T23:59:59.000Z

    In 2011, the NAHB Research Center began the first part of the multi-year effort by assessing the needs and motivations of residential remodelers regarding energy performance remodeling. The scope is multifaceted - all perspectives will be sought related to remodeling firms ranging in size from small-scale, sole proprietor to national. This will allow the Research Center to gain a deeper understanding of the remodeling and energy retrofit business and the needs of contractors when offering energy upgrade services. To determine the gaps and the motivation for energy performance remodeling, the NAHB Research Center conducted (1) an initial series of focus groups with remodelers at the 2011 International Builders' Show, (2) a second series of focus groups with remodelers at the NAHB Research Center in conjunction with the NAHB Spring Board meeting in DC, and (3) quantitative market research with remodelers based on the findings from the focus groups. The goal was threefold, to: Understand the current remodeling industry and the role of energy efficiency; Identify the gaps and barriers to adding energy efficiency into remodeling; and Quantify and prioritize the support needs of professional remodelers to increase sales and projects involving improving home energy efficiency. This report outlines all three of these tasks with remodelers.

  13. Asymptotic Approximations to the Distributed Activation Energy Model

    E-Print Network [OSTI]

    McGuinness, Mark

    applicability in situations, such as computational fluid dynamics modelling of coal-fired boilers, where- plex chemical systems such as coal pyrolysis. MRM assumes that the process can be represented Energy Model (DAEM) or multiple reaction model (MRM) for coal pyrolysis [4] may be applied to either

  14. NREL's Building Component Library for Use with Energy Models

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    The Building Component Library (BCL) is the U.S. Department of Energy’s comprehensive online searchable library of energy modeling building blocks and descriptive metadata. Novice users and seasoned practitioners can use the freely available and uniquely identifiable components to create energy models and cite the sources of input data, which will increase the credibility and reproducibility of their simulations. The BCL contains components which are the building blocks of an energy model. They can represent physical characteristics of the building such as roofs, walls, and windows, or can refer to related operational information such as occupancy and equipment schedules and weather information. Each component is identified through a set of attributes that are specific to its type, as well as other metadata such as provenance information and associated files. The BCL also contains energy conservation measures (ECM), referred to as measures, which describe a change to a building and its associated model. For the BCL, this description attempts to define a measure for reproducible application, either to compare it to a baseline model, to estimate potential energy savings, or to examine the effects of a particular implementation. The BCL currently contains more than 30,000 components and measures. A faceted search mechanism has been implemented on the BCL that allows users to filter through the search results using various facets. Facet categories include component and measure types, data source, and energy modeling software type. All attributes of a component or measure can also be used to filter the results.

  15. Brazil LULUCF Modeling | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:EzfeedflagBiomass ConversionsSouthbyBostonBrattleboro, Vermont: Energy

  16. System Advisor Model (SAM) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual Siteof Energy 2,AUDITCalifornia Sector:Shrenik IndustriesState ofSwitchpower JumpSystem

  17. Advanced Financing Models Webinar | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directed off Energy.gov. Are you0 ARRA Newsletters 20103-03 AUDITProductsletter No.10-006Advanced Financing

  18. Systems Advisor Model | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn April 23, 2014,Zaleski -BlueprintThis documentEnergy(SHINES)Full DocumentSystem

  19. NREL: Energy Analysis - Models and Tools

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItemResearch > The EnergyCenterDioxide CaptureSee theOilNREL in the

  20. Transport Modeling Working Group | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn April 23,EnergyChicopeeTechnologyfact sheetTransferring thefor Analyzing andThe

  1. Portfolio Risk Modeling | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County, Nebraska: EnergyPiratiniEdwards, Wisconsin:Porter County,Porter-CologneRisk

  2. Biotrans: Cost Optimization Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia: EnergyAvignon,Belcher HomesLyons

  3. Model Fire Protection Program | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742Energy ChinaofSchaeferApril 1,(EAC)TABLE OF CONTENTSTogetherThe highDepartment of Energy

  4. System Advisor Model (SAM) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty Edit with formSoutheastern ILSunseeker EnergySuzhouSynergy Biofuels LLC

  5. Modeling-Computer Simulations | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville,Missoula, Montana: EnergyAnalysis ofDecker, 1983)(Roberts,(Laney,| Jump

  6. Advanced Electrolyte Model - Energy Innovation Portal

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041cloth DocumentationProducts (VAP) VAP7-0973 1BP-14 Power andAdvanced Components andEnergyEnergy

  7. Energy-economy interactions revisited within a comprehensive sectoral model

    SciTech Connect (OSTI)

    Hanson, D. A.; Laitner, J. A.

    2000-07-24T23:59:59.000Z

    This paper describes a computable general equilibrium (CGE) model with considerable sector and technology detail, the ``All Modular Industry Growth Assessment'' Model (AMIGA). It is argued that a detailed model is important to capture and understand the several rolls that energy plays within the economy. Fundamental consumer and industrial demands are for the services from energy; hence, energy demand is a derived demand based on the need for heating, cooling mechanical, electrical, and transportation services. Technologies that provide energy-services more efficiently (on a life cycle basis), when adopted, result in increased future output of the economy and higher paths of household consumption. The AMIGA model can examine the effects on energy use and economic output of increases in energy prices (e.g., a carbon charge) and other incentive-based policies or energy-efficiency programs. Energy sectors and sub-sector activities included in the model involve energy extraction conversion and transportation. There are business opportunities to produce energy-efficient goods (i.e., appliances, control systems, buildings, automobiles, clean electricity). These activities are represented in the model by characterizing their likely production processes (e.g., lighter weight motor vehicles). Also, multiple industrial processes can produce the same output but with different technologies and inputs. Secondary recovery, i.e., recycling processes, are examples of these multiple processes. Combined heat and power (CHP) is also represented for energy-intensive industries. Other modules represent residential and commercial building technologies to supply energy services. All sectors of the economy command real resources (capital services and labor).

  8. Model and control for cooperative energy management

    E-Print Network [OSTI]

    Ranade, Vinayak V

    2010-01-01T23:59:59.000Z

    Proto/Amorphous Cooperative Energy Management (PACEM) aims to build and deploy a highly scalable system for smart power grids that will enable efficient demand shaping for small-user networks. Two key problems are to provide ...

  9. Energy laboratory data and model directory

    E-Print Network [OSTI]

    Lahiri, S.

    1981-01-01T23:59:59.000Z

    Over the past several years M.I.T. faculty, staff, and students have produced a substantial body of research and analysis relating to the production, conversion, and use of energy in domestic and international markets. ...

  10. Energy standards and model codes development, adoption, implementation, and enforcement

    SciTech Connect (OSTI)

    Conover, D.R.

    1994-08-01T23:59:59.000Z

    This report provides an overview of the energy standards and model codes process for the voluntary sector within the United States. The report was prepared by Pacific Northwest Laboratory (PNL) for the Building Energy Standards Program and is intended to be used as a primer or reference on this process. Building standards and model codes that address energy have been developed by organizations in the voluntary sector since the early 1970s. These standards and model codes provide minimum energy-efficient design and construction requirements for new buildings and, in some instances, existing buildings. The first step in the process is developing new or revising existing standards or codes. There are two overall differences between standards and codes. Energy standards are developed by a consensus process and are revised as needed. Model codes are revised on a regular annual cycle through a public hearing process. In addition to these overall differences, the specific steps in developing/revising energy standards differ from model codes. These energy standards or model codes are then available for adoption by states and local governments. Typically, energy standards are adopted by or adopted into model codes. Model codes are in turn adopted by states through either legislation or regulation. Enforcement is essential to the implementation of energy standards and model codes. Low-rise residential construction is generally evaluated for compliance at the local level, whereas state agencies tend to be more involved with other types of buildings. Low-rise residential buildings also may be more easily evaluated for compliance because the governing requirements tend to be less complex than for commercial buildings.

  11. Advanced Financing Models | Department of Energy

    Energy Savers [EERE]

    models, third-party vendors, and green bonds. In addition to providing details about renewable project financing, presenters will cover federal and state incentives, local...

  12. Models for Tribal Energy Development Organizations | Department...

    Energy Savers [EERE]

    that significantly contribute to community economic development. Learn about available business models such as the Section 17 corporation and the tribal utility. Also, get tips...

  13. Sandia Energy - Computational Modeling & Simulation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiation Protection245C Unlimited ReleaseWelcomeLong Lifetime ofColin

  14. Photovoltaic Theory and Modeling - Energy Innovation Portal

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's Possible for RenewableSpeedingBiomassPPPOPetroleum Reserves Vision,4newsSolar

  15. Sandia Energy » Modeling & Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementingnpitche Home About npitche This

  16. Carrots and Sticks: A Comprehensive Business Model for the Successful Achievement of Energy Efficiency Resource Standards Environmental Energy Technologies Division March 2011

    E-Print Network [OSTI]

    Satchwell, Andrew

    2011-01-01T23:59:59.000Z

    framework of the energy efficiency business model in furthera comprehensive energy efficiency business model on utilitya comprehensive energy efficiency business model on utility

  17. Linear Free Energy Relationships between Dissolution Rates and Molecular Modeling Energies of Rhombohedral

    E-Print Network [OSTI]

    Linear Free Energy Relationships between Dissolution Rates and Molecular Modeling Energies, and Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185-0750 Received July 24, 2003. In Final Form: December 18, 2003 Bulk and surface energies are calculated for endmembers

  18. Modeling Interregional Transmission Congestion in the National Energy Modeling System

    E-Print Network [OSTI]

    Gumerman, Etan; Chan, Peter; Lesieutre, Bernard; Marnay, Chris; Wang, Juan

    2006-01-01T23:59:59.000Z

    5 Figure 1-4 Four-Node Example Based on the WECC6 Figure 1-5 Four-Node Example Based on the WECCPAE PBA RA RON SERC TWh WECC Annual Energy Outlook U.S.

  19. Modeling compaction-induced energy dissipation of granular HMX

    SciTech Connect (OSTI)

    Gonthier, K.A. [Lamar Univ., Beaumont, TX (US). Dept. of Mechanical Engineering; Menikoff, R.; Son, S.F.; Asay, B.W. [Los Alamos National Lab., NM (US)

    1998-12-31T23:59:59.000Z

    A thermodynamically consistent model is developed for the compaction of granular solids. The model is an extension of the single phase limit of two-phase continuum models used to describe Deflagration-to-Detonation Transition (DDT) experiments. The focus is on the energetics and dissipation of the compaction process. Changes in volume fraction are partitioned into reversible and irreversible components. Unlike conventional DDT models, the model is applicable from the quasi-static to dynamic compaction regimes for elastic, plastic, or brittle materials. When applied to the compaction of granular HMX (a brittle material), the model predicts results commensurate with experiments including stress relaxation, hysteresis, and energy dissipation. The model provides a suitable starting point for the development of thermal energy localization sub-scale models based on compaction-induced dissipation.

  20. Petroleum Market Model of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1997-01-01T23:59:59.000Z

    The purpose of this report is to define the objectives of the Petroleum Market Model (PMM), describe its basic approach, and provide detail on how it works. This report is intended as a reference document for model analysts, users, and the public. The PMM models petroleum refining activities, the marketing of petroleum products to consumption regions. The production of natural gas liquids in gas processing plants, and domestic methanol production. The PMM projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil, both domestic and imported; other inputs including alcohols and ethers; natural gas plant liquids production; petroleum product imports; and refinery processing gain. In addition, the PMM estimates domestic refinery capacity expansion and fuel consumption. Product prices are estimated at the Census division level and much of the refining activity information is at the Petroleum Administration for Defense (PAD) District level. This report is organized as follows: Chapter 2, Model Purpose; Chapter 3, Model Overview and Rationale; Chapter 4, Model Structure; Appendix A, Inventory of Input Data, Parameter Estimates, and Model Outputs; Appendix B, Detailed Mathematical Description of the Model; Appendix C, Bibliography; Appendix D, Model Abstract; Appendix E, Data Quality; Appendix F, Estimation methodologies; Appendix G, Matrix Generator documentation; Appendix H, Historical Data Processing; and Appendix I, Biofuels Supply Submodule.

  1. Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents

    SciTech Connect (OSTI)

    Sanyal, Jibonananda [ORNL] [ORNL; New, Joshua Ryan [ORNL] [ORNL; Edwards, Richard [ORNL] [ORNL; Parker, Lynne Edwards [ORNL] [ORNL

    2014-01-01T23:59:59.000Z

    Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building energy modeling unfeasible for smaller projects. In this paper, we describe the Autotune research which employs machine learning algorithms to generate agents for the different kinds of standard reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models.

  2. A New Model to Simulate Energy Performance of VRF Systems

    SciTech Connect (OSTI)

    Hong, Tianzhen; Pang, Xiufeng; Schetrit, Oren; Wang, Liping; Kasahara, Shinichi; Yura, Yoshinori; Hinokuma, Ryohei

    2014-03-30T23:59:59.000Z

    This paper presents a new model to simulate energy performance of variable refrigerant flow (VRF) systems in heat pump operation mode (either cooling or heating is provided but not simultaneously). The main improvement of the new model is the introduction of the evaporating and condensing temperature in the indoor and outdoor unit capacity modifier functions. The independent variables in the capacity modifier functions of the existing VRF model in EnergyPlus are mainly room wet-bulb temperature and outdoor dry-bulb temperature in cooling mode and room dry-bulb temperature and outdoor wet-bulb temperature in heating mode. The new approach allows compliance with different specifications of each indoor unit so that the modeling accuracy is improved. The new VRF model was implemented in a custom version of EnergyPlus 7.2. This paper first describes the algorithm for the new VRF model, which is then used to simulate the energy performance of a VRF system in a Prototype House in California that complies with the requirements of Title 24 ? the California Building Energy Efficiency Standards. The VRF system performance is then compared with three other types of HVAC systems: the Title 24-2005 Baseline system, the traditional High Efficiency system, and the EnergyStar Heat Pump system in three typical California climates: Sunnyvale, Pasadena and Fresno. Calculated energy savings from the VRF systems are significant. The HVAC site energy savings range from 51 to 85percent, while the TDV (Time Dependent Valuation) energy savings range from 31 to 66percent compared to the Title 24 Baseline Systems across the three climates. The largest energy savings are in Fresno climate followed by Sunnyvale and Pasadena. The paper discusses various characteristics of the VRF systems contributing to the energy savings. It should be noted that these savings are calculated using the Title 24 prototype House D under standard operating conditions. Actual performance of the VRF systems for real houses under real operating conditions will vary.

  3. Sandia Energy - Sandia, NREL Release Wave Energy Converter Modeling and

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution GridDocuments HomeDatabaseInternational

  4. NREL: Energy Analysis - Energy Forecasting and Modeling Staff

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Saleshttp://www.fnal.gov/directorate/nalcal/nalcal02_07_05_files/nalcal.gifNRELPower SystemsDebbie Brodt-Giles PhotoElla

  5. A new alternative model to dark energy

    E-Print Network [OSTI]

    Gong, Y; Duan, C K

    2004-01-01T23:59:59.000Z

    The recent observations of type Ia supernovae strongly support that the universe is accelerating now and decelerated in the recent past. This may be the evidence of the breakdown of the standard Friedmann equation. Instead of a linear function of the matter density, we consider a general function of the matter density to modify the Freidmann equation. We propose a new model which explains the recent acceleration and the past deceleration. Furthermore, the new model also gives a decelerated universe in the future. The new model gives $\\Omega_{m0}=0.46$ and $z_T=0.44$.

  6. A new alternative model to dark energy

    E-Print Network [OSTI]

    Yungui Gong; Xi-Ming Chen; Chang-Kui Duan

    2004-05-08T23:59:59.000Z

    The recent observations of type Ia supernovae strongly support that the universe is accelerating now and decelerated in the recent past. This may be the evidence of the breakdown of the standard Friedmann equation. Instead of a linear function of the matter density, we consider a general function of the matter density to modify the Freidmann equation. We propose a new model which explains the recent acceleration and the past deceleration. Furthermore, the new model also gives a decelerated universe in the future. The new model gives $\\Omega_{m0}=0.46$ and $z_T=0.44$.

  7. Building Energy Modeling (BEM) Program Overview

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny: Theof Energy Future ofHydronicBuildingDepartmentDavidDepartment ofAmir Roth,

  8. CTG Sustainable Communities Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:EzfeedflagBiomassSustainableCSL Gas Recovery Biomass Facility Jump to:ROW Forms JumpCSU

  9. Category:Analytical Modeling | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:EzfeedflagBiomassSustainableCSLInformationMissouri:Catalyst2-Mpage? For detailed

  10. Sandia Energy - Computational Modeling & Simulation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiation Protection245C Unlimited ReleaseWelcomeLong Lifetime ofColinMELCOR Permalink

  11. Sandia Energy - Reference Model Project (RMP)

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution GridDocuments Home Stationary Power Energy ConversionProject

  12. Brophy Occurrence Models | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:Power LP Biomass Facility Jump to: navigation,Biogen1Broomfield

  13. Property:BrophyModel | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar Group BV Jump to:InformationCaseType JumpProperty Edit with form

  14. Sandia Energy - Climate Measurement & Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItemResearch > TheNuclear Press ReleasesInApplied &Climate Measurement &

  15. IDRISI Land Change Modeler | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEIHesperia,IDGWP Wind Farm Jump to: navigation, search NameIDRISI Land

  16. MAPSS Vegetation Modeling | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOf KilaueaInformation Other4Q07) WindLowM2E Power Inc JumpMAKMAMAMAPSS

  17. Annual Energy Outlook 2014 Modeling Updates

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade Energy IDecade Year-0 Year-1 Year-2Cubic Feet)AugustAnalysis;

  18. Sectional Model Flume Facilities | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ |Rippey JumpAir Jump to:ScottsSearch

  19. Gold Standard Program Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetec AG Contracting Jump to:Echo,GEF JumpGloverville,GogebicGold Standard

  20. Vehicle Model Validation | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your DensityEnergy U.S.-China Electric Vehicle and03/02 TUEValidation of& Systems Simulation|

  1. Electrochemistry Cell Model | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny:RevisedAdvisory BoardNucleateElectrochemical Hydrogen CompressionEnergy2 DOE

  2. Electrochemistry Cell Model | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny:RevisedAdvisory BoardNucleateElectrochemical Hydrogen CompressionEnergy2 DOE1

  3. Electrochemistry Cell Model | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny:RevisedAdvisory BoardNucleateElectrochemical Hydrogen CompressionEnergy2 DOE10

  4. Electrochemistry Cell Model | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny:RevisedAdvisory BoardNucleateElectrochemical Hydrogen CompressionEnergy2

  5. Mixed Solvent Electrolyte Model | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't YourTransport(FactDepartment3311,Official FileEnergyAERMOD-PRIME,Department ofMixed Solvent

  6. System Advisor Model (SAM) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries PvtStratosolar Jump to:Holdings Co08.0 - WarehousesSymerton,EV Jump

  7. System Advisor Model (SAM) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries PvtStratosolar Jump to:Holdings Co08.0 - WarehousesSymerton,EV JumpSolar Advisor

  8. Coastal Harbors Modeling Facility | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:PowerCER.png El CER esDatasetCityFundCo-benefitsCoalogix Inc

  9. Coastal Inlet Model Facility | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:PowerCER.png El CER esDatasetCityFundCo-benefitsCoalogix Inc Basic

  10. Regional Dynamics Model (REDYN) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt Ltd Jump to: navigation, searchRayreviewAl., 2005) | OpenRegan,Virginia

  11. Sandia Energy » Computational Modeling & Simulation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiationImplementingnpitche Home About npitche This author has notExpansion ofNewDigital

  12. Form:Buildings Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump to:ar-80m.pdfFillmoreGabbs ValleyCity,Forked River, NewDENERGY Input

  13. Modeling Solar Energy Technology Evolution breakout session

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't YourTransport(FactDepartment3311,OfficialProducts | Department of|of Energy 09|

  14. Quantisation of the holographic Ricci dark energy model

    E-Print Network [OSTI]

    Albarran, Imanol

    2015-01-01T23:59:59.000Z

    While general relativity is an extremely robust theory to describe the gravitational interaction in our Universe, it is expected to fail close to singularities like the cosmological ones. On the other hand, it is well known that some dark energy models might induce future singularities; this can be the case for example within the setup of the Holographic Ricci Dark Energy model (HRDE). On this work, we perform a cosmological quantisation of the HRDE model and obtain under which conditions a cosmic doomsday can be avoided within the quantum realm. We show as well that this quantum model not only avoid future singularities but also the past Big Bang.

  15. Building an Efficient Model for Afterburn Energy Release

    SciTech Connect (OSTI)

    Alves, S; Kuhl, A; Najjar, F; Tringe, J; McMichael, L; Glascoe, L

    2012-02-03T23:59:59.000Z

    Many explosives will release additional energy after detonation as the detonation products mix with the ambient environment. This additional energy release, referred to as afterburn, is due to combustion of undetonated fuel with ambient oxygen. While the detonation energy release occurs on a time scale of microseconds, the afterburn energy release occurs on a time scale of milliseconds with a potentially varying energy release rate depending upon the local temperature and pressure. This afterburn energy release is not accounted for in typical equations of state, such as the Jones-Wilkins-Lee (JWL) model, used for modeling the detonation of explosives. Here we construct a straightforward and efficient approach, based on experiments and theory, to account for this additional energy release in a way that is tractable for large finite element fluid-structure problems. Barometric calorimeter experiments have been executed in both nitrogen and air environments to investigate the characteristics of afterburn for C-4 and other materials. These tests, which provide pressure time histories, along with theoretical and analytical solutions provide an engineering basis for modeling afterburn with numerical hydrocodes. It is toward this end that we have constructed a modified JWL equation of state to account for afterburn effects on the response of structures to blast. The modified equation of state includes a two phase afterburn energy release to represent variations in the energy release rate and an afterburn energy cutoff to account for partial reaction of the undetonated fuel.

  16. Model Predictive Control for Energy Efficient Buildings

    E-Print Network [OSTI]

    Ma, Yudong

    2012-01-01T23:59:59.000Z

    T mixed T amb d OA ?T supply Cooling Fan Heating 20 Time (models for supply fan (5.6), cooling and heating coils (5.7)Solar radiation u cooling/heating coils supply fan dampers

  17. Constraints on alternative models to dark energy

    E-Print Network [OSTI]

    Gong, Y; Gong, Yungui; Duan, Chang-Kui

    2003-01-01T23:59:59.000Z

    The recent observations of type Ia supernovae strongly support that the universe is accelerating now and decelerated in the recent past. This may be the evidence of the breakdown of the standard Friemann equation. We consider a general modified Friedmann equation. Three different models are analyzed in detail. The current supernovae data and the Wilkinson microwave anisotropy probe data are used to constrain these models. A detailed analysis of the transition from the deceleration phase to the acceleration phase is also performed.

  18. Constraints on alternative models to dark energy

    E-Print Network [OSTI]

    Yungui Gong; Chang-Kui Duan

    2005-07-13T23:59:59.000Z

    The recent observations of type Ia supernovae strongly support that the universe is accelerating now and decelerated in the recent past. This may be the evidence of the breakdown of the standard Friemann equation. We consider a general modified Friedmann equation. Three different models are analyzed in detail. The current supernovae data and the Wilkinson microwave anisotropy probe data are used to constrain these models. A detailed analysis of the transition from the deceleration phase to the acceleration phase is also performed.

  19. A dark energy model alternative to generalized Chaplygin gas

    E-Print Network [OSTI]

    Hoavo Hova; Huanxiong Yang

    2010-11-22T23:59:59.000Z

    We propose a new fluid model of dark energy for $-1 \\leq \\omega_{\\text{eff}} \\leq 0$ as an alternative to the generalized Chaplygin gas models. The energy density of dark energy fluid is severely suppressed during barotropic matter dominant epochs, and it dominates the universe evolution only for eras of small redshift. From the perspective of fundamental physics, the fluid is a tachyon field with a scalar potential flatter than that of power-law decelerated expansion. Different from the standard $\\Lambda\\text{CDM}$ model, the suggested dark energy model claims that the cosmic acceleration at present epoch can not continue forever but will cease in the near future and a decelerated cosmic expansion will recover afterwards.

  20. A dark energy model alternative to generalized Chaplygin gas

    E-Print Network [OSTI]

    Hova, Hoavo

    2010-01-01T23:59:59.000Z

    We propose a new fluid model of dark energy for $-1 \\leq \\omega_{\\text{eff}} \\leq 0$ as an alternative to the generalized Chaplygin gas models. The energy density of dark energy fluid is severely suppressed during barotropic matter dominant epochs, and it dominates the universe evolution only for eras of small redshift. From the perspective of fundamental physics, the fluid is a tachyon field with a scalar potential flatter than that of power-law decelerated expansion. Different from the standard $\\Lambda\\text{CDM}$ model, the suggested dark energy model claims that the cosmic acceleration at present epoch can not continue forever but will cease in the near future and a decelerated cosmic expansion will recover afterwards.

  1. Recommendations concerning energy information model documentation, public access, and evaluation

    E-Print Network [OSTI]

    Wood, David O.

    1979-01-01T23:59:59.000Z

    In this study we provide an analysis of the factors underlying Congressional concern regarding model documentation, policies for public access, and evaluation procedures of the Energy Information Administration (EIA) and ...

  2. An evaluation of the ORNL residential energy use model

    E-Print Network [OSTI]

    McFadden, Daniel

    1981-01-01T23:59:59.000Z

    This report provides an evaluation of the architecture, empirical foundation, and applications of the Oak Ridge National Laboratory (ORNL) residential energy use model. A particular effort is made to identify the strengths ...

  3. Modeling and design of a MEMS piezoelectric vibration energy harvester

    E-Print Network [OSTI]

    Du Toit, Noël Eduard

    2005-01-01T23:59:59.000Z

    The modeling and design of MEMS-scale piezoelectric-based vibration energy harvesters (MPVEH) are presented. The work is motivated by the need for pervasive and limitless power for wireless sensor nodes that have application ...

  4. Cosmological viability conditions for f(T) dark energy models

    SciTech Connect (OSTI)

    Setare, M.R.; Mohammadipour, N., E-mail: rezakord@ipm.ir, E-mail: N.Mohammadipour@uok.ac.ir [Department of Science, University of Kurdistan, Sanandaj (Iran, Islamic Republic of)

    2012-11-01T23:59:59.000Z

    Recently f(T) modified teleparallel gravity where T is the torsion scalar has been proposed as the natural gravitational alternative for dark energy. We perform a detailed dynamical analysis of these models and find conditions for the cosmological viability of f(T) dark energy models as geometrical constraints on the derivatives of these models. We show that in the phase space exists two cosmologically viable trajectory which (i) The universe would start from an unstable radiation point, then pass a saddle standard matter point which is followed by accelerated expansion de sitter point. (ii) The universe starts from a saddle radiation epoch, then falls onto the stable matter era and the system can not evolve to the dark energy dominated epoch. Finally, for a number of f(T) dark energy models were proposed in the more literature, the viability conditions are investigated.

  5. Online Modeling in the Process Industry for Energy Optimization

    E-Print Network [OSTI]

    Alexander, J.

    "This paper discusses how steady state models are being used in the process industry to perform online energy optimization of steam and electrical systems. It presents process demands commonly found in the processing industry in terms of steam...

  6. Heat Pump Water Heater Modeling in EnergyPlus (Presentation)

    SciTech Connect (OSTI)

    Wilson, E.; Christensen, C.

    2012-03-01T23:59:59.000Z

    This presentation summarizes NREL's development of a HPWH model for use in hourly building energy simulation programs, such as BEopt; this presentation was given at the Building America Stakeholder meeting on March 1, 2012, in Austin, Texas.

  7. Model documentation report: Industrial sector demand module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1997-01-01T23:59:59.000Z

    This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description of the NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects. The NEMS Industrial Demand Model is a dynamic accounting model, bringing together the disparate industries and uses of energy in those industries, and putting them together in an understandable and cohesive framework. The Industrial Model generates mid-term (up to the year 2015) forecasts of industrial sector energy demand as a component of the NEMS integrated forecasting system. From the NEMS system, the Industrial Model receives fuel prices, employment data, and the value of industrial output. Based on the values of these variables, the Industrial Model passes back to the NEMS system estimates of consumption by fuel types.

  8. Integrating Empirical Measures of Energy Efficiency into an Energy Modeling Framework

    E-Print Network [OSTI]

    Boyd, G.

    2006-01-01T23:59:59.000Z

    Integrating Empirical Measures of Energy Efficiency Into An Energy Modeling Framework Gale Boyd, Argonne National Laboratory Tools such as Data Envelopment Analysis and Stochastic Frontier Regressions provide a basis for empirical measures... of efficiency. The definition of efficiency these tools encompass can be as broadly defined as total factor productivity, or narrowly defined in terms of single inputs like energy. Given the ability to generate empirical measures of energy efficiency...

  9. New agegraphic dark energy model with generalized uncertainty principle

    E-Print Network [OSTI]

    Yong-Wan Kim; Hyung Won Lee; Yun Soo Myung; Mu-In Park

    2008-08-07T23:59:59.000Z

    We investigate the new agegraphic dark energy models with generalized uncertainty principle (GUP). It turns out that although the GUP affects the early universe, it does not change the current and future dark energy-dominated universe significantly. Furthermore, this model could describe the matter-dominated universe in the past only when the parameter $n$ is chosen to be $n>n_c$, where the critical value determined to be $n_c=2.799531478$.

  10. Energy dissipation statistics in a shell model of turbulence

    E-Print Network [OSTI]

    G. Boffetta; A. Celani; D. Roagna

    1999-09-30T23:59:59.000Z

    The Reynolds number dependence of the statistics of energy dissipation is investigated in a shell model of fully developed turbulence. The results are in agreement with a model which accounts for fluctuations of the dissipative scale with the intensity of energy dissipation. It is shown that the assumption of a fixed dissipative scale leads to a different scaling with Reynolds which is not compatible with numerical results.

  11. Regional Economic Models, Inc. (REMI) Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ | Roadmap Jump to:bJumpRedSeismic Imaging,WesternRegional

  12. Delayed energy injection model for gamma-ray burst afterglows

    SciTech Connect (OSTI)

    Geng, J. J.; Huang, Y. F.; Yu, Y. B. [Department of Astronomy, Nanjing University, Nanjing 210093 (China); Wu, X. F., E-mail: hyf@nju.edu.cn, E-mail: xfwu@pmo.ac.cn [Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210008 (China)

    2013-12-10T23:59:59.000Z

    The shallow decay phase and flares in the afterglows of gamma-ray bursts (GRBs) are widely believed to be associated with the later activation of the central engine. Some models of energy injection involve a continuous energy flow since the GRB trigger time, such as the magnetic dipole radiation from a magnetar. However, in the scenario involving a black hole accretion system, the energy flow from the fall-back accretion may be delayed for a fall-back time ?t {sub fb}. Thus, we propose a delayed energy injection model. The delayed energy would cause a notable rise to the Lorentz factor of the external shock, which will 'generate' a bump in the multiple band afterglows. If the delayed time is very short, our model degenerates to the previous models. Our model can explain the significant re-brightening in the optical and infrared light curves of GRB 081029 and GRB 100621A. A considerable fall-back mass is needed to provide the later energy; this indicates that GRBs accompanied with fall-back material may be associated with a low energy supernova so that the fraction of the envelope can survive during eruption. The fall-back time can give meaningful information on the properties of GRB progenitor stars.

  13. Spherical Collapse Model And Dark Energy(I)

    E-Print Network [OSTI]

    Ding-fang Zeng; Yi-hong Gao

    2005-05-09T23:59:59.000Z

    In existing literatures about the top-hat spherical collapse model of galaxy clusters formation in cosmology containing dark energies, dark energies are usually assumed not to cluster on this scale. But all these literatures ignored the current describing the flowing of dark energies outside the clusters which should exist under this assumption, so the conclusions of these literatures are worth further explorations. In this paper we study this model in QCDM or Phantom-CDM cosmologies(flat) by assuming that dark energies will cluster synchronously with ordinary matters on the scale of galaxy clusters so the dark energy current flowing outside the clusters does not exist at all and find that in this case, the key parameters of the model exhibit rather non-trivial and remarkable dependence on the equation of state coefficients of dark energies. We then apply the results in Press-Scheter theory and calculate the number density of galaxy clusters and its evolutions. We find that this two quantities are both affected exponentially by the equation of state coefficients of dark energies. We leave the study of this model with the assumption that dark energies do not cluster on the scale of galaxy clusters at all as the topic of another paper where similar conclusions will be obtained also.

  14. Comparison of Building Energy Modeling Programs: HVAC Systems

    E-Print Network [OSTI]

    LBNL-6432E Comparison of Building Energy Modeling Programs: HVAC Systems Xin Zhou1 , Tianzhen Hong2 programs (BEMPs) for HVAC calculations: EnergyPlus, DeST, and DOE-2.1E. This is a joint effort between purposes, BEMPs can be divided into load modules and HVAC system modules. This technical report

  15. Expand the Modeling Capabilities of DOE's EnergyPlus Building Energy Simulation Program

    SciTech Connect (OSTI)

    Don Shirey

    2008-02-28T23:59:59.000Z

    EnergyPlus{trademark} is a new generation computer software analysis tool that has been developed, tested, and commercialized to support DOE's Building Technologies (BT) Program in terms of whole-building, component, and systems R&D (http://www.energyplus.gov). It is also being used to support evaluation and decision making of zero energy building (ZEB) energy efficiency and supply technologies during new building design and existing building retrofits. Version 1.0 of EnergyPlus was released in April 2001, followed by semiannual updated versions over the ensuing seven-year period. This report summarizes work performed by the University of Central Florida's Florida Solar Energy Center (UCF/FSEC) to expand the modeling capabilities of EnergyPlus. The project tasks involved implementing, testing, and documenting the following new features or enhancement of existing features: (1) A model for packaged terminal heat pumps; (2) A model for gas engine-driven heat pumps with waste heat recovery; (3) Proper modeling of window screens; (4) Integrating and streamlining EnergyPlus air flow modeling capabilities; (5) Comfort-based controls for cooling and heating systems; and (6) An improved model for microturbine power generation with heat recovery. UCF/FSEC located existing mathematical models or generated new model for these features and incorporated them into EnergyPlus. The existing or new models were (re)written using Fortran 90/95 programming language and were integrated within EnergyPlus in accordance with the EnergyPlus Programming Standard and Module Developer's Guide. Each model/feature was thoroughly tested and identified errors were repaired. Upon completion of each model implementation, the existing EnergyPlus documentation (e.g., Input Output Reference and Engineering Document) was updated with information describing the new or enhanced feature. Reference data sets were generated for several of the features to aid program users in selecting proper model inputs. An example input data file, suitable for distribution to EnergyPlus users, was created for each new or improved feature to illustrate the input requirements for the model.

  16. Natural gas transmission and distribution model of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1997-02-01T23:59:59.000Z

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. NEMS was developed in the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA). NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the EIA and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. From 1982 through 1993, the Intermediate Future Forecasting System (IFFS) was used by the EIA for its analyses, and the Gas Analysis Modeling System (GAMS) was used within IFFS to represent natural gas markets. Prior to 1982, the Midterm Energy Forecasting System (MEFS), also referred to as the Project Independence Evaluation System (PIES), was employed. NEMS was developed to enhance and update EIA`s modeling capability by internally incorporating models of energy markets that had previously been analyzed off-line. In addition, greater structural detail in NEMS permits the analysis of a broader range of energy issues. The time horizon of NEMS is the midterm period (i.e., through 2015). In order to represent the regional differences in energy markets, the component models of NEMS function at regional levels appropriate for the markets represented, with subsequent aggregation/disaggregation to the Census Division level for reporting purposes.

  17. Transportation Sector Model of the National Energy Modeling System. Volume 2 -- Appendices: Part 1

    SciTech Connect (OSTI)

    NONE

    1998-01-01T23:59:59.000Z

    This volume contains input data and parameters used in the model of the transportation sector of the National Energy Modeling System. The list of Transportation Sector Model variables includes parameters for the following: Light duty vehicle modules (fuel economy, regional sales, alternative fuel vehicles); Light duty vehicle stock modules; Light duty vehicle fleet module; Air travel module (demand model and fleet efficiency model); Freight transport module; Miscellaneous energy demand module; and Transportation emissions module. Also included in these appendices are: Light duty vehicle market classes; Maximum light duty vehicle market penetration parameters; Aircraft fleet efficiency model adjustment factors; and List of expected aircraft technology improvements.

  18. activation energy model: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    energy model First Page Previous Page 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 Time-dependent Stochastic Modeling of...

  19. Univariate Modeling and Forecasting of Monthly Energy Demand Time Series

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural dedicated models to forecast the 12 individual months directly. Results indicate better performance is superior to naĂŻve forecasts based on persistence and seasonality, and is better than results quoted

  20. Battery Life Predictor Model - Energy Innovation Portal

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041cloth DocumentationProductsAlternativeOperationalAugustDecade5-F,INITIALoperatorBassi

  1. NREL: Energy Analysis - BSM: Biomass Scenario Model

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC)Integrated CodesTransparency Visit | NationalWebmaster To contactK-12BSM - Biomass

  2. NREL: Energy Analysis - Models and Tools Archive

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC)Integrated CodesTransparency Visit | NationalWebmaster To contactK-12BSM -JEDI

  3. Sandia Energy - Tutorial on PV System Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home DistributionTransportation Safety Home Stationary Power NuclearTutorial on

  4. Sandia Energy - Computational Modeling & Simulation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Scienceand RequirementsCoatings Initiated at PNNL's Sequim BayCaptureCloud Computingfor

  5. Sandia Energy - PV Reliability & Performance Model

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution Grid IntegrationOffshore Wind RD&D:PVReliability &

  6. Hydrogen Delivery Analysis Models | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't YourTransport(Fact Sheet), GeothermalGridHYDROGEND D e e& Fuel

  7. Model documentation renewable fuels module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1997-04-01T23:59:59.000Z

    This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the 1997 Annual Energy Outlook forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs. and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. This documentation report serves three purposes. First, it is a reference document for model analysts, model users, and the public interested in the construction and application of the RFM. Second, it meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. Finally, such documentation facilitates continuity in EIA model development by providing information sufficient to perform model enhancements and data updates as part of EIA`s ongoing mission to provide analytical and forecasting information systems.

  8. White paper on VU for Modeling Nuclear Energy Systems

    SciTech Connect (OSTI)

    Klein, R; Turinsky, P

    2009-05-07T23:59:59.000Z

    The purpose of this whitepaper is to provide a framework for understanding the role that Verification and Validation (V&V), Uncertainty Quantification (UQ) and Risk Quantification, collectively referred to as VU, is expected to play in modeling nuclear energy systems. We first provide background for the modeling of nuclear energy based systems. We then provide a brief discussion that emphasizes the critical elements of V&V as applied to nuclear energy systems but is general enough to cover a broad spectrum of scientific and engineering disciplines that include but are not limited to astrophysics, chemistry, physics, geology, hydrology, chemical engineering, mechanical engineering, civil engineering, electrical engineering, nu nuclear engineering material clear science science, etc. Finally, we discuss the critical issues and challenges that must be faced in the development of a viable and sustainable VU program in support of modeling nuclear energy systems.

  9. Infiltration modeling guidelines for commercial building energy analysis

    SciTech Connect (OSTI)

    Gowri, Krishnan; Winiarski, David W.; Jarnagin, Ronald E.

    2009-09-30T23:59:59.000Z

    This report presents a methodology for modeling air infiltration in EnergyPlus to account for envelope air barrier characteristics. Based on a review of various infiltration modeling options available in EnergyPlus and sensitivity analysis, the linear wind velocity coefficient based on DOE-2 infiltration model is recommended. The methodology described in this report can be used to calculate the EnergyPlus infiltration input for any given building level infiltration rate specified at known pressure difference. The sensitivity analysis shows that EnergyPlus calculates the wind speed based on zone altitude, and the linear wind velocity coefficient represents the variation in infiltration heat loss consistent with building location and weather data.

  10. Water supply and demand in an energy supply model

    SciTech Connect (OSTI)

    Abbey, D; Loose, V

    1980-12-01T23:59:59.000Z

    This report describes a tool for water and energy-related policy analysis, the development of a water supply and demand sector in a linear programming model of energy supply in the United States. The model allows adjustments in the input mix and plant siting in response to water scarcity. Thus, on the demand side energy conversion facilities can substitute more costly dry cooling systems for conventional evaporative systems. On the supply side groundwater and water purchased from irrigators are available as more costly alternatives to unappropriated surface water. Water supply data is developed for 30 regions in 10 Western states. Preliminary results for a 1990 energy demand scenario suggest that, at this level of spatial analysis, water availability plays a minor role in plant siting. Future policy applications of the modeling system are discussed including the evaluation of alternative patterns of synthetic fuels development.

  11. How to obtain the National Energy Modeling System (NEMS)

    Reports and Publications (EIA)

    2013-01-01T23:59:59.000Z

    The National Energy Modeling System (NEMS) NEMS is used by the modelers at the U. S. Energy Information Administration (EIA) who understand its structure and programming. NEMS has only been used by a few organizations outside of the EIA, because most people that requested NEMS found out that it was too difficult or rigid to use. NEMS is not typically used for state-level analysis and is poorly suited for application to other countries. However, many do obtain the model simply to use the data in its input files or to examine the source code.

  12. Modeling-Computer Simulations (Laney, 2005) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville,Missoula, Montana: EnergyAnalysis of Energy DemandModeling-Computer

  13. Toward an energy-conserving model of spontaneous wavefunction collapse

    E-Print Network [OSTI]

    Andrea Smirne; Angelo Bassi

    2014-08-27T23:59:59.000Z

    Collapse models explain the absence of quantum superpositions at the macroscopic scale, while giving practically the same predictions as quantum mechanics for microscopic systems. A well-known problem of the original models is the steady and unlimited increase of the energy induced by the collapse noise. Here we define the first collapse model which, besides applying also to (non-relativistic) identical particles, guarantees a finite energy during the entire system's evolution, thus making a crucial step toward a realistic energy-conserving collapse model. This is achieved by introducing a proper non-linear stochastic modification of the Schr\\"odinger equation, which represents the action of a dissipative finite-temperature collapse noise.

  14. Model documentation: Renewable Fuels Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    Not Available

    1994-04-01T23:59:59.000Z

    This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it related to the production of the 1994 Annual Energy Outlook (AEO94) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. This documentation report serves two purposes. First, it is a reference document for model analysts, model users, and the public interested in the construction and application of the RFM. Second, it meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. The RFM consists of six analytical submodules that represent each of the major renewable energy resources -- wood, municipal solid waste (MSW), solar energy, wind energy, geothermal energy, and alcohol fuels. Of these six, four are documented in the following chapters: municipal solid waste, wind, solar and biofuels. Geothermal and wood are not currently working components of NEMS. The purpose of the RFM is to define the technological and cost characteristics of renewable energy technologies, and to pass these characteristics to other NEMS modules for the determination of mid-term forecasted renewable energy demand.

  15. Reference Model 6 (RM6): Oscillating Wave Energy Converter.

    SciTech Connect (OSTI)

    Bull, Diana L; Smith, Chris; Jenne, Dale Scott; Jacob, Paul; Copping, Andrea; Willits, Steve; Fontaine, Arnold; Brefort, Dorian; Gordon, Margaret Ellen; Copeland, Robert; Jepsen, Richard A.

    2014-10-01T23:59:59.000Z

    This report is an addendum to SAND2013-9040: Methodology for Design and Economic Analysis of Marine Energy Conversion (MEC) Technologies. This report describes an Oscillating Water Column Wave Energy Converter reference model design in a complementary manner to Reference Models 1-4 contained in the above report. In this report, a conceptual design for an Oscillating Water Column Wave Energy Converter (WEC) device appropriate for the modeled reference resource site was identified, and a detailed backward bent duct buoy (BBDB) device design was developed using a combination of numerical modeling tools and scaled physical models. Our team used the methodology in SAND2013-9040 for the economic analysis that included costs for designing, manufacturing, deploying, and operating commercial-scale MEC arrays, up to 100 devices. The methodology was applied to identify key cost drivers and to estimate levelized cost of energy (LCOE) for this RM6 Oscillating Water Column device in dollars per kilowatt-hour (%24/kWh). Although many costs were difficult to estimate at this time due to the lack of operational experience, the main contribution of this work was to disseminate a detailed set of methodologies and models that allow for an initial cost analysis of this emerging technology. This project is sponsored by the U.S. Department of Energy's (DOE) Wind and Water Power Technologies Program Office (WWPTO), within the Office of Energy Efficiency & Renewable Energy (EERE). Sandia National Laboratories, the lead in this effort, collaborated with partners from National Laboratories, industry, and universities to design and test this reference model.

  16. Urban Form Energy Use and Emissions in China: Preliminary Findings and Model Proof of Concept

    E-Print Network [OSTI]

    Aden, Nathaniel

    2011-01-01T23:59:59.000Z

    China's building sector--A review of energy and climate models forecast,China's building sector--A review of energy and climate models forecast,

  17. Comprehensive country energy assessments using the MARKAL-MACRO model

    SciTech Connect (OSTI)

    Reisman, A.W.

    1997-07-01T23:59:59.000Z

    A number of comprehensive country energy assessments were performed in the late 1970s and early 1980s in cooperation with the governments of various countries. The assessments provided a framework for analyzing the impacts of various national strategies for meeting energy requirements. These analyses considered the total energy framework. Economics, energy supply, national resources, energy use, environmental impacts, technologies, energy efficiencies, and sociopolitical impacts were some of the factors addressed. These analyses incorporated the best available data bases and computer models to facilitate the analyses. National policy makers identified the various strategies to examine. The results of the analyses were provided to the national policy makers to support their decision making. Almost 20 years have passed since these assessments were performed. There have been major changes in energy supply and use, technologies, economics, available resources, and environmental concerns. The available tools for performing the assessments have improved drastically. The availability of improved computer modeling, i.e., MARKAL-MACRO, and improved data collection methods and data bases now permit such assessments to be performed in a more sophisticated manner to provide state of the art support to policy makers. The MARKAL-MACRO model was developed by Brookhaven National Laboratory over the last 25 years to support strategic energy planning. It is widely used in the international community for integrating analyses of environmental options, such as reduction of greenhouse gas emissions. It was used to perform the analyses in the least cost energy strategy study for the Energy Policy Act of 1992. Improvements continue to be made to MARKAL-MACRO and its capabilities extended. A methodology to conduct Country Energy Assessments using MARKAL-MACRO is discussed.

  18. Emissions and Energy: An Integral Approach Using an Online Energy Management and Optimization Model

    E-Print Network [OSTI]

    Ruiz, D.; Ruiz, C.; Santollani, O.; Reitmeier, T.

    2010-01-01T23:59:59.000Z

    examples and results corresponding to the application of such systems to refineries will be discussed. In addition, the integration of CO2 emission costs and constraints into the online energy system models and their optimization is also explained....

  19. The Importance of High Temporal Resolution in Modeling Renewable Energy Penetration Scenarios

    E-Print Network [OSTI]

    Nicolosi, Marco

    2011-01-01T23:59:59.000Z

    for Competitive Renewable Energy Zones in Texas, Report,Supply, National Renewable Energy Laboratory. Sensfuß, F. ,in Modeling Renewable Energy Penetration Scenarios Marco

  20. Multiobjective calibration and sensitivity of a distributed land surface water and energy balance model

    E-Print Network [OSTI]

    Houser, Paul R; Gupta, Hoshin V; Shuttleworth, W. James; Famiglietti, James S

    2001-01-01T23:59:59.000Z

    identification and energy balance models on a tallgrassdata for surface energy balance evaluation of a semiaridWatershed. We are energy balance components over a semiarid

  1. COMBINING DIVERSE DATA SOURCES FOR CEDSS, AN AGENT-BASED MODEL OF DOMESTIC ENERGY DEMAND

    E-Print Network [OSTI]

    Gotts, Nicholas Mark; Polhill, Gary; Craig, Tony; Galan-Diaz, Carlos

    2014-01-01T23:59:59.000Z

    Model CEDSS (Community Energy Demand Social Simulator) wasthe determinants of domestic energy demand and covering fivescenarios of domestic energy demand to 2050, and for its

  2. ResPoNSe: modeling the wide variability of residential energy consumption.

    E-Print Network [OSTI]

    Peffer, Therese; Burke, William; Auslander, David

    2010-01-01T23:59:59.000Z

    affect appliance energy consumption. For example, differentStates, 2005 Residential Energy Consumption Survey: HousingModeling of End-Use Energy Consumption in the Residential

  3. Dark Energy Models and Laws of Thermodynamics in Bianchi I Model

    E-Print Network [OSTI]

    M. Sharif; Rabia Saleem

    2013-02-20T23:59:59.000Z

    This paper is devoted to check validity of the laws of thermodynamics for LRS Bianchi type I universe model which is filled with combination of dark matter and dark energy. We take two types of dark energy models, i.e., generalized holographic dark energy and generalized Ricci dark energy. It is proved that the first and generalized second law of thermodynamics are valid on the apparent horizon for both the models. Further, we take fixed radius $L$ of the apparent horizon with original holographic or Ricci dark energy. We conclude that the first and generalized second laws of thermodynamics do not hold on the horizon of fixed radius $L$ for both the models.

  4. Models and Tools for Evaluating Energy Efficiency and Renewable Energy Project Opportunities

    Broader source: Energy.gov [DOE]

    In this webinar, attendees will learn about the models and tools developed by DOE and its partners to assist Tribes in assessing renewable energy and energy efficiency project potential. The webinar is held from 11:00 a.m. to 12:30 p.m. Mountain Standard Time on May 27, 2015.

  5. Structure formation in modified gravity models alternative to dark energy

    E-Print Network [OSTI]

    Kazuya Koyama

    2006-01-10T23:59:59.000Z

    We study structure formation in phenomenological models in which the Friedmann equation receives a correction of the form $H^{\\alpha}/r_c^{2-\\alpha}$, which realize an accelerated expansion without dark energy. In order to address structure formation in these model, we construct simple covariant gravitational equations which give the modified Friedmann equation with $\\alpha=2/n$ where $n$ is an integer. For $n=2$, the underlying theory is known as a 5D braneworld model (the DGP model). Thus the models interpolate between the DGP model ($n=2, \\alpha=1$) and the LCDM model in general relativity ($n \\to \\infty, \\alpha \\to 0$). Using the covariant equations, cosmological perturbations are analyzed. It is shown that in order to satisfy the Bianchi identity at a perturbative level, we need to introduce a correction term $E_{\\mu \

  6. Structure formation in modified gravity models alternative to dark energy

    E-Print Network [OSTI]

    Koyama, K

    2006-01-01T23:59:59.000Z

    We study structure formation in phenomenological models in which the Friedmann equation receives a correction of the form $H^{\\alpha}/r_c^{2-\\alpha}$, which realize an accelerated expansion without dark energy. In order to address structure formation in these model, we construct simple covariant gravitational equations which give the modified Friedmann equation with $\\alpha=2/n$ where $n$ is an integer. For $n=2$, the underlying theory is known as a 5D braneworld model (the DGP model). Thus the models interpolate between the DGP model ($n=2, \\alpha=1$) and the LCDM model in general relativity ($n \\to \\infty, \\alpha \\to 0$). Using the covariant equations, cosmological perturbations are analyzed. It is shown that in order to satisfy the Bianchi identity at a perturbative level, we need to introduce a correction term $E_{\\mu \

  7. Model documentation renewable fuels module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1995-06-01T23:59:59.000Z

    This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the 1995 Annual Energy Outlook (AEO95) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. The RFM consists of six analytical submodules that represent each of the major renewable energy resources--wood, municipal solid waste (MSW), solar energy, wind energy, geothermal energy, and alcohol fuels. The RFM also reads in hydroelectric facility capacities and capacity factors from a data file for use by the NEMS Electricity Market Module (EMM). The purpose of the RFM is to define the technological, cost and resource size characteristics of renewable energy technologies. These characteristics are used to compute a levelized cost to be competed against other similarly derived costs from other energy sources and technologies. The competition of these energy sources over the NEMS time horizon determines the market penetration of these renewable energy technologies. The characteristics include available energy capacity, capital costs, fixed operating costs, variable operating costs, capacity factor, heat rate, construction lead time, and fuel product price.

  8. Renewable Energy and Efficiency Modeling Analysis Partnership: An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    SciTech Connect (OSTI)

    Blair, N.; Jenkin, T.; Milford, J.; Short, W.; Sullivan, P.; Evans, D.; Lieberman, E.; Goldstein, G.; Wright, E.; Jayaraman, K.; Venkatech, B.; Kleiman, G.; Namovicz, C.; Smith, B.; Palmer, K.; Wiser, R.; Wood, F.

    2009-09-30T23:59:59.000Z

    The Renewable Energy and Efficiency Modeling and Analysis Partnership (REMAP) sponsors ongoing workshops to discuss individual 'renewable' technologies, energy/economic modeling, and - to some extent - policy issues related to renewable energy. Since 2002, the group has organized seven workshops, each focusing on a different renewable technology (geothermal, solar, wind, etc.). These workshops originated and continue to be run under an informal partnership of the Environmental Protection Agency (EPA), the Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE), the National Renewable Energy Laboratory (NREL), and the American Council on Renewable Energy (ACORE). EPA originally funded the activities, but support is now shared between EPA and EERE. REMAP has a wide range of participating analysts and models/modelers that come from government, the private sector, and academia. Modelers include staff from the Energy Information Administration (EIA), the American Council for an Energy-Efficient Economy (ACEEE), NREL, EPA, Resources for the Future (RFF), Argonne National Laboratory (ANL), Northeast States for Coordinated Air Use Management (NESCAUM), Regional Economic Models Inc. (REMI), ICF International, OnLocation Inc., and Boston University. The working group has more than 40 members, which also includes representatives from DOE, Lawrence Berkeley National Laboratory (LBNL), Union of Concerned Scientists (UCS), Massachusetts Renewable Energy Trust, Federal Energy Regulatory Commission (FERC), and ACORE. This report summarizes the activities and findings of the REMAP activity that started in late 2006 with a kickoff meeting, and concluded in mid-2008 with presentations of final results. As the project evolved, the group compared results across models and across technologies rather than just examining a specific technology or activity. The overall goal was to better understand how and why different energy models give similar and/or different answers in response to a set of focused energy-related questions. The focus was on understanding reasons for model differences, not on policy implications, even though a policy of high renewable penetration was used for the analysis. A group process was used to identify the potential question (or questions) to be addressed through the project. In late 2006, increasing renewable energy penetration in the electricity sector was chosen from among several options as the general policy to model. From this framework, the analysts chose a renewable portfolio standard (RPS) as the way to implement the required renewable energy market penetration in the models. An RPS was chosen because it was (i) of interest and represented the group's consensus choice, and (ii) tractable and not too burdensome for the modelers. Because the modelers and analysts were largely using their own resources, it was important to consider the degree of effort required. In fact, several of the modelers who started this process had to discontinue participation because of other demands on their time. Federal and state RPS policy is an area of active political interest and debate. Recognizing this, participants used this exercise to gain insight into energy model structure and performance. The results are not intended to provide any particular insight into policy design or be used for policy advocacy, and participants are not expected to form a policy stance based on the outcomes of the modeling. The goals of this REMAP project - in terms of the main topic of renewable penetration - were to: (1) Compare models and understand why they may give different results to the same question, (2) Improve the rigor and consistency of assumptions used across models, and (3) Evaluate the ability of models to measure the impacts of high renewable-penetration scenarios.

  9. Ground state energy fluctuations in the Nuclear Shell Model

    E-Print Network [OSTI]

    Victor Velazquez; Jorge G. Hirsch; Alejandro Frank; Jose Barea; Andres P. Zuker

    2005-03-29T23:59:59.000Z

    Statistical fluctuations of the nuclear ground state energies are estimated using shell model calculations in which particles in the valence shells interact through well defined forces, and are coupled to an upper shell governed by random 2-body interactions. Induced ground-state energy fluctuations are found to be one order of magnitude smaller than those previously associated with chaotic components, in close agreement with independent perturbative estimates based on the spreading widths of excited states.

  10. Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1995-02-01T23:59:59.000Z

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This report serves three purposes. First, it is a reference document providing a detailed description for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports (Public Law 93-275, section 57(b)(1)). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

  11. Nuclear Hybrid Energy System Modeling: RELAP5 Dynamic Coupling Capabilities

    SciTech Connect (OSTI)

    Piyush Sabharwall; Nolan Anderson; Haihua Zhao; Shannon Bragg-Sitton; George Mesina

    2012-09-01T23:59:59.000Z

    The nuclear hybrid energy systems (NHES) research team is currently developing a dynamic simulation of an integrated hybrid energy system. A detailed simulation of proposed NHES architectures will allow initial computational demonstration of a tightly coupled NHES to identify key reactor subsystem requirements, identify candidate reactor technologies for a hybrid system, and identify key challenges to operation of the coupled system. This work will provide a baseline for later coupling of design-specific reactor models through industry collaboration. The modeling capability addressed in this report focuses on the reactor subsystem simulation.

  12. Winding vacuum energies in a deformed O(4) sigma model

    E-Print Network [OSTI]

    Vladimir V. Bazhanov; Gleb A. Kotousov; Sergei L. Lukyanov

    2014-09-01T23:59:59.000Z

    We consider the problem of calculating the Casimir energies in the winding sectors of Fateev's SS-model, which is an integrable two-parameter deformation of the O(4) non-linear sigma model in two dimensions. This problem lies beyond the scope of all traditional methods of integrable quantum field theory including the thermodynamic Bethe ansatz and non-linear integral equations. Here we propose a solution based on a remarkable correspondence between classical and quantum integrable systems and express the winding energies in terms of certain solutions of the classical sinh-Gordon equation.

  13. Energy conserving Anisotropic Anhysteretic Magnetic Modelling for Finite Element Analysis

    E-Print Network [OSTI]

    Jens Krause

    2012-12-20T23:59:59.000Z

    To model ferromagnetic material in finite element analysis a correct description of the constitutive relationship (BH-law) must be found from measured data. This article proposes to use the energy density function as a centrepiece. Using this function, which turns out to be a convex function of the flux density, guarantees energy conservative modelling. The magnetic field strength can be seen as a derivative with respect to the flux density. Especially for anisotropic materials (from lamination and/or grain orientation) this method has advantages. Strictly speaking this method is only valid for anhysteretic and thermodynamically stable material.

  14. Category:Data and Modeling Techniques | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual Model Add.png Add aTechniques page? For

  15. Property:Buildings/ModelType | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar GroupInformationInformationYearConstruction1ModelName JumpModelType

  16. Property:Buildings/ModelYear | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar GroupInformationInformationYearConstruction1ModelNameModelYear Jump

  17. Differences between the 1993 and 1995 CABO Model Energy Codes

    SciTech Connect (OSTI)

    Conover, D.R.; Lucas, R.G.

    1995-10-01T23:59:59.000Z

    The Energy Policy Act of 1992 requires the US DOE to determine if changes to the Council of American Building Officials` (CABO) 1993 Model Energy Code (MEC) (CABO 1993), published in the 1995 edition of the MEC (CABO 1995), will improve energy efficiency in residential buildings. The DOE, the states, and others have expressed an interest in the differences between the 1993 and 1995 editions of the MEC. This report describes each change to the 1993 MEC, and its impact. Referenced publications are also listed along with discrepancies between code changes approved in the 1994 and 1995 code-change cycles and what actually appears in the 1995 MEC.

  18. Conceptual Model At Raft River Geothermal Area (1988) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, clickInformationNew| Open Energy InformationJerseyOpen2003)

  19. Conceptual Model At Raft River Geothermal Area (1990) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, clickInformationNew| Open Energy

  20. Conceptual Models of Geothermal Systems - Introduction | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, clickInformationNew| Open EnergyInformation Faulds, Et Al.,Et Al.,

  1. Text-Alternative Version: Webcast of the Renewable Energy Competency Model: An Aid to Build a Renewable Energy Skilled Workforce

    Broader source: Energy.gov [DOE]

    This is the Webcast of Renewable Energy and Competency model. Led by Linda Silverman an economist at Energy Efficiency and Renewable Energy and Pam Frugoli Department of Labor. This webcast was on...

  2. Building environment modeling and minimum-energy control

    E-Print Network [OSTI]

    Godfrey, James Bradford

    1980-01-01T23:59:59.000Z

    be expanded to study energy loss due to vapor condensation. The mathematical model of the building environment is simplified so that optimal temperature control can be studied. Simulations of the building environment heating system using feed- back.... Heating System Simulation. . OPTIMAL TEMPERATURE CONTROL. . . A. Def i ni ti ons 8, Model for the Dynamic Programming Algorithm C. The Dynamic Programming Algorithm. . D. Stochastic External Forcing Terms. . E. Optimal Stochastic Heating Control...

  3. Thermalization at lowest energies? A view from a transport model

    E-Print Network [OSTI]

    C Hartnack; H Oeschler; J Aichelin

    2010-10-05T23:59:59.000Z

    Using the Isospin Quantum Molecular Dynamics (IQMD) model we analyzed the production of pions and kaons in the energy range of 1-2 AGeV in order to study the question why thermal models could achieve a successful description. For this purpose we study the variation of pion and kaon yields using different elementary cross sections. We show that several ratios appear to be rather robust versus their variations.

  4. Model documentation report: Residential sector demand module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1995-03-01T23:59:59.000Z

    This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This document serves three purposes. First, it is a reference document providing a detailed description for energy analysts, other users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports according to Public Law 93-275, section 57(b)(1). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

  5. A stochastic reorganizational bath model for electronic energy transfer

    E-Print Network [OSTI]

    Takatoshi Fujita; Joonsuk Huh; Alan Aspuru-Guzik

    2014-06-06T23:59:59.000Z

    The fluctuations of optical gap induced by the environment play crucial roles in electronic energy transfer dynamics. One of the simplest approaches to incorporate such fluctuations in energy transfer dynamics is the well known Haken-Strobl-Reineker model, in which the energy-gap fluctuation is approximated as a white noise. Recently, several groups have employed molecular dynamics simulations and excited-state calculations in conjunction to take the thermal fluctuation of excitation energies into account. Here, we discuss a rigorous connection between the stochastic and the atomistic bath models. If the phonon bath is treated classically, time evolution of the exciton-phonon system can be described by Ehrenfest dynamics. To establish the relationship between the stochastic and atomistic bath models, we employ a projection operator technique to derive the generalized Langevin equations for the energy-gap fluctuations. The stochastic bath model can be obtained as an approximation of the atomistic Ehrenfest equations via the generalized Langevin approach. Based on the connection, we propose a novel scheme to correct reorganization effects within the framework of stochastic models. The proposed scheme provides a better description of the population dynamics especially in the regime of strong exciton-phonon coupling. Finally, we discuss the effect of the bath reorganization in the absorption and fluorescence spectra of ideal J-aggregates in terms of the Stokes shifts. For this purpose, we introduce a simple relationship that relates the reorganization contribution to the Stokes shifts - the reorganization shift - to three parameters: the monomer reorganization energy, the relaxation time of the optical gap, and the exciton delocalization length. This simple relationship allows one to classify the origin of the Stokes shifts in molecular aggregates.

  6. Energy Models for Drawing Signed Graphs Anne-Marie Kermarrec and Afshin Moin

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    by proposing a dual energy model for graphs containing uniquely negative edges, and combining it linearly

  7. Model documentation Coal Market Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1996-04-30T23:59:59.000Z

    This report documents objectives and conceptual and methodological approach used in the development of the National Energy Modeling System (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1996 (AEO96). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s three submodules: Coal Production Submodule, Coal Export Submodule, and Coal Distribution Submodule.

  8. Spreading of energy in the Ding-Dong Model

    E-Print Network [OSTI]

    S. Roy; A. Pikovsky

    2012-06-26T23:59:59.000Z

    We study properties of energy spreading in a lattice of elastically colliding harmonic oscillators (Ding-Dong model). We demonstrate that in the regular lattice the spreading from a localized initial state is mediated by compactons and chaotic breathers. In a disordered lattice the compactons do not exist, and the spreading eventually stops, resulting in a finite configuration with a few chaotic spots.

  9. Models of the Prompt and High Energy Emission of GRB

    SciTech Connect (OSTI)

    Meszaros, Peter; Toma, Kenji; Wu Xuefeng; He Haoning [Center for Particle Astrophysics, Dept. of Astronomy and Astrophysics and Dept. of Physics, Pennsylvania State University, University Park, PA 16802 (United States)

    2010-10-15T23:59:59.000Z

    Gamma-ray bursts have been detected at photon energies up to tens of GeV. We review some recent developments in the X-ray to GeV photon phenomenology in the light of Swift and Fermi observations, and some of the theoretical models developed to explain them.

  10. Sudden Future Singularity models as an alternative to Dark Energy?

    E-Print Network [OSTI]

    Hoda Ghodsi; Martin A. Hendry; Mariusz P. Dabrowski; Tomasz Denkiewicz

    2011-03-11T23:59:59.000Z

    Current observational evidence does not yet exclude the possibility that dark energy could be in the form of phantom energy. A universe consisting of a phantom constituent will be driven toward a drastic end known as the `Big Rip' singularity where all the matter in the universe will be destroyed. Motivated by this possibility, other evolutionary scenarios have been explored by Barrow, including the phenomena which he called Sudden Future Singularities (SFS). In such a model it is possible to have a blow up of the pressure occurring at sometime in the future evolution of the universe while the energy density would remain unaffected. The particular evolution of the scale factor of the universe in this model that results in a singular behaviour of the pressure also admits acceleration in the current era. In this paper we will present the results of our confrontation of one example class of SFS models with the available cosmological data from high redshift supernovae, baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB). We then discuss the viability of the model in question as an alternative to dark energy.

  11. Development of nuclear models for higher energy calculations

    SciTech Connect (OSTI)

    Bozoian, M.; Siciliano, E.R.; Smith, R.D.

    1988-01-01T23:59:59.000Z

    Two nuclear models for higher energy calculations have been developed in the regions of high and low energy transfer, respectively. In the former, a relativistic hybrid-type preequilibrium model is compared with data ranging from 60 to 800 MeV. Also, the GNASH exciton preequilibrium-model code with higher energy improvements is compared with data at 200 and 318 MeV. In the region of low energy transfer, nucleon-nucleus scattering is predominately a direct reaction involving quasi-elastic collisions with one or more target nucleons. We discuss various aspects of quasi-elastic scattering which are important in understanding features of cross sections and spin observables. These include (1) contributions from multi-step processes; (2) damping of the continuum response from 2p-2h excitations; (3) the ''optimal'' choice of frame in which to evaluate the nucleon-nucleon amplitudes; and (4) the effect of optical and spin-orbit distortions, which are included in a model based on the RPA the DWIA and the eikonal approximation. 33 refs., 15 figs.

  12. Modeling Windows in Energy Plus with Simple Performance Indices

    E-Print Network [OSTI]

    indices (U-factor, Solar Heat Gain Coefficient, and Visible Transmittance) to model the energy impacts, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily

  13. A cosmographic analysis of holographic dark energy models

    E-Print Network [OSTI]

    Supriya Pan; Subenoy Chakraborty

    2014-11-10T23:59:59.000Z

    The present work deals with a detailed study of interacting holographic dark energy model for three common choices of the interaction term. Also, two standard choices of IR cut-off, namely, Ricci length scale and radius of the event horizon are considered here. Finally, the cosmographic parameters are presented both analytically and graphically.

  14. Performance and Energy Modeling for Live Migration of Virtual Machines

    E-Print Network [OSTI]

    Xu, Cheng-Zhong

    Performance and Energy Modeling for Live Migration of Virtual Machines Haikun Liu , Cheng-Zhong Xu , Hai Jin , Jiayu Gong , Xiaofei Liao School of Computer Science and Technology Huazhong University of Science and Technology Wuhan, 430074, China {hjin, xfliao}@hust.edu.cn Department of Electrical

  15. Modelling the Energy Demand of Households in a Combined

    E-Print Network [OSTI]

    Steininger, Karl W.

    . Emissions from passenger transport, households'electricity and heat consumption are growing rapidly despite demand analysis for electricity (e.g. Larsen and Nesbakken, 2004; Holtedahl and Joutz, 2004Modelling the Energy Demand of Households in a Combined Top Down/Bottom Up Approach Kurt Kratena

  16. Strategic Eurasian Natural Gas Model for Energy Security

    E-Print Network [OSTI]

    Chyong, Chi-Kong; Hobbs, Benjamin F.

    2011-04-06T23:59:59.000Z

    in (Egging et al., 2008). Lise and Hobbs (2008) assumed that only producers have market power. The primary purpose of extending the GASTALE model to include dynamic investment is to address the policy question of energy corridors to Europe. The dynamic...

  17. Steady states for Streater's energy-transport models

    E-Print Network [OSTI]

    Esteban, Maria J.

    Steady states for Streater's energy-transport models of self/4, 50-384 Wroc_law, Poland Piotr.Biler@math.uni.wroc.pl, 2Ceremade Matematyki, Politechnika Zielonog'orska, ul. Podg'orna 50, 65-246 Zielona G'ora, Poland

  18. Advanced Modeling of Renewable Energy Market Dynamics: May 2006

    SciTech Connect (OSTI)

    Evans, M.; Little, R.; Lloyd, K.; Malikov, G.; Passolt, G.; Arent, D.; Swezey, B.; Mosey, G.

    2007-08-01T23:59:59.000Z

    This report documents a year-long academic project, presenting selected techniques for analysis of market growth, penetration, and forecasting applicable to renewable energy technologies. Existing mathematical models were modified to incorporate the effects of fiscal policies and were evaluated using available data. The modifications were made based on research and classification of current mathematical models used for predicting market penetration. An analysis of the results was carried out, based on available data. MATLAB versions of existing and new models were developed for research and policy analysis.

  19. Nonlocal String Tachyon as a Model for Cosmological Dark Energy

    SciTech Connect (OSTI)

    Aref'eva, Irina Ya. [Steklov Mathematical Institute, Russian Academy of Sciences, Gubkin st. 8, Moscow, 119991 (Russian Federation)

    2006-03-29T23:59:59.000Z

    There are many different phenomenological models describing the cosmological dark energy and accelerating Universe by choosing adjustable functions. In this paper we consider a specific model of scalar tachyon field which is derived from the NSR string field theory and study its cosmological applications. We find that in the effective field theory approximation the equation of state parameter w < -1, i.e. one has a phantom Universe. It is shown that due to nonlocal effects there is no quantum instability that the usual phantom models suffer from. Moreover due to a flip effect of the potential the Universe does not enter to a future singularity.

  20. Crystal Structure Representations for Machine Learning Models of Formation Energies

    E-Print Network [OSTI]

    Faber, Felix; von Lilienfeld, O Anatole; Armiento, Rickard

    2015-01-01T23:59:59.000Z

    We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organic molecules have been successful using a Coulomb matrix representation of the molecule. We consider three ways to generalize such representations to periodic systems: (i) a matrix where each element is related to the Ewald sum of the electrostatic interaction between two different atoms in the unit cell repeated over the lattice; (ii) an extended Coulomb-like matrix that takes into account a number of neighboring unit cells; and (iii) an Ansatz that mimics the periodicity and the basic features of the elements in the Ewald sum matrix by using a sine function of the crystal coordinates of the atoms. The representations are compared for a Laplacian kernel with Manhattan norm, trained to reproduce formation energies using a data set of 3938 crystal structures obtained from the Materials Project. For training sets consi...

  1. Differences between the 1992 and 1993 CABO Model Energy Codes

    SciTech Connect (OSTI)

    Conover, D.R.; Lucas, R.G.

    1995-01-01T23:59:59.000Z

    This report is one in a series of documents describing research activities in support of the US Department of Energy (DOE) Building Energy Standards Program. The Pacific Northwest Laboratory (PNL) leads the program for DOE. The goal of the Program is to develop and encourage the implementation Of Performance standards to achieve the maximum practicable energy efficiency in the design of new buildings. The program approach to meeting the goal is to initiate and manage individual research and standards and guidelines development efforts that are planned and conducted in cooperation with representatives from throughout the buildings community. Projects under way involve practicing architects and engineers, Professional societies and code organizations, industry representatives, and researchers from the private sector and national laboratories. Research results and technical justifications for standards criteria are provided to standards development and model code organizations and to Federal, State, and local jurisdictions as a basis to update their codes and standards. This effort helps to ensure that building standards incorporate the latest research results to achieve maximum energy savings in new buildings, Yet remain responsive to the needs of the affected professions, organizations, and jurisdictions. Our efforts also support the implementation, deployment, and use of energy-efficient codes and standards. This report identifies the differences between the 1992 and 1993 editions of the Council of American Building Officials, (CABO) Model Energy Code (MEC) and briefly highlights the technical and administrative impacts of these changes.

  2. New holographic Chaplygin gas model of dark energy

    E-Print Network [OSTI]

    M. Malekjani; A. Khodam-Mohammadi

    2010-11-20T23:59:59.000Z

    In this work, we investigate the holographic dark energy model with new infrared cut-off (new HDE model) proposed by Granda and Oliveros. Using this new definition for infrared cut-off, we establish the correspondence between new HDE model and standard Chaplygin gas (SCG), generalized Chaplygin gas (GCG) and modified Chaplygin gas (MCG) scalar field models in non-flat universe. The potential and dynamics for these scalar field models, which describe the accelerated expansion of the universe are reconstructed. According to the evolutionary behavior of new HDE model, we derive the same form of dynamics and potential for different SCG, GCG and MCG models. We also calculate the squared sound speed of new HDE model as well as for SCG, GCG and MCG models and investigate the new HDE Chaplygin gas models from the viewpoint of linear perturbation theory. All results in non-flat universe are also discussed in the limiting case of flat universe, i.e. $k=0$.

  3. PSCAD/EMTDC-Based Modeling and Analysis of a Microgrid with Renewable Energy Sources

    E-Print Network [OSTI]

    Chu, Zhengguo

    2010-07-14T23:59:59.000Z

    . The proposed microgrid system includes fundamental power system component models, two renewable energy source models (wind and solar) and one energy storage source model. Different case studies were conducted. The results from the simulation case studies...

  4. PSCAD/EMTDC-Based Modeling and Analysis of a Microgrid with Renewable Energy Sources 

    E-Print Network [OSTI]

    Chu, Zhengguo

    2010-07-14T23:59:59.000Z

    . The proposed microgrid system includes fundamental power system component models, two renewable energy source models (wind and solar) and one energy storage source model. Different case studies were conducted. The results from the simulation case studies...

  5. A Three-Dimensional Model of Residential Energy Consumer Archetypes for Local Energy Policy Design in the UK

    E-Print Network [OSTI]

    Aickelin, Uwe

    residential energy consumers in the UK by considering property energy efficiency levels, the greenness1 A Three-Dimensional Model of Residential Energy Consumer Archetypes for Local Energy Policy lines of research in residential energy consumption in the UK, i.e. economic/infrastructure, behaviour

  6. Impacts of Modeled Recommendations of the National Commission on Energy Policy

    Reports and Publications (EIA)

    2005-01-01T23:59:59.000Z

    This report provides the Energy Information Administration's analysis of those National Commission on Energy Policy (NCEP) energy policy recommendations that could be simulated using the National Energy Modeling System (NEMS).

  7. Numerical study of energy diffusion in King models

    E-Print Network [OSTI]

    Tom Theuns

    1995-11-07T23:59:59.000Z

    The energy diffusion coefficients D_n(E) (n=1,2) for a system of equal mass particles moving self-consistently in an N-body realisation of a King model are computed from the probability per unit time, P(E, Delta E), that a star with initial energy E will undergo an energy change Delta E. In turn, P is computed from the number of times during the simulation that a particle in a state of given energy undergoes a transition to another state. These particle states are defined directly from the time evolution of E by identifying them with the event occuring between two local maxima in the E(t) curve. If one assumes next that energy changes are uncorrelated between different states, one can use diffusion theory to compute D_n(E). The simulations employ N=512, 2048,... , 32768 particles and are performed using an implementation of Aarseth's direct integrator N-body1 on a massively parallel computer. The more than seven million transitions measured in the largest N simulation provide excellent statistics. The numerically determined D(E)'s are compared against their theoretical counterparts which are computed from phase-space averaged rates of energy change due to independent binary encounters. The overall agreement between them is impressive over most of the energy range, notwithstanding the very different type of approximations involved, giving considerable support to the valid usage of these theoretical expressions to simulate dynamical evolution in Fokker-Planck type calculations.

  8. Solar-hydrogen energy system model for Libya

    SciTech Connect (OSTI)

    Eljrushi, G.S.

    1987-01-01T23:59:59.000Z

    A solar-hydrogen energy-system model for Libya was developed, obtaining relationships for and between the main energy and energy related parameters of Libya and the world. The parameters included are: population, energy demand, fossil-fuel production, fossil-fuel resources, hydrogen production, hydrogen introduction rates, energy prices, gross domestic product, pollution and quality of life. The trends of these parameters with and without hydrogen introduction were investigated over a period of time - through the year 2100. The results indicate that the fossil-fuel resources in Libya could be exhausted, due to production for local and export demands, within three to four decades unless serious measures for reducing production are taken. The results indicate that adopting solar-hydrogen energy system would extend the availability of fossil-fuel resources for a longer time period, reduce pollution, improve quality of life and establish a permanent energy system for Libya. It also shows that eventually Libya could export hydrogen in lieu of oil and natural gas.

  9. Understanding Energy Models and Modeling (ENV 715) Spring 2013 Tuesday/Thursday 10:05-11:20 am

    E-Print Network [OSTI]

    Ferrari, Silvia

    topics for Congress, and publishes the Annual Energy Outlook. At Duke, NI-NEMS has recently been used1 Understanding Energy Models and Modeling (ENV 715) Spring 2013 Tuesday/Thursday 10:05-11:20 am) Course Description and Learning Objectives Energy models are widely used for policy analysis, scenario

  10. New holographic dark energy model inspired by the DGP braneworld

    E-Print Network [OSTI]

    Sheykhi, A; Ghaffari, S

    2015-01-01T23:59:59.000Z

    The energy density of the holographic dark energy is based on the area law of entropy, and thus any modification of the area law leads to a modified holographic energy density. Inspired by the entropy expression associated with the apparent horizon of a Friedmann-Robertson-Walker (FRW) Universe in DGP braneworld, we propose a new model for the holographic dark energy in the framework of DGP brane cosmology. We investigate the cosmological consequences of this new model and calculate the equation of state parameter by choosing the Hubble radius, $L = H^{-1}$, as the system's IR cutoff. Our study show that, due to the effects of the extra dimension (bulk), the identification of IR-cutoff with Hubble radius, can reproduce the present acceleration of the Universe expansion. This is in contrast to the ordinary holographic dark energy in standard cosmology which leads to the zero equation of state parameter in the case of choosing the Hubble radius as system's IR cutoff in the absence of interaction between dark ma...

  11. Reference Model 5 (RM5): Oscillating Surge Wave Energy Converter

    SciTech Connect (OSTI)

    Yu, Y. H.; Jenne, D. S.; Thresher, R.; Copping, A.; Geerlofs, S.; Hanna, L. A.

    2015-01-01T23:59:59.000Z

    This report is an addendum to SAND2013-9040: Methodology for Design and Economic Analysis of Marine Energy Conversion (MEC) Technologies. This report describes an Oscillating Water Column Wave Energy Converter (OSWEC) reference model design in a complementary manner to Reference Models 1-4 contained in the above report. A conceptual design for a taut moored oscillating surge wave energy converter was developed. The design had an annual electrical power of 108 kilowatts (kW), rated power of 360 kW, and intended deployment at water depths between 50 m and 100 m. The study includes structural analysis, power output estimation, a hydraulic power conversion chain system, and mooring designs. The results were used to estimate device capital cost and annual operation and maintenance costs. The device performance and costs were used for the economic analysis, following the methodology presented in SAND2013-9040 that included costs for designing, manufacturing, deploying, and operating commercial-scale MEC arrays up to 100 devices. The levelized cost of energy estimated for the Reference Model 5 OSWEC, presented in this report, was for a single device and arrays of 10, 50, and 100 units, and it enabled the economic analysis to account for cost reductions associated with economies of scale. The baseline commercial levelized cost of energy estimate for the Reference Model 5 device in an array comprised of 10 units is $1.44/kilowatt-hour (kWh), and the value drops to approximately $0.69/kWh for an array of 100 units.

  12. Equilibrium Statistical-Thermal Models in High-Energy Physics

    E-Print Network [OSTI]

    Abdel Nasser Tawfik

    2014-10-25T23:59:59.000Z

    We review some recent highlights from the applications of statistical-thermal models to different experimental measurements and lattice QCD thermodynamics, that have been made during the last decade. We start with a short review of the historical milestones on the path of constructing statistical-thermal models for heavy-ion physics. We discovered that Heinz Koppe formulated in 1948 an almost complete recipe for the statistical-thermal models. In 1950, Enrico Fermi generalized this statistical approach, in which he started with a general cross-section formula and inserted into it simplifying assumptions about the matrix element of the interaction process that likely reflects many features of the high-energy reactions dominated by density in the phase space of final states. In 1964, Hagedorn systematically analysed the high-energy phenomena using all tools of statistical physics and introduced the concept of limiting temperature based on the statistical bootstrap model. It turns to be quite often that many-particle systems can be studied with the help of statistical-thermal methods. The analysis of yield multiplicities in high-energy collisions gives an overwhelming evidence for the chemical equilibrium in the final state. The strange particles might be an exception, as they are suppressed at lower beam energies. However, their relative yields fulfill statistical equilibrium, as well. We review the equilibrium statistical-thermal models for particle production, fluctuations and collective flow in heavy-ion experiments. We also review their reproduction of the lattice QCD thermodynamics at vanishing and finite chemical potential. During the last decade, five conditions have been suggested to describe the universal behavior of the chemical freeze out parameters.

  13. Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1998-01-01T23:59:59.000Z

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. The NEMS Commercial Sector Demand Module is a simulation tool based upon economic and engineering relationships that models commercial sector energy demands at the nine Census Division level of detail for eleven distinct categories of commercial buildings. Commercial equipment selections are performed for the major fuels of electricity, natural gas, and distillate fuel, for the major services of space heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The algorithm also models demand for the minor fuels of residual oil, liquefied petroleum gas, steam coal, motor gasoline, and kerosene, the renewable fuel sources of wood and municipal solid waste, and the minor services of office equipment. Section 2 of this report discusses the purpose of the model, detailing its objectives, primary input and output quantities, and the relationship of the Commercial Module to the other modules of the NEMS system. Section 3 of the report describes the rationale behind the model design, providing insights into further assumptions utilized in the model development process to this point. Section 3 also reviews alternative commercial sector modeling methodologies drawn from existing literature, providing a comparison to the chosen approach. Section 4 details the model structure, using graphics and text to illustrate model flows and key computations.

  14. Property:Buildings/ModelClimateZone | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar GroupInformationInformationYearConstruction1 JumpModelClimateZone

  15. Property:Buildings/ModelName | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar GroupInformationInformationYearConstruction1ModelName Jump to:

  16. Property:Buildings/ModelTargetType | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar GroupInformationInformationYearConstruction1ModelName Jump

  17. Property:Buildings/ModelXmlFile | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,Pillar GroupInformationInformationYearConstruction1ModelName

  18. ENV-Linkages-KEI Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOE FacilityDimondale,South, NewDyer County,ECO2Ltd Place: Nr.ENV-Linkages-KEI

  19. Electricity Markets Analysis (EMA) Model | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOEHazel Crest, Illinois:EdinburghEldorado IvanpahGas WellsColumbiaArea,

  20. Community Wind Handbook/Research Turbine Models | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:PowerCER.png ElColumbia, NorthCommunity ManagementMaintenanceModels

  1. Property:Data Comparison to Computational Models | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County, Nebraska:PrecourtOid Jump to: navigation, searchto Computational Models Jump

  2. Transportation Sector Model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    NONE

    1998-01-01T23:59:59.000Z

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. The current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.

  3. Turkey energy and environmental review - Task 7 energy sector modeling : executive summary.

    SciTech Connect (OSTI)

    Conzelmann, G.; Koritarov, V.; Decision and Information Sciences

    2008-02-28T23:59:59.000Z

    Turkey's demand for energy and electricity is increasing rapidly. Since 1990, energy consumption has increased at an annual average rate of 4.3%. As would be expected, the rapid expansion of energy production and consumption has brought with it a wide range of environmental issues at the local, regional and global levels. With respect to global environmental issues, Turkey's carbon dioxide (CO2) emissions have grown along with its energy consumption. Emissions in 2000 reached 211 million metric tons. With GDP projected to grow at over 6% per year over the next 25 years, both the energy sector and the pollution associated with it are expected to increase substantially. This is expected to occur even if assuming stricter controls on lignite and hard coal-fired power generation. All energy consuming sectors, that is, power, industrial, residential, and transportation, will contribute to this increased emissions burden. Turkish Government authorities charged with managing the fundamental problem of carrying on economic development while protecting the environment include the Ministry of Environment (MOE), the Ministry of Energy and Natural Resources (MENR), and the Ministry of Health, as well as the Turkish Electricity Generation & Transmission Company (TEAS). The World Bank, working with these agencies, is planning to assess the costs and benefits of various energy policy alternatives under an Energy and Environment Review (EER). Eight individual studies have been conducted under this activity to analyze certain key energy technology issues and use this analysis to fill in the gaps in data and technical information. This will allow the World Bank and Turkish authorities to better understand the trade-offs in costs and impacts associated with specific policy decisions. The purpose of Task 7-Energy Sector Modeling, is to integrate information obtained in other EER tasks and provide Turkey's policy makers with an integrated systems analysis of the various options for addressing the various energy and environmental concerns. The work presented in this report builds on earlier analyses presented at the COP 6 conference in Bonn.

  4. Modeling Activities in the Department of Energy’s Atmospheric Sciences Program

    SciTech Connect (OSTI)

    Fast, Jerome D.; Ghan, Steven J.; Schwartz, Stephen E.

    2009-03-01T23:59:59.000Z

    The Department of Energy's Atmospheric Science Program (ASP) conducts research pertinent to radiative forcing of climate change by atmospheric aerosols. The program consists of approximately 40 highly interactive peer-reviewed research projects that examine aerosol properties and processes and the evolution of aerosols in the atmosphere. Principal components of the program are instrument development, laboratory experiments, field studies, theoretical investigations, and modeling. The objectives of the Program are to 1) improve the understanding of aerosol processes associated with light scattering and absorption properties and interactions with clouds that affect Earth's radiative balance and to 2) develop model-based representations of these processes that enable the effects of aerosols on Earth's climate system to be properly represented in global-scale numerical climate models. Although only a few of the research projects within ASP are explicitly identified as primarily modeling activities, modeling actually comprises a substantial component of a large fraction of ASP research projects. This document describes the modeling activities within the Program as a whole, the objectives and intended outcomes of these activities, and the linkages among the several modeling components and with global-scale modeling activities conducted under the support of the Department of Energy's Climate Sciences Program and other aerosol and climate research programs.

  5. Energy-Casimir stability of hybrid Vlasov-MHD models

    E-Print Network [OSTI]

    Cesare Tronci; Emanuele Tassi; Philip J. Morrison

    2014-10-07T23:59:59.000Z

    Different variants of hybrid kinetic-fluid models are considered for describing the interaction of a bulk fluid plasma obeying MHD and an energetic component obeying a kinetic theory. Upon using the Vlasov kinetic theory for energetic particles, two planar Vlasov-MHD models are compared in terms of their stability properties. This is made possible by the Hamiltonian structures underlying the considered hybrid systems, whose infinite number of invariants makes the energy-Casimir method effective for determining stability. Equilibrium equations for the models are obtained from a variational principle and in particular a generalized hybrid Grad-Shafranov equation follows for one of the considered models. The stability conditions are then derived and discussed with particular emphasis on kinetic particle effects on classical MHD stability.

  6. Models for Optimization of Energy Consumption of Pumps in a Wastewater Processing Plant

    E-Print Network [OSTI]

    Kusiak, Andrew

    ; Energy consumption; Data collection; Neural networks; Dynamic models; Statics; Water treatment plants. Author keywords: Wastewater pump models; Energy consumption; Pump energy; Data mining; Head influenceModels for Optimization of Energy Consumption of Pumps in a Wastewater Processing Plant Zijun Zhang

  7. Solar Radiation Modeling and Measurements for Renewable Energy Applications: Data and Model Quality; Preprint

    SciTech Connect (OSTI)

    Myers, D. R.

    2003-03-01T23:59:59.000Z

    Measurement and modeling of broadband and spectral terrestrial solar radiation is important for the evaluation and deployment of solar renewable energy systems. We discuss recent developments in the calibration of broadband solar radiometric instrumentation and improving broadband solar radiation measurement accuracy. An improved diffuse sky reference and radiometer calibration and characterization software and for outdoor pyranometer calibrations is outlined. Several broadband solar radiation model approaches, including some developed at the National Renewable Energy Laboratory, for estimating direct beam, total hemispherical and diffuse sky radiation are briefly reviewed. The latter include the Bird clear sky model for global, direct beam, and diffuse terrestrial solar radiation; the Direct Insolation Simulation Code (DISC) for estimating direct beam radiation from global measurements; and the METSTAT (Meteorological and Statistical) and Climatological Solar Radiation (CSR) models that estimate solar radiation from meteorological data. We conclude that currently the best model uncertainties are representative of the uncertainty in measured data.

  8. Optimal Control of Distributed Energy Resources using Model Predictive Control

    SciTech Connect (OSTI)

    Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.; Zhang, Wei; Lu, Shuai; Samaan, Nader A.; Butler-Purry, Karen

    2012-07-22T23:59:59.000Z

    In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.

  9. Results from Development of Model Specifications for Multifamily Energy Retrofits

    SciTech Connect (OSTI)

    Brozyna, K.

    2012-08-01T23:59:59.000Z

    Specifications, modeled after CSI MasterFormat, provide the trade contractors and builders with requirements and recommendations on specific building materials, components and industry practices that comply with the expectations and intent of the requirements within the various funding programs associated with a project. The goal is to create a greater level of consistency in execution of energy efficiency retrofits measures across the multiple regions a developer may work. IBACOS and Mercy Housing developed sample model specifications based on a common building construction type that Mercy Housing encounters.

  10. A Finite Element Algorithm of a Nonlinear Diffusive Climate Energy Balance Model

    E-Print Network [OSTI]

    DĂ­az, JesĂşs Ildefonso

    A Finite Element Algorithm of a Nonlinear Diffusive Climate Energy Balance Model R. BERMEJO,1 J. This model belongs to the category of energy balance models introduced independently by the climatologists M climate. The energy balance model we are dealing with consists of a two-dimensional nonlinear parabolic

  11. Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

    E-Print Network [OSTI]

    Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models 2011 # Springer Science+Business Media B.V. 2011 Abstract In energy-economy modeling, new hybrid models) backcasting a hybrid energy- economy model over a historical time period; and (3) the application of Markov

  12. Nationwide water availability data for energy-water modeling.

    SciTech Connect (OSTI)

    Tidwell, Vincent Carroll; Zemlick, Katie M.; Klise, Geoffrey Taylor

    2013-11-01T23:59:59.000Z

    The purpose of this effort is to explore where the availability of water could be a limiting factor in the siting of new electric power generation. To support this analysis, water availability is mapped at the county level for the conterminous United States (3109 counties). Five water sources are individually considered, including unappropriated surface water, unappropriated groundwater, appropriated water (western U.S. only), municipal wastewater and brackish groundwater. Also mapped is projected growth in non-thermoelectric consumptive water demand to 2035. Finally, the water availability metrics are accompanied by estimated costs associated with utilizing that particular supply of water. Ultimately these data sets are being developed for use in the National Renewable Energy Laboratories' (NREL) Regional Energy Deployment System (ReEDS) model, designed to investigate the likely deployment of new energy installations in the U.S., subject to a number of constraints, particularly water.

  13. NREL's System Advisor Model Simplifies Complex Energy Analysis (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2015-01-01T23:59:59.000Z

    NREL has developed a tool -- the System Advisor Model (SAM) -- that can help decision makers analyze cost, performance, and financing of any size grid-connected solar, wind, or geothermal power project. Manufacturers, engineering and consulting firms, research and development firms, utilities, developers, venture capital firms, and international organizations use SAM for end-to-end analysis that helps determine whether and how to make investments in renewable energy projects.

  14. Transport Modeling Working Group Meeting Reports | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn'tOriginEducationVideoStrategic|IndustrialCenter Gets PeopleTransmissionModeling Working Group

  15. Live Webinar on Better Buildings Case Competition: Energy Efficiency in the Restaurant Franchise Model Case Study

    Broader source: Energy.gov [DOE]

    The Energy Department will present a live webinar titled "A Side of Savings: Energy Efficiency in the Restaurant Franchise Model Case Study."

  16. Sudden Future Singularity models as an alternative to Dark Energy?

    E-Print Network [OSTI]

    Ghodsi, Hoda; Dabrowski, Mariusz P; Denkiewicz, Tomasz

    2011-01-01T23:59:59.000Z

    Current observational evidence does not yet exclude the possibility that dark energy could be in the form of phantom energy. A universe consisting of a phantom constituent will be driven toward a drastic end known as the `Big Rip' singularity where all the matter in the universe will be destroyed. Motivated by this possibility, other evolutionary scenarios have been explored by Barrow, including the phenomena which he called Sudden Future Singularities (SFS). In such a model it is possible to have a blow up of the pressure occurring at sometime in the future evolution of the universe while the energy density would remain unaffected. The particular evolution of the scale factor of the universe in this model that results in a singular behaviour of the pressure also admits acceleration in the current era. In this paper we will present the results of our confrontation of one example class of SFS models with the available cosmological data from high redshift supernovae, baryon acoustic oscillations (BAO) and the c...

  17. Carrots and Sticks: A Comprehensive Business Model for the Successful Achievement of Energy Efficiency Resource Standards Environmental Energy Technologies Division March 2011

    E-Print Network [OSTI]

    Satchwell, Andrew

    2011-01-01T23:59:59.000Z

    Business Model for the Successful Achievement of Energy Efficiency ResourceBusiness Model for the Successful Achievement of Energy Efficiency Resourcebusiness model on utility ROE 13   Table 1. Lifetime savings, resource costs and benefits of alternative energy efficiency

  18. Simulation and Big Data Challenges in Tuning Building Energy Models

    SciTech Connect (OSTI)

    Sanyal, Jibonananda [ORNL] [ORNL; New, Joshua Ryan [ORNL] [ORNL

    2013-01-01T23:59:59.000Z

    EnergyPlus is the flagship building energy simulation software used to model whole building energy consumption for residential and commercial establishments. A typical input to the program often has hundreds, sometimes thousands of parameters which are typically tweaked by a buildings expert to get it right . This process can sometimes take months. Autotune is an ongoing research effort employing machine learning techniques to automate the tuning of the input parameters for an EnergyPlus input description of a building. Even with automation, the computational challenge faced to run the tuning simulation ensemble is daunting and requires the use of supercomputers to make it tractable in time. In this proposal, we describe the scope of the problem, the technical challenges faced and overcome, the machine learning techniques developed and employed, and the software infrastructure developed/in development when taking the EnergyPlus engine, which was primarily designed to run on desktops, and scaling it to run on shared memory supercomputers (Nautilus) and distributed memory supercomputers (Frost and Titan). The parametric simulations produce data in the order of tens to a couple of hundred terabytes.We describe the approaches employed to streamline and reduce bottlenecks in the workflow for this data, which is subsequently being made available for the tuning effort as well as made available publicly for open-science.

  19. Exploring holographic dark energy model with Sandage-Loeb test

    E-Print Network [OSTI]

    Hongbao Zhang; Wuhan Zhong; Zong-Hong Zhu; Song He

    2007-11-14T23:59:59.000Z

    Taking into account that Sandage-Loeb test is unique in its coverage of the redshift desert and available in the near future, we explore the cosmic time evolution behavior of the source redshift for holographic dark energy model, an important competing cosmological model. As a result, we find that Sandage-Loeb test can provide a extremely strong bound on $\\Omega^0_m$, while its constraint on another dimensionless parameter $\\lambda$ is weak. In addition, it is proposed here for the first time that we can also constrain various cosmological model by measuring the value of $z_{max}$ at which the peak of redshift velocity occurs. Combining this new proposed method with the traditional Sandage-Loeb test, we should be able to provide a better constraint on $\\lambda$, at least from the theoretical perspective.

  20. A dynamic model of industrial energy demand in Kenya

    SciTech Connect (OSTI)

    Haji, S.H.H. [Gothenburg Univ. (Sweden)

    1994-12-31T23:59:59.000Z

    This paper analyses the effects of input price movements, technology changes, capacity utilization and dynamic mechanisms on energy demand structures in the Kenyan industry. This is done with the help of a variant of the second generation dynamic factor demand (econometric) model. This interrelated disequilibrium dynamic input demand econometric model is based on a long-term cost function representing production function possibilities and takes into account the asymmetry between variable inputs (electricity, other-fuels and Tabour) and quasi-fixed input (capital) by imposing restrictions on the adjustment process. Variations in capacity utilization and slow substitution process invoked by the relative input price movement justifies the nature of input demand disequilibrium. The model is estimated on two ISIS digit Kenyan industry time series data (1961 - 1988) using the Iterative Zellner generalized least square method. 31 refs., 8 tabs.

  1. Oneida Tribe of Indians of Wisconsin Energy Optimization Model

    SciTech Connect (OSTI)

    Troge, Michael [Project Manager

    2014-12-30T23:59:59.000Z

    Oneida Nation is located in Northeast Wisconsin. The reservation is approximately 96 square miles (8 miles x 12 miles), or 65,000 acres. The greater Green Bay area is east and adjacent to the reservation. A county line roughly splits the reservation in half; the west half is in Outagamie County and the east half is in Brown County. Land use is predominantly agriculture on the west 2/3 and suburban on the east 1/3 of the reservation. Nearly 5,000 tribally enrolled members live in the reservation with a total population of about 21,000. Tribal ownership is scattered across the reservation and is about 23,000 acres. Currently, the Oneida Tribe of Indians of Wisconsin (OTIW) community members and facilities receive the vast majority of electrical and natural gas services from two of the largest investor-owned utilities in the state, WE Energies and Wisconsin Public Service. All urban and suburban buildings have access to natural gas. About 15% of the population and five Tribal facilities are in rural locations and therefore use propane as a primary heating fuel. Wood and oil are also used as primary or supplemental heat sources for a small percent of the population. Very few renewable energy systems, used to generate electricity and heat, have been installed on the Oneida Reservation. This project was an effort to develop a reasonable renewable energy portfolio that will help Oneida to provide a leadership role in developing a clean energy economy. The Energy Optimization Model (EOM) is an exploration of energy opportunities available to the Tribe and it is intended to provide a decision framework to allow the Tribe to make the wisest choices in energy investment with an organizational desire to establish a renewable portfolio standard (RPS).

  2. Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building

    E-Print Network [OSTI]

    Dudley, Junqiao Han

    2010-01-01T23:59:59.000Z

    of Automated Demand Response in a Large Office Building”, inBuilding Control Strategies and Techniques for Demand Response.Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building

  3. Carrots and Sticks: A Comprehensive Business Model for the Successful Achievement of Energy Efficiency Resource Standards Environmental Energy Technologies Division March 2011

    E-Print Network [OSTI]

    Satchwell, Andrew

    2011-01-01T23:59:59.000Z

    and energy costs. Model Inputs Utility Characterization Business-energy efficiency business model on utility ROE 13   Table 1. Lifetime savings, resource costs

  4. The ABC's of snowmelt: a topographically factorized energy component snowmelt model

    E-Print Network [OSTI]

    Tarboton, David

    of models: conceptual index models and more intricate energy balance models. The index models, like purposes; while the energy balance models, though they are complicated and require large amounts of data States as well as in many other parts of the world. In the western United States, approximately 75

  5. Improving behavioral realism in hybrid energy-economy models using discrete choice

    E-Print Network [OSTI]

    Improving behavioral realism in hybrid energy-economy models using discrete choice studies Abstract Hybrid energy-economy models combine top-down and bottom-up approaches to explore behaviorally models to inform key behavioral parameters in CIMS, a hybrid model. The discrete choice models

  6. Modeling of customer adoption of distributed energy resources

    SciTech Connect (OSTI)

    Marnay, Chris; Chard, Joseph S.; Hamachi, Kristina S.; Lipman, Timothy; Moezzi, Mithra M.; Ouaglal, Boubekeur; Siddiqui, Afzal S.

    2001-08-01T23:59:59.000Z

    This report describes work completed for the California Energy Commission (CEC) on the continued development and application of the Distributed Energy Resources Customer Adoption Model (DER-CAM). This work was performed at Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) between July 2000 and June 2001 under the Consortium for Electric Reliability Technology Solutions (CERTS) Distributed Energy Resources Integration (DERI) project. Our research on distributed energy resources (DER) builds on the concept of the microgrid ({mu}Grid), a semiautonomous grouping of electricity-generating sources and end-use sinks that are placed and operated for the benefit of its members. Although a {mu}Grid can operate independent of the macrogrid (the utility power network), the {mu}Grid is usually interconnected, purchasing energy and ancillary services from the macrogrid. Groups of customers can be aggregated into {mu}Grids by pooling their electrical and other loads, and the most cost-effective combination of generation resources for a particular {mu}Grid can be found. In this study, DER-CAM, an economic model of customer DER adoption implemented in the General Algebraic Modeling System (GAMS) optimization software is used, to find the cost-minimizing combination of on-site generation customers (individual businesses and a {mu}Grid) in a specified test year. DER-CAM's objective is to minimize the cost of supplying electricity to a specific customer by optimizing the installation of distributed generation and the self-generation of part or all of its electricity. Currently, the model only considers electrical loads, but combined heat and power (CHP) analysis capability is being developed under the second year of CEC funding. The key accomplishments of this year's work were the acquisition of increasingly accurate data on DER technologies, including the development of methods for forecasting cost reductions for these technologies, and the creation of a credible example California {mu}Grid for use in this study and in future work. The work performed during this year demonstrates the viability of DER-CAM and of our approach to analyzing adoption of DER.

  7. Distributed Energy: Modeling Penetration in Industrial Sector Over the Long-Term

    E-Print Network [OSTI]

    Greening, L.

    2006-01-01T23:59:59.000Z

    -established industrial energy model, ITEMS (Industrial Technology and Energy Modeling System), and is calibrated to MECS 1994 and 1998. However, as compared to ITEMS, MARKAL is an optimization framework. And, this particular version of MARKAL has a forecast horizon...

  8. A Simple Energy Model for the Harvesting and Leakage in a Supercapacitor

    E-Print Network [OSTI]

    Ingram, Mary Ann

    A Simple Energy Model for the Harvesting and Leakage in a Supercapacitor Aravind Kailas Dept.brunelli@disi.unitn.it Abstract--The modeling of energy storage devices such as supercapacitors for wireless sensor networks

  9. Comparison of Software Models for Energy Savings from Cool Roofs

    SciTech Connect (OSTI)

    New, Joshua Ryan [ORNL; Miller, William A [ORNL; Huang, Yu (Joe) [White Box Technologies; Levinson, Ronnen [Lawrence Berkeley National Laboratory (LBNL)

    2014-01-01T23:59:59.000Z

    A web-based Roof Savings Calculator (RSC) has been deployed for the United States Department of Energy as an industry-consensus tool to help building owners, manufacturers, distributors, contractors and researchers easily run complex roof and attic simulations. This tool employs modern web technologies, usability design, and national average defaults as an interface to annual simulations of hour-by-hour, whole-building performance using the world-class simulation tools DOE-2.1E and AtticSim in order to provide estimated annual energy and cost savings. In addition to cool reflective roofs, RSC simulates multiple roof and attic configurations including different roof slopes, above sheathing ventilation, radiant barriers, low-emittance roof surfaces, duct location, duct leakage rates, multiple substrate types, and insulation levels. A base case and energy-efficient alternative can be compared side-by-side to estimate monthly energy. RSC was benchmarked against field data from demonstration homes in Ft. Irwin, California; while cooling savings were similar, heating penalty varied significantly across different simulation engines. RSC results reduce cool roofing cost-effectiveness thus mitigating expected economic incentives for this countermeasure to the urban heat island effect. This paper consolidates comparison of RSC s projected energy savings to other simulation engines including DOE-2.1E, AtticSim, Micropas, and EnergyPlus, and presents preliminary analyses. RSC s algorithms for capturing radiant heat transfer and duct interaction in the attic assembly are considered major contributing factors to increased cooling savings and heating penalties. Comparison to previous simulation-based studies, analysis on the force multiplier of RSC cooling savings and heating penalties, the role of radiative heat exchange in an attic assembly, and changes made for increased accuracy of the duct model are included.

  10. ENERGY INVESTMENTS UNDER CLIMATE POLICY: A COMPARISON OF GLOBAL MODELS

    SciTech Connect (OSTI)

    McCollum, David; Nagai, Yu; Riahi, Keywan; Marangoni, Giacomo; Calvin, Katherine V.; Pietzcker, Robert; Van Vliet, Jasper; van der Zwaan, Bob

    2013-11-01T23:59:59.000Z

    The levels of investment needed to mobilize an energy system transformation and mitigate climate change are not known with certainty. This paper aims to inform the ongoing dialogue and in so doing to guide public policy and strategic corporate decision making. Within the framework of the LIMITS integrated assessment model comparison exercise, we analyze a multi-IAM ensemble of long-term energy and greenhouse gas emissions scenarios. Our study provides insight into several critical but uncertain areas related to the future investment environment, for example in terms of where capital expenditures may need to flow regionally, into which sectors they might be concentrated, and what policies could be helpful in spurring these financial resources. We find that stringent climate policies consistent with a 2°C climate change target would require a considerable upscaling of investments into low-carbon energy and energy efficiency, reaching approximately $45 trillion (range: $30–$75 trillion) cumulative between 2010 and 2050, or about $1.1 trillion annually. This represents an increase of some $30 trillion ($10–$55 trillion), or $0.8 trillion per year, beyond what investments might otherwise be in a reference scenario that assumes the continuation of present and planned emissions-reducing policies throughout the world. In other words, a substantial "clean-energy investment gap" of some $800 billion/yr exists — notably on the same order of magnitude as present-day subsidies for fossil energy and electricity worldwide ($523 billion). Unless the gap is filled rather quickly, the 2°C target could potentially become out of reach.

  11. Distributed generation capabilities of the national energy modeling system

    SciTech Connect (OSTI)

    LaCommare, Kristina Hamachi; Edwards, Jennifer L.; Marnay, Chris

    2003-01-01T23:59:59.000Z

    This report describes Berkeley Lab's exploration of how the National Energy Modeling System (NEMS) models distributed generation (DG) and presents possible approaches for improving how DG is modeled. The on-site electric generation capability has been available since the AEO2000 version of NEMS. Berkeley Lab has previously completed research on distributed energy resources (DER) adoption at individual sites and has developed a DER Customer Adoption Model called DER-CAM. Given interest in this area, Berkeley Lab set out to understand how NEMS models small-scale on-site generation to assess how adequately DG is treated in NEMS, and to propose improvements or alternatives. The goal is to determine how well NEMS models the factors influencing DG adoption and to consider alternatives to the current approach. Most small-scale DG adoption takes place in the residential and commercial modules of NEMS. Investment in DG ultimately offsets purchases of electricity, which also eliminates the losses associated with transmission and distribution (T&D). If the DG technology that is chosen is photovoltaics (PV), NEMS assumes renewable energy consumption replaces the energy input to electric generators. If the DG technology is fuel consuming, consumption of fuel in the electric utility sector is replaced by residential or commercial fuel consumption. The waste heat generated from thermal technologies can be used to offset the water heating and space heating energy uses, but there is no thermally activated cooling capability. This study consists of a review of model documentation and a paper by EIA staff, a series of sensitivity runs performed by Berkeley Lab that exercise selected DG parameters in the AEO2002 version of NEMS, and a scoping effort of possible enhancements and alternatives to NEMS current DG capabilities. In general, the treatment of DG in NEMS is rudimentary. The penetration of DG is determined by an economic cash-flow analysis that determines adoption based on the n umber of years to a positive cash flow. Some important technologies, e.g. thermally activated cooling, are absent, and ceilings on DG adoption are determined by some what arbitrary caps on the number of buildings that can adopt DG. These caps are particularly severe for existing buildings, where the maximum penetration for any one technology is 0.25 percent. On the other hand, competition among technologies is not fully considered, and this may result in double-counting for certain applications. A series of sensitivity runs show greater penetration with net metering enhancements and aggressive tax credits and a more limited response to lowered DG technology costs. Discussion of alternatives to the current code is presented in Section 4. Alternatives or improvements to how DG is modeled in NEMS cover three basic areas: expanding on the existing total market for DG both by changing existing parameters in NEMS and by adding new capabilities, such as for missing technologies; enhancing the cash flow analysis but incorporating aspects of DG economics that are not currently represented, e.g. complex tariffs; and using an external geographic information system (GIS) driven analysis that can better and more intuitively identify niche markets.

  12. Comparative Analysis of Modeling Studies on China's Future Energy and Emissions Outlook

    E-Print Network [OSTI]

    Zheng, Nina

    2010-01-01T23:59:59.000Z

    International Energy Agency (IEA). 2009. World EnergyChina-specific section of the IEA World Energy Outlook 2009.while LBNL, McKinsey and IEA all employed bottom-up modeling

  13. Experimental investigation of opacity models for stellar interior, inertial fusion, and high energy density plasmasa...

    E-Print Network [OSTI]

    Experimental investigation of opacity models for stellar interior, inertial fusion, and high energy for calculating energy transport in plasmas. In particular, understanding stellar interiors, inertial fusion more energy and the backlight must be bright enough to overwhelm the plasma self

  14. Testing models of vacuum energy interacting with cold dark matter

    E-Print Network [OSTI]

    Li, Yun-He; Zhang, Xin

    2015-01-01T23:59:59.000Z

    We test the models of vacuum energy interacting with cold dark matter, and try to probe the possible deviation from the $\\Lambda$CDM model using current observations. We focus on two specific models, $Q=3\\beta H\\rho_{\\Lambda}$ and $Q=3\\beta H\\rho_c$. The data combinations come from the Planck 2013 data, the baryon acoustic oscillations measurements, the Type-Ia supernovae data, the Hubble constant measurement, the redshift space distortions data and the galaxy weak lensing data. For the $Q=3\\beta H\\rho_c$ model, we find that it can be tightly constrained by all the data combinations, while for the $Q=3\\beta H\\rho_{\\Lambda}$ model there still exist significant degeneracies between parameters. The tightest constraints for the coupling constant are $\\beta=-0.026^{+0.036}_{-0.053}$ (for $Q=3\\beta H\\rho_{\\Lambda}$) and $\\beta=-0.00045\\pm0.00069$ (for $Q=3\\beta H\\rho_c$) at $1\\sigma$ level. For all the fit results, we find that the null interaction $\\beta=0$ is always consistent with data. Our work completes the di...

  15. Modeling the Transfer Function for the Dark Energy Survey

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Chang, C.; Busha, M. T.; Wechsler, R. H.; Refregier, A.; Amara, A.; Rykoff, E.; Becker, M. R.; Bruderer, C.; Gamper, L.; Leistedt, B.; Peiris, H.; Abbott, T.; Abdalla, F. B.; Balbinot, E.; Banerji, M.; Bernstein, R. A.; Bertin, E.; Brooks, D.; Carnero, A.; Desai, S.; da Costa, L. N.; Cunha, C. E; Eifler, T.; Evrard, A. E.; Fausti Neto, A.; Gerdes, D.; Gruen, D.; James, D.; Kuehn, K.; Maia, M. A. G.; Makler, M.; Ogando, R.; Plazas, A.; Sanchez, E.; Santiago, B.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, C.; Soares-Santos, M.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Zuntz, J.

    2015-03-10T23:59:59.000Z

    We present a forward-modelling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function -- a mapping from cosmological and astronomical signals to the final data products used by the scientists. Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator, Berge et al. 2013) and catalogs representative of the DES data. In this work we simulate the 244 deg2 coadd images and catalogs in 5 bands for the DES Science Verification (SV) data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured. We also point out several directions of future improvements. Two practical examples, star/galaxy classification and proximity effects on object detection, are then used to demonstrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modelling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies which is sufficiently realistic and highly controllable.

  16. Modeling the Transfer Function for the Dark Energy Survey

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Chang, C.; Busha, M. T.; Wechsler, R. H.; Refregier, A.; Amara, A.; Rykoff, E.; Becker, M. R.; Bruderer, C.; Gamper, L.; Leistedt, B.; et al

    2015-03-10T23:59:59.000Z

    We present a forward-modelling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function -- a mapping from cosmological and astronomical signals to the final data products used by the scientists. Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator, Berge et al. 2013) and catalogs representative of the DES data. In this work we simulate the 244 deg2 coadd images and catalogs in 5 bands for the DES Science Verification (SV) data. The simulationmore »output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured. We also point out several directions of future improvements. Two practical examples, star/galaxy classification and proximity effects on object detection, are then used to demonstrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modelling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies which is sufficiently realistic and highly controllable.« less

  17. Modelling the Transfer Function for the Dark Energy Survey

    SciTech Connect (OSTI)

    Chang, C.

    2014-10-31T23:59:59.000Z

    We present a forward-modelling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function -- a mapping from cosmological and astronomical signals to the final data products used by the scientists. Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator, Berge et al. 2013) and catalogs representative of the DES data. In this work we simulate the 244 sq. deg coadd images and catalogs in 5 bands for the DES Science Verification (SV) data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured. We also point out several directions of future improvements. Two practical examples, star/galaxy classification and proximity effects on object detection, are then used to demonstrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modelling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies which is sufficiently realistic and highly controllable.

  18. Avoiding Boltzmann Brain domination in holographic dark energy models

    E-Print Network [OSTI]

    R. Horvat

    2015-02-23T23:59:59.000Z

    In a spatially infinite and eternal universe approaching ultimately a de Sitter (or quasi-de Sitter) regime, structure can form by thermal fluctuations as such a space is thermal. The models of Dark Energy invoking holographic principle fit naturally into such a category, and spontaneous formation of isolated brains in otherwise empty space seems the most perplexing, creating the paradox of Boltzmann Brains (BB). It is thus appropriate to ask if such models can be made free from domination by Boltzmann Brains. Here we consider only the simplest model, but adopt both the local and the global viewpoint in the description of the Universe. In the former case, we find that if a parameter $c$, which modulates the Dark Energy density, lies outside the exponentially narrow strip around the most natural $c = 1$ line, the theory is rendered BB-safe. In the later case, the bound on $c$ is exponentially stronger, and seemingly at odds with those bounds on $c$ obtained from various observational tests.

  19. Avoiding Boltzmann Brain domination in holographic dark energy models

    E-Print Network [OSTI]

    Horvat, R

    2015-01-01T23:59:59.000Z

    In a spatially infinite and eternal universe approaching ultimately a de Sitter (or quasi-de Sitter) regime, structure can form by thermal fluctuations as such a space is thermal. The models of Dark Energy invoking holographic principle fit naturally into such a category, and spontaneous formation of isolated brains in otherwise empty space seems the most perplexing, creating the paradox of Boltzmann Brains (BB). It is thus appropriate to ask if such models can be made free from domination by Boltzmann Brains. Here we consider only the simplest model, but adopt both the local and the global viewpoint in the description of the Universe. In the former case, we find that if a parameter $c$, which modulates the Dark Energy density, lies outside the exponentially narrow strip around the most natural $c = 1$ line, the theory is rendered BB-safe. In the later case, the bound on $c$ is exponentially stronger, and seemingly at odds with those bounds on $c$ obtained from various observational tests.

  20. Neural Network Based Energy Storage System Modeling for Hybrid Electric Vehicles

    SciTech Connect (OSTI)

    Bhatikar, S. R.; Mahajan, R. L.; Wipke, K.; Johnson, V.

    1999-08-01T23:59:59.000Z

    Demonstrates the application of an artificial neural network (ANN) for modeling the energy storage system of a hybrid electric vehicle.

  1. Ocean Heat Transport, Sea Ice, and Multiple Climate States: Insights from Energy Balance Models

    E-Print Network [OSTI]

    Rose, Brian Edward James

    Several extensions of energy balance models (EBMs) are explored in which (i) sea ice acts to insulate the

  2. On the internal consistency of holographic dark energy models

    SciTech Connect (OSTI)

    Horvat, R, E-mail: horvat@lei3.irb.hr [Rudjer Boskovic Institute, POB 180, 10002 Zagreb (Croatia)] [Rudjer Boskovic Institute, POB 180, 10002 Zagreb (Croatia)

    2008-10-15T23:59:59.000Z

    Holographic dark energy (HDE) models, underpinned by an effective quantum field theory (QFT) with a manifest UV/IR connection, have become convincing candidates for providing an explanation of the dark energy in the universe. On the other hand, the maximum number of quantum states that a conventional QFT for a box of size L is capable of describing relates to those boxes which are on the brink of experiencing a sudden collapse to a black hole. Another restriction on the underlying QFT is that the UV cut-off, which cannot be chosen independently of the IR cut-off and therefore becomes a function of time in a cosmological setting, should stay the largest energy scale even in the standard cosmological epochs preceding a dark energy dominated one. We show that, irrespective of whether one deals with the saturated form of HDE or takes a certain degree of non-saturation in the past, the above restrictions cannot be met in a radiation dominated universe, an epoch in the history of the universe which is expected to be perfectly describable within conventional QFT.

  3. Cost effectiveness of the 1995 model energy code in Massachusetts

    SciTech Connect (OSTI)

    Lucas, R.G.

    1996-02-01T23:59:59.000Z

    This report documents an analysis of the cost effectiveness of the Council of American Building Officials` 1995 Model Energy Code (MEC) building thermal-envelope requirements for single-family houses and multifamily housing units in Massachusetts. The goal was to compare the cost effectiveness of the 1995 MEC to the energy conservation requirements of the Massachusetts State Building Code-based on a comparison of the costs and benefits associated with complying with each.. This comparison was performed for three cities representing three geographical regions of Massachusetts--Boston, Worcester, and Pittsfield. The analysis was done for two different scenarios: a ``move-up`` home buyer purchasing a single-family house and a ``first-time`` financially limited home buyer purchasing a multifamily condominium unit. Natural gas, oil, and electric resistance heating were examined. The Massachusetts state code has much more stringent requirements if electric resistance heating is used rather than other heating fuels and/or equipment types. The MEC requirements do not vary by fuel type. For single-family homes, the 1995 MEC has requirements that are more energy-efficient than the non-electric resistance requirements of the current state code. For multifamily housing, the 1995 MEC has requirements that are approximately equally energy-efficient to the non-electric resistance requirements of the current state code. The 1995 MEC is generally not more stringent than the electric resistance requirements of the state code, in fact; for multifamily buildings the 1995 MEC is much less stringent.

  4. A Phenomenological Cost Model for High Energy Particle Accelerators

    E-Print Network [OSTI]

    Vladimir Shiltsev

    2014-04-15T23:59:59.000Z

    Accelerator-based high-energy physics have been in the forefront of scientific discoveries for more than half a century. The accelerator technology of the colliders has progressed immensely, while the beam energy, luminosity, facility size, and cost have grown by several orders of magnitude. The method of colliding beams has not fully exhausted its potential but has slowed down considerably in its progress. In this paper we derive a simple scaling model for the cost of large accelerators and colliding beam facilities based on costs of 17 big facilities which have been either built or carefully estimated. Although this approach cannot replace an actual cost estimate based on an engineering design, this parameterization is to indicate a somewhat realistic cost range for consideration of what future frontier accelerator facilities might be fiscally realizable.

  5. Sub-grid parameterization of snow distribution for an energy and mass balance snow cover model

    E-Print Network [OSTI]

    Sub-grid parameterization of snow distribution for an energy and mass balance snow cover model model of the lumped snowpack mass and energy balance applied to a 26-ha rangeland catchment with high (Af). The energy state variable is evolved through an energy balance. The snow water equivalence state

  6. A graphical model approach for predicting free energies of association for protein-protein

    E-Print Network [OSTI]

    Langmead, Christopher James

    A graphical model approach for predicting free energies of association for protein University, Pittsburgh, PA 1 Corresponding Author: cjl@cs.cmu.edu #12;Keywords: Graphical Models, Free Energy in free energy, and the ability to predict binding free energies provides both better understanding

  7. Modelling Office Energy Consumption: An Agent Based Approach , Peer-Olaf Siebers1

    E-Print Network [OSTI]

    Aickelin, Uwe

    1 Modelling Office Energy Consumption: An Agent Based Approach Tao Zhang1 , Peer-Olaf Siebers1 integrates four important elements, i.e. organisational energy management policies/regulations, energy, to simulate the energy consumption in office buildings. With the model, we test the effectiveness of different

  8. Modeling and Analysis of Energy Harvesting Nodes in Body Sensor Networks

    E-Print Network [OSTI]

    Sikdar, Biplab

    , or that the energy supply is monotonically decreasing with a fixed initial value. Since energy harvesting sensors canModeling and Analysis of Energy Harvesting Nodes in Body Sensor Networks Alireza Seyedi Department@ecse.rpi.edu Abstract--A Markov based unified model for an energy har- vesting node in a body sensor network

  9. MARS15 study of the Energy Production Demonstrator Model for Megawatt

    E-Print Network [OSTI]

    McDonald, Kirk

    MARS15 study of the Energy Production Demonstrator Model for Megawatt proton beams in the 0.5 ­ 120 Targetry Workshop HPT5, Fermilab #12;Energy Production Demonstrator MARS15 Model · Solid targets · R= 60 cm · Energy Production/Materials Testing · LAQGSM/CEM generators were usedU-nat, 3 GeV, Energy deposition, Ge

  10. Energy Aware Algorithm Design via Probabilistic Computing: From Algorithms and Models to Moore's Law

    E-Print Network [OSTI]

    Palem, Krishna V.

    Energy Aware Algorithm Design via Probabilistic Computing: From Algorithms and Models to Moore opportunities for being energy-aware, the most fundamental limits are truly rooted in the physics of energy of models of computing for energy-aware al- gorithm design and analysis, culminating in establishing

  11. Energy consumption in cellular network: ON-OFF model and impact of mobility

    E-Print Network [OSTI]

    Energy consumption in cellular network: ON-OFF model and impact of mobility Thanh Tung Vu Telecom consumption in cellular network and we focus on the distribution of energy consumed by a base station. We first define the energy consumption model, in which the consumed energy is divided into two parts

  12. Towards a Very Low Energy Building Stock: Modeling the US Commercial Building Sector

    E-Print Network [OSTI]

    Towards a Very Low Energy Building Stock: Modeling the US Commercial Building Sector to Support and continuing development of a model of time varying energy consumption in the US commercial building stock targeting very low future energy consumption in the building stock. Model use has highlighted the scale

  13. Carrots and Sticks: A Comprehensive Business Model for the Successful Achievement of Energy Efficiency Resource Standards

    E-Print Network [OSTI]

    Satchwell, Andrew

    2013-01-01T23:59:59.000Z

    business model for energy efficiency Historically, utilities in Arizona have been allowed to recover prudently incurred EE program costs;costs. We presented a comprehensive business model to achieve aggressive energyCosts Net Benefits Figure 1 Flowchart for analyzing impacts of portfolio of energy efficiency programs on stakeholders Model Inputs Business-

  14. Model Reduction for Indoor-Air Behavior in Control Design for Energy-Efficient Buildings

    E-Print Network [OSTI]

    Gugercin, Serkan

    Model Reduction for Indoor-Air Behavior in Control Design for Energy-Efficient Buildings Jeff models for the indoor-air environment in control design for energy efficient buildings. In one method by a desire to incorporate models of the indoor-air environment in the design of energy efficient buildings

  15. Technical Note Comparing Dynamic Causal Models using AIC, BIC and Free Energy

    E-Print Network [OSTI]

    Penny, Will

    Technical Note Comparing Dynamic Causal Models using AIC, BIC and Free Energy W.D. Penny Wellcome) and Dynamic Causal Models (DCMs). We find that the Free Energy has the best model selection ability, to instead score DCMs using the Free Energy (Friston et al., 2007a). However, until now there has been

  16. Stochastic Models of Energy Commodity Prices and Their Applications: Mean-reversion with Jumps and

    E-Print Network [OSTI]

    California at Berkeley. University of

    PWP-073 Stochastic Models of Energy Commodity Prices and Their Applications: Mean.ucei.berkeley.edu/ucei #12;Stochastic Models of Energy Commodity Prices and Their Applications: Mean-reversion with Jumps-switching and stochastic volatility into these models in order to capture the salient features of energy commodity prices

  17. Stochastic Models of Energy Commodity Prices and Their Applications: Mean-reversion with

    E-Print Network [OSTI]

    Stochastic Models of Energy Commodity Prices and Their Applications: Mean-reversion with Jumps usion models to describe energy commodity spot prices. We incorporate multiple jumps, regime-switching and stochastic volatility in these models. Prices of various energy commodity derivatives are obtained under each

  18. APPLICATION OF A HYBRID MODEL TO EXPLORE ENERGY EMISSIONS ABATEMENT POLICIES IN CHINA

    E-Print Network [OSTI]

    APPLICATION OF A HYBRID MODEL TO EXPLORE ENERGY EMISSIONS ABATEMENT POLICIES IN CHINA by Jianjun Tu: Application of a Hybrid Model to Explore Energy Emissions Abatement Policies in China Project No. 360 control and energy security goals; 3) to use a hybrid model ­ CIMS, as this incorporates improvements

  19. TTP 289A Syllabus: Energy Modeling for Policy Analysis TTP 289A-002

    E-Print Network [OSTI]

    California at Davis, University of

    TTP 289A Syllabus: Energy Modeling for Policy Analysis 1 TTP 289A-002 (CRN81927) Energy Modeling for Policy Analysis Quarter: Winter 2014 When: T/Th: 2:10-4 pm for policy analysis. We will explore several facets of energy systems modeling including supply and demand

  20. Battery Thermal Modeling and Testing | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny: The FutureComments fromofBatteries from Brine Batteries fromThermal Modeling and

  1. LDV HVAC Model Development and Validation | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't YourTransport(FactDepartment ofLetter Report:40PM to 2:05PMDOE-STD-1107-97LSEED:LDV HVAC Model

  2. Sandia Energy - PV Performance Modeling Collaborative's New and

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home Distribution Grid IntegrationOffshore Wind RD&D:PV Modeling

  3. Advanced Modeling Grid Research Program | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergyDepartment ofATVM LoanActiveMission »Advanced Modeling

  4. Combustion Model for Engine Concept Development | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny: Theof"Wave the White Flag"DepartmentToward Targets of EfficientModel for

  5. Descriptive Model of a Generic WAMS | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny:Revised Finding of No53197E T A * S H I E L D *Department ofDescriptive Model of a

  6. H2A Delivery Models and Results | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't YourTransport(Fact Sheet), GeothermalGrid Integration0-1 MarchH-TankModels and Results H2A

  7. Accelerated Climate Modeling For Energy Marcia Branstetter Katherine Evans

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041cloth DocumentationProducts (VAP) VAP7-0973 1 Introduction In theACME - Accelerated Climate Modeling

  8. A/C Model Development and Validation | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't Your Destiny: The Future of1 A Strategic Framework for SMRA View from the BridgeA/C Model

  9. An Energy Savings Model for the Heat Treatment of Castings

    SciTech Connect (OSTI)

    Y. Rong; R. Sisson; J. Morral; H. Brody

    2006-12-31T23:59:59.000Z

    An integrated system of software, databases, and design rules have been developed, verified, and to be marketed to enable quantitative prediction and optimization of the heat treatment of aluminum castings to increase quality, increase productivity, reduce heat treatment cycle times and reduce energy consumption. The software predicts the thermal cycle in critical locations of individual components in a furnace, the evolution of microstructure, and the attainment of properties in heat treatable aluminum alloy castings. The model takes into account the prior casting process and the specific composition of the component. The heat treatment simulation modules can be used in conjunction with software packages for simulation of the casting process. The system is built upon a quantitative understanding of the kinetics of microstructure evolution in complex multicomponent alloys, on a quantitative understanding of the interdependence of microstructure and properties, on validated kinetic and thermodynamic databases, and validated quantitative models.

  10. Natural Gas Transmission and Distribution Model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    NONE

    1998-01-01T23:59:59.000Z

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. The NGTDM is the model within the NEMS that represents the transmission, distribution, and pricing of natural gas. The model also includes representations of the end-use demand for natural gas, the production of domestic natural gas, and the availability of natural gas traded on the international market based on information received from other NEMS models. The NGTDM determines the flow of natural gas in an aggregate, domestic pipeline network, connecting domestic and foreign supply regions with 12 demand regions. The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic design, provides detail on the methodology employed, and describes the model inputs, outputs, and key assumptions. Subsequent chapters of this report provide: an overview of NGTDM; a description of the interface between the NEMS and NGTDM; an overview of the solution methodology of the NGTDM; the solution methodology for the Annual Flow Module; the solution methodology for the Distributor Tariff Module; the solution methodology for the Capacity Expansion Module; the solution methodology for the Pipeline Tariff Module; and a description of model assumptions, inputs, and outputs.

  11. Model documentation, Renewable Fuels Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1998-01-01T23:59:59.000Z

    This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the Annual Energy Outlook 1998 (AEO98) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. For AEO98, the RFM was modified in three principal ways, introducing capital cost elasticities of supply for new renewable energy technologies, modifying biomass supply curves, and revising assumptions for use of landfill gas from municipal solid waste (MSW). In addition, the RFM was modified in general to accommodate projections beyond 2015 through 2020. Two supply elasticities were introduced, the first reflecting short-term (annual) cost increases from manufacturing, siting, and installation bottlenecks incurred under conditions of rapid growth, and the second reflecting longer term natural resource, transmission and distribution upgrade, and market limitations increasing costs as more and more of the overall resource is used. Biomass supply curves were also modified, basing forest products supplies on production rather than on inventory, and expanding energy crop estimates to include states west of the Mississippi River using information developed by the Oak Ridge National Laboratory. Finally, for MSW, several assumptions for the use of landfill gas were revised and extended.

  12. Precise estimation of shell model energy by second order extrapolation method

    E-Print Network [OSTI]

    Takahiro Mizusaki; Masatoshi Imada

    2003-02-20T23:59:59.000Z

    A second order extrapolation method is presented for shell model calculations, where shell model energies of truncated spaces are well described as a function of energy variance by quadratic curves and exact shell model energies can be obtained by the extrapolation. This new extrapolation can give more precise energy than those of first order extrapolation method. It is also clarified that first order extrapolation gives a lower limit of shell model energy. In addition to the energy, we derive the second order extrapolation formula for expectation values of other observables.

  13. Modeling and simulations of electrical energy storage in electrochemical capacitors

    E-Print Network [OSTI]

    Wang, Hainan

    2013-01-01T23:59:59.000Z

    3D nanoarchitec- tures for energy storage and conversion,”functionality in energy storage materials and devices byto electrochemical energy storage in TiO 2 (anatase)

  14. Modeling of customer adoption of distributed energy resources

    E-Print Network [OSTI]

    2001-01-01T23:59:59.000Z

    of Dispersed Energy Resources Deployment. Berkeley, LawrenceAdoption of Distributed Energy Resources Ozbek, A. 2001.Adoption of Distributed Energy Resources Figure 39. Figure

  15. SIMMODEL: A DOMAIN DATA MODEL FOR WHOLE BUILDING ENERGY SIMULATION

    E-Print Network [OSTI]

    O'Donnell, James

    2013-01-01T23:59:59.000Z

    whole building energy simulation program. In: IBPSA BuildingExchange Protocols for Energy Simulation of HVAC&R EquipmentInteroperability for Energy Simulation. buildingSmart (2010)

  16. Modeling and simulations of electrical energy storage in electrochemical capacitors

    E-Print Network [OSTI]

    Wang, Hainan

    2013-01-01T23:59:59.000Z

    electrochemical capacitor energy storage systems. 1.2 Energyto electrochemical energy storage in TiO 2 (anatase)3D nanoarchitec- tures for energy storage and conversion,”

  17. Modelling the impact of user behaviour on heat energy consumption

    E-Print Network [OSTI]

    Combe, Nicola Miss; Harrison, David Professor; Way, Celia Miss

    2011-01-01T23:59:59.000Z

    strategies impact on energy consumption in residentialBEHAVIOUR ON HEAT ENERGY CONSUMPTION Nicola Combe 1 ,2 ,nearly 60% of domestic energy consumption and 27% of total

  18. The ABC's of Snowmelt: A Topographically Factorized Energy Component Snowmelt Model

    E-Print Network [OSTI]

    Tarboton, David

    index models and more intricate energy balance models. The index models, like the degree- day the energy balance models, though they are complicated and require large amounts of data, can represent plays a crucial role in the hydrology of the United States as well as in many other parts of the world

  19. Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial1 Neural Network2

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Transitional Modeling of Building Heating Energy Demand Using Artificial1 Neural Network2 Subodh Paudel a, it is39 essential to know energy flows and energy demand of the buildings for the control of heating and40 cooling energy production from plant systems. The energy demand of the building system, thus,41

  20. Construction of energy-stable Galerkin reduced order models.

    SciTech Connect (OSTI)

    Kalashnikova, Irina; Barone, Matthew Franklin; Arunajatesan, Srinivasan; van Bloemen Waanders, Bart Gustaaf

    2013-05-01T23:59:59.000Z

    This report aims to unify several approaches for building stable projection-based reduced order models (ROMs). Attention is focused on linear time-invariant (LTI) systems. The model reduction procedure consists of two steps: the computation of a reduced basis, and the projection of the governing partial differential equations (PDEs) onto this reduced basis. Two kinds of reduced bases are considered: the proper orthogonal decomposition (POD) basis and the balanced truncation basis. The projection step of the model reduction can be done in two ways: via continuous projection or via discrete projection. First, an approach for building energy-stable Galerkin ROMs for linear hyperbolic or incompletely parabolic systems of PDEs using continuous projection is proposed. The idea is to apply to the set of PDEs a transformation induced by the Lyapunov function for the system, and to build the ROM in the transformed variables. The resulting ROM will be energy-stable for any choice of reduced basis. It is shown that, for many PDE systems, the desired transformation is induced by a special weighted L2 inner product, termed the %E2%80%9Csymmetry inner product%E2%80%9D. Attention is then turned to building energy-stable ROMs via discrete projection. A discrete counterpart of the continuous symmetry inner product, a weighted L2 inner product termed the %E2%80%9CLyapunov inner product%E2%80%9D, is derived. The weighting matrix that defines the Lyapunov inner product can be computed in a black-box fashion for a stable LTI system arising from the discretization of a system of PDEs in space. It is shown that a ROM constructed via discrete projection using the Lyapunov inner product will be energy-stable for any choice of reduced basis. Connections between the Lyapunov inner product and the inner product induced by the balanced truncation algorithm are made. Comparisons are also made between the symmetry inner product and the Lyapunov inner product. The performance of ROMs constructed using these inner products is evaluated on several benchmark test cases.