National Library of Energy BETA

Sample records for demand module assumption

  1. Assumption to the Annual Energy Outlook 2014 - Residential Demand Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and GasDemand

  2. Assumption to the Annual Energy Outlook 2014 - Industrial Demand Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market Module This pageIndustrial

  3. Assumption to the Annual Energy Outlook 2014 - Transportation Demand Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and

  4. Heap Assumptions on Demand Andreas Podelski1

    E-Print Network [OSTI]

    Wies, Thomas

    Heap Assumptions on Demand Andreas Podelski1 , Andrey Rybalchenko2 , and Thomas Wies1 1 University checker and shape analysis. The shape analysis pro- duces heap assumptions on demand to eliminate.e., it applies shape analysis on demand. The shape analysis produces a heap assumption, which is an assertion

  5. Residential Demand Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Model Documentation - 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.

  6. Heap Assumptions on Demand Andreas Podelski1

    E-Print Network [OSTI]

    Wies, Thomas

    PROTOTYPE IMPLEMENTATION OF A DEMAND DRIVEN NETWORK MONITORING ARCHITECTURE Augusto Ciuffoletti for demand driven monitoring, named gd2, that can be potentially integrated in the gLite framework. We capable of managing the scalability challenge offered by a Grid environment: i) demand driven

  7. Assumption to the Annual Energy Outlook 2014 - Commercial Demand Module

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

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  8. Assumption to the Annual Energy Outlook 2014 - Electricity Market Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market Module This page

  9. Assumption to the Annual Energy Outlook 2014 - International Energy Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market Module This

  10. Assumption to the Annual Energy Outlook 2014 - Macroeconomic Activity Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market Module ThisMacroeconomic

  11. Assumption to the Annual Energy Outlook 2014 - Renewable Fuels Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and Gas

  12. Assumptions to the Annual Energy Outlook 2014 - Coal Market Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and ThisMarket

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

    SciTech Connect (OSTI)

    1998-01-01

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

    SciTech Connect (OSTI)

    1997-01-01

    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 that provides 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.

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

    SciTech Connect (OSTI)

    NONE

    1995-02-01

    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.

  16. Assumption to the Annual Energy Outlook 2014 - Natural Gas Transmission and Distribution Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market Module

  17. Assumption to the Annual Energy Outlook 2014 - Oil and Gas Supply Module

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and Gas Supply

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

    SciTech Connect (OSTI)

    NONE

    1998-01-01

    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.

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

    SciTech Connect (OSTI)

    NONE

    1997-01-01

    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.

  20. Transportation Sector Demand Module of the National Energy Modeling System: Model Documentation

    U.S. Energy Information Administration (EIA) 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 PageMonthly","10/2015"4,"Ames5 Tables July 1996 Energy Information Administration Office of Coal,Demand Module of the National Energy Modeling System: Model Documentation

  1. U.S. Energy Information Administration NEMS Residential Demand Module Documentation Report 2011

    U.S. Energy Information Administration (EIA) 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 PageMonthly","10/2015"4,"Ames5 Tables July 1996 Energy Information Administration Office of Coal,Demand Module of the NationalSales (Million Barrels)New

  2. declaration assumption,

    E-Print Network [OSTI]

    Ábrahám, Erika

    Initial Type Assumption A 0 A 0 (x) = 8a: a for all x 2 V A 0 (c) = pre-de#12;ned type schema in haskell, for all c 2 C 0 A 0 (constr) = 8 (type 1 ! : : : ! type n ! (tyconstr a 1 : : : am )); A 0 (bot;) or the computation fails because of a failing uni#12;cation problem. Let c 2 C [ V. #15; W(A + fc :: 8a 1

  3. Commercial Sector Demand Module

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet)DecadeYear Jan Feb Mar Apr May Jun Jul Aug Sep3,118,592 3,102,59399 2006-20105)

  4. Residential Sector Demand Module

    U.S. Energy Information Administration (EIA) 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 PageMonthly","10/2015"4,"Ames5 Tables July 1996 Energy Information Administration Office of Coal, Nuclear,DecadeYearby the(Dollars1.840 2.318 3.1195) Model8)3 November

  5. Assessing Vehicle Electricity Demand Impacts on California Electricity Supply

    E-Print Network [OSTI]

    McCarthy, Ryan W.

    2009-01-01

    fuel efficiency and electricity demand assumptions used into added vehicle electricity demand in the BAU (no IGCC)to added vehicle electricity demand in the Mixed technology

  6. Design, Implementation, and Formal Verification of On-demand Connection Establishment Scheme for TCP Module of MPICH2 Library 

    E-Print Network [OSTI]

    Muthukrishnan, Sankara Subbiah

    2012-10-19

    developed at Argonne National Laboratory. The scalability of MPI implementations is very important for building high performance parallel computing applications. The initial TCP (Transmission Control Protocol) network module developed for Nemesis...

  7. Key Assumptions Policy Issues

    E-Print Network [OSTI]

    Supply Limitations 8 Withi h B l i8. Within-hour Balancing 9. Capacity and Energy Values for Wind · Independent Power Producers C t ti· Current assumptions · Winter: full availability ~ 3,200 MW · Summer: 1 t b it d d li d· Thermal: must be sited and licensed · Wind/solar: must be sited and licensed · EE

  8. Modeling the Capacity and Emissions Impacts of Reduced Electricity Demand. Part 1. Methodology and Preliminary Results.

    E-Print Network [OSTI]

    Coughlin, Katie

    2013-01-01

    pdf. ———. 2011b. Residential Demand Module of the Nationaland the Commercial and Residential Demand Modules (DOE EIAcommercial and residential electricity demand projections

  9. Pricing Cloud Bandwidth Reservations under Demand Uncertainty

    E-Print Network [OSTI]

    Li, Baochun

    Heap Assumptions on Demand Andreas Podelski1 , Andrey Rybalchenko2 , and Thomas Wies1 1 University analysis produces heap assumptions on demand to eliminate counterexamples, i.e., non-terminating abstract of a non-terminating abstract computation, i.e., it applies shape analysis on demand. The shape analysis

  10. Demand Reduction

    Office of Energy Efficiency and Renewable Energy (EERE)

    Grantees may use funds to coordinate with electricity supply companies and utilities to reduce energy demands on their power systems. These demand reduction programs are usually coordinated through...

  11. Solar in Demand | Department of Energy

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

    Solar in Demand Solar in Demand June 15, 2012 - 10:23am Addthis Kyle Travis, left and Jon Jackson, with Lighthouse Solar, install microcrystalline PV modules on top of Kevin...

  12. QPE: Using assumption-based truth maintenance for qualitative simulation

    E-Print Network [OSTI]

    Forbus, Kenneth D.

    writin g such programs. This paper identifies several abstractions for organizing ATMS-based problem a mechanism for making closed-world assumptions . We sketch the design of the Qualitative Process Engine, QPE simulation as a module in a larger task . All of these research directions require substantially more

  13. Electricity Demand and Energy Consumption Management System

    E-Print Network [OSTI]

    Sarmiento, Juan Ojeda

    2008-01-01

    This project describes the electricity demand and energy consumption management system and its application to the Smelter Plant of Southern Peru. It is composted of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks, with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows management the peak demand before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules allow the proper planning because it allows knowing the behavior of the hourly demand and the consumption patterns of the plant, in...

  14. InDemandInDemandInDemand Energize Your Career

    E-Print Network [OSTI]

    Wolberg, George

    InDemandInDemandInDemand Energize Your Career You can join the next generation of workers who in Energy #12;#12;In Demand | 1 No, this isn't a quiz...but if you answered yes to any or all and Training Administration wants you to have this publication, In Demand: Careers in Energy. It will let you

  15. VideoonDemandVideoonDemandVideoonDemand Video on Demand Testbed

    E-Print Network [OSTI]

    Eleftheriadis, Alexandros

    VideoonDemandVideoonDemandVideoonDemand Columbia's Video on Demand Testbed and Interoperability Experiment Columbia's Video on Demand Testbed and Interoperability Experiment S.-F. Chang and A Columbia UniversityColumbia University www.www.ctrctr..columbiacolumbia..eduedu/advent/advent #12;VideoonDemandVideoonDemandVideoonDemand

  16. VideoonDemandVideoonDemandVideoonDemand Video on Demand Testbed

    E-Print Network [OSTI]

    Eleftheriadis, Alexandros

    #12;VideoonDemandVideoonDemandVideoonDemand Columbia's Video on Demand Testbed and Interoperability Experiment Columbia's Video on Demand Testbed and Interoperability Experiment H.H. KalvaKalva, A.www.eeee..columbiacolumbia..eduedu/advent/advent #12;VideoonDemandVideoonDemandVideoonDemand VoD Testbed ArchitectureVoD Testbed Architecture Video

  17. INJECTIVE MODULES FOR UNIFORM ALGEBRAS

    E-Print Network [OSTI]

    White, Michael C.

    of right Banach A-modules as mod-A and the class of Banach A-bimodules as A-mod-A. We shall refer to the module maps in each case as morphisms. Modules will be denoted by upper-case Roman letters, morphisms ), bounded linear maps are denoted by lower-case Roman letters. The assumption that all modules

  18. Demand Response and Open Automated Demand Response

    E-Print Network [OSTI]

    LBNL-3047E Demand Response and Open Automated Demand Response Opportunities for Data Centers G described in this report was coordinated by the Demand Response Research Center and funded by the California. Demand Response and Open Automated Demand Response Opportunities for Data Centers. California Energy

  19. Draft Fourth Northwest Conservation and Electric Power Plan, Appendix D ECONOMIC AND DEMAND FORECASTS

    E-Print Network [OSTI]

    , and high) based on different assumptions about the key determinants of electricity demand. Much economy is the dominant determinant of electricity demand both now and in the future. The demand of alternative energy forms, such as natural gas, are also important determinants of electricity demand. Demand

  20. Current Status and Future Assumptions INTRODUCTION

    E-Print Network [OSTI]

    of the current and future electricity situation are the demand for electricity, the amount and cost and national energy and environmental policies. Demand defines the need for electricity while generation and its costs. DEMAND FOR ELECTRICITY It has been 20 years since the Council's first power plan in 1983

  1. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1992-04-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  2. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1991-09-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  3. High Temperatures & Electricity Demand

    E-Print Network [OSTI]

    High Temperatures & Electricity Demand An Assessment of Supply Adequacy in California Trends.......................................................................................................1 HIGH TEMPERATURES AND ELECTRICITY DEMAND.....................................................................................................................7 SECTION I: HIGH TEMPERATURES AND ELECTRICITY DEMAND ..........................9 BACKGROUND

  4. Advanced Demand Responsive Lighting

    E-Print Network [OSTI]

    Advanced Demand Responsive Lighting Host: Francis Rubinstein Demand Response Research Center demand responsive lighting systems ­ Importance of dimming ­ New wireless controls technologies · Advanced Demand Responsive Lighting (commenced March 2007) #12;Objectives · Provide up-to-date information

  5. Sensitivity of Rooftop PV Projections in the SunShot Vision Study to Market Assumptions

    SciTech Connect (OSTI)

    Drury, E.; Denholm, P.; Margolis, R.

    2013-01-01

    The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The SolarDS model was used to simulate rooftop PV demand for this study, based on several PV market assumptions--future electricity rates, customer access to financing, and others--in addition to the SunShot PV price projections. This paper finds that modeled PV demand is highly sensitive to several non-price market assumptions, particularly PV financing parameters.

  6. Development and Demonstration of the Open Automated Demand Response Standard for the Residential Sector

    E-Print Network [OSTI]

    Herter, Karen

    2014-01-01

    of the Open Automated Demand Response Standard for theOpen Automated Demand Response (OpenADR) Price Schedule Time3.3.2. General Electric Demand Response Module Figure 7. GE’

  7. Assessing Vehicle Electricity Demand Impacts on California Electricity Supply

    E-Print Network [OSTI]

    McCarthy, Ryan W.

    2009-01-01

    Designing Markets for Electricity, Wiley-IEEE Press. CEC (in Major Drivers in U.S. Electricity Markets, NREL/CP-620-and fuel efficiency and electricity demand assumptions used

  8. Manufacturing Energy and Carbon Footprint Definitions and Assumptions...

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

    Definitions and Assumptions, October 2012 Manufacturing Energy and Carbon Footprint Definitions and Assumptions, October 2012 footprintsassumptionsdefinitions2012.pdf More...

  9. Projecting Electricity Demand in 2050

    SciTech Connect (OSTI)

    Hostick, Donna J.; Belzer, David B.; Hadley, Stanton W.; Markel, Tony; Marnay, Chris; Kintner-Meyer, Michael CW

    2014-07-01

    This paper describes the development of end-use electricity projections and load curves that were developed for the Renewable Electricity (RE) Futures Study (hereafter RE Futures), which explored the prospect of higher percentages (30% ? 90%) of total electricity generation that could be supplied by renewable sources in the United States. As input to RE Futures, two projections of electricity demand were produced representing reasonable upper and lower bounds of electricity demand out to 2050. The electric sector models used in RE Futures required underlying load profiles, so RE Futures also produced load profile data in two formats: 8760 hourly data for the year 2050 for the GridView model, and in 2-year increments for 17 time slices as input to the Regional Energy Deployment System (ReEDS) model. The process for developing demand projections and load profiles involved three steps: discussion regarding the scenario approach and general assumptions, literature reviews to determine readily available data, and development of the demand curves and load profiles.

  10. Counterexamples to commonly held Assumptions on

    E-Print Network [OSTI]

    Gatterbauer, Wolfgang

    Counterexamples to commonly held Assumptions on Unit Commitment and Market Power Assessment NAPS and Decentralized Unit Commitment (PoolCo vs. PX) · Determination of Market Power revisiting the fundamental Information 1: PoolCo vs PX · Unit Commitment: Technological constraints (minimum up-time, starting costs

  11. Preliminary Assumptions for Natural Gas Peaking

    E-Print Network [OSTI]

    ; adjusted to 2012$, state construction cost index, vintage of cost estimate, scope of estimate to extent's Discussion Aeroderivative Gas Turbine Technology Proposed reference plant and assumptions Preliminary cost Robbins 2 #12;Peaking Power Plant Characteristics 6th Power Plan ($2006) Unit Size (MW) Capital Cost ($/k

  12. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    of integrating demand response and energy efficiencyand D. Kathan (2009), Demand Response in U.S. ElectricityFRAMEWORKS THAT PROMOTE DEMAND RESPONSE 3.1. Demand Response

  13. Integrating demand into the U.S. electric power system : technical, economic, and regulatory frameworks for responsive load

    E-Print Network [OSTI]

    Black, Jason W. (Jason Wayne)

    2005-01-01

    The electric power system in the US developed with the assumption of exogenous, inelastic demand. The resulting evolution of the power system reinforced this assumption as nearly all controls, monitors, and feedbacks were ...

  14. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01

    benefits of Demand Side Management (DSM) are insufficient toefficiency, demand side management (DSM) cost effectivenessResearch Center Demand Side Management Demand Side Resources

  15. Residential Demand Sector Data, Commercial Demand Sector Data, Industrial Demand Sector Data - Annual Energy Outlook 2006

    SciTech Connect (OSTI)

    2009-01-18

    Tables describing consumption and prices by sector and census division for 2006 - includes residential demand, commercial demand, and industrial demand

  16. Preliminary Assumptions for Natural Gas Peaking

    E-Print Network [OSTI]

    plants and capital cost estimates for peaking technologies Frame, Aeroderivative, Intercooled, Reciprocating Engines Next steps 2 #12;Definitions Baseload Energy: power generated (or conserved) across a period of time to serve system demands for electricity Peaking Capacity: capability of power generating

  17. GridLAB-D Technical Support Document: Residential End-Use Module Version 1.0

    SciTech Connect (OSTI)

    Taylor, Zachary T.; Gowri, Krishnan; Katipamula, Srinivas

    2008-07-31

    1.0 Introduction The residential module implements the following end uses and characteristics to simulate the power demand in a single family home: • Water heater • Lights • Dishwasher • Range • Microwave • Refrigerator • Internal gains (plug loads) • House (heating/cooling loads) The house model considers the following four major heat gains/losses that contribute to the building heating/cooling load: 1. Conduction through exterior walls, roof and fenestration (based on envelope UA) 2. Air infiltration (based on specified air change rate) 3. Solar radiation (based on CLTD model and using tmy data) 4. Internal gains from lighting, people, equipment and other end use objects. The Equivalent Thermal Parameter (ETP) approach is used to model the residential loads and energy consumption. The following sections describe the modeling assumptions for each of the above end uses and the details of power demand calculations in the residential module.

  18. Module Handbook Module title

    E-Print Network [OSTI]

    . Students learn about selected topics from inorganic chemistry, biochemistry, materials chemistryModule Handbook Module title Module title in English Credits Degree of compulsion Level Learning Theory of Functional Materials 6 Compulsory Basic A systematic foundation for quantum physics

  19. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions to

  20. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions to00

  1. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions

  2. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions33

  3. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions3302

  4. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions330247

  5. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions330247

  6. Assumptions to the Annual Energy Outlook 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.101 (Million Short Tons)U.S.Assumptions330247

  7. DEMAND INTERPROCEDURAL PROGRAM ANALYSIS

    E-Print Network [OSTI]

    Reps, Thomas W.

    1 DEMAND INTERPROCEDURAL PROGRAM ANALYSIS USING LOGIC DATABASES Thomas W. Reps Computer Sciences@cs.wisc.edu ABSTRACT This paper describes how algorithms for demand versions of inerprocedural program­ analysis for all elements of the program. This paper concerns the solution of demand versions of interprocedural

  8. Capacity Demand Power (GW)

    E-Print Network [OSTI]

    California at Davis, University of

    Capacity Demand Power (GW) Hour of the Day The "Dip" Electricity Demand in Electricity Demand Every weekday, Japan's electricity use dips about 6 GW at 12 but it also shows that: · Behavior affects naHonal electricity use in unexpected ways

  9. Demand Response Assessment INTRODUCTION

    E-Print Network [OSTI]

    Demand Response Assessment INTRODUCTION This appendix provides more detail on some of the topics raised in Chapter 4, "Demand Response" of the body of the Plan. These topics include 1. The features, advantages and disadvantages of the main options for stimulating demand response (price mechanisms

  10. Manufacturing Energy and Carbon Footprint Definitions and Assumptions, October 2012

    Broader source: Energy.gov [DOE]

    Definitions of parameters and table of assumptions for the Manufacturing Energy and Carbon Footprint

  11. A Privacy-Preserving Scheme for Incentive-Based Demand Response in the Smart Grid

    E-Print Network [OSTI]

    Fang, Yuguang "Michael"

    1 A Privacy-Preserving Scheme for Incentive-Based Demand Response in the Smart Grid Yanmin Gong to both grid operators and customers, exploiting the full potential of demand response. However to be attributable to individuals. However, this assumption does not hold in incentive-based demand response (IDR

  12. Macroeconomic Activity Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Macroeconomic Activity Module (MAM) used to develop the Annual Energy Outlook for 2014 (AEO2014). The report catalogues and describes the module assumptions, computations, methodology, parameter estimation techniques, and mainframe source code

  13. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    DECC aggregator managed portfolio automated demand responseaggregator designs their own programs, and offers demand responseaggregator is responsible for designing and implementing their own demand response

  14. The imperfect price-reversibility of world oil demand

    SciTech Connect (OSTI)

    Gately, D. [New York Univ., NY (United States)

    1993-12-31

    This paper examines the price-reversibility of world oil demand, using price-decomposition methods employed previously on other energy demand data. We conclude that the reductions in world oil demand following the oil price increases of the 1970s will not be completely reversed by the price cuts of the 1980s. The response to price cuts in the 1980s is perhaps only one-fifth that for price increases in the 1970s. This has dramatic implications for projections of oil demand, especially under low-price assumptions. We also consider the effect on demand of a price recovery (sub-maximum increase) in the 1990s - due either to OPEC or to a carbon tax-specifically whether the effects would be as large as for the price increases of the 1970s or only as large as the smaller demand reversals of the 1980s. On this the results are uncertain, but a tentative conclusion is that the response to a price recovery would lie midway between the small response to price cuts and the larger response to increases in the maximum historical price. Finally, we demonstrate two implications of wrongly assuming that demand is perfectly price-reversible. First, such an assumption will grossly overestimate the demand response to price declines of the 1980s. Secondly, and somewhat surprisingly, it causes an underestimate of the effect of income growth on future demand. 21 refs., 11 figs., 1 tab.

  15. Utility Sector Impacts of Reduced Electricity Demand

    SciTech Connect (OSTI)

    Coughlin, Katie

    2014-12-01

    This report presents a new approach to estimating the marginal utility sector impacts associated with electricity demand reductions. The method uses publicly available data and provides results in the form of time series of impact factors. The input data are taken from the Energy Information Agency's Annual Energy Outlook (AEO) projections of how the electric system might evolve in the reference case, and in a number of side cases that incorporate different effciency and other policy assumptions. The data published with the AEO are used to define quantitative relationships between demand-side electricity reductions by end use and supply-side changes to capacity by plant type, generation by fuel type and emissions of CO2, Hg, NOx and SO2. The impact factors define the change in each of these quantities per unit reduction in site electricity demand. We find that the relative variation in these impacts by end use is small, but the time variation can be significant.

  16. International Oil Supplies and Demands. Volume 1

    SciTech Connect (OSTI)

    Not Available

    1991-09-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  17. International Oil Supplies and Demands. Volume 2

    SciTech Connect (OSTI)

    Not Available

    1992-04-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  18. Assumptions to the Annual Energy Outlook 2014

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and

  19. Assumptions to the Annual Energy Outlook 2014

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and This page

  20. Assumptions to the Annual Energy Outlook 2014

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and This page

  1. Automated Demand Response and Commissioning

    E-Print Network [OSTI]

    Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

    2005-01-01

    Fully-Automated Demand Response Test in Large Facilities14in DR systems. Demand Response using HVAC in Commercialof Fully Automated Demand Response in Large Facilities”

  2. Demand Response Spinning Reserve Demonstration

    E-Print Network [OSTI]

    2007-01-01

    F) Enhanced ACP Date RAA ACP Demand Response – SpinningReserve Demonstration Demand Response – Spinning Reservesupply spinning reserve. Demand Response – Spinning Reserve

  3. Demand Response Programs for Oregon

    E-Print Network [OSTI]

    Demand Response Programs for Oregon Utilities Public Utility Commission May 2003 Public Utility ....................................................................................................................... 1 Types of Demand Response Programs............................................................................ 3 Demand Response Programs in Oregon

  4. Exponential Demand Simulation Tool

    E-Print Network [OSTI]

    Reed, Derek D.

    2015-05-15

    Operant behavioral economics investigates the relation between environmental constraint and reinforcer consumption. The standard approach to quantifying this relation is through the use of behavioral economic demand curves. ...

  5. Managing Increased Charging Demand

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

    Managing Increased Charging Demand Carrie Giles ICF International, Supporting the Workplace Charging Challenge Workplace Charging Challenge Do you already own an EV? Are you...

  6. ZBB EnerStore(tm): Deep Discharge Zinc-Bromine Battery Module...

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

    Battery Module Long-Lasting Electrical Energy Storage Module Allows Off-Peak Power Generation Electricity consumption during peak demand can overload utilities, forcing them to...

  7. Efficient Pseudorandom Functions From the Decisional Linear Assumption and Weaker

    E-Print Network [OSTI]

    International Association for Cryptologic Research (IACR)

    Efficient Pseudorandom Functions From the Decisional Linear Assumption and Weaker Variants Allison to yield a construction of pseudorandom functions under the decisional k-Linear Assumption, for each k 1 In this paper, we generalize Naor and Reingold's construction of pseudorandom functions under the DDH Assumption

  8. Electrical Demand Management 

    E-Print Network [OSTI]

    Fetters, J. L.; Teets, S. J.

    1983-01-01

    The Demand Management Plan set forth in this paper has proven to be a viable action to reduce a 3 million per year electric bill at the Columbus Works location of Western Electric. Measures are outlined which have reduced the peak demand 5% below...

  9. Demand Dispatch-Intelligent

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

    such as wind, solar, and electric vehicles as well as dispatchable loads and microgrids. Many of these resources will be "behind-the-meter" (i.e., demand resources) and...

  10. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    World crude oil and natural gas: a demand and supply model.analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.

  11. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    World crude oil and natural gas: a demand and supply model.analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.

  12. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    H. , and James M. Gri¢ n. 1983. Gasoline demand in the OECDof dynamic demand for gasoline. Journal of Econometrics 77(An empirical analysis of gasoline demand in Denmark using

  13. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    shift in the short-run price elasticity of gasoline demand.A meta-analysis of the price elasticity of gasoline demand.2007. Consumer demand un- der price uncertainty: Empirical

  14. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    Sterner. 1991. Analysing gasoline demand elasticities: A2011. Measuring global gasoline and diesel price and incomeMutairi. 1995. Demand for gasoline in Kuwait: An empirical

  15. A Hierarchical Demand Response Framework for Data Center Power Cost Optimization Under Real-World Electricity Pricing

    E-Print Network [OSTI]

    Giles, C. Lee

    1 A Hierarchical Demand Response Framework for Data Center Power Cost Optimization Under Real bills. Our focus is on a subset of this work that carries out demand response (DR) by modulating

  16. A Privacy-Aware Architecture For Demand Response Systems Stephen Wicker, Robert Thomas

    E-Print Network [OSTI]

    Wicker, Stephen

    A Privacy-Aware Architecture For Demand Response Systems Stephen Wicker, Robert Thomas School architectures that realize the benefits of demand response without requiring that AMI data be centrally-based demand response efforts in the face of public outcry. We also show that Trusted Platform Modules can

  17. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01

    No. ER06-615-000 CAISO Demand Response Resource User Guide -8 2.1. Demand Response Provides a Range of Benefits to8 2.2. Demand Response Benefits can be Quantified in Several

  18. Optimal Demand Response Libin Jiang

    E-Print Network [OSTI]

    Optimal Demand Response Libin Jiang Steven Low Computing + Math Sciences Electrical Engineering Caltech Oct 2011 #12;Outline Caltech smart grid research Optimal demand response #12;Global trends 1

  19. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

    ....................................................................................................1-16 Energy Consumption Data...............................................1-15 Data Sources for Energy Demand Forecasting ModelsCALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report

  20. Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand

    E-Print Network [OSTI]

    Carlini, David

    Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand Amos Golan* Jeffrey an almost ideal demand system for five types of meat using cross-sectional data from Mexico, where most households did not buy at least one type of meat during the survey week. The system of demands is shown

  1. Peer-Assisted On-Demand Streaming: Characterizing Demands and

    E-Print Network [OSTI]

    Li, Baochun

    Peer-Assisted On-Demand Streaming: Characterizing Demands and Optimizing Supplies Fangming Liu Abstract--Nowadays, there has been significant deployment of peer-assisted on-demand streaming services over the Internet. Two of the most unique and salient features in a peer-assisted on-demand streaming

  2. Assumption-Commitment Support for CSP Model Checking

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    AVoCS 2006 Assumption-Commitment Support for CSP Model Checking Nick Moffat1 Systems Assurance using CSP. In our formulation, an assumption-commitment style property of a process SYS takes the form-Guarantee, CSP, Model Checking, Compositional Reasoning 1 Introduction The principle of compositional program

  3. Energy Demand Staff Scientist

    E-Print Network [OSTI]

    Eisen, Michael

    #12;Sources: China National Bureau of Statistics; U.S. Energy Information Administration, Annual Energy Outlook. Overview:Overview: Energy Use in China and the U.S.Energy Use in China and the U.S. 5 0Energy Demand in China Lynn Price Staff Scientist February 2, 2010 #12;Founded in 1988 Focused

  4. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    fraction of residential and commercial demands, leading16 Residential electricity demand endspecific residential electricity demands into electricity

  5. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

    Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required in electricity demand is, of course, crucial to determining the need for new electricity resources and helping of any forecast of electricity demand and developing ways to reduce the risk of planning errors

  6. Coal Market Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System's (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 2014 (AEO2014). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM's two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS).

  7. Behavioral Economics Applied to Energy Demand Analysis: A Foundation

    Reports and Publications (EIA)

    2014-01-01

    Neoclassical economics has shaped our understanding of human behavior for several decades. While still an important starting point for economic studies, neoclassical frameworks have generally imposed strong assumptions, for example regarding utility maximization, information, and foresight, while treating consumer preferences as given or external to the framework. In real life, however, such strong assumptions tend to be less than fully valid. Behavioral economics refers to the study and formalizing of theories regarding deviations from traditionally-modeled economic decision-making in the behavior of individuals. The U.S. Energy Information Administration (EIA) has an interest in behavioral economics as one influence on energy demand.

  8. STUDENT FORM GENERAL RELEASE FORM & ASSUMPTION OF RISK

    E-Print Network [OSTI]

    Schaefer, Marcus

    STUDENT FORM GENERAL RELEASE FORM & ASSUMPTION OF RISK DePaul University School of Cinematic Arts I that while enrolling in the Course may be a requirement for achieving my degree in Cinematic Arts at De

  9. Computational soundness for standard assumptions of formal cryptography

    E-Print Network [OSTI]

    Herzog, Jonathan, 1975-

    2004-01-01

    This implementation is conceptually simple, and relies only on general assumptions. Specifically, it can be thought of as a 'self-referential' variation on a well-known encryption scheme. 4. Lastly, we show how the ...

  10. World Energy Projection System Plus (WEPS ): Global Activity Module

    Reports and Publications (EIA)

    2013-01-01

    World Energy Projection System Plus Model Documentation: Global Activity Module Documents the objectives, analytical approach, and development of the World Energy Projection Plus (WEPS ) Global Activity Module (GAM) used to develop the International Energy Outlook for 2013 (IEO2013). The report catalogues and describes the module assumptions, computations, methodology, parameter estimation techniques, and mainframe source code.

  11. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    retail regulatory authority prohibit such activity. Demand response integration into US wholesale power marketsretail or wholesale level. 17 While demand response began participating at scale in wholesale power markets

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

    SciTech Connect (OSTI)

    NONE

    1998-01-01

    This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The international area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.

  13. Demand Dispatch-Intelligent

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power Administration would like submit theCovalent Bonding Low-Cost2DepartmentDelta Dental Claim Form PDF iconDemand

  14. Revelation on Demand Nicolas Anciaux

    E-Print Network [OSTI]

    Revelation on Demand Nicolas Anciaux 1 · Mehdi Benzine1,2 · Luc Bouganim1 · Philippe Pucheral1 "revelation on demand". Keywords: Confidentiality and privacy, Secure device, Data warehousing, Indexing model

  15. by popular demand: Addiction II

    E-Print Network [OSTI]

    Niv, Yael

    by popular demand: Addiction II PSY/NEU338:Animal learning and decision making: Psychological, size of other non-drug rewards, and cost (but ultimately the demand is inelastic, or at least

  16. Demand Response: Load Management Programs 

    E-Print Network [OSTI]

    Simon, J.

    2012-01-01

    Management Programs CATEE Conference October, 2012 Agenda Outline I. General Demand Response Definition II. General Demand Response Program Rules III. CenterPoint Commercial Program IV. CenterPoint Residential Programs V. Residential Discussion... Points Demand Response Definition of load management per energy efficiency rule 25.181: ? Load control activities that result in a reduction in peak demand, or a shifting of energy usage from a peak to an off-peak period or from high-price periods...

  17. Chord on Demand Alberto Montresor

    E-Print Network [OSTI]

    Jelasity, Márk

    Chord on Demand Alberto Montresor University of Bologna, Italy montresor@cs.unibo.it M´ark Jelasity to solve a specific task on demand. We introduce T- CHORD, that can build a Chord network efficiently to solve a specific task on demand. Existing join protocols are not designed to handle the massive

  18. Supply Chain Supernetworks Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Supply Chain Supernetworks with Random Demands June Dong and Ding Zhang Department of Marketing of three tiers of decision-makers: the manufacturers, the distributors, and the retailers, with the demands equilibrium model with electronic commerce and with random demands for which modeling, qualitative analysis

  19. Chord on Demand Alberto Montresor

    E-Print Network [OSTI]

    Chord on Demand Alberto Montresor University of Bologna, Italy montresor@cs.unibo.it Mark Jelasity to solve a specific task on demand. We introduce T- CHORD, that can build a Chord network efficiently on demand. Existing join protocols are not designed to handle the massive concurrency involved in a jump

  20. ERCOT Demand Response Paul Wattles

    E-Print Network [OSTI]

    Mohsenian-Rad, Hamed

    ERCOT Demand Response Paul Wattles Senior Analyst, Market Design & Development, ERCOT Whitacre;Definitions of Demand Response · `The short-term adjustment of energy use by consumers in response to price to market or reliability conditions.' (NAESB) #12;Definitions of Demand Response · The common threads

  1. Assessment of Demand Response Resource

    E-Print Network [OSTI]

    Assessment of Demand Response Resource Potentials for PGE and Pacific Power Prepared for: Portland January 15, 2004 K:\\Projects\\2003-53 (PGE,PC) Assess Demand Response\\Report\\Revised Report_011504.doc #12;#12;quantec Assessment of Demand Response Resource Potentials for I-1 PGE and Pacific Power I. Introduction

  2. Unconscious Biases and Assumptions: The Origins of Discrimination?

    E-Print Network [OSTI]

    Sheridan, Jennifer

    #12;Unconscious Biases and Assumptions: The Origins of Discrimination? #12;Outline Examples of subtle discrimination What is "unconscious bias" and do I have it? What to do? #12;Applications is causing these phenomena? Discrimination? Or... Unconscious Bias? #12;Mind-blindness Count

  3. Utilizing Symmetry when Model Checking under Fairness Assumptions: An Automatatheoretic

    E-Print Network [OSTI]

    Emerson, E. Allen

    ­ current Programming General Terms: Verification, Model Checking, Temporal Logic, Abstraction AdditionalUtilizing Symmetry when Model Checking under Fairness Assumptions: An Automata­theoretic Approach E temporal logic model checking. In previous work it is shown how, using some basic notions of group theory

  4. Basing Obfuscation on Simple Tamper-Proof Hardware Assumptions

    E-Print Network [OSTI]

    International Association for Cryptologic Research (IACR)

    Basing Obfuscation on Simple Tamper-Proof Hardware Assumptions Nico D¨ottling, Thilo Mie, J¨orn M for obfuscation, and using tamper-proof hardware tokens to achieve general code obfuscation. Following this last on the security of this CRS. Keywords: Obfuscation, Stateless Tamper-Proof hardware, Universal Composability

  5. COMPARING ALASKA'S OIL PRODUCTION TAXES: INCENTIVES AND ASSUMPTIONS1

    E-Print Network [OSTI]

    Pantaleone, Jim

    1 COMPARING ALASKA'S OIL PRODUCTION TAXES: INCENTIVES AND ASSUMPTIONS1 Matthew Berman In a recent analysis comparing the current oil production tax, More Alaska Production Act (MAPA, also known as SB 21 oil prices, production rates, and costs. He noted that comparative revenues are highly sensitive

  6. Demand Response Programs, 6. edition

    SciTech Connect (OSTI)

    2007-10-15

    The report provides a look at the past, present, and future state of the market for demand/load response based upon market price signals. It is intended to provide significant value to individuals and companies who are considering participating in demand response programs, energy providers and ISOs interested in offering demand response programs, and consultants and analysts looking for detailed information on demand response technology, applications, and participants. The report offers a look at the current Demand Response environment in the energy industry by: defining what demand response programs are; detailing the evolution of program types over the last 30 years; discussing the key drivers of current initiatives; identifying barriers and keys to success for the programs; discussing the argument against subsidization of demand response; describing the different types of programs that exist including:direct load control, interruptible load, curtailable load, time-of-use, real time pricing, and demand bidding/buyback; providing examples of the different types of programs; examining the enablers of demand response programs; and, providing a look at major demand response programs.

  7. NEMS integrating module documentation report

    SciTech Connect (OSTI)

    Not Available

    1993-12-14

    The National Energy Modeling System (NEMS) is a computer modeling system that produces a general equilibrium solution for energy supply and demand in the US energy markets. The model achieves a supply and demand balance in the end-use demand regions, defined as the nine Census Divisions, by solving for the prices of each energy type such that the quantities producers are willing to supply equal the quantities consumers wish to consume. The system reflects market economics, industry structure, and energy policies and regulations that influence market behavior. The NEMS Integrating Module is the central integrating component of a complex modeling system. As such, a thorough understanding of its role in the modeling process can only be achieved by placing it in the proper context with respect to the other modules. To that end, this document provides an overview of the complete NEMS model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

  8. Generating Demand for Multifamily Building Upgrades | Department...

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

    Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades Better Buildings Residential Network Peer Exchange Call Series: Generating Demand for...

  9. China's Coal: Demand, Constraints, and Externalities

    E-Print Network [OSTI]

    Aden, Nathaniel

    2010-01-01

    raising transportation oil demand. Growing internationalcoal by wire could reduce oil demand by stemming coal roadEastern oil production. The rapid growth of coal demand

  10. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    of Energy demand-side management energy information systemdemand response. Demand-side management (DSM) program goalsa goal for demand-side management (DSM) coordination and

  11. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01

    3 2.1 Demand-Side Managementbuildings. The demand side management framework is discussedIssues 2.1 Demand-Side Management Framework Forecasting

  12. Home Network Technologies and Automating Demand Response

    E-Print Network [OSTI]

    McParland, Charles

    2010-01-01

    LBNL Commercial and Residential Demand Response Overview ofmarket [5]. Residential demand reduction programs have beenin the domain of residential demand response. There are a

  13. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

    Kiliccote, Sila; Global Energy Partners; Pacific Gas and Electric Company

    2008-01-01

    their partnership in demand response automation research andand Techniques for Demand Response. LBNL Report 59975. Mayof Fully Automated Demand Response in Large Facilities.

  14. Coupling Renewable Energy Supply with Deferrable Demand

    E-Print Network [OSTI]

    Papavasiliou, Anthony

    2011-01-01

    8.4 Demand Response Integration . . . . . . . . . . .for each day type for the demand response study - moderatefor each day type for the demand response study - deep

  15. Strategies for Demand Response in Commercial Buildings

    E-Print Network [OSTI]

    Watson, David S.; Kiliccote, Sila; Motegi, Naoya; Piette, Mary Ann

    2006-01-01

    Fully Automated Demand Response Tests in Large Facilities”of Fully Automated Demand Response in Large Facilities”,was coordinated by the Demand Response Research Center and

  16. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01

    2 2.0 Demand ResponseFully Automated Demand Response Tests in Large Facilities,was coordinated by the Demand Response Research Center and

  17. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    and D. Kathan (2009). Demand Response in U.S. ElectricityEnergy Financial Group. Demand Response Research Center [2008). Assessment of Demand Response and Advanced Metering.

  18. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01

    Like HECO actual utility demand response implementations canindustry-wide utility demand response applications tend toobjective. Figure 4. Demand Response Objectives 17  

  19. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01

    23 ii Retail Demand Response in SPP List of Figures and10 Figure 3. Demand Response Resources by11 Figure 4. Existing Demand Response Resources by Type of

  20. Demand Response - Policy | Department of Energy

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

    Coordination of Energy Efficiency and Demand Response Demand Response in U.S. Electricity Markets: Empirical Evidence 2009 Retail Demand Response in Southwest Power Pool (January...

  1. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01

    Barat, and D. Watson. 2007. Demand Response Spinning ReserveKueck, and B. Kirby. 2009. Demand Response Spinning Reserveand B. Kirby. 2012. The Demand Response Spinning Reserve

  2. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    duty fuel demand in alternate scenarios. ..for light-duty fuel demand in alternate scenarios. Minimum52 Heavy-duty vehicle fuel demand for each alternate

  3. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    2006-2016: Staff energy demand forecast (Revised SeptemberCEC (2005b) Energy demand forecast methods report.California energy demand 2003-2013 forecast. California

  4. Industrial Sector Demand Module of the National Energy Modeling System

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet)DecadeYear Jan Feb Mar Apr MayYearYear JanDecade Year-0per6,167,371 6,826,1925)

  5. Residential Sector Demand Module of the National Energy Modeling System

    U.S. Energy Information Administration (EIA) 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 PageMonthly","10/2015"4,"Ames5 Tables July 1996 Energy Information Administration Office of Coal, Nuclear,DecadeYearby the(Dollars1.840 2.318 3.1195) Model8)3 November4)

  6. Demand Response Technology Roadmap A

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

    meetings and workshops convened to develop content for the Demand Response Technology Roadmap. The project team has developed this companion document in the interest of providing...

  7. Supply Chain Supernetworks With Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Supply Chain Supernetworks With Random Demands June Dong Ding Zhang School of Business State Field Warehouses: stocking points Customers, demand centers sinks Production/ purchase costs Inventory Customer Demand Customer Demand Retailer OrdersRetailer Orders Distributor OrdersDistributor Orders

  8. Module Configuration

    DOE Patents [OSTI]

    Oweis, Salah (Ellicott City, MD); D'Ussel, Louis (Bordeaux, FR); Chagnon, Guy (Cockeysville, MD); Zuhowski, Michael (Annapolis, MD); Sack, Tim (Cockeysville, MD); Laucournet, Gaullume (Paris, FR); Jackson, Edward J. (Taneytown, MD)

    2002-06-04

    A stand alone battery module including: (a) a mechanical configuration; (b) a thermal management configuration; (c) an electrical connection configuration; and (d) an electronics configuration. Such a module is fully interchangeable in a battery pack assembly, mechanically, from the thermal management point of view, and electrically. With the same hardware, the module can accommodate different cell sizes and, therefore, can easily have different capacities. The module structure is designed to accommodate the electronics monitoring, protection, and printed wiring assembly boards (PWAs), as well as to allow airflow through the module. A plurality of modules may easily be connected together to form a battery pack. The parts of the module are designed to facilitate their manufacture and assembly.

  9. Marketing & Driving Demand Collaborative - Social Media Tools...

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

    & Driving Demand Collaborative - Social Media Tools & Strategies Marketing & Driving Demand Collaborative - Social Media Tools & Strategies Presentation slides from the Better...

  10. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01

    Data Collection for Demand-side Management for QualifyingPrepared by Demand-side Management Task Force of the

  11. Honeywell Demonstrates Automated Demand Response Benefits for...

    Office of Environmental Management (EM)

    Honeywell Demonstrates Automated Demand Response Benefits for Utility, Commercial, and Industrial Customers Honeywell Demonstrates Automated Demand Response Benefits for Utility,...

  12. Effects of the drought on California electricity supply and demand

    E-Print Network [OSTI]

    Benenson, P.

    2010-01-01

    Acknowledgments SUMMARY Electricity Demand ElectricityAdverse Impacts ELECTRICITY DEMAND . . . .Demand forElectricity Sales Electricity Demand by Major Utility

  13. Demand Response for Ancillary Services

    SciTech Connect (OSTI)

    Alkadi, Nasr E; Starke, Michael R

    2013-01-01

    Many demand response resources are technically capable of providing ancillary services. In some cases, they can provide superior response to generators, as the curtailment of load is typically much faster than ramping thermal and hydropower plants. Analysis and quantification of demand response resources providing ancillary services is necessary to understand the resources economic value and impact on the power system. Methodologies used to study grid integration of variable generation can be adapted to the study of demand response. In the present work, we describe and illustrate a methodology to construct detailed temporal and spatial representations of the demand response resource and to examine how to incorporate those resources into power system models. In addition, the paper outlines ways to evaluate barriers to implementation. We demonstrate how the combination of these three analyses can be used to translate the technical potential for demand response providing ancillary services into a realizable potential.

  14. Transportation Sector Module

    U.S. Energy Information Administration (EIA) 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 PageMonthly","10/2015"4,"Ames5 Tables July 1996 Energy Information Administration Office of Coal,Demand Module of the National Energy Modeling System: Model Documentation7)

  15. Sensitivity of Utility-Scale Solar Deployment Projections in the SunShot Vision Study to Market and Performance Assumptions

    SciTech Connect (OSTI)

    Eurek, K.; Denholm, P.; Margolis, R.; Mowers, M.

    2013-04-01

    The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The ReEDS model was used to simulate utility PV and CSP deployment for this present study, based on several market and performance assumptions - electricity demand, natural gas prices, coal retirements, cost and performance of non-solar renewable technologies, PV resource variability, distributed PV deployment, and solar market supply growth - in addition to the SunShot solar price projections. This study finds that utility-scale solar deployment is highly sensitive to solar prices. Other factors can have significant impacts, particularly electricity demand and natural gas prices.

  16. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

  17. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

  18. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    electricity. In this manner, demand side management is directly integrated into the wholesale capacity marketcapacity market U.S. Federal Energy Regulatory Commission Florida Reliability Coordinating Council incremental auctions independent electricity

  19. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    global gasoline and diesel price and income elasticities.shift in the short-run price elasticity of gasoline demand.Habits and Uncertain Relative Prices: Simulating Petrol Con-

  20. California Baseline Energy Demands to 2050 for Advanced Energy Pathways

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    demands. Residential and commercial demand has a significantDemand by Sector Residential Peak Demand (MW) Commercialwe convert residential electricity demand based upon climate

  1. Demand Response for Ancillary Services

    Broader source: Energy.gov [DOE]

    Methodologies used to study grid integration of variable generation can be adapted to the study of demand response. In the present work, we describe and implement a methodology to construct detailed temporal and spatial representations of demand response resources and to incorporate those resources into power system models. In addition, the paper outlines ways to evaluate barriers to implementation. We demonstrate how the combination of these three analyses can be used to assess economic value of the realizable potential of demand response for ancillary services.

  2. Physically-based demand modeling 

    E-Print Network [OSTI]

    Calloway, Terry Marshall

    1980-01-01

    nts on the demand. Of course the demand of a real a1r cond1t1oner has lower and upper bounds equal to 0 and 0 , respec- u tively. A constra1ned system can be simulated numerically, but there 1s no explicit system response formula s1m11ar... sect1on. It may now be instruct1ve to relate this model to that of Jones and Bri ce [5] . The average demand pred1 cted by their model is the expected value of the product of a load response factor 0 and a U sw1tching process H(t), which depends...

  3. Assumption Parish, Louisiana: Energy Resources | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EAandAmminex A SOpenAshley, Ohio: Energy-Resource |CarbonAssumption Parish,

  4. Seasonality in air transportation demand

    E-Print Network [OSTI]

    Reichard Megwinoff, H?tor Nicolas

    1988-01-01

    This thesis investigates the seasonality of demand in air transportation. It presents three methods for computing seasonal indices. One of these methods, the Periodic Average Method, is selected as the most appropriate for ...

  5. Demand response enabling technology development

    E-Print Network [OSTI]

    2006-01-01

    Monitoring in an Agent-Based Smart Home, Proceedings of theConference on Smart Homes and Health Telematics, September,Smart Meter Motion sensors Figure 1: Schematic of the Demand Response Electrical Appliance Manager in a Home.

  6. Full Rank Rational Demand Systems

    E-Print Network [OSTI]

    LaFrance, Jeffrey T; Pope, Rulon D.

    2006-01-01

    Dover Publications 1972. Barnett, W.A. and Y.W. Lee. “TheEconometrica 53 (1985): 1421- Barnett, W.A. , Lee, Y.W. ,Laurent demand systems (Barnett and Lee 1985; Barnett, Lee,

  7. Marketing Demand-Side Management 

    E-Print Network [OSTI]

    O'Neill, M. L.

    1988-01-01

    Demand-Side Management is an organizational tool that has proven successful in various realms of the ever changing business world in the past few years. It combines the multi-faceted desires of the customers with the increasingly important...

  8. Testing the assumptions of linear prediction analysis in normal vowels

    E-Print Network [OSTI]

    Max Little; Patrick E. McSharry; Irene M. Moroz; Stephen J. Roberts

    2006-01-04

    This paper develops an improved surrogate data test to show experimental evidence, for all the simple vowels of US English, for both male and female speakers, that Gaussian linear prediction analysis, a ubiquitous technique in current speech technologies, cannot be used to extract all the dynamical structure of real speech time series. The test provides robust evidence undermining the validity of these linear techniques, supporting the assumptions of either dynamical nonlinearity and/or non-Gaussianity common to more recent, complex, efforts at dynamical modelling speech time series. However, an additional finding is that the classical assumptions cannot be ruled out entirely, and plausible evidence is given to explain the success of the linear Gaussian theory as a weak approximation to the true, nonlinear/non-Gaussian dynamics. This supports the use of appropriate hybrid linear/nonlinear/non-Gaussian modelling. With a calibrated calculation of statistic and particular choice of experimental protocol, some of the known systematic problems of the method of surrogate data testing are circumvented to obtain results to support the conclusions to a high level of significance.

  9. The contour method cutting assumption: error minimization and correction

    SciTech Connect (OSTI)

    Prime, Michael B; Kastengren, Alan L

    2010-01-01

    The recently developed contour method can measure 2-D, cross-sectional residual-stress map. A part is cut in two using a precise and low-stress cutting technique such as electric discharge machining. The contours of the new surfaces created by the cut, which will not be flat if residual stresses are relaxed by the cutting, are then measured and used to calculate the original residual stresses. The precise nature of the assumption about the cut is presented theoretically and is evaluated experimentally. Simply assuming a flat cut is overly restrictive and misleading. The critical assumption is that the width of the cut, when measured in the original, undeformed configuration of the body is constant. Stresses at the cut tip during cutting cause the material to deform, which causes errors. The effect of such cutting errors on the measured stresses is presented. The important parameters are quantified. Experimental procedures for minimizing these errors are presented. An iterative finite element procedure to correct for the errors is also presented. The correction procedure is demonstrated on experimental data from a steel beam that was plastically bent to put in a known profile of residual stresses.

  10. Demand Response Spinning Reserve Demonstration

    SciTech Connect (OSTI)

    Eto, Joseph H.; Nelson-Hoffman, Janine; Torres, Carlos; Hirth,Scott; Yinger, Bob; Kueck, John; Kirby, Brendan; Bernier, Clark; Wright,Roger; Barat, A.; Watson, David S.

    2007-05-01

    The Demand Response Spinning Reserve project is a pioneeringdemonstration of how existing utility load-management assets can providean important electricity system reliability resource known as spinningreserve. Using aggregated demand-side resources to provide spinningreserve will give grid operators at the California Independent SystemOperator (CAISO) and Southern California Edison (SCE) a powerful, newtool to improve system reliability, prevent rolling blackouts, and lowersystem operating costs.

  11. Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    A. Barat, D. Watson. 2006 Demand Response Spinning ReserveKueck, and B. Kirby 2008. Demand Response Spinning ReserveReport 2009. Open Automated Demand Response Communications

  12. Demand Response and Open Automated Demand Response Opportunities for Data Centers

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    Standardized Automated Demand Response Signals. Presented atand Automated Demand Response in Industrial RefrigeratedActions for Industrial Demand Response in California. LBNL-

  13. Optimal Demand Response and Power Flow

    E-Print Network [OSTI]

    Willett, Rebecca

    Optimal Demand Response and Power Flow Steven Low Computing + Math Sciences Electrical Engineering #12;Outline Optimal demand response n With L. Chen, L. Jiang, N. Li Optimal power flow n With S. Bose;Optimal demand response Model Results n Uncorrelated demand: distributed alg n Correlated demand

  14. Cost and Performance Assumptions for Modeling Electricity Generation Technologies

    SciTech Connect (OSTI)

    Tidball, R.; Bluestein, J.; Rodriguez, N.; Knoke, S.

    2010-11-01

    The goal of this project was to compare and contrast utility scale power plant characteristics used in data sets that support energy market models. Characteristics include both technology cost and technology performance projections to the year 2050. Cost parameters include installed capital costs and operation and maintenance (O&M) costs. Performance parameters include plant size, heat rate, capacity factor or availability factor, and plant lifetime. Conventional, renewable, and emerging electricity generating technologies were considered. Six data sets, each associated with a different model, were selected. Two of the data sets represent modeled results, not direct model inputs. These two data sets include cost and performance improvements that result from increased deployment as well as resulting capacity factors estimated from particular model runs; other data sets represent model input data. For the technologies contained in each data set, the levelized cost of energy (LCOE) was also evaluated, according to published cost, performance, and fuel assumptions.

  15. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  16. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    high economic/demographic growth, relatively low electricity and natural gas rates, and relatively low CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION

  17. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand Gough Office Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  18. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01

    Barat, and D. Watson. 2007. Demand Response Spinning ReserveKueck, and B. Kirby. 2009. Demand Response Spinning ReserveFormat of 2009-2011 Demand Response Activity Applications.

  19. CRITICAL ASSUMPTIONS IN THE F-TANK FARM CLOSURE OPERATIONAL DOCUMENTATION REGARDING WASTE TANK INTERNAL CONFIGURATIONS

    SciTech Connect (OSTI)

    Hommel, S.; Fountain, D.

    2012-03-28

    The intent of this document is to provide clarification of critical assumptions regarding the internal configurations of liquid waste tanks at operational closure, with respect to F-Tank Farm (FTF) closure documentation. For the purposes of this document, FTF closure documentation includes: (1) Performance Assessment for the F-Tank Farm at the Savannah River Site (hereafter referred to as the FTF PA) (SRS-REG-2007-00002), (2) Basis for Section 3116 Determination for Closure of F-Tank Farm at the Savannah River Site (DOE/SRS-WD-2012-001), (3) Tier 1 Closure Plan for the F-Area Waste Tank Systems at the Savannah River Site (SRR-CWDA-2010-00147), (4) F-Tank Farm Tanks 18 and 19 DOE Manual 435.1-1 Tier 2 Closure Plan Savannah River Site (SRR-CWDA-2011-00015), (5) Industrial Wastewater Closure Module for the Liquid Waste Tanks 18 and 19 (SRRCWDA-2010-00003), and (6) Tank 18/Tank 19 Special Analysis for the Performance Assessment for the F-Tank Farm at the Savannah River Site (hereafter referred to as the Tank 18/Tank 19 Special Analysis) (SRR-CWDA-2010-00124). Note that the first three FTF closure documents listed apply to the entire FTF, whereas the last three FTF closure documents listed are specific to Tanks 18 and 19. These two waste tanks are expected to be the first two tanks to be grouted and operationally closed under the current suite of FTF closure documents and many of the assumptions and approaches that apply to these two tanks are also applicable to the other FTF waste tanks and operational closure processes.

  20. Exponential Communication Ine ciency of Demand Queries

    E-Print Network [OSTI]

    Sandholm, Tuomas W.

    FORECAST COMBINATION IN REVENUE MANAGEMENT DEMAND FORECASTING SILVIA RIEDEL A thesissubmitted Combination in RevenueManagement Demand Forecasting Abstract The domain of multi level forecastcombination

  1. Generating Demand for Multifamily Building Upgrades | Department...

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

    Generating Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades Better Buildings Residential Network Peer Exchange Call Series: Generating...

  2. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    demand response: ? Distribution utility ? ISO ? Aggregator (demand response less obstructive and inconvenient for the customer (particularly if DR resources are aggregated by a load aggregator).

  3. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    annual per-capita electricity consumption by demand15 California electricity consumption projections by demandannual per-capita electricity consumption by demand

  4. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    California Long-term Energy Efficiency Strategic Plan. B-2 Coordination of Energy Efficiency and Demand Response> B-4 Coordination of Energy Efficiency and Demand Response

  5. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    Energy Efficiency, Demand Response, and Peak Load Managementdemand response, and load management programs in the Ebefore they undertake load management and demand response

  6. Supply chain planning decisions under demand uncertainty

    E-Print Network [OSTI]

    Huang, Yanfeng Anna

    2008-01-01

    Sales and operational planning that incorporates unconstrained demand forecasts has been expected to improve long term corporate profitability. Companies are considering such unconstrained demand forecasts in their decisions ...

  7. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    > B-2 Coordination of Energy Efficiency and Demand Response> B-4 Coordination of Energy Efficiency and Demand Responseand integration is: Energy efficiency, energy conservation,

  8. Generating Demand for Multifamily Building Upgrades | Department...

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

    Generating Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades May 14, 2015 12:30PM to 2:00PM EDT Learn more...

  9. Demand Response Programs Oregon Public Utility Commission

    E-Print Network [OSTI]

    Demand Response Programs Oregon Public Utility Commission January 6, 2005 Mike Koszalka Director;Demand Response Results, 2004 Load Control ­ Cool Keeper ­ ID Irrigation Load Control Price Responsive

  10. Turkey's energy demand and supply

    SciTech Connect (OSTI)

    Balat, M. [Sila Science, Trabzon (Turkey)

    2009-07-01

    The aim of the present article is to investigate Turkey's energy demand and the contribution of domestic energy sources to energy consumption. Turkey, the 17th largest economy in the world, is an emerging country with a buoyant economy challenged by a growing demand for energy. Turkey's energy consumption has grown and will continue to grow along with its economy. Turkey's energy consumption is high, but its domestic primary energy sources are oil and natural gas reserves and their production is low. Total primary energy production met about 27% of the total primary energy demand in 2005. Oil has the biggest share in total primary energy consumption. Lignite has the biggest share in Turkey's primary energy production at 45%. Domestic production should be to be nearly doubled by 2010, mainly in coal (lignite), which, at present, accounts for almost half of the total energy production. The hydropower should also increase two-fold over the same period.

  11. Assumptions to the Annual Energy Outlook 2014 - Abbreviations

    Annual Energy Outlook [U.S. Energy Information Administration (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (BillionProved Reserves (BillionTechnical InformationDecade Year-0 2Market ModuleOil and This

  12. Approved Module Information for BS2241, 2014/5 Module Title/Name: Principles of Macro Economics Module Code: BS2241

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    2 Classical Economics ? Closed Economy ? Supply side and Demand side ? Equilibrium in goods Credits: 10 Module Management Information Module Leader Name Rakesh Bissoondeeal Email Address r if employed by policymakers such as central banks and fiscal policymakers. If students are managing business

  13. Demand Response and Energy Efficiency 

    E-Print Network [OSTI]

    2009-01-01

    stream_source_info ESL-IC-09-11-05.pdf.txt stream_content_type text/plain stream_size 14615 Content-Encoding ISO-8859-1 stream_name ESL-IC-09-11-05.pdf.txt Content-Type text/plain; charset=ISO-8859-1 Demand Response... 4 An Innovative Solution to Get the Ball Rolling ? Demand Response (DR) ? Monitoring Based Commissioning (MBCx) EnerNOC has a solution involving two complementary offerings. ESL-IC-09-11-05 Proceedings of the Ninth International Conference...

  14. Reduce Demand Rather than Increase Supply

    E-Print Network [OSTI]

    Shoup, Donald C.

    2006-01-01

    Assumptions Conservative Optimistic 1. In-lieu parking fee ($/parking space) (Mountain View) 2.Parking requirement (Palo Alto) (Mountain View) (

  15. Revelation on Demand Nicolas Anciaux

    E-Print Network [OSTI]

    is willing to reveal the aggregate response (according to his company's policy) to the customer dataRevelation on Demand Nicolas Anciaux 1 · Mehdi Benzine1,2 · Luc Bouganim1 · Philippe Pucheral1 time to support epidemiological studies. In these and many other situations, aggregate data or partial

  16. Demand Response Providing Ancillary Services

    E-Print Network [OSTI]

    1 Demand Response Providing Ancillary Services: A Comparison of Opportunities and Challenges in US to operate (likely price takers) ­ Statistical reliability (property of large aggregations of small resources size based on Mid-Atlantic Reserve Zone #12;Market Rules: Resource Size Min. Size (MW) Aggregation

  17. Water demand management in Kuwait

    E-Print Network [OSTI]

    Milutinovic, Milan, M. Eng. Massachusetts Institute of Technology

    2006-01-01

    Kuwait is an arid country located in the Middle East, with limited access to water resources. Yet water demand per capita is much higher than in other countries in the world, estimated to be around 450 L/capita/day. There ...

  18. On-demand data broadcasting 

    E-Print Network [OSTI]

    Kothandaraman, Kannan

    1998-01-01

    related to on-demand data broadcasting. We look at the problem of data broadcasting in an environment where clients make explicit requests to the server. The server broadcasts requested data items to all the clients, including those who have not requested...

  19. Promising Technology: Demand Control Ventilation

    Broader source: Energy.gov [DOE]

    Demand control ventilation (DCV) measures carbon dioxide concentrations in return air or other strategies to measure occupancy, and accurately matches the ventilation requirement. This system reduces ventilation when spaces are vacant or at lower than peak occupancy. When ventilation is reduced, energy savings are accrued because it is not necessary to heat, cool, or dehumidify as much outside air.

  20. Effects of the drought on California electricity supply and demand

    E-Print Network [OSTI]

    Benenson, P.

    2010-01-01

    DEMAND . . . .Demand for Electricity and Power PeakDemand . . • . . ELECTRICITY REQUIREMENTS FOR AGRICULTUREResults . . Coriclusions ELECTRICITY SUPPLY Hydroelectric

  1. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01

    Interoperable Automated Demand Response Infrastructure,study of automated demand response in wastewater treatmentopportunities for demand response control strategies in

  2. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    Report 2009. Open Automated Demand Response Communicationsand Techniques for Demand Response. California Energyand S. Kiliccote. Estimating Demand Response Load Impacts:

  3. Opportunities, Barriers and Actions for Industrial Demand Response in California

    E-Print Network [OSTI]

    McKane, Aimee T.

    2009-01-01

    and Techniques for Demand Response, report for theand Reliability Demand Response Programs: Final Report.Demand Response

  4. Incorporating Demand Response into Western Interconnection Transmission Planning

    E-Print Network [OSTI]

    Satchwell, Andrew

    2014-01-01

    Aggregator Programs. Demand Response Measurement andIncorporating Demand Response into Western Interconnection13 Demand Response Dispatch

  5. Upply Chain Supernetworks with Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Upply Chain Supernetworks with Random Demands June Dong & Ding Zhang School of Business State Warehouses: stocking points Field Warehouses: stocking points Customers, demand centers sinks Production Commerce and Value Chain Management, 1998 Customer Demand Customer Demand Retailer OrdersRetailer Orders

  6. Assessment of Demand Response and Advanced Metering

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    #12;#12;2008 Assessment of Demand Response and Advanced Metering Staff Report Federal Energy metering penetration and potential peak load reduction from demand response have increased since 2006. Significant activity to promote demand response or to remove barriers to demand response occurred at the state

  7. The alchemy of demand response: turning demand into supply

    SciTech Connect (OSTI)

    Rochlin, Cliff

    2009-11-15

    Paying customers to refrain from purchasing products they want seems to run counter to the normal operation of markets. Demand response should be interpreted not as a supply-side resource but as a secondary market that attempts to correct the misallocation of electricity among electric users caused by regulated average rate tariffs. In a world with costless metering, the DR solution results in inefficiency as measured by deadweight losses. (author)

  8. Addressing Energy Demand through Demand Response. International Experiences and Practices

    SciTech Connect (OSTI)

    Shen, Bo; Ghatikar, Girish; Ni, Chun Chun; Dudley, Junqiao; Martin, Phil; Wikler, Greg

    2012-06-01

    Demand response (DR) is a load management tool which provides a cost-effective alternative to traditional supply-side solutions to address the growing demand during times of peak electrical load. According to the US Department of Energy (DOE), demand response reflects “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” 1 The California Energy Commission (CEC) defines DR as “a reduction in customers’ electricity consumption over a given time interval relative to what would otherwise occur in response to a price signal, other financial incentives, or a reliability signal.” 2 This latter definition is perhaps most reflective of how DR is understood and implemented today in countries such as the US, Canada, and Australia where DR is primarily a dispatchable resource responding to signals from utilities, grid operators, and/or load aggregators (or DR providers).

  9. Thermionic modules

    DOE Patents [OSTI]

    King, Donald B. (Albuquerque, NM); Sadwick, Laurence P. (Salt Lake City, UT); Wernsman, Bernard R. (Clairton, PA)

    2002-06-18

    Modules of assembled microminiature thermionic converters (MTCs) having high energy-conversion efficiencies and variable operating temperatures manufactured using MEMS manufacturing techniques including chemical vapor deposition. The MTCs incorporate cathode to anode spacing of about 1 micron or less and use cathode and anode materials having work functions ranging from about 1 eV to about 3 eV. The MTCs also exhibit maximum efficiencies of just under 30%, and thousands of the devices and modules can be fabricated at modest costs.

  10. The Power of a Few Large Blocks: A credible assumption with incredible efficiency

    E-Print Network [OSTI]

    Foster, Dean P.

    i.i.d. assumption about the error structure, the two-sample t-statistic for oil was significantThe Power of a Few Large Blocks: A credible assumption with incredible efficiency Dongyu Lin and Dean P. Foster Abstract The most powerful assumption in data analysis is that of independence. Unfortu

  11. Module No: 420244International Humanitarian Module Title

    E-Print Network [OSTI]

    Module No: 420244International Humanitarian law Module Title: Co-requisite:public international law 1Pre-requisite: Module Type: specialization requirementModule level: Second year Evening Study-mailOffice Number Office Phone Academic rank Instructor Name E-mailOffice Number Office Phone Academic rank Module

  12. module.h

    E-Print Network [OSTI]

    /* OS-9 module header definitions */ /* Executable memory module */ typedef struct { unsigned m_sync, /* sync bytes ($87cd) */ m_size, /* module size ...

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

    SciTech Connect (OSTI)

    Not Available

    1994-04-01

    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.

  14. The Future of Food Demand: Understanding Differences in Global Economic Models

    SciTech Connect (OSTI)

    Valin, Hugo; Sands, Ronald; van der Mensbrugghe, Dominique; Nelson, Gerald; Ahammad, Helal; Blanc, Elodie; Bodirsky, Benjamin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Heyhoe, Edwina; Kyle, G. Page; Mason d'Croz, Daniel; Paltsev, S.; Rolinski, Susanne; Tabeau, Andrzej; van Meijl, Hans; von Lampe, Martin; Willenbockel, Dirk

    2014-01-01

    Understanding the capacity of agricultural systems to feed the world population under climate change requires a good prospective vision on the future development of food demand. This paper reviews modeling approaches from ten global economic models participating to the AgMIP project, in particular the demand function chosen and the set of parameters used. We compare food demand projections at the horizon 2050 for various regions and agricultural products under harmonized scenarios. Depending on models, we find for a business as usual scenario (SSP2) an increase in food demand of 59-98% by 2050, slightly higher than FAO projection (54%). The prospective for animal calories is particularly uncertain with a range of 61-144%, whereas FAO anticipates an increase by 76%. The projections reveal more sensitive to socio-economic assumptions than to climate change conditions or bioenergy development. When considering a higher population lower economic growth world (SSP3), consumption per capita drops by 9% for crops and 18% for livestock. Various assumptions on climate change in this exercise do not lead to world calorie losses greater than 6%. Divergences across models are however notable, due to differences in demand system, income elasticities specification, and response to price change in the baseline.

  15. Renewable Electricity Futures Study Volume 3: End-Use Electricity Demand

    Broader source: Energy.gov [DOE]

    This volume details the end-use electricity demand and efficiency assumptions. The projection of electricity demand is an important consideration in determining the extent to which a predominantly renewable electricity future is feasible. Any scenario regarding future electricity use must consider many factors, including technological, sociological, demographic, political, and economic changes (e.g., the introduction of new energy-using devices; gains in energy efficiency and process improvements; changes in energy prices, income, and user behavior; population growth; and the potential for carbon mitigation).

  16. Thermoelectric module

    DOE Patents [OSTI]

    Kortier, William E. (Columbus, OH); Mueller, John J. (Columbus, OH); Eggers, Philip E. (Columbus, OH)

    1980-07-08

    A thermoelectric module containing lead telluride as the thermoelectric mrial is encapsulated as tightly as possible in a stainless steel canister to provide minimum void volume in the canister. The lead telluride thermoelectric elements are pressure-contacted to a tungsten hot strap and metallurgically bonded at the cold junction to iron shoes with a barrier layer of tin telluride between the iron shoe and the p-type lead telluride element.

  17. STEO December 2012 - coal demand

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home RoomPreservation of Fe(II) byMultidayAlumni > The2/01/12 Page 1NEWSSupportcoal demand seen below

  18. Scaling Microblogging Services with Divergent Traffic Demands

    E-Print Network [OSTI]

    Fu, Xiaoming

    Scaling Microblogging Services with Divergent Traffic Demands Tianyin Xu, Yang Chen, Lei Jiao, Ben-server architecture has not scaled with user demands, lead- ing to server overload and significant impairment

  19. Michel Meulpolder Managing Supply and Demand of

    E-Print Network [OSTI]

    Michel Meulpolder Managing Supply and Demand of Bandwidth in Peer-to-Peer Communities #12;#12;Managing Supply and Demand of Bandwidth in Peer-to-Peer Communities Proefschrift ter verkrijging van de

  20. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

  1. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    /demographic growth, relatively low electricity and natural gas rates, and relatively low efficiency program CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity Manager Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY

  2. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P. Oglesby Executive

  3. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

  4. Demand Effects in Productivity and Efficiency Analysis 

    E-Print Network [OSTI]

    Lee, Chia-Yen

    2012-07-16

    Demand fluctuations will bias the measurement of productivity and efficiency. This dissertation described three ways to characterize the effect of demand fluctuations. First, a two-dimensional efficiency decomposition (2DED) of profitability...

  5. Industrial Equipment Demand and Duty Factors 

    E-Print Network [OSTI]

    Dooley, E. S.; Heffington, W. M.

    1998-01-01

    Demand and duty factors have been measured for selected equipment (air compressors, electric furnaces, injection molding machines, centrifugal loads, and others) in industrial plants. Demand factors for heavily loaded air ...

  6. Coordination of Energy Efficiency and Demand Response

    SciTech Connect (OSTI)

    none,

    2010-01-01

    Summarizes existing research and discusses current practices, opportunities, and barriers to coordinating energy efficiency and demand response programs.

  7. Decentralized demand management for water distribution 

    E-Print Network [OSTI]

    Zabolio, Dow Joseph

    1989-01-01

    OF THE DEMAND CURVE 30 31 35 39 Model Development Results 39 45 VI CONTROLLER DESIGN AND COSTS 49 Description of Controller Production and Installation Costs 49 50 VII SYSTEM EVALUATION AND ECONOMICS 53 System Response and Degree of Control... Patterns 9 Typical Winter Diurnal Patterns 10 Trace of Marginal Pump Efficiency and Hourly Demand 11 Original Demand Distribution and Possible Redistributions 33 34 40 41 43 46 12 Typical Nodal Responses to Demand Change 54 ix LIST OF TABLES...

  8. Demand Response Valuation Frameworks Paper

    SciTech Connect (OSTI)

    Heffner, Grayson

    2009-02-01

    While there is general agreement that demand response (DR) is a valued component in a utility resource plan, there is a lack of consensus regarding how to value DR. Establishing the value of DR is a prerequisite to determining how much and what types of DR should be implemented, to which customers DR should be targeted, and a key determinant that drives the development of economically viable DR consumer technology. Most approaches for quantifying the value of DR focus on changes in utility system revenue requirements based on resource plans with and without DR. This ''utility centric'' approach does not assign any value to DR impacts that lower energy and capacity prices, improve reliability, lower system and network operating costs, produce better air quality, and provide improved customer choice and control. Proper valuation of these benefits requires a different basis for monetization. The review concludes that no single methodology today adequately captures the wide range of benefits and value potentially attributed to DR. To provide a more comprehensive valuation approach, current methods such as the Standard Practice Method (SPM) will most likely have to be supplemented with one or more alternative benefit-valuation approaches. This report provides an updated perspective on the DR valuation framework. It includes an introduction and four chapters that address the key elements of demand response valuation, a comprehensive literature review, and specific research recommendations.

  9. Demand Queries with Preprocessing Uriel Feige

    E-Print Network [OSTI]

    Demand Queries with Preprocessing Uriel Feige and Shlomo Jozeph May 1, 2014 )>IJH=?J Given a set of items and a submodular set-function f that determines the value of every subset of items, a demand query, the value of S minus its price. The use of demand queries is well motivated in the context of com

  10. DemandDriven Pointer Analysis Nevin Heintze

    E-Print Network [OSTI]

    Tardieu, Olivier

    Demand­Driven Pointer Analysis Nevin Heintze Research, Agere Systems (formerly Lucent Technologies analysis of a pro­ gram or program component. In this paper we introduce a demand­driven approach for pointer analysis. Specifically, we describe a demand­driven flow­insensitive, subset­based, context

  11. APPLICATION-FORM DEMANDED'ADMISSION

    E-Print Network [OSTI]

    Opportunities and Challenges for Data Center Demand Response Adam Wierman Zhenhua Liu Iris Liu of renewable energy into the grid as well as electric power peak-load shaving: data center demand response. Data center demand response sits at the intersection of two growing fields: energy efficient data

  12. Airline Pilot Demand Projections What this is-

    E-Print Network [OSTI]

    Bustamante, Fabián E.

    60 Mobile applications constantly demand additional memory, and traditional designs increase but also e-mail, Internet access, digital camera features, and video on demand. With feature expansion demanding additional storage and memory in all com- puting devices, DRAM and flash memory densities

  13. Algorithms Demands and Bounds Applications of Flow

    E-Print Network [OSTI]

    Kabanets, Valentine

    2/28/2014 1 Algorithms ­ Demands and Bounds Applications of Flow Networks Design and Analysis of Algorithms Andrei Bulatov Algorithms ­ Demands and Bounds 12-2 Lower Bounds The problem can be generalized) capacities (ii) demands (iii) lower bounds A circulation f is feasible if (Capacity condition) For each e E

  14. Adapton: Composable, Demand-Driven Incremental Computation

    E-Print Network [OSTI]

    Hicks, Michael

    Adapton: Composable, Demand-Driven Incremental Computation CS-TR-5027 -- July 12, 2013 Matthew A demands on the program output; that is, if a program input changes, all depen- dencies will be recomputed. To address these problems, we present cdd ic , a core calculus that applies a demand-driven seman- tics

  15. Demand And Response Transportation Rider's Guide

    E-Print Network [OSTI]

    Acton, Scott

    Demand And Response Transportation Rider's Guide http://www.virginia.edu/parking/disabilities/dart Version 14.5 (8/13/14) Welcome DART Rider: The Demand and Response Transportation (DART) Service rides: #12;Demand And Response Transportation Rider's Guide http

  16. Scaling Microblogging Services with Divergent Traffic Demands

    E-Print Network [OSTI]

    Almeroth, Kevin C.

    Scaling Microblogging Services with Divergent Traffic Demands Tianyin Xu1 , Yang Chen1 , Lei Jiao1 client-server architecture has not scaled with user demands, leading to server overload and significant #12;Scaling Microblogging Services with Divergent Traffic Demands 21 producing effective predictions

  17. Demande de diplmes NOM,Prnom : ......................................................................................................................

    E-Print Network [OSTI]

    Chamroukhi, Faicel

    Optimal demand response: problem formulation and deterministic case Lijun Chen, Na Li, Libin Jiang load through real-time demand response and purchases balancing power on the spot market to meet the aggregate demand. Hence optimal supply procurement by the LSE and the consumption decisions by the users

  18. Precision On Demand: An Improvement in Probabilistic

    E-Print Network [OSTI]

    Precision On Demand: An Improvement in Probabilistic Hashing Igor Melatti, Robert Palmer approach Precision on Demand or POD). #12;This paper provides a scientific evaluation of the pros and cons time likely to increase by a factor of 1.8 or less. #12;Precision On Demand: An Improvement

  19. ADAPTON: Composable, Demand-Driven Incremental Computation

    E-Print Network [OSTI]

    Hicks, Michael

    ADAPTON: Composable, Demand- Driven Incremental Computation Abstract Many researchers have proposed important drawbacks. First, recomputation is oblivious to specific demands on the program output; that is ic , a core calculus that applies a demand-driven semantics to incremental computa- tion, tracking

  20. Constructing Speculative Demand Functions in Equilibrium Markets

    E-Print Network [OSTI]

    On the Convergence of Statistical Techniques for Inferring Network Traffic Demands Alberto Medina1 of traffic demands in a communication net- work enables or enhances a variety of traffic engineering and net set of these demands is prohibitively expensive because of the huge amounts of data that must

  1. Appeld'offrespublic Demanded'approvisionnement

    E-Print Network [OSTI]

    Montréal, Université de

    ATM for Video and Audio on Demand David Greaves. University of Cambridge and ATM Ltd. email: djg fast, particularly for video- on-demand. These digital streams require constant-rate digi- tal channels of the Cambridge Digital Interactive Television Trial, where Video and Audio on demand are transported to the Home

  2. Precision On Demand: An Improvement in Probabilistic

    E-Print Network [OSTI]

    Precision On Demand: An Improvement in Probabilistic Hashing Igor Melatti, Robert Palmer approach Precision on Demand or POD). #12; This paper provides a scientific evaluation of the pros and cons time likely to increase by a factor of 1.8 or less. #12; Precision On Demand: An Improvement

  3. FORECAST COMBINATION IN REVENUE MANAGEMENT DEMAND FORECASTING

    E-Print Network [OSTI]

    Fernandez, Thomas

    Demandness in Rewriting and Narrowing Sergio Antoy1 and Salvador Lucas2 1 Computer Science by a strategy to compute a step. The notion of demandness provides a suitable framework for pre- senting that the notion of demandness is both atomic and fundamental to the study of strategies. 1 Introduction Modern

  4. Resolution on Demand Bianka BuschbeckWolf

    E-Print Network [OSTI]

    Reyle, Uwe

    Resolution on Demand Bianka Buschbeck­Wolf Universit¨at Stuttgart Report 196 May 1997 #12; May 1997¨ur den Inhalt dieser Arbeit liegt bei der Autorin. #12; Resolution on Demand Abstract Following the strategy of resolution on demand, the transfer component triggers inference processes in analysis

  5. Pricing Cloud Bandwidth Reservations under Demand Uncertainty

    E-Print Network [OSTI]

    Li, Baochun

    Pricing Cloud Bandwidth Reservations under Demand Uncertainty Di Niu, Chen Feng, Baochun Li's utility depends not only on its bandwidth usage, but more importantly on the portion of its demand that can be made by all tenants and the cloud provider, even with the presence of demand uncertainty

  6. Transportation Energy: Supply, Demand and the Future

    E-Print Network [OSTI]

    Saldin, Dilano

    trends in China, India, Eastern Europe and other developing areas. China oil demand +104% by 2030, India 2000 2020 2040 2060 Supply demand Energy UWM-CUTS 14 U.S. DOE viewpoint, source:http://tonto.eia.doe.gov/FTPROOT/features/longterm.pdf#search='oilTransportation Energy: Supply, Demand and the Future http://www.uwm.edu/Dept/CUTS//2050/energy05

  7. 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- response paradigm. When the energy provider needs to reduce the current energy demand on the grid, it can

  8. INTEGRATION OF PV IN DEMAND RESPONSE

    E-Print Network [OSTI]

    Perez, Richard R.

    INTEGRATION OF PV IN DEMAND RESPONSE PROGRAMS Prepared by Richard Perez et al. NREL subcontract the case that distributed PV generation deserves a substantial portion of the credit allotted to demand response programs. This is because PV generation acts as a catalyst to demand response, markedly enhancing

  9. Demand Response for Computing Jerey S. Chase

    E-Print Network [OSTI]

    Chase, Jeffrey S.

    Chapter 1 Demand Response for Computing Centers Jerey S. Chase Duke University 1.1 Introduction ............................................................... 3 1.2 Demand Response in the Emerging Smart Grid .......................... 5 1.2.1 Importance of Demand Response for Energy E ciency .......... 6 1.2.2 The Role of Renewable Energy

  10. Response to changes in demand/supply

    E-Print Network [OSTI]

    Response to changes in demand/supply through improved marketing 21.2 http with the mill consuming 450 000 m3 , amounting to 30% of total plywood log demand in 1995. The composites board, statistics of demand and supply of wood, costs and competitiveness were analysed. The reactions

  11. Response to changes in demand/supply

    E-Print Network [OSTI]

    Response to changes in demand/supply through improved marketing 21.2 #12;#12;111 Impacts of changes log demand in 1995. The composites board mills operating in Korea took advantage of flexibility environment changes on the production mix, some economic indications, statistics of demand and supply of wood

  12. Demand Response and Ancillary Services September 2008

    E-Print Network [OSTI]

    Demand Response and Ancillary Services September 2008 #12;© 2008 EnerNOC, Inc. All Rights Reserved programs The purpose of this presentation is to offer insight into the mechanics of demand response and industrial demand response resources across North America in both regulated and restructured markets As of 6

  13. THE STATE OF DEMAND RESPONSE IN CALIFORNIA

    E-Print Network [OSTI]

    THE STATE OF DEMAND RESPONSE IN CALIFORNIA Prepared For: California Energy in this report. #12; ABSTRACT By reducing system loads during criticalpeak times, demand response (DR) can.S. and internationally and lay out ideas that could help move California forward. KEY WORDS demand response, peak

  14. Demand Response Resources in Pacific Northwest

    E-Print Network [OSTI]

    Demand Response Resources in Pacific Northwest Chuck Goldman Lawrence Berkeley National Laboratory cagoldman@lbl.gov Pacific Northwest Demand Response Project Portland OR May 2, 2007 #12;Overview · Typology Annual Reports ­ Journal articles/Technical reports #12;Demand Response Resources · Incentive

  15. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    LBNL-62226 Demand Responsive Lighting: A Scoping Study F. Rubinstein, S. Kiliccote Energy Environmental Technologies Division January 2007 #12;LBNL-62226 Demand Responsive Lighting: A Scoping Study in this report was coordinated by the Demand Response Research Center and funded by the California Energy

  16. Barrier Immune Radio Communications for Demand Response

    E-Print Network [OSTI]

    LBNL-2294E Barrier Immune Radio Communications for Demand Response F. Rubinstein, G. Ghatikar, J Ann Piette of Lawrence Berkeley National Laboratory's (LBNL) Demand Response Research Center (DRRC and Environment's (CIEE) Demand Response Emerging Technologies Development (DRETD) Program, under Work for Others

  17. THE STATE OF DEMAND RESPONSE IN CALIFORNIA

    E-Print Network [OSTI]

    THE STATE OF DEMAND RESPONSE IN CALIFORNIA Prepared For: California Energy in this report. #12; ABSTRACT By reducing system loads during criticalpeak times, demand response can help reduce the threat of planned rotational outages. Demand response is also widely regarded as having

  18. Photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan; Graves, Simon; Lenox, Carl J. S.; Culligan, Matthew; Danning, Matt

    2013-08-27

    A photovoltaic (PV) module including a PV device and a frame, The PV device has a PV laminate defining a perimeter and a major plane. The frame is assembled to and encases the laminate perimeter, and includes leading, trailing, and side frame members, and an arm that forms a support face opposite the laminate. The support face is adapted for placement against a horizontal installation surface, to support and orient the laminate in a non-parallel or tilted arrangement. Upon final assembly, the laminate and the frame combine to define a unitary structure. The frame can orient the laminate at an angle in the range of 3.degree.-7.degree. from horizontal, and can be entirely formed of a polymeric material. Optionally, the arm incorporates integral feature(s) that facilitate interconnection with corresponding features of a second, identically formed PV module.

  19. Photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan (El Cerrito, CA); Graves, Simon (Berkeley, CA); Lenox, Carl J. S. (Oakland, CA); Culligan, Matthew (Berkeley, CA); Danning, Matt (Oakland, CA)

    2012-07-17

    A photovoltaic (PV) module including a PV device and a frame. The PV device has a PV laminate defining a perimeter and a major plane. The frame is assembled to and encases the laminate perimeter, and includes leading, trailing, and side frame members, and an arm that forms a support face opposite the laminate. The support face is adapted for placement against a horizontal installation surface, to support and orient the laminate in a non-parallel or tilted arrangement. Upon final assembly, the laminate and the frame combine to define a unitary structure. The frame can orient the laminate at an angle in the range of 3.degree.-7.degree. from horizontal, and can be entirely formed of a polymeric material. Optionally, the arm incorporates integral feature(s) that facilitate interconnection with corresponding features of a second, identically formed PV module.

  20. Energy demand and population changes

    SciTech Connect (OSTI)

    Allen, E.L.; Edmonds, J.A.

    1980-12-01

    Since World War II, US energy demand has grown more rapidly than population, so that per capita consumption of energy was about 60% higher in 1978 than in 1947. Population growth and the expansion of per capita real incomes have led to a greater use of energy. The aging of the US population is expected to increase per capita energy consumption, despite the increase in the proportion of persons over 65, who consume less energy than employed persons. The sharp decline in the population under 18 has led to an expansion in the relative proportion of population in the prime-labor-force age groups. Employed persons are heavy users of energy. The growth of the work force and GNP is largely attributable to the growing participation of females. Another important consequence of female employment is the growth in ownership of personal automobiles. A third factor pushing up labor-force growth is the steady influx of illegal aliens.

  1. Demande de casier 20142015 1. Demande ( remplir par l'lve)

    E-Print Network [OSTI]

    Demande de casier 20142015 1. Demande (à remplir par l'élève) Nom : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demande l'attribution d'un casier pour y déposer) : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . En cas d'acceptation de ma demande, je retirerai ma clé contre un chèque de caution d'un montant de

  2. DEMANDE DE CONGE Cette demande doit tre effectue un mois avant le dbut du semestre.

    E-Print Network [OSTI]

    Halazonetis, Thanos

    DEMANDE DE CONGE Cette demande doit être effectuée un mois avant le début du semestre. Date de la demande .......................................................... NOM-mail .......................................................................................................................................................................... @etu.unige.ch Demande à être mis au bénéfice d'un congé pour le(s) semestre(s) suivant(s) (2 semestres

  3. Risk Management for Video-on-Demand Servers leveraging Demand Forecast

    E-Print Network [OSTI]

    Li, Baochun

    Risk Management for Video-on-Demand Servers leveraging Demand Forecast Di Niu, Hong Xu, Baochun Li}@eecg.toronto.edu Shuqiao Zhao Multimedia Development Group UUSee, Inc. shuqiao.zhao@gmail.com ABSTRACT Video-on-demand (VoD) servers are usually over-provisioned for peak demands, incurring a low average resource effi- ciency

  4. Secure Demand Shaping for Smart Grid On constructing probabilistic demand response schemes

    E-Print Network [OSTI]

    Sastry, S. Shankar

    Secure Demand Shaping for Smart Grid On constructing probabilistic demand response schemes. Developing novel schemes for demand response in smart electric gird is an increasingly active research area/SCADA for demand response in smart infrastructures face the following dilemma: On one hand, in order to increase

  5. Assessing the Control Systems Capacity for Demand Response in California Industries

    SciTech Connect (OSTI)

    Ghatikar, Girish; McKane, Aimee; Goli, Sasank; Therkelsen, Peter; Olsen, Daniel

    2012-01-18

    California's electricity markets are moving toward dynamic pricing models, such as real-time pricing, within the next few years, which could have a significant impact on an industrial facility's cost of energy use during the times of peak use. Adequate controls and automated systems that provide industrial facility managers real-time energy use and cost information are necessary for successful implementation of a comprehensive electricity strategy; however, little is known about the current control capacity of California industries. To address this gap, Lawrence Berkeley National Laboratory, in close collaboration with California industrial trade associations, conducted a survey to determine the current state of controls technologies in California industries. This,study identifies sectors that have the technical capability to implement Demand Response (DR) and Automated Demand Response (Auto-DR). In an effort to assist policy makers and industry in meeting the challenges of real-time pricing, facility operational and organizational factors were taken into consideration to generate recommendations on which sectors Demand Response efforts should be focused. Analysis of the survey responses showed that while the vast majority of industrial facilities have semi- or fully automated control systems, participation in Demand Response programs is still low due to perceived barriers. The results also showed that the facilities that use continuous processes are good Demand Response candidates. When comparing facilities participating in Demand Response to those not participating, several similarities and differences emerged. Demand Response-participating facilities and non-participating facilities had similar timings of peak energy use, production processes, and participation in energy audits. Though the survey sample was smaller than anticipated, the results seemed to support our preliminary assumptions. Demonstrations of Auto-Demand Response in industrial facilities with good control capabilities are needed to dispel perceived barriers to participation and to investigate industrial subsectors suggested of having inherent Demand Response potential.

  6. Model documentation coal market module of the National Energy Modeling System

    SciTech Connect (OSTI)

    1995-03-01

    This report documents the approaches used in developing the Annual Energy Outlook 1995 (AEO95). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of the coal market module`s three submodules. These are the Coal Production Submodule (CPS), the Coal Export Submodule (CES), the Coal Expert Submodule (CES), and the Coal Distribution Submodule (CDS).

  7. Supported PV module assembly

    SciTech Connect (OSTI)

    Mascolo, Gianluigi; Taggart, David F.; Botkin, Jonathan D.; Edgett, Christopher S.

    2013-10-15

    A supported PV assembly may include a PV module comprising a PV panel and PV module supports including module supports having a support surface supporting the module, a module registration member engaging the PV module to properly position the PV module on the module support, and a mounting element. In some embodiments the PV module registration members engage only the external surfaces of the PV modules at the corners. In some embodiments the assembly includes a wind deflector with ballast secured to a least one of the PV module supports and the wind deflector. An array of the assemblies can be secured to one another at their corners to prevent horizontal separation of the adjacent corners while permitting the PV modules to flex relative to one another so to permit the array of PV modules to follow a contour of the support surface.

  8. Module Embedding Atanas Radenski

    E-Print Network [OSTI]

    Radenski, Atanas

    Module Embedding 1 Atanas Radenski Computer Science Department UNC-WSSU, P. O. Box 19479 Winston module embedding that enables the building of new modules from existing ones through inheritance for this mechanism. Module embedding is beneficial when modules and classes are used in combination and need

  9. BEHAVIORAL ASSUMPTION-BASED PREDICTION FOR HIGH-LATENCY HIDING IN MOBILE GAMES

    E-Print Network [OSTI]

    Bidarra, Rafael

    Carpentier and Rafael Bidarra Computer Graphics and CAD/CAM Group Faculty of Electrical Engineering assumptions, designed to be suitable for racing titles. Although these assumptions limit the accuracy it possible to create a real-time multi-player race game using today's GPRS network. INTRODUCTION The widely

  10. Model documentation: Electricity Market Module, Electricity Fuel Dispatch Submodule

    SciTech Connect (OSTI)

    Not Available

    1994-04-08

    This report documents the objectives, analytical approach and development of the National Energy Modeling System Electricity Fuel Dispatch Submodule (EFD), a submodule of the Electricity Market Module (EMM). 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.

  11. Opportunities, Barriers and Actions for Industrial Demand Response in California

    E-Print Network [OSTI]

    McKane, Aimee T.

    2009-01-01

    13 Table 2. Demand Side Management Framework for IndustrialDR Strategies The demand-side management (DSM) frameworkpresented in Table 2. Demand Side Management Framework for

  12. Direct versus Facility Centric Load Control for Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01

    Interoperable Automated Demand Response Infrastructure.and Techniques for Demand Response. LBNL Report 59975. Mayand Communications for Demand Response and Energy Efficiency

  13. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01

    of Fully Automated Demand  Response in Large Facilities.  Fully Automated Demand Response Tests in Large Facilities.  Open Automated  Demand Response Communication Standards: 

  14. Climate, extreme heat, and electricity demand in California

    E-Print Network [OSTI]

    Miller, N.L.

    2008-01-01

    warming and electricity demand: A study of California.Extreme Heat, and Electricity Demand in California Norman L.high temperature and electricity demand for air-conditioned

  15. Residential Electricity Demand in China -- Can Efficiency Reverse the Growth?

    E-Print Network [OSTI]

    Letschert, Virginie

    2010-01-01

    with Residential Electricity Demand in India's Future - How2008). The Boom of Electricity Demand in the residential2005). Forecasting Electricity Demand in Developing

  16. California Baseline Energy Demands to 2050 for Advanced Energy Pathways

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    Figure 16 Annual peak electricity demand by sector. Tableincludes an hourly electricity demand (i.e. power) profileof aggregating sectoral electricity demands into a statewide

  17. SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY | Department of...

    Energy Savers [EERE]

    SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY As a city that experiences seasonal...

  18. Residential Electricity Demand in China -- Can Efficiency Reverse the Growth?

    E-Print Network [OSTI]

    Letschert, Virginie

    2010-01-01

    2007). Coping with Residential Electricity Demand in India'sResidential Electricity Demand in China –Can EfficiencyBoom of Electricity Demand in the residential sector in the

  19. Climate, extreme heat, and electricity demand in California

    E-Print Network [OSTI]

    Miller, N.L.

    2008-01-01

    Peirson. 1998. Residential energy demand and the interactionresponse of residential cooling energy demand to climaterise in residential and commercial electricity demand can be

  20. Coordination of Retail Demand Response with Midwest ISO Markets

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2008-01-01

    Robinson, Michael, 2008, "Demand Response in Midwest ISOPresentation at MISO Demand Response Working Group Meeting,Coordination of Retail Demand Response with Midwest ISO

  1. Rates and technologies for mass-market demand response

    E-Print Network [OSTI]

    Herter, Karen; Levy, Roger; Wilson, John; Rosenfeld, Arthur

    2002-01-01

    Roger. 2002. Using Demand Response to Link Wholesale andfor advanced metering, demand response, and dynamic pricing.EPRI. 2001. Managing Demand-Response To Achieve Multiple

  2. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

    Goodin. 2009. “Open Automated Demand Response Communicationsin Demand Response for Wholesale Ancillary Services. ” InOpen Automated Demand Response Demonstration Project. LBNL-

  3. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    A. Barat, D. Watson. Demand Response Spinning ReserveOpen Automated Demand Response Communication Standards:Dynamic Controls for Demand Response in a New Commercial

  4. Demand Response in U.S. Electricity Markets: Empirical Evidence

    E-Print Network [OSTI]

    Cappers, Peter

    2009-01-01

    Reliability Corporation. Demand response data task force:Energy. Benefits of demand response in electricity marketsAssessment of demand response & advanced metering, staff

  5. LEED Demand Response Credit: A Plan for Research towards Implementation

    E-Print Network [OSTI]

    Kiliccote, Sila

    2014-01-01

    C. McParland, Open Automated Demand Response Communicationsand Open Automated Demand Response", Grid Interop Forum,Testing of Automated Demand Response for Integration of

  6. Open Automated Demand Response Communications Specification (Version 1.0)

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    and Techniques for Demand Response. May 2007. LBNL-59975.to facilitate automating  demand response actions at the Interoperable Automated Demand Response Infrastructure,

  7. Demand Response Opportunities in Industrial Refrigerated Warehouses in California

    E-Print Network [OSTI]

    Goli, Sasank

    2012-01-01

    and Open Automated Demand Response. In Grid Interop Forum.work was sponsored by the Demand Response Research Center (load-management.php. Demand Response Research Center (2009).

  8. Results and commissioning issues from an automated demand response pilot

    E-Print Network [OSTI]

    Piette, Mary Ann; Watson, Dave; Sezgen, Osman; Motegi, Naoya

    2004-01-01

    of Fully Automated Demand Response in Large Facilities"Management and Demand Response in Commercial Buildings", L Band Commissioning Issues from an Automated Demand Response.

  9. California Baseline Energy Demands to 2050 for Advanced Energy Pathways

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    CEC (2005b) Energy demand forecast methods report.growth in California energy demands forecast in the baseline2006-2016: Staff energy demand forecast (Revised September

  10. National Action Plan on Demand Response | Department of Energy

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

    Working Group (FUPWG) Fall 2008 meeting-discusses the National Assessment of Demand Response study, the National Action Plan for Demand Response, and demand response as...

  11. A new scenario framework for climate change research: The concept of Shared Climate Policy Assumptions

    SciTech Connect (OSTI)

    Kriegler, Elmar; Edmonds, James A.; Hallegatte, Stephane; Ebi, Kristie L.; Kram, Tom; Riahi, Keywan; Winkler, Harald; Van Vuuren, Detlef

    2014-04-01

    The paper presents the concept of shared climate policy assumptions as an important element of the new scenario framework. Shared climate policy assumptions capture key climate policy dimensions such as the type and scale of mitigation and adaptation measures. They are not specified in the socio-economic reference pathways, and therefore introduce an important third dimension to the scenario matrix architecture. Climate policy assumptions will have to be made in any climate policy scenario, and can have a significant impact on the scenario description. We conclude that a meaningful set of shared climate policy assumptions is useful for grouping individual climate policy analyses and facilitating their comparison. Shared climate policy assumptions should be designed to be policy relevant, and as a set to be broad enough to allow a comprehensive exploration of the climate change scenario space.

  12. Working with Modules within Python

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

    Using Modules within Python The EnvironmentModules python package gives access to the module system from within python. The EnvironmentModules python package has a single...

  13. Electric Utility Demand-Side Evaluation Methodologies 

    E-Print Network [OSTI]

    Treadway, N.

    1986-01-01

    UTILITY DEMAND-SIDE EVALUATION METHODOLOGIES* Nat Treadway Public Utility Commission of Texas Austin, Texas ABSTRACT The electric. util ity industry's demand-side management programs can be analyzed ?from various points of view using a standard... cost and certification proceedings. A s~andard benefit-cost methodology analyzes demand-slde management programs from various ~oints of view. The benefit-cost methodology now ln use by several electric utilities and the * The views presented...

  14. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01

    3 3.0 Previous Experience with Demand Responsive Lighting11 4.3. Prevalence of Lighting13 4.4. Impact of Title 24 on Lighting

  15. Geographically Based Hydrogen Demand and Infrastructure Rollout...

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

    Rollout Scenario Analysis Geographically Based Hydrogen Demand and Infrastructure Rollout Scenario Analysis Presentation by Margo Melendez at the 2010-2025 Scenario Analysis for...

  16. Geographically Based Hydrogen Demand and Infrastructure Analysis...

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

    Analysis Geographically Based Hydrogen Demand and Infrastructure Analysis Presentation by NREL's Margo Melendez at the 2010 - 2025 Scenario Analysis for Hydrogen Fuel Cell Vehicles...

  17. Operationalizing demand forecasts in the warehouse

    E-Print Network [OSTI]

    Li, Dan, Ph. D. University of Rochester

    2015-01-01

    Demand planning affects the subsequent business activities including distribution center operational planning and management. Today's competitive environment requires distribution centers to rapidly respond to changes in ...

  18. Marketing & Driving Demand: Social Media Tools & Strategies ...

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

    January 16, 2011 Conference Call transcript: "Marketing & Driving Demand: Social Media Tools & Strategies," from the U.S. Department of Energy. Conference call transcript More...

  19. Integration of Demand Side Management, Distributed Generation...

    Open Energy Info (EERE)

    Integration of Demand Side Management, Distributed Generation, Renewable Energy Sources, and Energy Storages: State-of-the-Art Report, Volume 1, Main Report Jump to: navigation,...

  20. Optimization of Demand Response Through Peak Shaving

    E-Print Network [OSTI]

    Jul 5, 2013 ... Optimization of Demand Response Through Peak Shaving. G. Zakeri(g.zakeri *** at*** auckland.ac.nz) D. Craigie(David.Craigie ***at*** ...

  1. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    National Action Plan for Energy Efficiency Energy efficiency programson energy efficiency program types, see National Action PlanNational Action Plan for Energy Efficiency Most demand response programs

  2. Demand Response in the ERCOT Markets

    SciTech Connect (OSTI)

    Patterson, Mark

    2011-10-25

    ERCOT grid serves 85% of Texas load over 40K+ miles transmission line. Demand response: voluntary load response, load resources, controllable load resources, and emergency interruptible load service.

  3. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01

    Demand  Response  Roadmap  Project   Final  Report  39   5.   Developing a Roadmap Actionproject was to develop a “roadmap” to guide the Hawaiian

  4. Optimization of Demand Response Through Peak Shaving

    E-Print Network [OSTI]

    2013-06-19

    Jun 19, 2013 ... efficient linear programming formulation for the demand response of such a consumer who could be a price taker, industrial or commercial user ...

  5. BPA, Energy Northwest launch demand response pilot

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

    BPA-Energy-Northwest-launch-demand-response-pilot Sign In About | Careers | Contact | Investors | bpa.gov Search News & Us Expand News & Us Projects & Initiatives Expand...

  6. Wireless Demand Response Controls for HVAC Systems

    E-Print Network [OSTI]

    Federspiel, Clifford

    2010-01-01

    conditioning. Figure 2: Wireless discharge air temperatureWireless Demand Response Controls for HVAC Systems Cliffordcontrol software and wireless hardware that could enable

  7. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    Forecasts of California transportation energy demand, 2005-alternative transportation energy pathways on California’salternative transportation energy pathways on California’s

  8. Reducing Logistics Footprints and Replenishment Demands: Nano...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Reducing Logistics Footprints and Replenishment Demands: Nano-engineered Silica Aerogels a Proven Method for Water Treatment Citation Details In-Document Search...

  9. Opportunities for Automated Demand Response in Wastewater Treatment Facilities in California - Southeast Water Pollution Control Plant Case Study

    SciTech Connect (OSTI)

    Olsen, Daniel; Goli, Sasank; Faulkner, David; McKane, Aimee

    2012-12-20

    This report details a study into the demand response potential of a large wastewater treatment facility in San Francisco. Previous research had identified wastewater treatment facilities as good candidates for demand response and automated demand response, and this study was conducted to investigate facility attributes that are conducive to demand response or which hinder its implementation. One years' worth of operational data were collected from the facility's control system, submetered process equipment, utility electricity demand records, and governmental weather stations. These data were analyzed to determine factors which affected facility power demand and demand response capabilities The average baseline demand at the Southeast facility was approximately 4 MW. During the rainy season (October-March) the facility treated 40% more wastewater than the dry season, but demand only increased by 4%. Submetering of the facility's lift pumps and centrifuges predicted load shifts capabilities of 154 kW and 86 kW, respectively, with large lift pump shifts in the rainy season. Analysis of demand data during maintenance events confirmed the magnitude of these possible load shifts, and indicated other areas of the facility with demand response potential. Load sheds were seen to be possible by shutting down a portion of the facility's aeration trains (average shed of 132 kW). Load shifts were seen to be possible by shifting operation of centrifuges, the gravity belt thickener, lift pumps, and external pump stations These load shifts were made possible by the storage capabilities of the facility and of the city's sewer system. Large load reductions (an average of 2,065 kW) were seen from operating the cogeneration unit, but normal practice is continuous operation, precluding its use for demand response. The study also identified potential demand response opportunities that warrant further study: modulating variable-demand aeration loads, shifting operation of sludge-processing equipment besides centrifuges, and utilizing schedulable self-generation.

  10. Resource Allocation With Non-Deterministic Demands and Profits

    E-Print Network [OSTI]

    Preece, Alun

    100000$ Appeld'offrespublic 1 Demanded'approvisionnement 25000$àDemanded'approvisionnementet Appeld'offresurinvitationou 3soumissions 2 5000$àDemanded'approvisionnementet Appeld services reliés Services de professionnels 3000$àDemanded'approvisionnementet 1soumission 2 4

  11. FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007

    E-Print Network [OSTI]

    ......................................................................... 11 3. Demand Side Management (DSM) Program Impacts................................... 13 4. Demand Sylvia Bender Manager DEMAND ANALYSIS OFFICE Scott W. Matthews Chief Deputy Director B.B. Blevins Forecast Methods and Models ....................................................... 14 5. Demand-Side

  12. High-Speed Link Modeling: Analog/Digital Equalization and Modulation Techniques 

    E-Print Network [OSTI]

    Lee, Keytaek

    2012-07-16

    High-speed serial input-output (I/O) link has required advanced equalization and modulation techniques to mitigate inter-symbol interference (ISI) caused by multi-Gb/s signaling over band-limited channels. Increasing demands ...

  13. Strategies for Aligning Program Demand with Contractor's Seasonal...

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

    Aligning Program Demand with Contractor's Seasonal Fluctuations Strategies for Aligning Program Demand with Contractor's Seasonal Fluctuations Better Buildings Neighborhood Program...

  14. Moldy Assumptions

    E-Print Network [OSTI]

    Heully, Gustave Paul

    2012-01-01

    historian of architecture and technology had waded out tillspores tie architecture’s tectonics and technologies to theon architecture’s connection to philosophy, technology and

  15. THE PERFORMANCE OF QUEUING THEORETIC VIDEO ON DEMAND ALGORITHMS

    E-Print Network [OSTI]

    THE PERFORMANCE OF QUEUING THEORETIC VIDEO ON DEMAND ALGORITHMS BOURAS C.(1)(2), GAROFALAKIS J.(1,Greece KEYWORDS Video On Demand (VOD), Performance of Algorithms, Simulation, Modeling ABSTRACT Video On Demand on state-of-the-art technologies is Video On Demand (VOD). A Video On Demand System provides on demand

  16. SUMMER 2007 ELECTRICITY SUPPLY AND DEMAND OUTLOOK

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION SUMMER 2007 ELECTRICITY SUPPLY AND DEMAND OUTLOOK DRAFTSTAFFREPORT May ELECTRICITY ANALYSIS OFFICE Sylvia Bender Acting Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION B. B assessment of the capability of the physical electricity system to provide power to meet electricity demand

  17. Demand response in adjustment markets for electricity

    E-Print Network [OSTI]

    : electricity consumption, adjustment market, demand response, information asymmetry JEL codes: D11, D21, Q41 in the consumption of electric energy by retail customers from their expected consumption inDemand response in adjustment markets for electricity Claude Crampes and Thomas-Olivier Léautier

  18. MODELLING WOODLAND RECREATION DEMAND USING GEOGRAPHICAL

    E-Print Network [OSTI]

    Bateman, Ian J.

    MODELLING WOODLAND RECREATION DEMAND USING GEOGRAPHICAL INFORMATION SYSTEMS: A BENEFIT TRANSFER;MODELLING WOODLAND RECREATION DEMAND USING GEOGRAPHICAL INFORMATION SYSTEMS: A BENEFIT TRANSFER STUDY by Ian Research Promotion Fund. ISSN 0967-8875 #12;Abstract This paper utilizes geographical information systems

  19. Optimal Trading Strategy Supply/Demand Dynamics

    E-Print Network [OSTI]

    Gabrieli, John

    prices through the changes in their supply/demand.2 Thus, to study how market participants trade can have interesting implications on the observed behavior of intraday volume, volatility and prices: November 15, 2004. This Draft: April 8, 2006 Abstract The supply/demand of a security in the market

  20. Value of Demand Response -Introduction Klaus Skytte

    E-Print Network [OSTI]

    -of-supply and DR 15 minutes DaysHoursSeconds Adjustments of planned production Prognosis errors Excess capacity in demand to prices. Similar to Least-cost planning and demand-side management. DR differs by using prices: Curtailment of load, Direct load control, e.g. central control of electric comfort heating. Reservation prices

  1. Ballasted photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan (El Cerrito, CA); Graves, Simon (Berkeley, CA); Danning, Matt (Oakland, CA)

    2011-11-29

    A photovoltaic (PV) module assembly including a PV module and a ballast tray. The PV module includes a PV device and a frame. A PV laminate is assembled to the frame, and the frame includes an arm. The ballast tray is adapted for containing ballast and is removably associated with the PV module in a ballasting state where the tray is vertically under the PV laminate and vertically over the arm to impede overt displacement of the PV module. The PV module assembly can be installed to a flat commercial rooftop, with the PV module and the ballast tray both resting upon the rooftop. In some embodiments, the ballasting state includes corresponding surfaces of the arm and the tray being spaced from one another under normal (low or no wind) conditions, such that the frame is not continuously subjected to a weight of the tray.

  2. Uranium 2009 resources, production and demand

    E-Print Network [OSTI]

    Organisation for Economic Cooperation and Development. Paris

    2010-01-01

    With several countries currently building nuclear power plants and planning the construction of more to meet long-term increases in electricity demand, uranium resources, production and demand remain topics of notable interest. In response to the projected growth in demand for uranium and declining inventories, the uranium industry – the first critical link in the fuel supply chain for nuclear reactors – is boosting production and developing plans for further increases in the near future. Strong market conditions will, however, be necessary to trigger the investments required to meet projected demand. The "Red Book", jointly prepared by the OECD Nuclear Energy Agency and the International Atomic Energy Agency, is a recognised world reference on uranium. It is based on information compiled in 40 countries, including those that are major producers and consumers of uranium. This 23rd edition provides a comprehensive review of world uranium supply and demand as of 1 January 2009, as well as data on global ur...

  3. The role of geologic assumptions in solving complex contaminant transport problems 

    E-Print Network [OSTI]

    Bustamante, Louis Sorola

    1995-01-01

    Commonly used ground-water flow and con t transport computer models often assume a homogeneous, isotropic medium. Most hydrogeological systems are very complex and violate the basic assumptions underlying these models. However, even complex...

  4. Washington International Renewable Energy Conference (WIREC) 2008 Pledges. Methodology and Assumptions Summary

    SciTech Connect (OSTI)

    Babiuch, Bill; Bilello, Daniel E.; Cowlin, Shannon C.; Mann, Margaret; Wise, Alison

    2008-08-01

    This report describes the methodology and assumptions used by NREL in quantifying the potential CO2 reductions resulting from more than 140 governments, international organizations, and private-sector representatives pledging to advance the uptake of renewable energy.

  5. Justification of the Modeling Assumptions in the Intermediate Fidelity Models for Portable Power Generation

    E-Print Network [OSTI]

    . For drastically different approaches, e.g., homogeneous combustion [7, 8] this assumption may not be appropriate. 2.1.2 3d-CFD Duct-Reactor Simulation Here we examine the effect of averaging the heat conductivity

  6. CBE UFAD cost analysis tool: Life cycle cost model, issues and assumptions

    E-Print Network [OSTI]

    Webster, Tom; Benedek, Corinne; Bauman, Fred

    2008-01-01

    Building Maintenance and Repair Cost Reference. ” WhitestoneJ. Wallis and H. Lin. 2008. “CBE UFAD Cost Analysis Tool:UFAD First Cost Model, Issues and Assumptions. ” Center for

  7. WVU Regional Research Institute grad assistant wins national award for throwing assumptions out the window

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    WVU Regional Research Institute grad assistant wins national award for throwing assumptions out the window Silicon Valley conjures images of leading edge technology. Las Vegas makes one think of gambling

  8. Coordination of Energy Efficiency and Demand Response

    SciTech Connect (OSTI)

    Goldman, Charles; Reid, Michael; Levy, Roger; Silverstein, Alison

    2010-01-29

    This paper reviews the relationship between energy efficiency and demand response and discusses approaches and barriers to coordinating energy efficiency and demand response. The paper is intended to support the 10 implementation goals of the National Action Plan for Energy Efficiency's Vision to achieve all cost-effective energy efficiency by 2025. Improving energy efficiency in our homes, businesses, schools, governments, and industries - which consume more than 70 percent of the nation's natural gas and electricity - is one of the most constructive, cost-effective ways to address the challenges of high energy prices, energy security and independence, air pollution, and global climate change. While energy efficiency is an increasingly prominent component of efforts to supply affordable, reliable, secure, and clean electric power, demand response is becoming a valuable tool in utility and regional resource plans. The Federal Energy Regulatory Commission (FERC) estimated the contribution from existing U.S. demand response resources at about 41,000 megawatts (MW), about 5.8 percent of 2008 summer peak demand (FERC, 2008). Moreover, FERC recently estimated nationwide achievable demand response potential at 138,000 MW (14 percent of peak demand) by 2019 (FERC, 2009).2 A recent Electric Power Research Institute study estimates that 'the combination of demand response and energy efficiency programs has the potential to reduce non-coincident summer peak demand by 157 GW' by 2030, or 14-20 percent below projected levels (EPRI, 2009a). This paper supports the Action Plan's effort to coordinate energy efficiency and demand response programs to maximize value to customers. For information on the full suite of policy and programmatic options for removing barriers to energy efficiency, see the Vision for 2025 and the various other Action Plan papers and guides available at www.epa.gov/eeactionplan.

  9. Patterns of crude demand: Future patterns of demand for crude oil as a func-

    E-Print Network [OSTI]

    Langendoen, Koen

    #12;2 #12;Patterns of crude demand: Future patterns of demand for crude oil as a func- tion;5 Summary The crude oil market is actually experiencing dramatic changes on a world wide scale. Most schemes, and/or change quality of the feedstock (crude). Demand for crude oil is growing, especially

  10. Shield Module Design Considerations

    E-Print Network [OSTI]

    McDonald, Kirk

    Shield Module Design Considerations Adam Carroll Van Graves July 3, 2014 #12;2 Managed by UT-Battelle for the U.S. Department of Energy Shield Module Design Considerations 3 July 2014 Overview · Capability to remotely remove and reinstall the shield modules is required · Shield module concept is He-cooled tungsten

  11. NGNP: High Temperature Gas-Cooled Reactor Key Definitions, Plant Capabilities, and Assumptions

    SciTech Connect (OSTI)

    Wayne Moe

    2013-05-01

    This document provides key definitions, plant capabilities, and inputs and assumptions related to the Next Generation Nuclear Plant to be used in ongoing efforts related to the licensing and deployment of a high temperature gas-cooled reactor. These definitions, capabilities, and assumptions were extracted from a number of NGNP Project sources such as licensing related white papers, previously issued requirement documents, and preapplication interactions with the Nuclear Regulatory Commission (NRC).

  12. Klystron Gun Arcing and Modulator Protection

    SciTech Connect (OSTI)

    Gold, S

    2004-05-04

    The demand for 500 kV and 265 amperes peak to power an X-Band klystron brings up protection issues for klystron faults and the energy dumped into the arc from the modulator. This situation is made worse when more than one klystron will be driven from a single modulator, such as the existing schemes for running two and eight klystrons. High power pulsed klystrons have traditionally be powered by line type modulators which match the driving impedance with the load impedance and therefore current limit at twice the operating current. Multiple klystrons have the added problems of a lower modulator source impedance and added stray capacitance, which converts into appreciable energy at high voltages like 500kV. SLAC has measured the energy dumped into klystron arcs in a single and dual klystron configuration at the 400 to 450 kV level and found interesting characteristics in the arc formation. The author will present measured data from klystron arcs powered from line-type modulators in several configurations. The questions arise as to how the newly designed solid-state modulators, running multiple tubes, will react to a klystron arc and how much energy will be dumped into the arc.

  13. Sixth Northwest Conservation and Electric Power Plan Chapter 2: Key Assumptions

    E-Print Network [OSTI]

    's power plan to include a forecast of electricity demand for the next 20 years. Demand, to a large extent, is....................................................................................................................................... 16 Forecast of Retail Electricity Prices................................................................................................................... 17 SUMMARY OF KEY FINDINGS Pacific Northwest population and energy costs are expected to increase

  14. Autonomous Demand Response for Primary Frequency Regulation

    SciTech Connect (OSTI)

    Donnelly, Matt; Trudnowski, Daniel J.; Mattix, S.; Dagle, Jeffery E.

    2012-02-28

    The research documented within this report examines the use of autonomous demand response to provide primary frequency response in an interconnected power grid. The work builds on previous studies in several key areas: it uses a large realistic model (i.e., the interconnection of the western United States and Canada); it establishes a set of metrics that can be used to assess the effectiveness of autonomous demand response; and it independently adjusts various parameters associated with using autonomous demand response to assess effectiveness and to examine possible threats or vulnerabilities associated with the technology.

  15. FERC sees huge potential for demand response

    SciTech Connect (OSTI)

    2010-04-15

    The FERC study concludes that U.S. peak demand can be reduced by as much as 188 GW -- roughly 20 percent -- under the most aggressive scenario. More moderate -- and realistic -- scenarios produce smaller but still significant reductions in peak demand. The FERC report is quick to point out that these are estimates of the potential, not projections of what could actually be achieved. The main varieties of demand response programs include interruptible tariffs, direct load control (DLC), and a number of pricing schemes.

  16. The Economics of Energy (and Electricity) Demand

    E-Print Network [OSTI]

    Platchkov, Laura M.; Pollitt, Michael G.

    25 3.3.2 Electrification of personal transport New sources of electricity demand may emerge which substantially change the total demand for electricity and the way electricity is consumed by the household. The Tesla Roadster12 stores 53 k... substantial battery storage capacity to the electricity grid, both when stationary at home and when at work. They may thus be very useful in providing short term back-up at system demand peaks or for dumping electricity to the batteries when supply is at a...

  17. Global Energy: Supply, Demand, Consequences, Opportunities

    ScienceCinema (OSTI)

    Majumdar, Arun

    2010-01-08

    July 29, 2008 Berkeley Lab lecture: Arun Majumdar, Director of the Environmental Energy Technologies Division, discusses current and future projections of economic growth, population, and global energy demand and supply, and explores the implications of these trends for the environment.

  18. Demand Controlled Ventilation and Classroom Ventilation

    E-Print Network [OSTI]

    Fisk, William J.

    2014-01-01

    columns indicate the energy and cost savings for  demand class size.   (The energy costs  of classroom ventilation $6.2 M in increased energy costs.   Further VR  increases 

  19. Essays on exchange rates and electricity demand

    E-Print Network [OSTI]

    Li, Xiangming, 1966-

    1999-01-01

    This thesis examines two important issues in economic development: exchange rates and electricity demand and addresses methodological issues of using time series and panel data analysis to investigate important policy ...

  20. Demand Response and Energy Storage Integration Study

    Broader source: Energy.gov [DOE]

    This study is a multi-national laboratory effort to assess the potential value of demand response and energy storage to electricity systems with different penetration levels of variable renewable...

  1. Capitalize on Existing Assets with Demand Response 

    E-Print Network [OSTI]

    Collins, J.

    2008-01-01

    Industrial facilities universally struggle with escalating energy costs. EnerNOC will demonstrate how commercial, industrial, and institutional end-users can capitalize on their existing assets—at no cost and no risk. Demand response, the voluntary...

  2. Integration of Demand Side Management, Distributed Generation...

    Open Energy Info (EERE)

    United States. Annex 8 provides a list of software tools for analysing various aspects of demand response, distributed generation, smart grid and energy storage. Annex 9 is a list...

  3. CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY

    E-Print Network [OSTI]

    supervised data preparation. Steven Mac and Keith O'Brien prepared the historical energy consumption data. Nahid Movassagh forecasted consumption for the agriculture and water pumping CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY FORECAST Volume 1

  4. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01

    and technology options should have general application across systems. However, MECO has unprecedented levels of wind energywind, solar, and clean energy initiatives have introduced many changes and created uncertainties that complicate utility demand response technology

  5. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01

    and Retails Electricity Markets in SPP The Southwest Powerand Retails Electricity Markets in SPP.3 2.1 Wholesale Markets in the Southwest PowerRetail Demand Response in SPP Wholesale Markets in the Southwest Power

  6. China's Coal: Demand, Constraints, and Externalities

    E-Print Network [OSTI]

    Aden, Nathaniel

    2010-01-01

    unit water requirement of coal-fired electricity generationin electricity demand. Coal-fired power generation accounted12, the absolute amount of coal-fired capacity grew at an

  7. Global Climate Change and Demand for Energy

    E-Print Network [OSTI]

    Subramanian, Venkat

    1 Global Climate Change and Demand for Energy Tyson Research Center and International Center et al. Climate Variability and Climate Change: The New Climate Dice http://data, 2012 Tyson Research Center International Center for Advanced Research and Sustainability (I

  8. Micro economics for demand-side management

    E-Print Network [OSTI]

    Kibune, Hisao

    1991-01-01

    This paper aims to interpret Demand-Side Management (DSM) activity and to point out its problems, adopting microeconomics as an analytical tool. Two major findings follow. first, the cost-benefit analysis currently in use ...

  9. Volatile coal prices reflect supply, demand uncertainties

    SciTech Connect (OSTI)

    Ryan, M.

    2004-12-15

    Coal mine owners and investors say that supply and demand are now finally in balance. But coal consumers find that both spot tonnage and new contract coal come at a much higher price.

  10. Climate policy implications for agricultural water demand

    SciTech Connect (OSTI)

    Chaturvedi, Vaibhav; Hejazi, Mohamad I.; Edmonds, James A.; Clarke, Leon E.; Kyle, G. Page; Davies, Evan; Wise, Marshall A.; Calvin, Katherine V.

    2013-03-28

    Energy, water and land are scarce resources, critical to humans. Developments in each affect the availability and cost of the others, and consequently human prosperity. Measures to limit greenhouse gas concentrations will inevitably exact dramatic changes on energy and land systems and in turn alter the character, magnitude and geographic distribution of human claims on water resources. We employ the Global Change Assessment Model (GCAM), an integrated assessment model to explore the interactions of energy, land and water systems in the context of alternative policies to limit climate change to three alternative levels: 2.5 Wm-2 (445 ppm CO2-e), 3.5 Wm-2 (535 ppm CO2-e) and 4.5 Wm-2 (645 ppm CO2-e). We explore the effects of two alternative land-use emissions mitigation policy options—one which taxes terrestrial carbon emissions equally with fossil fuel and industrial emissions, and an alternative which only taxes fossil fuel and industrial emissions but places no penalty on land-use change emissions. We find that increasing populations and economic growth could be anticipated to almost triple demand for water for agricultural systems across the century even in the absence of climate policy. In general policies to mitigate climate change increase agricultural demands for water still further, though the largest changes occur in the second half of the century, under both policy regimes. The two policies examined profoundly affected both the sources and magnitudes of the increase in irrigation water demands. The largest increases in agricultural irrigation water demand occurred in scenarios where only fossil fuel emissions were priced (but not land-use change emission) and were primarily driven by rapid expansion in bioenergy production. In these scenarios water demands were large relative to present-day total available water, calling into question whether it would be physically possible to produce the associated biomass energy. We explored the potential of improved water delivery and irrigation system efficiencies. These could potentially reduce demands substantially. However, overall demands remained high under our fossil-fuel-only tax policy. In contrast, when all carbon was priced, increases in agricultural water demands were smaller than under the fossil-fuel-only policy and were driven primarily by increased demands for water by non-biomass crops such as rice. Finally we estimate the geospatial pattern of water demands and find that regions such as China, India and other countries in south and east Asia might be expected to experience greatest increases in water demands.?

  11. Measuring the capacity impacts of demand response

    SciTech Connect (OSTI)

    Earle, Robert; Kahn, Edward P.; Macan, Edo

    2009-07-15

    Critical peak pricing and peak time rebate programs offer benefits by increasing system reliability, and therefore, reducing capacity needs of the electric power system. These benefits, however, decrease substantially as the size of the programs grows relative to the system size. More flexible schemes for deployment of demand response can help address the decreasing returns to scale in capacity value, but more flexible demand response has decreasing returns to scale as well. (author)

  12. Demand Response and Electric Grid Reliability 

    E-Print Network [OSTI]

    Wattles, P.

    2012-01-01

    and Regional Transmission Organizations are the ?air traffic controllers? of the bulk electric power grids 4 Power supply (generation) must match load (demand) CATEE Conference October 10, 2012 ? The fundamental concept behind ERCOT operations... changes or incentives.? (FERC) ? ?Changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times...

  13. The Assumption of Class-Conditional Independence in Category Learning Jana Jarecki (jarecki@mpib-berlin.mpg.de)

    E-Print Network [OSTI]

    Nelson, Jonathan D.

    The Assumption of Class-Conditional Independence in Category Learning Jana Jarecki (jarecki Berlin, Germany Abstract This paper investigates the role of the assumption of class- conditional. Treating features as class- conditionally independent can in many situations substantially facilitate

  14. Ethanol Demand in United States Gasoline Production

    SciTech Connect (OSTI)

    Hadder, G.R.

    1998-11-24

    The Oak Ridge National Laboratory (OWL) Refinery Yield Model (RYM) has been used to estimate the demand for ethanol in U.S. gasoline production in year 2010. Study cases examine ethanol demand with variations in world oil price, cost of competing oxygenate, ethanol value, and gasoline specifications. For combined-regions outside California summer ethanol demand is dominated by conventional gasoline (CG) because the premised share of reformulated gasoline (RFG) production is relatively low and because CG offers greater flexibility for blending high vapor pressure components like ethanol. Vapor pressure advantages disappear for winter CG, but total ethanol used in winter RFG remains low because of the low RFG production share. In California, relatively less ethanol is used in CG because the RFG production share is very high. During the winter in California, there is a significant increase in use of ethanol in RFG, as ethanol displaces lower-vapor-pressure ethers. Estimated U.S. ethanol demand is a function of the refiner value of ethanol. For example, ethanol demand for reference conditions in year 2010 is 2 billion gallons per year (BGY) at a refiner value of $1.00 per gallon (1996 dollars), and 9 BGY at a refiner value of $0.60 per gallon. Ethanol demand could be increased with higher oil prices, or by changes in gasoline specifications for oxygen content, sulfur content, emissions of volatile organic compounds (VOCS), and octane numbers.

  15. An Operational Model for Optimal NonDispatchable Demand Response

    E-Print Network [OSTI]

    Grossmann, Ignacio E.

    An Operational Model for Optimal NonDispatchable Demand Response for Continuous PowerintensiveFACTS, $ Demand Response Energy Storage HVDC Industrial Customer PEV Renewable Energy Source: U.S.-Canada Power: To balance supply and demand of a power system, one can manipulate both: supply and demand demand response

  16. Advanced silicon photonic modulators

    E-Print Network [OSTI]

    Sorace, Cheryl M

    2010-01-01

    Various electrical and optical schemes used in Mach-Zehnder (MZ) silicon plasma dispersion effect modulators are explored. A rib waveguide reverse biased silicon diode modulator is designed, tested and found to operate at ...

  17. Demand Response This is the first of the Council's power plans to treat demand response as a resource.1

    E-Print Network [OSTI]

    . WHAT IS DEMAND RESPONSE? Demand response is a change in customers' demand for electricity corresponding. Demand response as defined here does not include involuntary curtailment imposed on electricity users to conditions in wholesale power markets, its electricity demand is not. This situation has a number of adverse

  18. Washington: Sustainability Training for Realtors in High Demand...

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

    Sustainability Training for Realtors in High Demand Washington: Sustainability Training for Realtors in High Demand March 6, 2014 - 5:50pm Addthis Demand has been high for a free...

  19. Forecasting Market Demand for New Telecommunications Services: An Introduction

    E-Print Network [OSTI]

    Parsons, Simon

    Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc in demand forecasting for new communication services. Acknowledgments: The writing of this paper commenced employers or consultancy clients. KEYWORDS: Demand Forecasting, New Product Marketing, Telecommunica- tions

  20. A Methodology for Estimating Interdomain Web Traffic Demand

    E-Print Network [OSTI]

    Maggs, Bruce M.

    A Methodology for Estimating Interdomain Web Traffic Demand Anja Feldmann , Nils Kammenhuber-varying) interdomain HTTP traffic demand matrix pairing several hundred thousand blocks of client IP addresses, Traffic demand, Interdomain, Es- timation 1. INTRODUCTION The reliable estimation and prediction

  1. Demand-Side Management and Energy Efficiency Revisited

    E-Print Network [OSTI]

    Auffhammer, Maximilian; Blumstein, Carl; Fowlie, Meredith

    2007-01-01

    EPRI). 1984. ”Demand Side Management. Vol. 1:Overview of Key1993. ”Industrial Demand-Side Management Programs: What’sJ. Kulick. 2004. ”Demand side management and energy e?ciency

  2. Coordination of Retail Demand Response with Midwest ISO Markets

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2008-01-01

    Data Collection for Demand-side Management for QualifyingPrepared by Demand-side Management Task Force of the4. Status of Demand Side Management in Midwest ISO 5.

  3. Demand Response Enabling Technologies and Approaches for Industrial Facilities 

    E-Print Network [OSTI]

    Epstein, G.; D'Antonio, M.; Schmidt, C.; Seryak, J.; Smith, C.

    2005-01-01

    , there are also huge opportunities for demand response in the industrial sector. This paper describes some of the demand response initiatives that are currently active in New York State, explaining applicability of industrial facilities. Next, we discuss demand...

  4. Demand Control Utilizing Energy Management Systems - Report of Field Tests 

    E-Print Network [OSTI]

    Russell, B. D.; Heller, R. P.; Perry, L. W.

    1984-01-01

    Energy Management systems and particularly demand controllers are becoming more popular as commercial and light industrial operations attempt to reduce their electrical usage and demand. Numerous techniques are used to control energy use and demand...

  5. Automated Demand Response Strategies and Commissioning Commercial Building Controls

    E-Print Network [OSTI]

    Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Linkugel, Eric

    2006-01-01

    4 9 . Piette et at Automated Demand Response Strategies andDynamic Controls for Demand Response in New and ExistingFully Automated Demand Response Tests in Large Facilities"

  6. Learning Energy Demand Domain Knowledge via Feature Transformation

    E-Print Network [OSTI]

    Povinelli, Richard J.

    -- Domain knowledge is an essential factor for forecasting energy demand. This paper introduces a method knowledge substantially improves energy demand forecasting accuracy. However, domain knowledge may differ. The first stage automatically captures energy demand forecasting domain knowledge through nonlinear

  7. Behavioral Aspects in Simulating the Future US Building Energy Demand

    E-Print Network [OSTI]

    Stadler, Michael

    2011-01-01

    Floor-space forecast to 2050 Gross demand for energy Macro-Floor-space forecast to 2050 Gross demand for energy Macro-Floor-space forecast to 2050 Gross demand for energy Macro-

  8. PIER: Demand Response Research Center Director, Mary Ann Piette

    E-Print Network [OSTI]

    1 PIER: Demand Response Research Center Director, Mary Ann Piette Program Development and Outreach Response Research Plan #12;2 Demand Response Research Center Objective Scope Stakeholders Develop, prioritize, conduct and disseminate multi- institutional research to facilitate Demand Response. Technologies

  9. Analysis of Open Automated Demand Response Deployments in California

    E-Print Network [OSTI]

    LBNL-6560E Analysis of Open Automated Demand Response Deployments in California and Guidelines The work described in this report was coordinated by the Demand Response Research. #12; #12;Abstract This report reviews the Open Automated Demand Response

  10. Modulating lignin in plants

    DOE Patents [OSTI]

    Apuya, Nestor; Bobzin, Steven Craig; Okamuro, Jack; Zhang, Ke

    2013-01-29

    Materials and methods for modulating (e.g., increasing or decreasing) lignin content in plants are disclosed. For example, nucleic acids encoding lignin-modulating polypeptides are disclosed as well as methods for using such nucleic acids to generate transgenic plants having a modulated lignin content.

  11. Agreement Template for Energy Conservation and Demand Side Management...

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

    Agreement Template for Energy Conservation and Demand Side Management Services Agreement Template for Energy Conservation and Demand Side Management Services Template agreement...

  12. Structuring Rebate and Incentive Programs for Sustainable Demand...

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

    Structuring Rebate and Incentive Programs for Sustainable Demand Structuring Rebate and Incentive Programs for Sustainable Demand Better Buildings Neighborhood Program Peer...

  13. Using Mobile Applications to Generate Customer Demand | Department...

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

    Using Mobile Applications to Generate Customer Demand Using Mobile Applications to Generate Customer Demand Better Buildings Residential Network Peer Exchange Call Series: Using...

  14. Reducing Energy Demand in Buildings Through State Energy Codes...

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

    Reducing Energy Demand in Buildings Through State Energy Codes Reducing Energy Demand in Buildings Through State Energy Codes Building Codes Project for the 2013 Building...

  15. Estimating Costs and Efficiency of Storage, Demand, and Heat...

    Energy Savers [EERE]

    Costs and Efficiency of Storage, Demand, and Heat Pump Water Heaters Estimating Costs and Efficiency of Storage, Demand, and Heat Pump Water Heaters A water heater's energy...

  16. Response to several FOIA requests - Renewable Energy. Demand...

    Energy Savers [EERE]

    Response to several FOIA requests - Renewable Energy. Demand for Fossil Fuels Response to several FOIA requests - Renewable Energy. Demand for Fossil Fuels Response to several FOIA...

  17. Using Wind and Solar to Reliably Meet Electricity Demand, Greening...

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

    and wind generation technologies. A variety of approaches can be deployed, including demand response, which can be used to shift demand to periods of greater renewable output,...

  18. Strategies for Marketing and Driving Demand for Commercial Financing...

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

    Strategies for Marketing and Driving Demand for Commercial Financing Products Strategies for Marketing and Driving Demand for Commercial Financing Products Better Buildings...

  19. Agreement for Energy Conservation and Demand Side Management...

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

    Agreement for Energy Conservation and Demand Side Management Services Template Agreement for Energy Conservation and Demand Side Management Services Template Document features a...

  20. Tool Improves Electricity Demand Predictions to Make More Room...

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

    Tool Improves Electricity Demand Predictions to Make More Room for Renewables Tool Improves Electricity Demand Predictions to Make More Room for Renewables October 3, 2011 -...

  1. FERC Presendation: Demand Response as Power System Resources...

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

    Federal Energy Regulatory Commission (FERC) presentation on demand response as power system resources before the Electicity Advisory Committee, October 29, 2010 Demand Response as...

  2. Implementation Proposal for the National Action Plan on Demand...

    Energy Savers [EERE]

    Implementation Proposal for the National Action Plan on Demand Response - July 2011 Implementation Proposal for the National Action Plan on Demand Response - July 2011 Report to...

  3. SGDP Report Now Available: Interoperability of Demand Response...

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

    SGDP Report Now Available: Interoperability of Demand Response Resources Demonstration in NY (February 2015) SGDP Report Now Available: Interoperability of Demand Response...

  4. Estimating Costs and Efficiency of Storage, Demand, and Heat...

    Office of Environmental Management (EM)

    Estimating Costs and Efficiency of Storage, Demand, and Heat Pump Water Heaters Estimating Costs and Efficiency of Storage, Demand, and Heat Pump Water Heaters A water heater's...

  5. Can Automotive Battery Recycling Help Meet Lithium Demand? |...

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

    Can Automotive Battery Recycling Help Meet Lithium Demand? Title Can Automotive Battery Recycling Help Meet Lithium Demand? Publication Type Presentation Year of Publication 2013...

  6. Automated Demand Response Strategies and Commissioning Commercial Building Controls

    E-Print Network [OSTI]

    Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Linkugel, Eric

    2006-01-01

    efficiency, daily peak load management and demand response.Loads Efficiency, Daily Load Management and Demand ResponseOperations Peak Load Management (Daily) - TOU Savings - Peak

  7. Robust Unit Commitment Problem with Demand Response and ...

    E-Print Network [OSTI]

    2010-10-31

    Oct 29, 2010 ... sion, both Demand Response (DR) strategy and intermittent renewable ... Key Words: unit commitment, demand response, wind energy, robust ...

  8. Demand Response and Smart Metering Policy Actions Since the Energy...

    Office of Environmental Management (EM)

    Demand Response and Smart Metering Policy Actions Since the Energy Policy Act of 2005: A Summary for State Officials Demand Response and Smart Metering Policy Actions Since the...

  9. China-Transportation Demand Management in Beijing: Mitigation...

    Open Energy Info (EERE)

    China-Transportation Demand Management in Beijing: Mitigation of Emissions in Urban Transport Jump to: navigation, search Name Transportation Demand Management in Beijing -...

  10. Estimating Costs and Efficiency of Storage, Demand, and Heat...

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

    Estimating Costs and Efficiency of Storage, Demand, and Heat Pump Water Heaters Estimating Costs and Efficiency of Storage, Demand, and Heat Pump Water Heaters March 10, 2015 -...

  11. Demand Response and Energy Storage Integration Study - Past Workshops...

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

    Demand Response and Energy Storage Integration Study - Past Workshops Demand Response and Energy Storage Integration Study - Past Workshops The project was initiated and informed...

  12. SGDP Report: Interoperability of Demand Response Resources Demonstrati...

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

    Report: Interoperability of Demand Response Resources Demonstration in NY (February 2015) SGDP Report: Interoperability of Demand Response Resources Demonstration in NY (February...

  13. ASSESSMENT OF VARIABLE EFFECTS OF SYSTEMS WITH DEMAND RESPONSE RESOURCES

    E-Print Network [OSTI]

    Gross, George

    ASSESSMENT OF VARIABLE EFFECTS OF SYSTEMS WITH DEMAND RESPONSE RESOURCES BY ANUPAMA SUNIL KOWLI B of consumers - called demand response resources (DRRs) - whose role has become increasingly important

  14. SGDP Report Now Available: Interoperability of Demand Response...

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

    Report Now Available: Interoperability of Demand Response Resources Demonstration in NY (February 2015) SGDP Report Now Available: Interoperability of Demand Response Resources...

  15. Water transfer in soil at low water content. Is the local equilibrium assumption still appropriate?

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Water transfer in soil at low water content. Is the local equilibrium assumption still appropriate Montpellier, France Abstract The dynamics of water content in the superficial layers of soils is critical a retardation time and a decrease in phase change rate as the water content gets lower. Therefore, the objective

  16. Human lightness perception is guided by simple assumptions about reflectance and lighting

    E-Print Network [OSTI]

    Murray, Richard

    Human lightness perception is guided by simple assumptions about reflectance and lighting Richard F 0009, Toronto, Ontario, Canada, M3J 1P3 ABSTRACT Lightness constancy is the remarkable ability of human successful approaches to understanding lightness perception that have developed along independent paths

  17. RETI Phase 1B Final Report Update NET SHORT RECALCULATION AND NEW PV ASSUMPTIONS

    E-Print Network [OSTI]

    RETI Phase 1B Final Report Update NET SHORT RECALCULATION AND NEW PV ASSUMPTIONS With Revisions distributed photovoltaic (PV) installations in the Report is unclear and perhaps misleading. At the direction-generation is required. The CEC forecast assumed that 1,082 GWh will be self-generated by consumers from new PV

  18. Biennial Assessment of the Fifth Power Plan Gas Turbine Power Plant Planning Assumptions

    E-Print Network [OSTI]

    from the heat recovery steam generator powers an additional steam turbine, providing extra electricBiennial Assessment of the Fifth Power Plan Gas Turbine Power Plant Planning Assumptions October 17, 2006 Simple- and combined-cycle gas turbine power plants fuelled by natural gas are among the bulk

  19. Complete Knowledge Assumption Often you want to assume that your knowledge is complete.

    E-Print Network [OSTI]

    Valtorta, Marco

    Complete Knowledge Assumption Often you want to assume that your knowledge is complete. Example: assume that a database of what students are enrolled in a course is complete. The definite clause language is monotonic: adding clauses can't invalidate a previous conclusion. Under the complete knowledge

  20. External review of the thermal energy storage (TES) cogeneration study assumptions. Final report

    SciTech Connect (OSTI)

    Lai, B.Y.; Poirier, R.N.

    1996-08-01

    This report is to provide a detailed review of the basic assumptions made in the design, sizing, performance, and economic models used in the thermal energy storage (TES)/cogeneration feasibility studies conducted by Pacific Northwest Laboratory (PNL) staff. This report is the deliverable required under the contract.

  1. 1 Relaxing the Multivariate Normality Assumption in the Simulation 2 of Transportation System Dependencies

    E-Print Network [OSTI]

    Kockelman, Kara M.

    1 1 Relaxing the Multivariate Normality Assumption in the Simulation 2 of Transportation System network analysis literature is the3 use of the multivariate normal (MVN) distribution. While in certain to sample from these case-specific multivariate distributions in simulation studies (see, e.g.,14 Ghosh

  2. Requirements Engineering for Cross-organizational ERP Implementation: Undocumented Assumptions and Potential Mismatches

    E-Print Network [OSTI]

    Wieringa, Roel

    Requirements Engineering for Cross-organizational ERP Implementation: Undocumented Assumptions) for Enterprise Resource Planning (ERP) in a cross- organizational context is how to find a match between the ERP for analyzing coordination requirements in inter-organizational ERP projects from a coordination theory

  3. Wireless Demand Response Controls for HVAC Systems

    SciTech Connect (OSTI)

    Federspiel, Clifford

    2009-06-30

    The objectives of this scoping study were to develop and test control software and wireless hardware that could enable closed-loop, zone-temperature-based demand response in buildings that have either pneumatic controls or legacy digital controls that cannot be used as part of a demand response automation system. We designed a SOAP client that is compatible with the Demand Response Automation Server (DRAS) being used by the IOUs in California for their CPP program, design the DR control software, investigated the use of cellular routers for connecting to the DRAS, and tested the wireless DR system with an emulator running a calibrated model of a working building. The results show that the wireless DR system can shed approximately 1.5 Watts per design CFM on the design day in a hot, inland climate in California while keeping temperatures within the limits of ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy.

  4. China's Coal: Demand, Constraints, and Externalities

    SciTech Connect (OSTI)

    Aden, Nathaniel; Fridley, David; Zheng, Nina

    2009-07-01

    This study analyzes China's coal industry by focusing on four related areas. First, data are reviewed to identify the major drivers of historical and future coal demand. Second, resource constraints and transport bottlenecks are analyzed to evaluate demand and growth scenarios. The third area assesses the physical requirements of substituting coal demand growth with other primary energy forms. Finally, the study examines the carbon- and environmental implications of China's past and future coal consumption. There are three sections that address these areas by identifying particular characteristics of China's coal industry, quantifying factors driving demand, and analyzing supply scenarios: (1) reviews the range of Chinese and international estimates of remaining coal reserves and resources as well as key characteristics of China's coal industry including historical production, resource requirements, and prices; (2) quantifies the largest drivers of coal usage to produce a bottom-up reference projection of 2025 coal demand; and (3) analyzes coal supply constraints, substitution options, and environmental externalities. Finally, the last section presents conclusions on the role of coal in China's ongoing energy and economic development. China has been, is, and will continue to be a coal-powered economy. In 2007 Chinese coal production contained more energy than total Middle Eastern oil production. The rapid growth of coal demand after 2001 created supply strains and bottlenecks that raise questions about sustainability. Urbanization, heavy industrial growth, and increasing per-capita income are the primary interrelated drivers of rising coal usage. In 2007, the power sector, iron and steel, and cement production accounted for 66% of coal consumption. Power generation is becoming more efficient, but even extensive roll-out of the highest efficiency units would save only 14% of projected 2025 coal demand for the power sector. A new wedge of future coal consumption is likely to come from the burgeoning coal-liquefaction and chemicals industries. If coal to chemicals capacity reaches 70 million tonnes and coal-to-liquids capacity reaches 60 million tonnes, coal feedstock requirements would add an additional 450 million tonnes by 2025. Even with more efficient growth among these drivers, China's annual coal demand is expected to reach 3.9 to 4.3 billion tonnes by 2025. Central government support for nuclear and renewable energy has not reversed China's growing dependence on coal for primary energy. Substitution is a matter of scale: offsetting one year of recent coal demand growth of 200 million tonnes would require 107 billion cubic meters of natural gas (compared to 2007 growth of 13 BCM), 48 GW of nuclear (compared to 2007 growth of 2 GW), or 86 GW of hydropower capacity (compared to 2007 growth of 16 GW). Ongoing dependence on coal reduces China's ability to mitigate carbon dioxide emissions growth. If coal demand remains on a high growth path, carbon dioxide emissions from coal combustion alone would exceed total US energy-related carbon emissions by 2010. Within China's coal-dominated energy system, domestic transportation has emerged as the largest bottleneck for coal industry growth and is likely to remain a constraint to further expansion. China has a low proportion of high-quality reserves, but is producing its best coal first. Declining quality will further strain production and transport capacity. Furthermore, transporting coal to users has overloaded the train system and dramatically increased truck use, raising transportation oil demand. Growing international imports have helped to offset domestic transport bottlenecks. In the long term, import demand is likely to exceed 200 million tonnes by 2025, significantly impacting regional markets.

  5. Centralized and Decentralized Control for Demand Response

    SciTech Connect (OSTI)

    Lu, Shuai; Samaan, Nader A.; Diao, Ruisheng; Elizondo, Marcelo A.; Jin, Chunlian; Mayhorn, Ebony T.; Zhang, Yu; Kirkham, Harold

    2011-04-29

    Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generation resources are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their relative advantages and disadvantages in terms of delay time, predictability, complexity, and reliability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the predictability and simplicity of centralized control to achieve the best performance of the smart grid.

  6. Uranium 2005 resources, production and demand

    E-Print Network [OSTI]

    Organisation for Economic Cooperation and Development. Paris

    2006-01-01

    Published every other year, Uranium Resources, Production, and Demand, or the "Red Book" as it is commonly known, is jointly prepared by the OECD Nuclear Energy Agency and the International Atomic Energy Agency. It is the recognised world reference on uranium and is based on official information received from 43 countries. This 21st edition presents the results of a thorough review of world uranium supplies and demand as of 1st January 2005 and provides a statistical profile of the world uranium industry in the areas of exploration, resource estimates, production and reactor-related requirements. It provides substantial new information from all major uranium production centres in Africa, Australia, Central Asia, Eastern Europe and North America. Projections of nuclear generating capacity and reactor-related uranium requirements through 2025 are provided as well as a discussion of long-term uranium supply and demand issues. This edition focuses on recent price and production increases that could signal major c...

  7. DEMAND CONTROLLED VENTILATION AND CLASSROOM VENTILATION

    SciTech Connect (OSTI)

    Fisk, William J.; Mendell, Mark J.; Davies, Molly; Eliseeva, Ekaterina; Faulkner, David; Hong, Tienzen; Sullivan, Douglas P.

    2014-01-06

    This document summarizes a research effort on demand controlled ventilation and classroom ventilation. The research on demand controlled ventilation included field studies and building energy modeling. Major findings included: ? The single-location carbon dioxide sensors widely used for demand controlled ventilation frequently have large errors and will fail to effectively control ventilation rates (VRs).? Multi-location carbon dioxide measurement systems with more expensive sensors connected to multi-location sampling systems may measure carbon dioxide more accurately.? Currently-available optical people counting systems work well much of the time but have large counting errors in some situations. ? In meeting rooms, measurements of carbon dioxide at return-air grilles appear to be a better choice than wall-mounted sensors.? In California, demand controlled ventilation in general office spaces is projected to save significant energy and be cost effective only if typical VRs without demand controlled ventilation are very high relative to VRs in codes. Based on the research, several recommendations were developed for demand controlled ventilation specifications in the California Title 24 Building Energy Efficiency Standards.The research on classroom ventilation collected data over two years on California elementary school classrooms to investigate associations between VRs and student illness absence (IA). Major findings included: ? Median classroom VRs in all studied climate zones were below the California guideline, and 40percent lower in portable than permanent buildings.? Overall, one additional L/s per person of VR was associated with 1.6percent less IA. ? Increasing average VRs in California K-12 classrooms from the current average to the required level is estimated to decrease IA by 3.4percent, increasing State attendance-based funding to school districts by $33M, with $6.2 M in increased energy costs. Further VR increases would provide additional benefits.? Confirming these findings in intervention studies is recommended. ? Energy costs of heating/cooling unoccupied classrooms statewide are modest, but a large portion occurs in relatively few classrooms.

  8. Reviving'demand+pull'perspec2ves:' The'effect'of'demand'uncertainty'and'

    E-Print Network [OSTI]

    Sussex, University of

    / Daniele&Rotolo& D.Rotolo@sussex.ac.uk/ Associate(Editors& Area& Florian&Kern& Energy& F.Kern@sussex.ac.ukReviving'demand+pull'perspec2ves:' The'effect'of'demand'uncertainty'and' stagnancy'on'R&D'strategy'which'case'the'Associate'Editors'may'decide'to'skip'internal'review'process.' Website' SWPS:'www.sussex.ac.uk

  9. US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier

    E-Print Network [OSTI]

    in Energy Economics, SEEC, University of Surrey, UK, 2010; the 11th IAEE European Conference, Vilnius strategy. One of the Department of Energy's missions are to promote energy efficiency to help the NationUS Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach Massimo

  10. Demand Responsive Lighting: A Scoping Study

    SciTech Connect (OSTI)

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-03

    The objective of this scoping study is: (1) to identify current market drivers and technology trends that can improve the demand responsiveness of commercial building lighting systems and (2) to quantify the energy, demand and environmental benefits of implementing lighting demand response and energy-saving controls strategies Statewide. Lighting systems in California commercial buildings consume 30 GWh. Lighting systems in commercial buildings often waste energy and unnecessarily stress the electrical grid because lighting controls, especially dimming, are not widely used. But dimmable lighting equipment, especially the dimming ballast, costs more than non-dimming lighting and is expensive to retrofit into existing buildings because of the cost of adding control wiring. Advances in lighting industry capabilities coupled with the pervasiveness of the Internet and wireless technologies have led to new opportunities to realize significant energy saving and reliable demand reduction using intelligent lighting controls. Manufacturers are starting to produce electronic equipment--lighting-application specific controllers (LAS controllers)--that are wirelessly accessible and can control dimmable or multilevel lighting systems obeying different industry-accepted protocols. Some companies make controllers that are inexpensive to install in existing buildings and allow the power consumed by bi-level lighting circuits to be selectively reduced during demand response curtailments. By intelligently limiting the demand from bi-level lighting in California commercial buildings, the utilities would now have an enormous 1 GW demand shed capability at hand. By adding occupancy and light sensors to the remotely controllable lighting circuits, automatic controls could harvest an additional 1 BkWh/yr savings above and beyond the savings that have already been achieved. The lighting industry's adoption of DALI as the principal wired digital control protocol for dimming ballasts and increased awareness of the need to standardize on emerging wireless technologies are evidence of this transformation. In addition to increased standardization of digital control protocols controller capabilities, the lighting industry has improved the performance of dimming lighting systems over the last two years. The system efficacy of today's current dimming ballasts is approaching that of non-dimming program start ballasts. The study finds that the benefits of applying digital controls technologies to California's unique commercial buildings market are enormous. If California were to embark on an concerted 20 year program to improve the demand responsiveness and energy efficiency of commercial building lighting systems, the State could avoid adding generation capacity, improve the elasticity of the grid, save Californians billion of dollars in avoided energy charges and significantly reduce greenhouse gas emissions.

  11. Real-Time Demand Side Energy Management 

    E-Print Network [OSTI]

    Victor, A.; Brodkorb, M.

    2006-01-01

    • Provides periodic energy consumption reports Demand-Side Energy Management • Compares actual energy cost against defined dynamic targets • Alerts responsible personnel when corrective action is needed • Provides a list of recommended actions... stream_source_info ESL-IE-06-05-24.pdf.txt stream_content_type text/plain stream_size 17485 Content-Encoding UTF-8 stream_name ESL-IE-06-05-24.pdf.txt Content-Type text/plain; charset=UTF-8 Real-Time Demand Side Energy...

  12. ERCOT's Weather Sensitive Demand Response Pilot 

    E-Print Network [OSTI]

    Carter, T.

    2013-01-01

    Demand Response Pilot CATEE 12-17-13 ESL-KT-13-12-21 CATEE 2013: Clean Air Through Energy Efficiency Conference, San Antonio, Texas Dec. 16-18 Disclaimer The information contained in this report has been obtained from sources which Constellation New... services along with other information about our business is available online at constellation.com. ESL-KT-13-12-21 CATEE 2013: Clean Air Through Energy Efficiency Conference, San Antonio, Texas Dec. 16-18 Demand Response in ERCOT CATEE 121313 - Tim Carter...

  13. Industrial Demand-Side Management in Texas 

    E-Print Network [OSTI]

    Jaussaud, D.

    1992-01-01

    stream_source_info ESL-IE-92-04-16.pdf.txt stream_content_type text/plain stream_size 25669 Content-Encoding ISO-8859-1 stream_name ESL-IE-92-04-16.pdf.txt Content-Type text/plain; charset=ISO-8859-1 INDUSTRIAL DEMAND.... Utilities may intervene to shape the load of their customers through demand-side management programs. In doing so, they can improve the efficiency of their system. Historically, utilities in Texas have offered industrial customers energy audits and other...

  14. Demand Response Initiatives at CPS Energy 

    E-Print Network [OSTI]

    Luna, R.

    2013-01-01

    stream_source_info ESL-KT-13-12-53.pdf.txt stream_content_type text/plain stream_size 4780 Content-Encoding UTF-8 stream_name ESL-KT-13-12-53.pdf.txt Content-Type text/plain; charset=UTF-8 Demand Response Initiatives... and Toyota combined. • Schools & Universities contributed 6 MW’s of Demand Response in 2013. 2013 DR Participants Trinity University - $5,654 Fort Sam ISD - $18,860 Judson ISD - $45,540 Alamo Colleges - $98,222 UTSA - $168,572 ESL-KT-13-12-53 CATEE 2013...

  15. Seasonal demand and supply analysis of turkeys 

    E-Print Network [OSTI]

    Blomo, Vito James

    1972-01-01

    (percentage) responsiveness of price to changes (usually one percent) in quantity. Assuming a linear demand function, flexibility is shown to be less than one in the upper half of the function, equal to one (unitary) at the midpoint, and greater than one...SEASONAL DEMAND AND SUPPLY ANALYSIS OF TURKEYS A Thesis by VITO JAMES BLOMO Submitted to the Graduate College of Texas A&M University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE May 1972 Ma)or Sub...

  16. Installation and Commissioning Automated Demand Response Systems

    SciTech Connect (OSTI)

    Global Energy Partners; Pacific Gas and Electric Company; Kiliccote, Sila; Kiliccote, Sila; Piette, Mary Ann; Wikler, Greg; Prijyanonda, Joe; Chiu, Albert

    2008-04-21

    Demand Response (DR) can be defined as actions taken to reduce electric loads when contingencies, such as emergencies and congestion, occur that threaten supply-demand balance, or market conditions raise supply costs. California utilities have offered price and reliability DR based programs to customers to help reduce electric peak demand. The lack of knowledge about the DR programs and how to develop and implement DR control strategies is a barrier to participation in DR programs, as is the lack of automation of DR systems. Most DR activities are manual and require people to first receive notifications, and then act on the information to execute DR strategies. Levels of automation in DR can be defined as follows. Manual Demand Response involves a labor-intensive approach such as manually turning off or changing comfort set points at each equipment switch or controller. Semi-Automated Demand Response involves a pre-programmed demand response strategy initiated by a person via centralized control system. Fully-Automated Demand Response does not involve human intervention, but is initiated at a home, building, or facility through receipt of an external communications signal. The receipt of the external signal initiates pre-programmed demand response strategies. We refer to this as Auto-DR (Piette et. al. 2005). Auto-DR for commercial and industrial facilities can be defined as fully automated DR initiated by a signal from a utility or other appropriate entity and that provides fully-automated connectivity to customer end-use control strategies. One important concept in Auto-DR is that a homeowner or facility manager should be able to 'opt out' or 'override' a DR event if the event comes at time when the reduction in end-use services is not desirable. Therefore, Auto-DR is not handing over total control of the equipment or the facility to the utility but simply allowing the utility to pass on grid related information which then triggers facility defined and programmed strategies if convenient to the facility. From 2003 through 2006 Lawrence Berkeley National Laboratory (LBNL) and the Demand Response Research Center (DRRC) developed and tested a series of demand response automation communications technologies known as Automated Demand Response (Auto-DR). In 2007, LBNL worked with three investor-owned utilities to commercialize and implement Auto-DR programs in their territories. This paper summarizes the history of technology development for Auto-DR, and describes the DR technologies and control strategies utilized at many of the facilities. It outlines early experience in commercializing Auto-DR systems within PG&E DR programs, including the steps to configure the automation technology. The paper also describes the DR sheds derived using three different baseline methodologies. Emphasis is given to the lessons learned from installation and commissioning of Auto-DR systems, with a detailed description of the technical coordination roles and responsibilities, and costs.

  17. CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY

    E-Print Network [OSTI]

    electricity and natural gas rates, and relatively low efficiency program and self: Electricity Demand by Utility Planning Area MAY 2013 CEC-200-2013-004-SD-V2 Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P. Oglesby Executive

  18. ConservationandDemand ManagementPlan

    E-Print Network [OSTI]

    Abolmaesumi, Purang

    to monitor system status for leaks and losses, energy management, Preventative Maintenance work and implement energy Conservation and Demand Management (CDM) plans starting in 2014. Requirementsofthe/replacements and minor construction projects to ensure energy efficient equipment is selected for systems and design

  19. Energy Demand (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    Growth in U.S. energy use is linked to population growth through increases in demand for housing, commercial floorspace, transportation, manufacturing, and services. This affects not only the level of energy use, but also the mix of fuels and consumption by sector.

  20. Demand Response Projects: Technical and Market Demonstrations

    E-Print Network [OSTI]

    signal · Reinforces conservation program efforts in areas such as insulation and heat pumps Voluntary DR Commercial & Industrial DR Pilot ­ Internet communication/control of imbedded load control devices on up storage Commercial & Industrial DR Pilot (8 customers)* · Open Automated Demand Response Communication

  1. Risk Management and Combinatorial Optimization for Large-Scale Demand Response and Renewable Energy Integration

    E-Print Network [OSTI]

    Yang, Insoon

    2015-01-01

    results: demand response . . . . . . . . . . . . . . . . . .Institute. “Automated Demand Response Today”. In: (2012). [Energy. “Benefits of demand response in electricity markets

  2. Market and Policy Barriers for Demand Response Providing Ancillary Services in U.S. Markets

    E-Print Network [OSTI]

    Cappers, Peter

    2014-01-01

    Wholesale Electricity Demand Response Program Comparison,J. (2009) Open Automated Demand Response Communicationsin Demand Response for Wholesale Ancillary Services.

  3. Adaptive Caching for Demand Prepaging Scott F. Kaplan

    E-Print Network [OSTI]

    Kaplan, Scott

    Bottleneck Links, Variable Demand, and the Tragedy of the Commons Richard Cole£ Yevgeniy DodisÝ Tim demand to resources whose performance degrades with increasing congestion. While fundamental of a resource and the demand for that resource. This coupling motivates allowing demand to vary with congestion

  4. Approximability of Partitioning Graphs with Supply and Demand

    E-Print Network [OSTI]

    Demaine, Erik

    Approximability of Partitioning Graphs with Supply and Demand Takehiro Ito a,, Erik D. Demaine b vertex or a demand vertex and is assigned a positive real number, called the supply or the demand. Each demand vertex can receive "power" from at most one supply vertex through edges in G. One thus wishes

  5. Control on Demand Customizing Control for Each Application

    E-Print Network [OSTI]

    Bhattacharjee, Samrat "Bobby"

    1 Control on Demand Customizing Control for Each Application Abstract Control on demand significant progress has been made in integrated network transport now offering ``bandwidth on demand control needs. In this paradigm paper we propose an architecture for control on demand. We de­ fine

  6. Approximability of Partitioning Graphs with Supply and Demand

    E-Print Network [OSTI]

    Demaine, Erik

    Approximability of Partitioning Graphs with Supply and Demand (Extended Abstract) Takehiro Ito1 vertex or a demand vertex and is assigned a positive real number, called the supply or the demand. Each demand vertex can receive "power" from at most one supply vertex through edges in G. One thus wishes

  7. DESIGN OF A SCALABLE VIDEO ON DEMAND ARCHITECTURE Philip Machanick

    E-Print Network [OSTI]

    Machanick, Philip

    DESIGN OF A SCALABLE VIDEO ON DEMAND ARCHITECTURE Philip Machanick Department of Computer Science Architecture for Video on Demand) approach to video on demand exploits the fact that end-user latency goals information across multiple users. Unlike other video on demand (VoD) approaches, SAVoD broadcasts a fixed

  8. Demand-Driven Type Inference with Subgoal Pruning

    E-Print Network [OSTI]

    Tobin-Hochstadt, Sam

    Preemptive & On-Demand IE Andrea Heyl Saarland University January, 18, 2007 #12;Introduction On-Demand Information Extraction Preemptive Information Extraction Evaluation Conclusions 1 Introduction 2 On-Demand Information Extraction 3 Preemptive Information Extraction 4 Evaluation 5 Conclusions #12;Introduction On-Demand

  9. Cuckoo: Scaling Microblogging Services with Divergent Traffic Demands.

    E-Print Network [OSTI]

    Fu, Xiaoming

    Airline Pilot Demand Projections #12;What this is- A Model of Pilot Demand for United States/Destination Airline Pilot Demand SouthWest, United, AA, JetBlue, Etc Airlines with their own branded marketing Retirements (note- if they retire early it just moves up the need for their replacement and adds little demand

  10. Bottleneck Links, Variable Demand, and the Tragedy of the Commons

    E-Print Network [OSTI]

    Dodis, Yevgeniy

    Bottleneck Links, Variable Demand, and the Tragedy of the Commons Richard Cole #3; Yevgeniy Dodis y a fixed demand to resources whose performance degrades with increasing congestion. While fundamental of a resource and the demand for that resource. This coupling motivates allowing demand to vary with congestion

  11. Museum-on-Demand: Dynamic management of resources

    E-Print Network [OSTI]

    Celentano, Augusto

    A DEMAND-DRIVEN APPROACH FOR EFFICIENT INTERPROCEDURAL DATA FLOW ANALYSIS by Evelyn Duesterwald M Duesterwald 1996 ii #12;A DEMAND-DRIVEN APPROACH FOR EFFICIENT INTERPROCEDURAL DATA FLOW ANALYSIS Evelyn to interprocedural data ow analysis that is demand-driven rather than exhaus- tive. Demand-driven analysis reduces

  12. Approximability of Partitioning Graphs with Supply and Demand

    E-Print Network [OSTI]

    Demaine, Erik

    Approximability of Partitioning Graphs with Supply and Demand (Extended Abstract) Takehiro Ito 1 vertex or a demand vertex and is assigned a positive real number, called the supply or the demand. Each demand vertex can receive ``power'' from at most one supply vertex through edges in G. One thus wishes

  13. The Role of Demand Response Policy Forum Series

    E-Print Network [OSTI]

    California at Davis, University of

    The Role of Demand Response Policy Forum Series Beyond 33 Percent: California's Renewable Future and Demand Response #12;Historic focus on Seasonal Grid Stress PG&E Demand Bid Test Day 0 2000 4000 6000 8000 Communication Latency #12;Bottom Up Review of End-Use Loads for Demand Response 5 Commercial Residential

  14. Autonomous Demand Response in Heterogeneous Smart Grid Topologies

    E-Print Network [OSTI]

    Mohsenian-Rad, Hamed

    1 Autonomous Demand Response in Heterogeneous Smart Grid Topologies Hamed Narimani and Hamed-mails: narimani-hh@ec.iut.ac.ir and hamed@ee.ucr.edu Abstract--Autonomous demand response (DR) is scalable and has demand response systems in heterogeneous smart grid topologies. Keywords: Autonomous demand response

  15. FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS

    E-Print Network [OSTI]

    Keller, Arturo A.

    Winter (November - April) water demand Developed by Limaye et al. 1993 Residential water demand ­ f {PPHFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil resources resulting in water stress. Effective water management ­ a solution Supply side management Demand

  16. Price-responsive demand management for a smart grid world

    SciTech Connect (OSTI)

    Chao, Hung-po

    2010-01-15

    Price-responsive demand is essential for the success of a smart grid. However, existing demand-response programs run the risk of causing inefficient price formation. This problem can be solved if each retail customer could establish a contract-based baseline through demand subscription before joining a demand-response program. (author)

  17. AUTOMATION OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S.

    E-Print Network [OSTI]

    Povinelli, Richard J.

    AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand of the energy demand forecasting are achieved by integrating nonlinear transformations within the models

  18. 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 networks, Neural networks, Modeling, Forecasting, Energy demand, Time series forecasting, Power system demand time series based only on data for six years to forecast the demand for the seventh year. Both

  19. Electricity Markets Meet the Home through Demand Response Lazaros Gkatzikis

    E-Print Network [OSTI]

    Electricity Markets Meet the Home through Demand Response Lazaros Gkatzikis CERTH, University Hegde, Laurent Massouli´e Technicolor Paris Research Lab Paris, France Abstract-- Demand response (DR the alternative option of dynamic demand adaptation. In this direction, demand response (DR) programs provide

  20. A Simulation Study of Demand Responsive Transit System Design

    E-Print Network [OSTI]

    Dessouky, Maged

    A Simulation Study of Demand Responsive Transit System Design Luca Quadrifoglio, Maged M. Dessouky changed the landscape for demand responsive transit systems. First, the demand for this type of transit experiencing increased usage for demand responsive transit systems. The National Transit Summaries and Trends

  1. NGNP: High Temperature Gas-Cooled Reactor Key Definitions, Plant Capabilities, and Assumptions

    SciTech Connect (OSTI)

    Phillip Mills

    2012-02-01

    This document is intended to provide a Next Generation Nuclear Plant (NGNP) Project tool in which to collect and identify key definitions, plant capabilities, and inputs and assumptions to be used in ongoing efforts related to the licensing and deployment of a high temperature gas-cooled reactor (HTGR). These definitions, capabilities, and assumptions are extracted from a number of sources, including NGNP Project documents such as licensing related white papers [References 1-11] and previously issued requirement documents [References 13-15]. Also included is information agreed upon by the NGNP Regulatory Affairs group's Licensing Working Group and Configuration Council. The NGNP Project approach to licensing an HTGR plant via a combined license (COL) is defined within the referenced white papers and reference [12], and is not duplicated here.

  2. Optimal Control of Distributed Energy Resources and Demand Response under Uncertainty

    E-Print Network [OSTI]

    Siddiqui, Afzal

    2010-01-01

    follows: • EDemand t : electricity demand during day t (incost of reducing electricity demand (in $/MWh e ) • HRDCost:maximum fraction of electricity demand to be met by demand

  3. What China Can Learn from International Experiences in Developing a Demand Response Program

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    2012. Addressing Electricity Demand through Demand Response:has been driving up the electricity demand while widespreadexperiences in addressing electricity demand This section is

  4. Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast

    E-Print Network [OSTI]

    Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast Energy Demand .............................................................. 23 Electricity Demand Growth in the West............................................................................................................................... 28 Estimating Electricity Demand in Data Centers

  5. Initial Type Assumption A0 A0(x) = a. a for all x V

    E-Print Network [OSTI]

    Ábrahám, Erika

    Initial Type Assumption A0 A0(x) = a. a for all x V A0(c) = pre-defined type schema in haskell, for all c C0 A0(constr) = (type1 . . . typen (tyconstr a1 . . . am)), A0(bot) = a. a A0(isa fails because of a failing unification problem. Let c C V. · W( A + {c :: a1, . . . , an. }, c

  6. Initial Type Assumption A0 A0(x) = a. a for all x V

    E-Print Network [OSTI]

    Ábrahám, Erika

    Initial Type Assumption A0 A0(x) = a. a for all x V A0(c) = pre-defined type schema in haskell, for all c C0 A0(constr) = (type1 . . . typen (tyconstr a1 . . . am)), A0(bot) = a. a A0(if) = a. Bool a pair (, ) or the computation fails because of a failing unification problem. Let c C V. · W( A + {c

  7. New Demand for Old Food: the U.S. Demand for Olive Oil Bo Xiong, William Matthews, Daniel Sumner

    E-Print Network [OSTI]

    Schladow, S. Geoffrey

    New Demand for Old Food: the U.S. Demand for Olive Oil Bo Xiong, William Matthews, Daniel Sumner, demand for oils differentiated by origin and quality is price-elastic. These olive oils are highly of olive oil and the spread of Mediterranean diet contribute significantly to the rising demand

  8. Bracket for photovoltaic modules

    DOE Patents [OSTI]

    Ciasulli, John; Jones, Jason

    2014-06-24

    Brackets for photovoltaic ("PV") modules are described. In one embodiment, a saddle bracket has a mounting surface to support one or more PV modules over a tube, a gusset coupled to the mounting surface, and a mounting feature coupled to the gusset to couple to the tube. The gusset can have a first leg and a second leg extending at an angle relative to the mounting surface. Saddle brackets can be coupled to a torque tube at predetermined locations. PV modules can be coupled to the saddle brackets. The mounting feature can be coupled to the first gusset and configured to stand the one or more PV modules off the tube.

  9. Module No: 410336Personal Statutes for Non-Module Title

    E-Print Network [OSTI]

    Module No: 410336Personal Statutes for Non- Muslims Module Title: Co-requisite:Introduction of Islamic Jurisprudence Pre-requisite: Module Type: specialization requirementModule level: Third Year Academic rank Module coordinator 307384Assistant Professor Dr. Fuad Sartawi ResearchTutorial Guidance

  10. Module No: 410319Copyrights and Neighboring Module Title

    E-Print Network [OSTI]

    Module No: 410319Copyrights and Neighboring Rights Module Title: Co-requisite:Effects of ObligationsPre-requisite: Module Type: specialization elective requirementModule level: Third Year Evening Academic rank Module coordinator e-bataineh@philadelphia.edu.joAssistant Professor Dr. Iyad Bataineh

  11. Home Network Technologies and Automating Demand Response

    SciTech Connect (OSTI)

    McParland, Charles

    2009-12-01

    Over the past several years, interest in large-scale control of peak energy demand and total consumption has increased. While motivated by a number of factors, this interest has primarily been spurred on the demand side by the increasing cost of energy and, on the supply side by the limited ability of utilities to build sufficient electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in electricity use through the use of price incentives. DR systems are also be design to shift or curtail energy demand at critical times when the generation, transmission, and distribution systems (i.e. the 'grid') are threatened with instabilities. To be effectively deployed on a large-scale, these proposed DR systems need to be automated. Automation will require robust and efficient data communications infrastructures across geographically dispersed markets. The present availability of widespread Internet connectivity and inexpensive, reliable computing hardware combined with the growing confidence in the capabilities of distributed, application-level communications protocols suggests that now is the time for designing and deploying practical systems. Centralized computer systems that are capable of providing continuous signals to automate customers reduction of power demand, are known as Demand Response Automation Servers (DRAS). The deployment of prototype DRAS systems has already begun - with most initial deployments targeting large commercial and industrial (C & I) customers. An examination of the current overall energy consumption by economic sector shows that the C & I market is responsible for roughly half of all energy consumption in the US. On a per customer basis, large C & I customers clearly have the most to offer - and to gain - by participating in DR programs to reduce peak demand. And, by concentrating on a small number of relatively sophisticated energy consumers, it has been possible to improve the DR 'state of the art' with a manageable commitment of technical resources on both the utility and consumer side. Although numerous C & I DR applications of a DRAS infrastructure are still in either prototype or early production phases, these early attempts at automating DR have been notably successful for both utilities and C & I customers. Several factors have strongly contributed to this success and will be discussed below. These successes have motivated utilities and regulators to look closely at how DR programs can be expanded to encompass the remaining (roughly) half of the state's energy load - the light commercial and, in numerical terms, the more important residential customer market. This survey examines technical issues facing the implementation of automated DR in the residential environment. In particular, we will look at the potential role of home automation networks in implementing wide-scale DR systems that communicate directly to individual residences.

  12. Approved Module Information for PD2003, 2014/5 Module Title/Name: Engineering Principles 2 Module Code: PD2003

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PD2003, 2014/5 Module Title/Name: Engineering Principles 2 Module Code: PD2003 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module? No Module Dependancies Pre-requisites: Engineering Principles (PD1803). Co-requisites: None Specified Module

  13. Approved Module Information for BF2210, 2014/5 Module Title/Name: Making Managerial Decisions Module Code: BF2210

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF2210, 2014/5 Module Title/Name: Making Managerial Decisions Module Code: BF2210 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Florian Gebreiter Email Address gebreif1

  14. Approved Module Information for LPM040, 2014/5 Module Title/Name: Rethinking European Integration Module Code: LPM040

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LPM040, 2014/5 Module Title/Name: Rethinking European Integration Module Code: LPM040 School: Languages and Social Sciences Module Type: Standard Module New Module? Yes Module Credits: 20 Module Management Information Module Leader Name Nathaniel Copsey Email Address n

  15. Approved Module Information for LEM039, 2014/5 Module Title/Name: Grammar Module Code: LEM039

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LEM039, 2014/5 Module Title/Name: Grammar Module Code: LEM039 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 20 Module Management Information Module Leader Name Urszula Clark Email Address u

  16. Approved Module Information for LS3006, 2014/5 Module Title/Name: Hispanic Film Module Code: LS3006

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LS3006, 2014/5 Module Title/Name: Hispanic Film Module Code: LS3006 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 10 Module Management Information Module Leader Name Raquel Medina Email Address r

  17. Approved Module Information for LT2102, 2014/5 Module Title/Name: Inventory Control Module Code: LT2102

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT2102, 2014/5 Module Title/Name: Inventory Control Module Code: LT2102 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name James Stone Email Address j

  18. Approved Module Information for PY2217, 2014/5 Module Title/Name: Personality Practical (JH) Module Code: PY2217

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY2217, 2014/5 Module Title/Name: Personality Practical (JH) Module Code: PY2217 School: Life and Health Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Ed Walford Email Address e

  19. Approved Module Information for BS3347, 2014/5 Module Title/Name: Economics of Entrepreneurship Module Code: BS3347

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BS3347, 2014/5 Module Title/Name: Economics of Entrepreneurship Module Code: BS3347 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Anna Rebmann Email Address rebmanna

  20. Approved Module Information for PY3351, 2014/5 Module Title/Name: Child Development Module Code: PY3351

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY3351, 2014/5 Module Title/Name: Child Development Module Code: PY3351 School: Life and Health Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Claire Farrow Email Address farrowc

  1. Approved Module Information for CE2110, 2014/5 Module Title/Name: Process Laboratory Module Code: CE2110

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE2110, 2014/5 Module Title/Name: Process Laboratory Module Code: CE2110 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name John Brammer Email Address brammejg

  2. Approved Module Information for LK2004, 2014/5 Module Title/Name: Global Society Module Code: LK2004

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LK2004, 2014/5 Module Title/Name: Global Society Module Code: LK2004 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Demelza Jones Email Address jonesd4@aston

  3. Approved Module Information for CH3117, 2014/5 Module Title/Name: Literature Research Project Module Code: CH3117

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CH3117, 2014/5 Module Title/Name: Literature Research Project Module Code: CH3117 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Andrew James Sutherland Email Address

  4. Approved Module Information for LE1008, 2014/5 Module Title/Name: Grammar & Meaning Module Code: LE1008

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LE1008, 2014/5 Module Title/Name: Grammar & Meaning Module Code: LE1008 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 10 Module Management Information Module Leader Name Jack Grieve Email Address grievej1

  5. Approved Module Information for BN3385, 2014/5 Module Title/Name: Effective Project Delivery Module Code: BN3385

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BN3385, 2014/5 Module Title/Name: Effective Project Delivery Module Code: BN3385 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Panagiotis Petridis Email Address petridip

  6. Approved Module Information for BS1163, 2014/5 Module Title/Name: Introduction to Microeconomics Module Code: BS1163

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BS1163, 2014/5 Module Title/Name: Introduction to Microeconomics Module Code: BS1163 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Morris Email Address morrisd5@aston

  7. Approved Module Information for LEM016, 2014/5 Module Title/Name: Methodology Module Code: LEM016

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LEM016, 2014/5 Module Title/Name: Methodology Module Code: LEM016 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Muna Morris-Adams Email Address adamsmm

  8. Approved Module Information for BF2251, 2014/5 Module Title/Name: Financial Management Module Code: BF2251

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF2251, 2014/5 Module Title/Name: Financial Management Module Code: BF2251 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Colin Chapman Email Address chapmac1@aston

  9. Approved Module Information for LT2315, 2014/5 Module Title/Name: Rail Transport Module Code: LT2315

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT2315, 2014/5 Module Title/Name: Rail Transport Module Code: LT2315 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name P Connor Email Address connorp

  10. Approved Module Information for ME2018, 2014/5 Module Title/Name: Quality Engineering Module Code: ME2018

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for ME2018, 2014/5 Module Title/Name: Quality Engineering Module Code: ME2018 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Upton Email Address uptondp

  11. Approved Module Information for LI2008, 2014/5 Module Title/Name: Communication across Cultures Module Code: LI2008

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LI2008, 2014/5 Module Title/Name: Communication across Cultures Module Code: LI2008 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Olga Castro Email Address o

  12. Approved Module Information for CE4018, 2014/5 Module Title/Name: Advanced Particle Processing Module Code: CE4018

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE4018, 2014/5 Module Title/Name: Advanced Particle Processing Module Code: CE4018 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Mark Leaper Email Address m

  13. Approved Module Information for SE4031, 2014/5 Module Title/Name: Extended Integrative Option Module Code: SE4031

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for SE4031, 2014/5 Module Title/Name: Extended Integrative Option Module Code: SE4031 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 30 Module Management Information Module Leader Name Trevor Oliver Email Address t

  14. Approved Module Information for LT1312, 2014/5 Module Title/Name: Literature Review Project Module Code: LT1312

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT1312, 2014/5 Module Title/Name: Literature Review Project Module Code: LT1312 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Carpenter Email Address d

  15. Approved Module Information for LS2017, 2014/5 Module Title/Name: Contemporary Latin America Module Code: LS2017

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LS2017, 2014/5 Module Title/Name: Contemporary Latin America Module Code: LS2017 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Stephanie Panichelli-Batalla Email Address

  16. Approved Module Information for CE3013, 2014/5 Module Title/Name: Particle Processing Module Code: CE3013

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE3013, 2014/5 Module Title/Name: Particle Processing Module Code: CE3013 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Mark Leaper Email Address m

  17. Approved Module Information for LE2057, 2014/5 Module Title/Name: Computer Mediated Communication Module Code: LE2057

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LE2057, 2014/5 Module Title/Name: Computer Mediated Communication Module Code: LE2057 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 20 Module Management Information Module Leader Name Nur Hooton Email Address n

  18. Approved Module Information for BS3325, 2014/5 Module Title/Name: Competition Policy -Theory Module Code: BS3325

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BS3325, 2014/5 Module Title/Name: Competition Policy - Theory Module Code: BS3325 School: Aston Business School Module Type: Standard Module New Module? Yes Module Credits: 10 Module Management Information Module Leader Name Matt Olczak Email Address olczakm

  19. Approved Module Information for BL1179, 2014/5 Module Title/Name: Accounting for Law Module Code: BL1179

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BL1179, 2014/5 Module Title/Name: Accounting for Law Module Code: BL1179 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Angela Stanhope Email Address a

  20. Approved Module Information for BFM120, 2014/5 Module Title/Name: Investment Management Module Code: BFM120

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BFM120, 2014/5 Module Title/Name: Investment Management Module Code: BFM120 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 15 Module Management Information Module Leader Name Colin Chapman Email Address chapmac1@aston

  1. Approved Module Information for PY2216, 2014/5 Module Title/Name: Neuroscience Practicals Module Code: PY2216

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY2216, 2014/5 Module Title/Name: Neuroscience Practicals Module Code: PY2216 School: Life and Health Sciences Module Type: Standard Module New Module? Yes Module Credits: 10 Module Management Information Module Leader Name Ed Walford Email Address e

  2. Approved Module Information for BHM348, 2014/5 Module Title/Name: Employee Relations & Counselling Module Code: BHM348

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BHM348, 2014/5 Module Title/Name: Employee Relations & Counselling Module Code: BHM348 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 15 Module Management Information Module Leader Name M Carter Email Address cartermr

  3. Approved Module Information for LT1307, 2014/5 Module Title/Name: Principles of Economics Module Code: LT1307

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT1307, 2014/5 Module Title/Name: Principles of Economics Module Code: LT1307 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Carpenter Email Address d

  4. Approved Module Information for ME2050, 2014/5 Module Title/Name: Dynamics and Control Module Code: ME2050

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for ME2050, 2014/5 Module Title/Name: Dynamics and Control Module Code: ME2050 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Xianghong Ma Email Address max

  5. Approved Module Information for BHM328, 2014/5 Module Title/Name: Strategic Business Sustainability Module Code: BHM328

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BHM328, 2014/5 Module Title/Name: Strategic Business Sustainability Module Code: BHM328 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 15 Module Management Information Module Leader Name H Borland Email Address borlanhm

  6. Approved Module Information for BF2244, 2014/5 Module Title/Name: Strategic Finance Module Code: BF2244

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF2244, 2014/5 Module Title/Name: Strategic Finance Module Code: BF2244 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Colin Chapman Email Address chapmac1@aston

  7. Approved Module Information for CE3102, 2014/5 Module Title/Name: Reaction Engineering Module Code: CE3102

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE3102, 2014/5 Module Title/Name: Reaction Engineering Module Code: CE3102 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Feroz Kabir Email Address kabirf

  8. Approved Module Information for LE2053, 2014/5 Module Title/Name: Variations of English Module Code: LE2053

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LE2053, 2014/5 Module Title/Name: Variations of English Module Code: LE2053 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 20 Module Management Information Module Leader Name Jack Grieve Email Address grievej1

  9. Approved Module Information for BF3314, 2014/5 Module Title/Name: Derivatives Module Code: BF3314

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF3314, 2014/5 Module Title/Name: Derivatives Module Code: BF3314 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Winifred Huang-Meier Email Address w

  10. Approved Module Information for PY3472, 2014/5 Module Title/Name: Autistic Spectrum Module Code: PY3472

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY3472, 2014/5 Module Title/Name: Autistic Spectrum Module Code: PY3472 School: Life and Health Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Gina Rippon Email Address rippong@aston.ac.uk Telephone

  11. Approved Module Information for CE3112, 2014/5 Module Title/Name: Nanomaterials Module Code: CE3112

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE3112, 2014/5 Module Title/Name: Nanomaterials Module Code: CE3112 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Qingchun Yuan Email Address q.yuan@aston.ac.uk Telephone

  12. Approved Module Information for CE1002, 2014/5 Module Title/Name: Design and Build Module Code: CE1002

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE1002, 2014/5 Module Title/Name: Design and Build Module Code: CE1002 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Paul Andrew Tack Email Address tackpa

  13. Approved Module Information for LG2018, 2014/5 Module Title/Name: Metropolis Berlin Module Code: LG2018

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LG2018, 2014/5 Module Title/Name: Metropolis Berlin Module Code: LG2018 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 10 Module Management Information Module Leader Name Uwe Schütte Email Address u

  14. Approved Module Information for PD2002, 2014/5 Module Title/Name: Commercial Practice Module Code: PD2002

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PD2002, 2014/5 Module Title/Name: Commercial Practice Module Code: PD2002 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Jon Hewitt Email Address Not Specified

  15. Approved Module Information for BN2290, 2014/5 Module Title/Name: Operational Research Techniques Module Code: BN2290

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BN2290, 2014/5 Module Title/Name: Operational Research Techniques Module Code: BN2290 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Ozren Despic Email Address o

  16. Approved Module Information for LT1319, 2014/5 Module Title/Name: Air Transport Module Code: LT1319

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT1319, 2014/5 Module Title/Name: Air Transport Module Code: LT1319 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name James Stone Email Address j.stone@aston.ac.uk Telephone

  17. What is a High Electric Demand Day?

    Broader source: Energy.gov [DOE]

    This presentation by T. McNevin of the New Jersey Bureau of Air Quality Planning was part of the July 2008 Webcast sponsored by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Weatherization and Intergovernmental Program Clean Energy and Air Quality Integration Initiative that was titled Role of Energy Efficiency and Renewable Energy in Improving Air Quality and Addressing Greenhouse Gas Reduction Goals on High Electric Demand Days.

  18. A hybrid inventory management system respondingto regular demand and surge demand

    SciTech Connect (OSTI)

    Mohammad S. Roni; Mingzhou Jin; Sandra D. Eksioglu

    2014-06-01

    This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.

  19. Encapsulation of High Temperature Thermoelectric Modules | Department...

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

    Encapsulation of High Temperature Thermoelectric Modules Encapsulation of High Temperature Thermoelectric Modules Presents concept for hermetic encapsulation of TE modules...

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

    SciTech Connect (OSTI)

    NONE

    1996-04-30

    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.