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1

Capacity Factor Risk At Nuclear Power Plants  

E-Print Network (OSTI)

We develop a model of the dynamic structure of capacity factor risk. It incorporates the risk that the capacity factor may vary widely from year-to-year, and also the risk that the reactor may be permanently shutdown prior ...

Du, Yangbo

2

Definition: Capacity factor | Open Energy Information  

Open Energy Info (EERE)

power)12 View on Wikipedia Wikipedia Definition The net capacity factor of a power plant is the ratio of its actual output over a period of time, to its potential output if...

3

EIA - Assumptions to the Annual Energy Outlook 2009 - Coal Market...  

Annual Energy Outlook 2012 (EIA)

of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply costs. The key assumptions underlying the coal production modeling are: As capacity...

4

NREL: Energy Analysis - Utility-Scale Energy Technology Capacity Factors  

NLE Websites -- All DOE Office Websites (Extended Search)

Utility-Scale Energy Technology Capacity Factors Utility-Scale Energy Technology Capacity Factors This chart indicates the range of recent capacity factor estimates for utility-scale renewable energy technologies. The dots indicate the average, and the vertical lines represent the range: Average +1 standard deviation and average -1 standard deviation. If you are seeking utility-scale technology cost and performance estimates, please visit the Transparent Cost Database website for NREL's information regarding vehicles, biofuels, and electricity generation. Capital Cost (September 2013 Update) Operations & Maintenance (September 2013 Update) Utility-Scale Capacity Factors Useful Life Land Use by System Technology LCOE Calculator Capacity factor for energy technologies. For more information, please download supporting data for energy technology costs.

5

November 2010CAPACITY FACTOR RISK AT NUCLEAR POWER PLANTS  

E-Print Network (OSTI)

We develop a model of the dynamic structure of capacity factor risk. It incorporates the risk that the capacity factor may vary widely from year-to-year, and also the risk that the reactor may be permanently shutdown prior to the end of its anticipated useful life. We then fit the parameters of the model to the IAEA’s PRIS dataset of historical capacity factors on reactors across the globe. The estimated capacity factor risk is greatest in the first year of operation. It then quickly declines over the next couple of years, after which it is approximately constant. Whether risk is constant or increasing in later years depends significantly on the probability of a premature permanent shutdown of the reactor. Because these should be very rare events, the probability is difficult to estimate reliably from the small historical sample of observations. Our base case is parameterized with a conservatively low probability of a premature permanent shutdown which yields the approximately constant variance. Our model, combined with the global historical dataset, also yields relatively low estimates for the expected level of the capacity factor through the life of the plant. Our base case estimate is approximately 74%. Focusing on alternative subsets of the data raises the estimated mean capacity factor marginally, but not significantly, unless the sample chosen is restricted to selected countries over select years. This emphasizes the need for judgment in exploiting the historical data to project future probabilities.

Yangbo Du; John E. Parsons; Yangbo Du; John E. Parsons

2010-01-01T23:59:59.000Z

6

AEO Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for the for the Annual Energy Outlook 1997 December 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Energy Information Administration/Assumptions for the Annual Energy Outlook 1997 Contents Page Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Commercial Demand Module . . . . . . . . . . . . . . . . . .

7

Wind turbine cost of electricity and capacity factor  

Science Conference Proceedings (OSTI)

Wind turbines are currently designed to minimize the cost of electricity at the wind turbine (the busbar cost) in a given wind regime, ignoring constraints on the capacity factor (the ratio of the average power output to the maximum power output). The trade-off between these two quantities can be examined in a straightforward fashion; it is found that the capacity factor can be increased by a factor of 30 percent above its value at the cost minimum for a ten percent increase in the busbar cost of electricity. This has important implications for the large-scale integration of wind electricity on utility grids where the cost of transmission may be a significant fraction of the cost of delivered electricity, or where transmission line capacity may be limited.

Cavallo, A.J. [Cavallo (A.J.), Princeton, NJ (United States)

1997-11-01T23:59:59.000Z

8

Monthly generator capacity factor data now available by ...  

U.S. Energy Information Administration (EIA)

weather; gasoline; capacity; exports; nuclear; forecast; ... Solar generators—particularly solar thermal—operate at a minimum during winter months, ...

9

A Review of Electric Vehicle Cost Studies: Assumptions, Methodologies, and Results  

E-Print Network (OSTI)

assumptions Battery costs and capacities: Lead acid batteryElectricity cost Battery cost and capacity: Lead acidElectricity cost Battery cost and capacity: N i C d

Lipman, Timothy

1999-01-01T23:59:59.000Z

10

Cost and Performance Assumptions for Modeling Electricity Generation Technologies  

Science Conference Proceedings (OSTI)

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.

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

2010-11-01T23:59:59.000Z

11

Refinery IGCC plants are exceeding 90% capacity factor after 3 years  

SciTech Connect

Steep learning curves for commercial IGCC plants in Italy show annual capacity factors of 55-60% in the first year of service and improvement to over 90% after the third year. The article reviews the success of three IGCC projects in Italy - those of ISAB Energy, Sarlux Saras and Api Energy. EniPower is commissioning a 250 MW IGCC plant that will burn syngas produced by gasification of residues at an adjacent Eni Sannazzaro refinery in north central Italy. The article lists 14 commercially operating IGCC plants worldwide that together provide close to 3900 MW of generating capacity. These use a variety of feedstock-coals, petroleum coke and refinery residues and biomass. Experience with commercial scale plants in Europe demonstrates that IGCC plants can operate at capacity factors comparable to if not better than conventional coal plants. 2 figs., 1 photo.

Jaeger, H.

2006-01-15T23:59:59.000Z

12

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2003, DOE/EIA-M060(2003) (Washington, DC, January 2003). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

13

State and National Wind Resource Potential at Various Capacity Factor Ranges for 80 and 100 Meters  

Wind Powering America (EERE)

February 4, 2010 (updated April 13, 2011 to add Alaska and Hawaii) February 4, 2010 (updated April 13, 2011 to add Alaska and Hawaii) State Total (km 2 ) Excluded 2 (km 2 ) Available (km 2 ) Available % of State % of Total Windy Land Excluded Installed Capacity 3 (MW) Annual Generation (GWh) Alabama 15.9 13.3 2.6 0.00% 83.4% 13.2 42 Alaska 267,897.7 209,673.4 58,224.3 3.87% 78.3% 291,121.3 1,051,210 Arizona 611.7 417.3 194.4 0.07% 68.2% 972.1 3,100 Arkansas 1,130.0 687.5 442.5 0.32% 60.8% 2,212.5 7,215 C lif i 11 456 4 8 650 1 2 806 3 0 69% 75 5% 14 031 7 49 073 Estimates of Windy 1 Land Area and Wind Energy Potential, by State, for areas >= 35% Capacity Factor at 80m These estimates show, for each of the 50 states and the total U.S., the windy land area with a gross capacity factor (without losses) of 35% and greater at 80-m height above ground and the wind energy potential that could be possible from development of the "available" windy land area

14

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA-M060(2007) (Washington, 7, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

15

Dynamic valuation model For wind development in regard to land value, proximity to transmission lines, and capacity factor  

E-Print Network (OSTI)

Developing a wind farm involves many variables that can make or break the success of a potential wind farm project. Some variables such as wind data (capacity factor, wind rose, wind speed, etc.) are readily available in ...

Nikandrou, Paul

2009-01-01T23:59:59.000Z

16

Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumption to the Annual Energy Outlook Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2004, DOE/EIA-M060(2004) (Washington, DC, 2004). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

17

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

1 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Petroleum Market Module. . . . . . . . . . . . .

18

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

to the to the Annual Energy Outlook 1998 December 1997 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Household Expenditures Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Oil and Gas Supply Module

19

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2003, DOE/EIA-M068(2003) April 2003. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

20

Annual Energy Outlook 96 Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for for the Annual Energy Outlook 1996 January 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 1996 (AEO96). In this context, assumptions include general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports listed in the Appendix. 1 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview. The National Energy Modeling System The projections

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

22

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.106

23

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, and (4) the implementation of recent regulatory reform. A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2003, DOE/EIA- M062(2003) (Washington, DC, January 2003).

24

Idaho National Engineering Laboratory installation roadmap assumptions document. Revision 1  

SciTech Connect

This document is a composite of roadmap assumptions developed for the Idaho National Engineering Laboratory (INEL) by the US Department of Energy Idaho Field Office and subcontractor personnel as a key element in the implementation of the Roadmap Methodology for the INEL Site. The development and identification of these assumptions in an important factor in planning basis development and establishes the planning baseline for all subsequent roadmap analysis at the INEL.

Not Available

1993-05-01T23:59:59.000Z

25

The disciplined use of simplifying assumptions  

Science Conference Proceedings (OSTI)

Simplifying assumptions --- everyone uses them but no one's programming tool explicitly supports them. In programming, as in other kinds of engineering design, simplifying assumptions are an important method for dealing with complexity. Given a complex ...

Charles Rich; Richard C. Waters

1982-04-01T23:59:59.000Z

26

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Macroeconomic Activity Module Macroeconomic Activity Module This page inTenTionally lefT blank 17 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents interactions between the U.S. economy and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP), is a key determinant of growth in the demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected

27

Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumption to the Annual Energy Outlook Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2004, DOE/EIA- M068(2004). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

28

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2006 The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2006, DOE/EIA- M068(2006). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

29

EIA - Assumptions to the Annual Energy Outlook 2008 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2008 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2008, DOE/EIA-M068(2008). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

30

Assumptions to the Annual Energy Outlook 2012  

U.S. Energy Information Administration (EIA)

Assumptions to the Annual Energy Outlook 2012 August 2012 www.eia.gov U.S. Department of Energy Washington, DC 20585

31

EIA - Assumptions to the Annual Energy Outlook 2008 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2008 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2008, DOE/EIA-M060(2008) (Washington, DC, 2008). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

32

EIA-Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2007 Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2007, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

33

EIA - Assumptions to the Annual Energy Outlook 2010 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2010 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2010, DOE/EIA-M060(2010) (Washington, DC, 2010). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply costs.

34

EIA - Assumptions to the Annual Energy Outlook 2009 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2009 Electricity Market Module figure 6. Electricity Market Model Supply Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2009, DOE/EIA-M068(2009). Based on fuel prices and electricity demands provided by the other modules

35

EIA - Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

7 7 Assumptions to the Annual Energy Outlook 2007 This report summarizes the major assumptions used in the NEMS to generate the AEO2007 projections. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800.

36

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

States. States. OGSM encompasses domestic crude oil and natural gas supply by both conventional and nonconventional recovery techniques. Nonconventional recovery includes unconventional gas recovery from low permeability formations of sandstone and shale, and coalbeds. Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 93 Figure 7. Oil and Gas Supply Model Regions Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Report #:DOE/EIA-0554(2007) Release date: April 2007 Next release date: March 2008 Primary inputs for the module are varied. One set of key assumptions concerns estimates of domestic technically recoverable oil and gas resources. Other factors affecting the projection include the assumed

37

Climate Action Planning Tool Formulas and Assumptions  

NLE Websites -- All DOE Office Websites (Extended Search)

CLIMATE ACTION PLANNING TOOL FORMULAS AND ASSUMPTIONS Climate Action Planning Tool Formulas and Assumptions The Climate Action Planning Tool calculations use the following formulas and assumptions to generate the business-as-usual scenario and the greenhouse gas emissions reduction goals for the technology options. Business-as-Usual Scenario All Scope 1 (gas, oil, coal, fleet, and electricity) and Scope 2 calculations increase at a rate equal to the building growth rate. Scope 3 calculations (commuters and business travel) increase at a rate equal to the population growth rate. Assumptions New buildings will consume energy at the same rate (energy use intensity) as existing campus buildings. Fleet operations will be proportional to total building area.

38

Hierarchy of Mesoscale Flow Assumptions and Equations  

Science Conference Proceedings (OSTI)

The present research proposes a standard nomenclature for mesoscale meteorological concepts and integrates existing concepts of atmospheric space scales, flow assumptions, governing equations, and resulting motions into a hierarchy useful in ...

P. Thunis; R. Bornstein

1996-02-01T23:59:59.000Z

39

Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumption to the Annual Energy Outlook Petroleum Market Module Figure 8. Petroleum Administration for Defense Districts. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohols, ethers, and bioesters natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of U.S. refining

40

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Introduction Introduction This page inTenTionally lefT blank 3 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S.

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

Assumptions to the Annual Energy Outlook 2000 - Electricity Market Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. The major assumptions are summarized below.

42

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Assumptions to the Annual Assumptions to the Annual Energy Outlook 2013 May 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Table of Contents Introduction .................................................................................................................................................. 3

43

Assumptions to the Annual Energy Outlook 2008  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Release date: June 2008 Next release date: March 2009 Assumptions to the Annual Energy Outlook 2008 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 113 Petroleum Market Module

44

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Energy Module Energy Module This page inTenTionally lefT blank 21 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 International Energy Module The LFMM International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the LFMM IEM computes BRENT and WTI prices, provides a supply curve of world crude-like liquids, and generates a worldwide oil supply- demand balance with regional detail. The IEM also provides, for each year of the projection period, endogenous and

45

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2004 Assumptions to the Annual Energy Outlook 2004 143 Appendix A: Handling of Federal and Selected State Legislation and Regulation in the Annual Energy Outlook Legislation Brief Description AEO Handling Basis Residential Sector A. National Appliance Energy Conservation Act of 1987 Requires Secretary of Energy to set minimum efficiency standards for 10 appliance categories a. Room Air Conditioners Current standard of 8.82 EER Federal Register Notice of Final Rulemaking, b. Other Air Conditioners (<5.4 tons) Current standard 10 SEER for central air conditioner and heat pumps, increasing to 12 SEER in 2006. Federal Register Notice of Final Rulemaking, c. Water Heaters Electric: Current standard .86 EF, incr easing to .90 EF in 2004. Gas: Curren

46

Assumptions to the Annual Energy Outlook - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumption to the Annual Energy Outlook Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20041 (AEO2004), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every two years. The National Energy Modeling System The projections in the AEO2004 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers in the U.S. Congress, the Administration, including DOE Program Offices, and other government agencies.

47

EIA - Assumptions to the Annual Energy Outlook 2009 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2009 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy.

48

Assumptions to the Annual Energy Outlook 1999 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

petroleum.gif (4999 bytes) petroleum.gif (4999 bytes) The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below. 75

49

EIA - Assumptions to the Annual Energy Outlook 2010 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2010 Petroleum Market Module The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. Figure 9. Petroleum Administration for Defense Districts. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9),

50

EIA - Assumptions to the Annual Energy Outlook 2008 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2008 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

51

Assumptions to the Annual Energy Outlook 2000 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2000, DOE/EIA-M062(2000), January 2000.

52

Assumptions to the Annual Energy Outlook 1999 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by obtaining market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation Report: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, DOE/EIA-MO62/1, January 1999.

53

Assumptions to Annual Energy Outlook - Energy Information Administrati...  

Annual Energy Outlook 2012 (EIA)

Assumptions to AEO2013 Release Date: May 14, 2013 | Next Release Date: May 2014 | full report Introduction This report presents the major assumptions of the National Energy...

54

Assumptions to the Annual Energy Outlook - Table 41  

Annual Energy Outlook 2012 (EIA)

> Forecasts >Assumptions to the Annual Energy Outlook> Download Report Assumption to the Annual Energy Outlook Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF...

55

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20031 (AEO2003), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2003 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of EnergyÂ’s Office of Policy and International Affairs, other DOE offices, and other government agencies.

56

Assumptions to the Annual Energy Outlook 2000 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.100

57

Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Tools Tools Printable Version Share this resource Send a link to Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology to someone by E-mail Share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Facebook Tweet about Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Twitter Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Google Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Delicious Rank Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Digg Find More places to share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on AddThis.com...

58

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Demand Module Demand Module This page inTenTionally lefT blank 39 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2040. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial.

59

Assumptions to the Annual Energy Outlook - Contacts  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts Assumption to the Annual Energy Outlook Contacts Specific questions about the information in this report may be directed to: Introduction Paul D. Holtberg 202/586-1284 Macroeconomic Activity Module Ronald F. Earley Yvonne Taylor 202/586-1398 202/586-1398 International Energy Module G. Daniel Butler 202/586-9503 Household Expenditures Module/ Residential Demand Module John H. Cymbalsky 202/586-4815 Commercial Demand Module Erin E. Boedecker 202/586-4791 Industrial Demand Module T. Crawford Honeycutt 202/586-1420 Transportation Demand Module John D. Maples 202/586-1757 Electricity Market Module Laura Martin 202/586-1494 Oil and Gas Supply Module/Natural Gas Transmission and Distribution Module Joseph Benneche 202/586-6132 Petroleum Market Module Bill Brown 202/586-8181

60

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Demand Module Demand Module This page inTenTionally lefT blank 27 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" (UEC) by appliance (in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Industrial Demand Module Industrial Demand Module This page inTenTionally lefT blank 53 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Industrial Demand Module The NEMS Industrial Demand Module (IDM) estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are subdivided further into the energy- intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure. The non-manufacturing industries are modeled with less detail because processes are simpler and there is less available data. The petroleum refining

62

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division. Key Assumptions The historical input data used to develop the HEM version for the AEO2003 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2003 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS).

63

An Assessment of Railway Capacity  

E-Print Network (OSTI)

In this paper, we review the main concepts and methods to perform capacity analyses, and we present an automated tool that is able to perform several capacity analyses. Capacity is extremely dependent on infrastructure, traffic, and operating parameters. Therefore, an in-depth study of the main factors that influence railway capacity is performed on several Spanish railway infrastructures. The results show how the capacity varies according to factors such as train speed, commercial stops, train heterogeneity, distance between railway signals, and timetable robustness.

M. Abril; F. Barber; A L. Ingolotti; A M. A. Salido; P. Tormos; B A. Lova

2007-01-01T23:59:59.000Z

64

GENERATING CAPACITY  

E-Print Network (OSTI)

Evidence from the U.S. and some other countries indicates that organized wholesale markets for electrical energy and operating reserves do not provide adequate incentives to stimulate the proper quantity or mix of generating capacity consistent with mandatory reliability criteria. A large part of the problem can be associated with the failure of wholesale spot market prices for energy and operating reserves to rise to high enough levels during periods when generating capacity is fully utilized. Reforms to wholesale energy markets, the introduction of well-design forward capacity markets, and symmetrical treatment of demand response and generating capacity resources to respond to market and institutional imperfections are discussed. This policy reform program is compatible with improving the efficiency of spot wholesale electricity markets, the continued evolution of competitive retail markets, and restores incentives for efficient investment in generating capacity consistent with operating reliability criteria applied by system operators. It also responds to investment disincentives that have been associated with volatility in wholesale energy prices, limited hedging opportunities and to concerns about regulatory opportunism. 1

Paul L. Joskow; Paul L. Joskow; Paul L. Joskow

2006-01-01T23:59:59.000Z

65

Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumption to the Annual Energy Outlook Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).109 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

66

EIA - Assumptions to the Annual Energy Outlook 2008 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2008 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

67

EIA-Assumptions to the Annual Energy Outlook - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2007 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

68

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2010 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Document>ation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2009), (Washington, DC, January 2009).

69

Assumptions to the Annual Energy Outlook 1999 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

coal.gif (4423 bytes) coal.gif (4423 bytes) The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Model Documentation: Coal Market Module of the National Energy Modeling System, DOE/EIA-MO60. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

70

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7), 7), (Washington, DC, January 2007). Key Assumptions The output of the U.S. economy, measured by GDP, is expected to increase by 2.9 percent between 2005 and 2030 in the reference case. Two key factors help explain the growth in GDP: the growth rate of nonfarm employment and the rate of productivity change associated with employment. As Table 3 indicates, for the Reference Case GDP growth slows down in each of the periods identified, from 3.0 percent between 2005 and 2010, to 2.9 percent between 2010 and 2020, to 2.8 percent in the between 2020 and 2030. In the near term from 2005 through 2010, the growth in nonfarm employment is low at 1.2 percent compared with 2.4 percent in the second half of the 1990s, while the economy is expected to experiencing relatively strong

71

EIA - Assumptions to the Annual Energy Outlook 2008 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2008 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

72

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2009 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2008), (Washington, DC, January 2008).

73

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).119 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

74

EIA-Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2007 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules-electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2007, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

75

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2003), (Washington, DC, January 2003).

76

EIA - Assumptions to the Annual Energy Outlook 2010  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2010 This report summarizes the major assumptions used in the NEMS to generate the AEO2010 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions Download the Report Assumptions to the Annual Energy Outlook 2010 Report Cover. Need help, contact the National Energy Information Center at 202-586-8800.

77

Assumptions to the Annual Energy Outlook - Macroeconomic Activity...  

Annual Energy Outlook 2012 (EIA)

Macroeconomic Activity Module Assumption to the Annual Energy Outlook Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the...

78

Assumption-Commitment Support for CSP Model Checking  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

79

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

6 6 Assumptions to the Annual Energy Outlook 2006 This report presents major assumptions of NEMS that are used to generate the projections in the AEO2006. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800.

80

North Dakota Refining Capacity Study  

Science Conference Proceedings (OSTI)

According to a 2008 report issued by the United States Geological Survey, North Dakota and Montana have an estimated 3.0 to 4.3 billion barrels of undiscovered, technically recoverable oil in an area known as the Bakken Formation. With the size and remoteness of the discovery, the question became 'can a business case be made for increasing refining capacity in North Dakota?' And, if so what is the impact to existing players in the region. To answer the question, a study committee comprised of leaders in the region's petroleum industry were brought together to define the scope of the study, hire a consulting firm and oversee the study. The study committee met frequently to provide input on the findings and modify the course of the study, as needed. The study concluded that the Petroleum Area Defense District II (PADD II) has an oversupply of gasoline. With that in mind, a niche market, naphtha, was identified. Naphtha is used as a diluent used for pipelining the bitumen (heavy crude) from Canada to crude markets. The study predicted there will continue to be an increase in the demand for naphtha through 2030. The study estimated the optimal configuration for the refinery at 34,000 barrels per day (BPD) producing 15,000 BPD of naphtha and a 52 percent refinery charge for jet and diesel yield. The financial modeling assumed the sponsor of a refinery would invest its own capital to pay for construction costs. With this assumption, the internal rate of return is 9.2 percent which is not sufficient to attract traditional investment given the risk factor of the project. With that in mind, those interested in pursuing this niche market will need to identify incentives to improve the rate of return.

Dennis Hill; Kurt Swenson; Carl Tuura; Jim Simon; Robert Vermette; Gilberto Marcha; Steve Kelly; David Wells; Ed Palmer; Kuo Yu; Tram Nguyen; Juliam Migliavacca

2011-01-05T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Assumptions to the Annual Energy Outlook 2000 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

82

EIA - Assumptions to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2009 The Early Release for next year's Annual Energy Outlook will be presented at the John Hopkins Kenney Auditorium on December 14th This report summarizes the major assumptions used in the NEMS to generate the AEO2009 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions

83

Capacity Markets for Electricity  

E-Print Network (OSTI)

ternative Approaches for Power Capacity Markets”, Papers andand Steven Stoft, “Installed Capacity and Price Caps: Oil onElectricity Markets Have a Capacity requirement? If So, How

Creti, Anna; Fabra, Natalia

2004-01-01T23:59:59.000Z

84

2. Gas Productive Capacity  

U.S. Energy Information Administration (EIA)

2. Gas Productive Capacity Gas Capacity to Meet Lower 48 States Requirements The United States has sufficient dry gas productive capacity at the wellhead to meet ...

85

Assumptions to Annual Energy Outlook - Energy Information Administration  

U.S. Energy Information Administration (EIA) Indexed Site

Assumptions to AEO2013 Assumptions to AEO2013 Release Date: May 14, 2013 | Next Release Date: May 2014 | full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S. Energy Information Administration (EIA). In addition to its use in developing the Annual

86

Assumptions to Annual Energy Outlook - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to AEO2012 Assumptions to AEO2012 Release Date: August 2, 2012 | Next Release Date: August 2013 | Full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2012 [1] (AEO2012), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in AEO2012 are generated using the NEMS, developed and maintained by the Office of Energy Analysis (OEA) of the U.S. Energy Information Administration (EIA). In addition to its use in developing the

87

Notes 01. The fundamental assumptions and equations of lubrication theory  

E-Print Network (OSTI)

The fundamental assumption in Lubrication Theory. Derivation of thin film flow equations from Navier-Stokes equations. Importance of fluid inertia effects in thin film flows. Some fluid physical properties

San Andres, Luis

2009-01-01T23:59:59.000Z

88

Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations  

Science Conference Proceedings (OSTI)

Using a spectral-type cumulus parameterization that includes moist downdrafts within a three-dimensional mesoscale model, various disparate closure assumptions are systematically tested within the generalized framework of dynamic control, static ...

Georg A. Grell

1993-03-01T23:59:59.000Z

89

Computational soundness for standard assumptions of formal cryptography  

E-Print Network (OSTI)

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 ...

Herzog, Jonathan, 1975-

2004-01-01T23:59:59.000Z

90

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology  

NLE Websites -- All DOE Office Websites (Extended Search)

5 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1 Francis X. Johnson, Richard E. Brown, James W. Hanford, Alan H. Sanstad and...

91

Assumptions to the Annual Energy Outlook 1999 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

bullet1.gif (843 bytes) Feedback link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Interactive Data Queries to the AEO99 bullet1.gif...

92

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2006 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20061 (AEO2006), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every few years. The National Energy Modeling System

93

Assumptions to the Annual Energy Outlook 1999 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

link.gif (1946 bytes) link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Supplemental Tables to the AEO99 bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage introduction.gif (4117 bytes) This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 19991 (AEO99), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3

94

FAQs about Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

about Storage Capacity about Storage Capacity How do I determine if my tanks are in operation or idle or non-reportable? Refer to the following flowchart. Should idle capacity be included with working capacity? No, only report working capacity of tanks and caverns in operation, but not for idle tanks and caverns. Should working capacity match net available shell in operation/total net available shell capacity? Working capacity should be less than net available shell capacity because working capacity excludes contingency space and tank bottoms. What is the difference between net available shell capacity in operation and total net available shell capacity? Net available shell capacity in operation excludes capacity of idle tanks and caverns. What do you mean by transshipment tanks?

95

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7) 7) Release date: April 2007 Next release date: March 2008 Assumptions to the Annual Energy Outlook 2007 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 107 Petroleum Market Module

96

A Comparison of the Free Ride and CISK Assumptions  

Science Conference Proceedings (OSTI)

In a recent paper Fraedrich and McBride have studied the relation between the “free ride” and CISK (conditional instability of the second kind) assumptions in a well-known two-layer model. Here the comparison is extended to a more general case. ...

Torben Strunge Pedersen

1991-08-01T23:59:59.000Z

97

Comparison of Productive Capacity  

U.S. Energy Information Administration (EIA)

Appendix B Comparison of Productive Capacity Comparisons of base case productive capacities for this and all previous studies were made (Figure B1).

98

Tables - Refinery Capacity Report  

U.S. Energy Information Administration (EIA)

Tables: 1: Number and Capacity of Operable Petroleum Refineries by PAD District and State as of January 1, 2009: PDF: 2: Production Capacity of Operable ...

99

EIA - Assumptions to the Annual Energy Outlook 2009 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2009 Petroleum Market Module Figure 9., Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. Table 11.1. Petroleum Product Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.2. Year Round Gasoline Specifications by Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.3. Gasolline Sulfur Content Assumptions, by Region and Gasoline Type, Parts per Million (PPM). Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version

100

Assumption Parish, Louisiana: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

Assumption Parish, Louisiana: Energy Resources Assumption Parish, Louisiana: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 29.9232544°, -91.09694° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":29.9232544,"lon":-91.09694,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

PROJECT MANGEMENT PLAN EXAMPLES Policy & Operational Decisions, Assumptions  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Policy & Operational Decisions, Assumptions Policy & Operational Decisions, Assumptions and Strategies Examples 1 & 2 Example 1 1.0 Summary The 322-M Metallurgical Laboratory is currently categorized as a Radiological Facility. It is inactive with no future DOE mission. In May of 1998 it was ranked Number 45 in the Inactive Facilities Risk Ranking database which the Facilities Decommissioning Division maintains. A short-term surveillance and maintenance program is in-place while the facility awaits final deactivation. Completion of the end points described in this deactivation project plan will place the 322-M facility into an End State that can be described as "cold and dark". The facility will be made passively safe requiring minimal surveillance and no scheduled maintenance.

102

Cost and Performance Assumptions for Modeling Electricity Generation Technologies  

NLE Websites -- All DOE Office Websites (Extended Search)

Cost and Performance Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia Subcontract Report NREL/SR-6A20-48595 November 2010 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401 303-275-3000 * www.nrel.gov Contract No. DE-AC36-08GO28308 Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia NREL Technical Monitor: Jordan Macknick

103

Assumptions to the Annual Energy Outlook 2002 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20021 (AEO2002), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2002 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of

104

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA- 7, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described. EMM Regions The supply regions used in EMM are based on the North American Electric Reliability Council regions and

105

Assumptions to the Annual Energy Outlook 2001 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Outlook2001 Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20011 (AEO2001), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2001 were produced with the National Energy

106

Assumptions to the Annual Energy Outlook 2000 - Errata  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2000 Assumptions to the Annual Energy Outlook 2000 as of 4/4/2000 1. On table 20 "the fractional fuel efficiency change for 4-Speed Automatic" should be .045 instead of .030. On table 20 "the fractional fuel efficiency change for 5-Speed Automatic" should be .065 instead of .045. (Change made on 3/6/2000) 2. Table 28 should be labeled: "Alternative-Fuel Vehicle Attribute Inputs for Compact Cars for Two Stage Logit Model". (Change made on 3/6/2000) 3. The capital costs in Table 29 should read 1998 dollars not 1988 dollars. (Change made on 3/6/2000) 4. Table 37 changed the label "Year Available" to "First Year Completed." Changed the second sentence of Footnote 1 to read "these estimates are costs of new projects

107

Assumptions to the Annual Energy Outlook 2000 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Key Assumptions Key Assumptions The historical input data used to develop the HEM version for the AEO2000 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2000 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and transportation sectors as inputs to the disaggregation algorithm that results in the direct fuel expenditure analysis. Household end-use and personal transportation service consumption are obtained by HEM from the NEMS Residential and Transportation Demand Modules. Household disposable income is adjusted with forecasts of total disposable income from the NEMS Macroeconomic Activity Module.

108

Effects of internal gain assumptions in building energy calculations  

DOE Green Energy (OSTI)

The utilization of direct solar gains in buildings can be affected by operating profiles, such as schedules for internal gains, thermostat controls, and ventilation rates. Building energy analysis methods use various assumptions about these profiles. The effects of typical internal gain assumptions in energy calculations are described. Heating and cooling loads from simulations using the DOE 2.1 computer code are compared for various internal-gain inputs: typical hourly profiles, constant average profiles, and zero gain profiles. Prototype single-family-detached and multi-family-attached residential units are studied with various levels of insulation and infiltration. Small detached commercial buildings and attached zones in large commercial buildings are studied with various levels of internal gains. The results of this study indicate that calculations of annual heating and cooling loads are sensitive to internal gains, but in most cases are relatively insensitive to hourly variations in internal gains.

Christensen, C.; Perkins, R.

1981-01-01T23:59:59.000Z

109

EIA - Assumptions to the Annual Energy Outlook 2009 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2009 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2009 (AEO2009),1 including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2009 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

110

EIA - Assumptions to the Annual Energy Outlook 2010 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2010 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2010 [1] (AEO2010), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in the AEO2010 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

111

EIA - Assumptions to the Annual Energy Outlook 2008 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2008 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20081 (AEO2008), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2008 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The AEO projections are also used by analysts and planners in other government agencies and outside organizations.

112

DOE Transmission Capacity Report | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Transmission Capacity Report Transmission Capacity Report DOE Transmission Capacity Report DOE Transmission Capacity Report: Transmission lines, substations, circuit breakers, capacitors, and other equipment provide more than just a highway to deliver energy and power from generating units to distribution systems. Transmission systems both complement and substitute for generation. Transmission generally enhances reliability; lowers the cost of electricity delivered to consumers; limits the ability of generators to exercise market power; and provides flexibility to protect against uncertainties about future fuel prices, load growth, generator construction, and other factors affecting the electric system. DOE Transmission Capacity Report More Documents & Publications Report to Congress:Impacts of the Federal Energy Regulatory Commission's

113

EIA-Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2007 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

114

Assumptions to the Annual Energy Outlook 2002 - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2002, DOE/EIA- M068(2002) January 2002. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are

115

Assumptions to the Annual Energy Outlook 2001 - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2001, DOE/EIA- M068(2001) January 2001. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are

116

Impact of 1980 scheduled capacity additions on electric-utility oil consumption  

SciTech Connect

The electric-utility sector currently consumes approximately 8% of the total oil used in the Nation. This oil represented about 15% of total fuel consumed by electric utilities in 1979. Two important factors that affect the level of utility oil consumption in 1980 are the substantial increase in coal-fired generating capacity and the uncertainty surrounding nuclear-plant licensing. With particular emphasis on these considerations, this report analyzes the potential for changes in electric-utility oil consumption in 1980 relative to the 1979 level. Plant conversions, oil to coal, for example, that may occur in 1980 are not considered in this analysis. Only the potential reduction in oil consumption resulting from new generating-capacity additions is analyzed. Changes in electric-utility oil consumption depend on, among other factors, regional-electricity-demand growth and generating-plant mix. Five cases are presented using various electricity-demand-growth rate assumptions, fuel-displacement strategies, and nuclear-plant-licensing assumptions. In general, it is likely that there will be a reduction in electric-utility oil consumption in 1980. Using the two reference cases of the report, this reduction is projected to amount to a 2 to 5% decrease from the 1979 oil-consumption level; 7% reduction is the largest reduction projected.

Gielecki, M.; Clark, G.; Roberts, B.

1980-08-01T23:59:59.000Z

117

Capacity Value of Concentrating Solar Power Plants  

DOE Green Energy (OSTI)

This study estimates the capacity value of a concentrating solar power (CSP) plant at a variety of locations within the western United States. This is done by optimizing the operation of the CSP plant and by using the effective load carrying capability (ELCC) metric, which is a standard reliability-based capacity value estimation technique. Although the ELCC metric is the most accurate estimation technique, we show that a simpler capacity-factor-based approximation method can closely estimate the ELCC value. Without storage, the capacity value of CSP plants varies widely depending on the year and solar multiple. The average capacity value of plants evaluated ranged from 45%?90% with a solar multiple range of 1.0-1.5. When introducing thermal energy storage (TES), the capacity value of the CSP plant is more difficult to estimate since one must account for energy in storage. We apply a capacity-factor-based technique under two different market settings: an energy-only market and an energy and capacity market. Our results show that adding TES to a CSP plant can increase its capacity value significantly at all of the locations. Adding a single hour of TES significantly increases the capacity value above the no-TES case, and with four hours of storage or more, the average capacity value at all locations exceeds 90%.

Madaeni, S. H.; Sioshansi, R.; Denholm, P.

2011-06-01T23:59:59.000Z

118

Assumptions to the Annual Energy Outlook - Household Expenditures Module  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module Assumption to the Annual Energy Outlook Household Expenditures Module Figure 5. United States Census Divisions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division (see

119

Network Routing Capacity  

E-Print Network (OSTI)

We define the routing capacity of a network to be the supremum of all possible fractional message throughputs achievable by routing. We prove that the routing capacity of every network is achievable and rational, we present an algorithm for its computation, and we prove that every non-negative rational number is the routing capacity of some network. We also determine the routing capacity for various example networks. Finally, we discuss the extension of routing capacity to fractional coding solutions and show that the coding capacity of a network is independent of the alphabet used.

Jillian Cannons; Randall Dougherty; Christopher Freiling; Kenneth Zeger

2005-01-01T23:59:59.000Z

120

Assumptions to the Annual Energy Outlook 2000 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20001 (AEO2000), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2000 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of EnergyÂ’s Office of Policy, other DOE offices, and other government agencies.

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumption to the Annual Energy Outlook Industrial Demand Module Table 17. Industry Categories Printer Friendly Version Energy-Intensive Manufacturing Nonenergy-Intensive Manufacturing Nonmanufacturing Industries Food and Kindred Products (NAICS 311) Metals-Based Durables (NAICS 332-336) Agricultural Production -Crops (NAICS 111) Paper and Allied Products (NAICS 322) Balance of Manufacturing (all remaining manufacturing NAICS) Other Agriculture Including Livestock (NAICS112- 115) Bulk Chemicals (NAICS 32B) Coal Mining (NAICS 2121) Glass and Glass Products (NAICS 3272) Oil and Gas Extraction (NAICS 211) Hydraulic Cement (NAICS 32731) Metal and Other Nonmetallic Mining (NAICS 2122- 2123) Blast Furnaces and Basic Steel (NAICS 331111) Construction (NAICS233-235)

122

EIA - Assumptions to the Annual Energy Outlook 2009 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

123

EIA - Assumptions to the Annual Energy Outlook 2009 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2009 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight, rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

124

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

clothes drying, ceiling fans, coffee makers, spas, home security clothes drying, ceiling fans, coffee makers, spas, home security systems, microwave ovens, set-top boxes, home audio equipment, rechargeable electronics, and VCR/DVDs. In addition to the major equipment-driven end-uses, the average energy consumption per household is projected for other electric and nonelectric appliances. The module's output includes number Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 19 Pacific East South Central South Atlantic Middle Atlantic New England West South Central West North Central East North Central Mountain AK WA MT WY ID NV UT CO AZ NM TX OK IA KS MO IL IN KY TN MS AL FL GA SC NC WV PA NJ MD DE NY CT VT ME RI MA NH VA WI MI OH NE SD MN ND AR LA OR CA HI Middle Atlantic New England East North Central West North Central Pacific West South Central East South Central

125

EIA - Assumptions to the Annual Energy Outlook 2010 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services [1].

126

EIA - Assumptions to the Annual Energy Outlook 2010 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2010 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and

127

EIA - Assumptions to the Annual Energy Outlook 2009 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2009 Industrial Demand Module Table 6.1. Industry Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 6.2.Retirement Rates. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process flow or end use accounting

128

Assumptions to the Annual Energy Outlook 2002 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2002, DOE/EIA-M060(2002) (Washington, DC, January 2002). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

129

Assumptions to the Annual Energy Outlook 1999 - Table 1  

Gasoline and Diesel Fuel Update (EIA)

Summary of AEO99 Cases Summary of AEO99 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.5 percent, compared to the reference case growth of 2.1 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.1 percent. Fully Integrated Low World Oil Price World oil prices are $14.57 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated High World Oil Price World oil prices are $29.35 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated Residential: 1999 Technology

130

EIA - Assumptions to the Annual Energy Outlook 2008 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2008 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

131

Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumption to the Annual Energy Outlook Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, sport utility vehicles and vans), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

132

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS27 data.

133

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2006 The International Energy Module determines changes in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market clearing method is used to determine the price at which worldwide demand for oil is equal to the worldwide supply. The module determines new values for oil production and demand for regions outside the United States, along with a new world oil price that balances supply and demand in the international oil market. A detailed description of the International Energy Module is provided in the EIA publication, Model Documentation Report: The International Energy Module of the National Energy Modeling System, DOE/EIA-M071(06), (Washington, DC, February 2006).

134

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7 7 1 (AEO2007), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant to formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports. 2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview 3 , which is updated once every few years. The National Energy Modeling System The projections in the AEO2007 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and

135

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

136

EIA - Assumptions to the Annual Energy Outlook 2008 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2008 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

137

EIA - Assumptions to the Annual Energy Outlook 2010 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2010 International Energy Module Figure 2. World Oil Prices in Three Cases, 1995-2035 Figure 2. World Oil Prices in three Cases, 1995-2035 (2008 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800.

138

Assumptions to the Annual Energy Outlook 2001 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Completed Copy in PDF Format Completed Copy in PDF Format Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Household Expenditures Module Key Assumptions The historical input data used to develop the HEM version for the AEO2001 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2001 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and

139

Assumptions to the Annual Energy Outlook 2001 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2001, DOE/EIA-M060(2001) January 2001. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

140

EIA - Assumptions to the Annual Energy Outlook 2009 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2009 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

EIA - Assumptions to the Annual Energy Outlook 2009 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2009 International Energy Module Figure 2. World Oil Prices in three Cases, 1995-2030 (2006 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids

142

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.14

143

EIA - Assumptions to the Annual Energy Outlook 2008 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2008 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module projects energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region projection using the SEDS1 data.

144

Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumption to the Annual Energy Outlook International Energy Module Figure 2. World Oil Prices in three Cases, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 3. OPEC Oil Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 4. Non-OPEC Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Table 4. Worldwide Oil Reserves as of January 1, 2002 (Billion Barrels) Printer Friendly Version Region Proved Oil Reserves Western Hemisphere 313.6 Western‘Europe 18.1 Asia-Pacific 38.7

145

EIA - Assumptions to the Annual Energy Outlook 2010 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2010 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock,

146

Assumptions to the Annual Energy Outlook - Natural Gas Transmission and  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumption to the Annual Energy Outlook Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

147

Assumptions to the Annual Energy Outlook 2001 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

148

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2006 Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment

149

EIA - Assumptions to the Annual Energy Outlook 2008 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2008 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

150

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2006 Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

151

Challenging Times for Making Refinery Capacity Decisions  

Reports and Publications (EIA)

This presentation was given at the National Petrochemical and Refiners Association's annual meeting in March 2004. The presentation covers a wide range of refining issues from near term to long term, and focuses on refining capacity and factors affecting decisions to alter that capacity.

Information Center

2004-03-01T23:59:59.000Z

152

ORISE: Capacity Building  

NLE Websites -- All DOE Office Websites (Extended Search)

Capacity Building Capacity Building Because public health agencies must maintain the resources to respond to public health challenges, critical situations and emergencies, the Oak Ridge Institute for Science and Education (ORISE) helps government agencies and organizations develop a solid infrastructure through capacity building. Capacity building refers to activities that improve an organization's ability to achieve its mission or a person's ability do his or her job more effectively. For organizations, capacity building may relate to almost any aspect of its work-from leadership and administration to program development and implementation. Strengthening an organizational infrastructure can help agencies and community-based organizations more quickly identify targeted audiences for

153

Modeling Capacity Reservation Contract  

E-Print Network (OSTI)

In this paper we model a scenario where a chip designer (buyer) buys capacity from chip manufacturers (suppliers) in the presence of demand uncertainty faced by the buyer. We assume that the buyer knows the probability distribution of his demand. The supplier offers the buyer to reserve capacity in advance at a price that is lower than the historical average of the spot price. The supplier’s price (if the buyer reserves capacity in advance) is function of her capacity, demand for her capacity, unit production cost, the average spot market price and the amount of capacity reserved by the buyer. Based on these parameters we derive the price the suppliers will charge. We formulate the problem from the buyer’s perspective. The buyer’s decisions are how much capacity to reserve and from how many suppliers. The optimal solution is obtained numerically. Our model addresses the following issues that are not covered in the current literature on capacity reservation models. In the existing literature the supplier’s price is an exogenous parameter. We model the supplier’s price from relevant parameters mentioned above. This makes our model richer. For example, if the expected capacity utilization for the supplier is likely to be low then the supplier will charge a lower price for capacity reservation. In reality, the buyer sources from multiple suppliers. Most mathematical models on capacity reservation, we are aware of, assumes a single buyer and a single supplier. We generalize this to a single buyer and multiple suppliers.

Jishnu Hazra; B. Mahadevan; Sudhi Seshadri

2002-01-01T23:59:59.000Z

154

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

155

Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumption to the Annual Energy Outlook Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

156

Assumptions to the Annual Energy Outlook 2000 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

157

EIA - Assumptions to the Annual Energy Outlook 2009 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2009 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the projection horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the projection horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

158

EIA - Assumptions to the Annual Energy Outlook 2008 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2008 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids supply and demand curves (1 curve per year; 2008-2030; approximated, isoelastic fit to previous NEMS results). These quantities are not modeled directly in NEMS. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2008 with IEO2007 and the STEO some functionality was removed from IEM while a new algorithm was implemented. Based on the difference between U.S. total petroleum liquids production (consumption) and the expected U.S. total liquids production (consumption) at the current WTI price, curves for global petroleum liquids consumption (production) were adjusted for each year. According to previous operations, a new WTI price path was generated. An exogenous oil supply module, Generate World Oil Balances (GWOB), was also used in IEM to provide annual regional (country) level production detail for conventional and unconventional liquids.

159

Natural Gas Underground Storage Capacity (Summary)  

Gasoline and Diesel Fuel Update (EIA)

Salt Caverns Storage Capacity Aquifers Storage Capacity Depleted Fields Storage Capacity Total Working Gas Capacity Working Gas Capacity of Salt Caverns Working Gas Capacity of...

160

Increasing State Capacity Through Clans  

E-Print Network (OSTI)

their role in increasing state capacity With the decline ofhere focus on state capacity and the associated discussionselements of state capacity during the transition from one

Doyle, Jr, Thomas Martin

2009-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Capacity Markets for Electricity  

E-Print Network (OSTI)

Designing Markets for Electricity. Wiley IEEE Press. [25]in the England and Wales Electricity Market”, Power WorkingFelder (1996), “Should Electricity Markets Have a Capacity

Creti, Anna; Fabra, Natalia

2004-01-01T23:59:59.000Z

162

ORISE: Capacity Building  

NLE Websites -- All DOE Office Websites (Extended Search)

Capacity Building Because public health agencies must maintain the resources to respond to public health challenges, critical situations and emergencies, the Oak Ridge Institute...

163

Assumptions to the Annual Energy Outlook 2002 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

164

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

unfinished oil imports, other refinery inputs (including alcohols, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9). The LP model is created by aggregating individual refineries within a PADD into one representative refinery, and linking all five PADD's via crude and product transit links. This representation provides the marginal costs of production for a number of conventional and new petroleum products. In order to interact with other NEMS modules with different regional representations,

165

Assumptions to the Annual Energy Outlook 2002 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).117 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

166

Assumptions to the Annual Energy Outlook 2001 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

167

Assumptions to the Annual Energy Outlook 2001 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

168

Assumptions to the Annual Energy Outlook 2001 - Table 3. Coal-Related  

Gasoline and Diesel Fuel Update (EIA)

Coal-Related Methane Assumptions Coal-Related Methane Assumptions Northern Appalachia Central Appalachia Southern Appalachia Eastern Interior Western Fraction of underground coal production at: Gassy mines 0.885 0.368 0.971 0.876 0.681 Nongassy mines 0.115 0.632 0.029 0.124 0.319 Production from mines with degasification systems (fraction of underground production) 0.541 0.074 0.810 0.067 0.056 Emission factors (kilograms methane per short ton of coal produced) Underground Mining Gassy mines 6.047 5.641 27.346 2.988 6.027 Nongassy mines 0.362 0.076 15.959 0.285 0.245 Degassified mines 4.085 37.724 22.025 0.310 0.000 Surface Mining 0.706 0.706 0.706 0.706 0.706 Post-Mining, underground-mined 1.505 1.505 1.505 1.505 1.505 Post-Mining, surface-mined 0.061 0.061 0.061 0.061 0.061 Methane recovery at active coal mines

169

Comparison of risk-dominant scenario assumptions for several TRU waste facilities in the DOE complex  

Science Conference Proceedings (OSTI)

In order to gain a risk management perspective, the DOE Rocky Flats Field Office (RFFO) initiated a survey of other DOE sites regarding risks from potential accidents associated with transuranic (TRU) storage and/or processing facilities. Recently-approved authorization basis documents at the Rocky Flats Environmental Technology Site (RFETS) have been based on the DOE Standard 3011 risk assessment methodology with three qualitative estimates of frequency of occurrence and quantitative estimates of radiological consequences to the collocated worker and the public binned into three severity levels. Risk Class 1 and 2 events after application of controls to prevent or mitigate the accident are designated as risk-dominant scenarios. Accident Evaluation Guidelines for selection of Technical Safety Requirements (TSRs) are based on the frequency and consequence bin assignments to identify controls that can be credited to reduce risk to Risk Class 3 or 4, or that are credited for Risk Class 1 and 2 scenarios that cannot be further reduced. This methodology resulted in several risk-dominant scenarios for either the collocated worker or the public that warranted consideration on whether additional controls should be implemented. RFFO requested the survey because of these high estimates of risks that are primarily due to design characteristics of RFETS TRU waste facilities (i.e., Butler-type buildings without a ventilation and filtration system, and a relatively short distance to the Site boundary). Accident analysis methodologies and key assumptions are being compared for the DOE sites responding to the survey. This includes type of accidents that are risk dominant (e.g., drum explosion, material handling breach, fires, natural phenomena, external events, etc.), source term evaluation (e.g., radionuclide material-at-risk, chemical and physical form, damage ratio, airborne release fraction, respirable fraction, leakpath factors), dispersion analysis (e.g., meteorological assumptions, distance to receptors, plume meander, deposition, and other factors affecting the calculated {chi}/Q), dose assessments (specific activities, inhalation dose conversion factors, breathing rates), designated frequency of occurrence, and risk assignment per the DOE Standard 3011 methodology. Information from the sites is being recorded on a spreadsheet to facilitate comparisons. The first response from Westinghouse Safety Management Solutions for the Savannah River Site (SRS) also provided a detailed analysis of the major differences in methods and assumptions between RFETS and SRS, which forms much of the basis for this paper. Other sites responding to the survey include the Idaho National Engineering and Environmental Laboratory (INEEL), Hanford, and the Los Alamos National Laboratory (LANL).

Foppe, T.L. [Foppe and Associates, Inc., Golden, CO (United States); Marx, D.R. [Westinghouse Safety Management Solutions, Inc., Aiken, SC (United States)

1999-06-01T23:59:59.000Z

170

A Statistical Analysis of the Dependency of Closure Assumptions in Cumulus Parameterization on the Horizontal Resolution  

Science Conference Proceedings (OSTI)

Simulated data from the UCLA cumulus ensemble model are used to investigate the quasi-universal validity of closure assumptions used in existing cumulus parameterizations. A closure assumption is quasi-universally valid if it is sensitive neither ...

Kuan-Man Xu

1994-12-01T23:59:59.000Z

171

Capacity on Finsler Spaces  

E-Print Network (OSTI)

Here, the concept of electric capacity on Finsler spaces is introduced and the fundamental conformal invariant property is proved, i.e. the capacity of a compact set on a connected non-compact Finsler manifold is conformal invariant. This work enables mathematicians and theoretical physicists to become more familiar with the global Finsler geometry and one of its new applications.

Bidabad, B

2009-01-01T23:59:59.000Z

172

Liquid heat capacity lasers  

DOE Patents (OSTI)

The heat capacity laser concept is extended to systems in which the heat capacity lasing media is a liquid. The laser active liquid is circulated from a reservoir (where the bulk of the media and hence waste heat resides) through a channel so configured for both optical pumping of the media for gain and for light amplification from the resulting gain.

Comaskey, Brian J. (Walnut Creek, CA); Scheibner, Karl F. (Tracy, CA); Ault, Earl R. (Livermore, CA)

2007-05-01T23:59:59.000Z

173

capacity | OpenEI  

Open Energy Info (EERE)

capacity capacity Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 9, and contains only the reference case. The dataset uses gigawatts. The data is broken down into power only, combined heat and power, cumulative planned additions, cumulative unplanned conditions, and cumulative retirements and total electric power sector capacity . Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO capacity consumption EIA Electricity generating Data application/vnd.ms-excel icon AEO2011: Electricity Generating Capacity- Reference Case (xls, 130.1 KiB) Quality Metrics Level of Review Peer Reviewed Comment

174

Battery capacity indicator  

SciTech Connect

This patent describes a battery capacity indicator for providing a continuous indication of battery capacity for a battery powered device. It comprises means for periodically effecting a first and a second positive discharge rate of the battery; voltage measurement means, for measuring the battery terminal voltage at the first and second positive discharge rates during the operation of the device, and for generating a differential battery voltage value in response thereto; memory means for storing a set of predetermined differential battery voltage values and a set of predetermined battery capacity values, each of the set of predetermined differential battery voltage values defining one of the set of predetermined battery capacity values; comparison means, coupled to the memory means and to the voltage measurement means, for comparing the measured differential battery voltage values with the set of predetermined differential battery voltage values, and for selecting the predetermined battery capacity value corresponding thereto.

Kunznicki, W.J.

1991-07-16T23:59:59.000Z

175

EIA-Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2007 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind.112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

176

Assumptions to the Annual Energy Outlook 1999 - Table 2  

Gasoline and Diesel Fuel Update (EIA)

Carbon Emission Factors (Kilograms-carbon per million Btu) Carbon Emission Factors (Kilograms-carbon per million Btu) Fuel Type Carbon Coefficient at Full Combustion Combustion Fraction Adjusted Emissions Factor Petroleum Motor Gasoline 19.35 0.990 19.16 Liquefied Petroleum Gas Used as Fuel 16.87 0.995 16.79 Used as Feedstock 17.11 0.200 3.42 Jet Fuel 19.33 0.990 19.14 Distillate Fuel 19.95 0.990 19.75 Residual Fuel 21.49 0.990 21.28 Asphalt and Road Oil 20.62 0.000 0.00 Lubricants 20.24 0.600 12.14 Petrochemical Feedstocks 19.37 0.200 3.87 Kerosene 19.72 0.990 19.52 Petroleum Coke 27.85 0.500 13.93 Petroleum Still Gas 17.51 0.995 17.42 Other Industrial 20.31 0.990 20.11 Coal Residential and Commercial 25.92 0.990 25.74 Metallurgical 25.55 0.990 25.28 Industrial Other 25.61 0.990 25.38 Electric Utility1 25.74 0.990 25.48 Natural Gas Used as Fuel

177

Lateral Capacity Exchange and Its Impact on Capacity Investment Decisions  

E-Print Network (OSTI)

We study the problem of capacity exchange between two …rms in anticipation of the mismatch between demand and capacity and its impact on …rm’s capacity investment decisions. For given capacity investment levels of the two …rms, we demonstrate how capacity price may be determined and how much capacity should be exchanged when either manufacturer acts as a Stackelberg leader in the capacity exchange game. By benchmarking against the centralized system, we show that a side payment may be used to coordinate the capacity exchange decisions. We then study the …rms’capacity investment decisions using a biform game framework in which capacity investment decisions are made individually and exchange decisions are made as in a centralized system. We demonstrate the existence and uniqueness of the Nash equilibrium capacity investment levels and study the impact of …rms’share of the capacity exchange surplus on their capacity investment levels.

Amiya K. Chakravartyz; Jun Zhangy

2005-01-01T23:59:59.000Z

178

Capacity Markets for Electricity  

E-Print Network (OSTI)

Global Agenda, August 15. [6] FERC, Docket No. EL01-63-003,at http://www.pjm.com. [7] FERC, Docket No. ER01-1440-capacity of the others” (FERC, 2001). Therefore, if an LSE

Creti, Anna; Fabra, Natalia

2004-01-01T23:59:59.000Z

179

Refinery Capacity Report 2007  

Reports and Publications (EIA)

Data series include fuel, electricity, and steam purchased for consumption at the refinery; refinery receipts of crude oil by method of transportation; current and projected capacities for atmospheric crude oil distillation, downstream charge, production, and storage capacities. Respondents are operators of all operating and idle petroleum refineries (including new refineries under construction) and refineries shut down during the previous year, located in the 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, and other U.S. possessions.

Information Center

2007-06-29T23:59:59.000Z

180

Refinery Capacity Report 2009  

Reports and Publications (EIA)

Data series include fuel, electricity, and steam purchased for consumption at the refinery; refinery receipts of crude oil by method of transportation; current and projected capacities for atmospheric crude oil distillation, downstream charge, production, and storage capacities. Respondents are operators of all operating and idle petroleum refineries (including new refineries under construction) and refineries shut down during the previous year, located in the 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, and other U.S. possessions.

Information Center

2009-06-25T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Refinery Capacity Report 2008  

Reports and Publications (EIA)

Data series include fuel, electricity, and steam purchased for consumption at the refinery; refinery receipts of crude oil by method of transportation; current and projected capacities for atmospheric crude oil distillation, downstream charge, production, and storage capacities. Respondents are operators of all operating and idle petroleum refineries (including new refineries under construction) and refineries shut down during the previous year, located in the 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, and other U.S. possessions.

Information Center

2008-06-20T23:59:59.000Z

182

Forward capacity market CONEfusion  

Science Conference Proceedings (OSTI)

In ISO New England and PJM it was assumed that sponsors of new capacity projects would offer them into the newly established forward centralized capacity markets at prices based on their levelized net cost of new entry, or ''Net CONE.'' But the FCCMs have not operated in the way their proponents had expected. To clear up the CONEfusion, FCCM designs should be reconsidered to adapt them to the changing circumstances and to be grounded in realistic expectations of market conduct. (author)

Wilson, James F.

2010-11-15T23:59:59.000Z

183

Assessing the Control Systems Capacity for Demand Response in California  

NLE Websites -- All DOE Office Websites (Extended Search)

the Control Systems Capacity for Demand Response in California the Control Systems Capacity for Demand Response in California Industries Title Assessing the Control Systems Capacity for Demand Response in California Industries Publication Type Report LBNL Report Number LBNL-5319E Year of Publication 2012 Authors Ghatikar, Girish, Aimee T. McKane, Sasank Goli, Peter L. Therkelsen, and Daniel Olsen Date Published 01/2012 Publisher CEC/LBNL Keywords automated dr, controls and automation, demand response, dynamic pricing, industrial controls, market sectors, openadr Abstract 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.

184

Assumptions to the Annual Energy Outlook 2001 - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module

185

Assumptions to the Annual Energy Outlook 1999 - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

macroeconomic.gif (5367 bytes) macroeconomic.gif (5367 bytes) The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065, (Washington, DC, February 1994).

186

Assumptions to the Annual Energy Outlook 2002 - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module

187

Assumptions to the Annual Energy Outlook 2000 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065, (Washington, DC, February 1994), plus Macroeconomic Activity Module (MAM): Kernel Regression Documentation of the National Energy Modeling System 1999, DOE/EIA-M065(99), Washington, DC, 1999).

188

Refinery Capacity Report  

U.S. Energy Information Administration (EIA) Indexed Site

Refinery Capacity Report Refinery Capacity Report With Data as of January 1, 2013 | Release Date: June 21, 2013 | Next Release Date: June 20, 2014 Previous Issues Year: 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1997 1995 1994 Go Data series include fuel, electricity, and steam purchased for consumption at the refinery; refinery receipts of crude oil by method of transportation; and current and projected atmospheric crude oil distillation, downstream charge, and production capacities. Respondents are operators of all operating and idle petroleum refineries (including new refineries under construction) and refineries shut down during the previous year, located in the 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, and other U.S. possessions.

189

Refinery Capacity Report  

U.S. Energy Information Administration (EIA) Indexed Site

Refinery Capacity Report Refinery Capacity Report June 2013 With Data as of January 1, 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Table 1. Number and Capacity of Operable Petroleum Refineries by PAD District and State as of January 1, 2013

190

Dual capacity reciprocating compressor  

DOE Patents (OSTI)

A multi-cylinder compressor particularly useful in connection with northern climate heat pumps and in which different capacities are available in accordance with reversing motor rotation is provided with an eccentric cam on a crank pin under a fraction of the connecting rods, and arranged for rotation upon the crank pin between opposite positions 180[degree] apart so that with cam rotation on the crank pin such that the crank throw is at its normal maximum value all pistons pump at full capacity, and with rotation of the crank shaft in the opposite direction the cam moves to a circumferential position on the crank pin such that the overall crank throw is zero. Pistons whose connecting rods ride on a crank pin without a cam pump their normal rate with either crank rotational direction. Thus a small clearance volume is provided for any piston that moves when in either capacity mode of operation. 6 figs.

Wolfe, R.W.

1984-10-30T23:59:59.000Z

191

Dual capacity reciprocating compressor  

DOE Patents (OSTI)

A multi-cylinder compressor 10 particularly useful in connection with northern climate heat pumps and in which different capacities are available in accordance with reversing motor 16 rotation is provided with an eccentric cam 38 on a crank pin 34 under a fraction of the connecting rods, and arranged for rotation upon the crank pin between opposite positions 180.degree. apart so that with cam rotation on the crank pin such that the crank throw is at its normal maximum value all pistons pump at full capacity, and with rotation of the crank shaft in the opposite direction the cam moves to a circumferential position on the crank pin such that the overall crank throw is zero. Pistons 24 whose connecting rods 30 ride on a crank pin 36 without a cam pump their normal rate with either crank rotational direction. Thus a small clearance volume is provided for any piston that moves when in either capacity mode of operation.

Wolfe, Robert W. (Wilkinsburg, PA)

1984-01-01T23:59:59.000Z

192

A kinematic wave theory of capacity drop  

E-Print Network (OSTI)

Capacity drop at active bottlenecks is one of the most puzzling traffic phenomena, but a thorough understanding is practically important for designing variable speed limit and ramp metering strategies. In this study, we attempt to develop a simple model of capacity drop within the framework of kinematic wave theory based on the observation that capacity drop occurs when an upstream queue forms at an active bottleneck. In addition, we assume that the fundamental diagrams are continuous in steady states. This assumption is consistent with observations and can avoid unrealistic infinite characteristic wave speeds in discontinuous fundamental diagrams. A core component of the new model is an entropy condition defined by a discontinuous boundary flux function. For a lane-drop area, we demonstrate that the model is well-defined, and its Riemann problem can be uniquely solved. We theoretically discuss traffic stability with this model subject to perturbations in density, upstream demand, and downstream supply. We clarify that discontinuous flow-density relations, or so-called "discontinuous" fundamental diagrams, are caused by incomplete observations of traffic states. Theoretical results are consistent with observations in the literature and are verified by numerical simulations and empirical observations. We finally discuss potential applications and future studies.

Wen-Long Jin; Qi-Jian Gan; Jean-Patrick Lebacque

2013-10-09T23:59:59.000Z

193

Quantum Zero-error Capacity  

E-Print Network (OSTI)

We define here a new kind of quantum channel capacity by extending the concept of zero-error capacity for a noisy quantum channel. The necessary requirement for which a quantum channel has zero-error capacity greater than zero is given. Finally, we point out some directions on how to calculate the zero-error capacity of such channels.

Rex A. C. Medeiros; Francisco M. De Assis

2006-11-08T23:59:59.000Z

194

Photovoltaics effective capacity: Interim final report 2  

DOE Green Energy (OSTI)

The authors provide solid evidence, based on more than 8 million data points, that regional photovoltaic (PV) effective capacity is largely unrelated to the region`s solar resource. They confirm, however, that effective capacity is strongly related to load-shape characteristics. The load-shape effective-capacity relationship appears to be valid for end-use loads as small as 100 kW, except possibly in the case of electrically heated buildings. This relationship was used as a tool to produce a US map of PV`s effective capacity. The regions of highest effective capacities include (1) the central US from the northern Great Plains to the metropolitan areas of Chicago and Detroit, down to the lower Mississippi Valley, (2) California and western Arizona, and (3) the northeast metropolitan corridor. The features of this map are considerably different from the traditional solar resource maps. They tend to reflect the socio-economic and climatic factors that indirectly drive PV`s effective capacity: e.g., commercial air-conditioning, little use of electric heat, and strong summer heat waves. The map provides a new and significant insight to a comprehensive valuation of the PV resource. The authors assembled preliminary evidence showing that end-use load type may be related to PV`s effective capacity. Highest effective capacities were found for (nonelectrically heated) office buildings, followed by hospitals. Lowest capacities were found for airports and residences. Many more data points are needed, however, to ascertain and characterize these preliminary findings.

Perez, R.; Seals, R. [State Univ. of New York, Albany, NY (United States). Atmospheric Sciences Research Center

1997-11-01T23:59:59.000Z

195

EIA - Natural Gas Pipeline Network - Pipeline Capacity and Utilization  

U.S. Energy Information Administration (EIA) Indexed Site

Pipeline Utilization & Capacity Pipeline Utilization & Capacity About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipeline Capacity & Utilization Overview | Utilization Rates | Integration of Storage | Varying Rates of Utilization | Measures of Utilization Overview of Pipeline Utilization Natural gas pipeline companies prefer to operate their systems as close to full capacity as possible to maximize their revenues. However, the average utilization rate (flow relative to design capacity) of a natural gas pipeline system seldom reaches 100%. Factors that contribute to outages include: Scheduled or unscheduled maintenance Temporary decreases in market demand Weather-related limitations to operations

196

Load Capacity of Bodies  

E-Print Network (OSTI)

For the stress analysis in a plastic body $\\Omega$, we prove that there exists a maximal positive number $C$, the \\emph{load capacity ratio,} such that the body will not collapse under any external traction field $t$ bounded by $Y_{0}C$, where $Y_0$ is the elastic limit. The load capacity ratio depends only on the geometry of the body and is given by $$ \\frac{1}{C}=\\sup_{w\\in LD(\\Omega)_D} \\frac{\\int_{\\partial\\Omega}|w|dA} {\\int_{\\Omega}|\\epsilon(w)|dV}=\\left\\|\\gamma_D\\right\\|. $$ Here, $LD(\\Omega)_D$ is the space of isochoric vector fields $w$ for which the corresponding stretchings $\\epsilon(w)$ are assumed to be integrable and $\\gamma_D$ is the trace mapping assigning the boundary value $\\gamma_D(w)$ to any $w\\in LD(\\Omega)_D$.

Reuven Segev

2005-11-01T23:59:59.000Z

197

Capacity Value of Solar Power  

Science Conference Proceedings (OSTI)

Evaluating the capacity value of renewable energy sources can pose significant challenges due to their variable and uncertain nature. In this paper the capacity value of solar power is investigated. Solar capacity value metrics and their associated calculation methodologies are reviewed and several solar capacity studies are summarized. The differences between wind and solar power are examined, the economic importance of solar capacity value is discussed and other assessments and recommendations are presented.

Duignan, Roisin; Dent, Chris; Mills, Andrew; Samaan, Nader A.; Milligan, Michael; Keane, Andrew; O'Malley, Mark

2012-11-10T23:59:59.000Z

198

Refinery Capacity Report  

U.S. Energy Information Administration (EIA) Indexed Site

1 1 Idle Operating Total Stream Day Barrels per Idle Operating Total Calendar Day Barrels per Atmospheric Crude Oil Distillation Capacity Idle Operating Total Operable Refineries Number of State and PAD District a b b 14 10 4 1,617,500 1,205,000 412,500 1,708,500 1,273,500 435,000 ............................................................................................................................................... PAD District I 1 0 1 182,200 0 182,200 190,200 0 190,200 ................................................................................................................................................................................................................................................................................................ Delaware......................................

199

Total Natural Gas Underground Storage Capacity  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity Working Gas Capacity of Salt Caverns Working Gas Capacity of Aquifers Working Gas Capacity of Depleted Fields Total Number of Existing Fields Number of Existing Salt...

200

Capacities associated with scalar signed Riesz kernels, and analytic capacity  

E-Print Network (OSTI)

The real and imaginari parts of the Cauchy kernel in the plane are scalar Riesz kernels of homogeneity -1. One can associate with each of them a natural notion of capacity related to bounded potentials. The main result of the paper asserts that these capacities are comparable to classical analytic capacity, thus stressing the real variables nature of analytic capacity. Higher dimensional versions of this result are also considered.

Mateu, Joan; Verdera, Joan

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

CCN predictions using simplified assumptions of organic aerosol composition and mixing state: A synthesis from six different locations  

SciTech Connect

An accurate but simple quantification of the fraction of aerosol particles that can act as cloud condensation nuclei (CCN) is needed for implementation in large-scale models. Data on aerosol size distribution, chemical composition, and CCN concentration from six different locations have been analyzed to explore the extent to which simple assumptions of composition and mixing state of the organic fraction can reproduce measured CCN number concentrations. Fresher pollution aerosol as encountered in Riverside, CA, and the ship channel in Houston, TX, cannot be represented without knowledge of more complex (size-resolved) composition. For aerosol that has experienced processing (Mexico City, Holme Moss (UK), Point Reyes (CA), and Chebogue Point (Canada)), CCN can be predicted within a factor of two assuming either externally or internally mixed soluble organics although these simplified compositions/mixing states might not represent the actual properties of ambient aerosol populations, in agreement with many previous CCN studies in the literature. Under typical conditions, a factor of two uncertainty in CCN concentration due to composition assumptions translates to an uncertainty of {approx}15% in cloud drop concentration, which might be adequate for large-scale models given the much larger uncertainty in cloudiness.

Ervens, B.; Wang, J.; Cubison, M. J.; Andrews, E.; Feingold, G.; Ogren, J. A.; Jimenez, J. L.; Quinn, P. K.; Bates, T. S.; Zhang, Q.; Coe, H.; Flynn, M.; Allan, J. D.

2010-05-01T23:59:59.000Z

202

Heterogeneous Correlation Modeling Based on the Wavelet Diagonal Assumption and on the Diffusion Operator  

Science Conference Proceedings (OSTI)

This article discusses several models for background error correlation matrices using the wavelet diagonal assumption and the diffusion operator. The most general properties of filtering local correlation functions, with wavelet formulations, are ...

Olivier Pannekoucke

2009-09-01T23:59:59.000Z

203

Microwave Properties of Ice-Phase Hydrometeors for Radar and Radiometers: Sensitivity to Model Assumptions  

Science Conference Proceedings (OSTI)

A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase ...

Benjamin T. Johnson; Grant W. Petty; Gail Skofronick-Jackson

2012-12-01T23:59:59.000Z

204

Heat capacity of a two-component superfluid Fermi gas  

E-Print Network (OSTI)

We investigate mean-field effects in two- component trapped Fermi gases in the superfluid phase, in the vicinity of s-wave Feshbach resonances. Within the resonance superfluidity approach (Holland et al., 2001) we calculate the ground state energy and the heat capacity as function of temperature. Heat capacity is analyzed for different trap aspect ratios. We find that trap anisotropy is an important factor in determining both the value of heat capacity near the transition temperature and the transition temperature itself.

Alexander V. Avdeenkov

2003-09-25T23:59:59.000Z

205

Multipath Channels of Unbounded Capacity  

E-Print Network (OSTI)

The capacity of discrete-time, noncoherent, multipath fading channels is considered. It is shown that if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the transmit power.

Koch, Tobias

2008-01-01T23:59:59.000Z

206

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

SciTech Connect

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).

Wayne Moe

2013-05-01T23:59:59.000Z

207

Heat capacities of elastic solids  

E-Print Network (OSTI)

The work function is embedded in the equation describing the relationship between the constant volume and constant pressure heat capacities. The modification of the work function results that the relationship between these quantities must be changed accordingly. Using the newly derived work functions of elastic solids the description of the heat capacities and the relationship between the heat capacities are given for solid phase.

Garai, J

2005-01-01T23:59:59.000Z

208

Symmetrical Symplectic Capacity with Applications  

E-Print Network (OSTI)

In this paper, we first introduce the concept of symmetrical symplectic capacity for symmetrical symplectic manifolds, and by using this symmetrical symplectic capacity theory we prove that there exists at least one symmetric closed characteristic (brake orbit and $S$-invariant brake orbit are two examples) on prescribed symmetric energy surface which has a compact neighborhood with finite symmetrical symplectic capacity.

Liu, Chungen

2010-01-01T23:59:59.000Z

209

GASCAP: Wellhead Gas Productive Capacity Model documentation, June 1993  

SciTech Connect

The Wellhead Gas Productive Capacity Model (GASCAP) has been developed by EIA to provide a historical analysis of the monthly productive capacity of natural gas at the wellhead and a projection of monthly capacity for 2 years into the future. The impact of drilling, oil and gas price assumptions, and demand on gas productive capacity are examined. Both gas-well gas and oil-well gas are included. Oil-well gas productive capacity is estimated separately and then combined with the gas-well gas productive capacity. This documentation report provides a general overview of the GASCAP Model, describes the underlying data base, provides technical descriptions of the component models, diagrams the system and subsystem flow, describes the equations, and provides definitions and sources of all variables used in the system. This documentation report is provided to enable users of EIA projections generated by GASCAP to understand the underlying procedures used and to replicate the models and solutions. This report should be of particular interest to those in the Congress, Federal and State agencies, industry, and the academic community, who are concerned with the future availability of natural gas.

Not Available

1993-07-01T23:59:59.000Z

210

Entangling capacity with local ancilla  

E-Print Network (OSTI)

We investigate the entangling capacity of a dynamical operation with access to local ancilla. A comparison is made between the entangling capacity with and without the assistance of prior entanglement. An analytic solution is found for the log-negativity entangling capacity of two-qubit gates, which equals the entanglement of the Choi matrix isomorphic to the unitary operator. Surprisingly, the availability of prior entanglement does not affect this result; a property we call resource independence of the entangling capacity. We prove several useful upper-bounds on the entangling capacity that hold for general qudit dynamical operations, and for a whole family of entanglement measures including log-negativity and log-robustness. The log-robustness entangling capacity is shown to be resource independent for general dynamics. We provide numerical results supporting a conjecture that the log-negativity entangling capacity is resource independence for all two-qudit unitaries.

Campbell, Earl T

2010-01-01T23:59:59.000Z

211

Lumpy capacity investment and disinvestment dynamics’, Operations Research forthcoming  

E-Print Network (OSTI)

Capacity addition and withdrawal decisions are among the most important strategic decisions made by …rms in oligopolistic industries. In this paper, we develop and analyze a fully dynamic model of an oligopolistic industry with lumpy capacity and lumpy investment/disinvestment. We use our model to answer two questions. First, what economic factors facilitate preemption races? Second, what economic factors facilitate capacity coordination? We show that low product di¤erentiation, low investment sunkness, and high depreciation promote preemption races. We also show that low product di¤erentiation and low investment sunkness promote capacity coordination. Although depreciation removes capacity, it may impede capacity coordination. Finally, we show that, at least over some range of parameter values, …rms’expectation plays a key role in determining whether or not industry dynamics are characterized by preemption races and capacity coordination. Taken together, our results suggest that preemption races and excess capacity in the short run often go hand-in-hand with capacity coordination in the long run. We thank Je ¤ Campbell, Ron Borkovsky, and Steve Kryukov for helpful comments and discussions. Besanko

David Besanko; Ulrich Doraszelski; Lauren Xiaoyuan; Lu Mark Satterthwaite

2010-01-01T23:59:59.000Z

212

Annual Energy Outlook 2001-Appendix G: Major Assumptions for the Forecasts  

Gasoline and Diesel Fuel Update (EIA)

Forecasts Forecasts Summary of the AEO2001 Cases/ Scenarios - Appendix Table G1 bullet1.gif (843 bytes) Model Results (Formats - PDF, ZIP) - Appendix Tables - Reference Case - 1998 to 2020 bullet1.gif (843 bytes) Download Report - Entire AEO2001 (PDF) - AEO2001 by Chapters (PDF) bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts Related Links bullet1.gif (843 bytes) Assumptions to the AEO2001 bullet1.gif (843 bytes) Supplemental Data to the AEO2001 (Only available on the Web) - Regional and more detailed AEO 2001 Reference Case Results - 1998, 2000 to 2020 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Forecast Homepage bullet1.gif (843 bytes) EIA Homepage Appendix G Major Assumptions for the Forecasts Component Modules Major Assumptions for the Annual Energy Outlook 2001

213

Decentralized capacity management and internal pricing  

E-Print Network (OSTI)

Press. Goex, R. (2002). Capacity planning and pricing undermanufacturing on innovation, capacity and pro?tability.Mieghem, V. J. (2003). Capacity management, investment and

Dutta, Sunil; Reichelstein, Stefan

2010-01-01T23:59:59.000Z

214

Capacity consideration of wireless ad hoc networks  

E-Print Network (OSTI)

Capacity ProblemCurrent Research on Capacity of Wireless Ad HocChapter 3 Upper Bound on the Capacity of Wireless Ad Hoc

Tan, Yusong

2008-01-01T23:59:59.000Z

215

Are there capacity limitations in symmetry perception?  

E-Print Network (OSTI)

1980). The demonstration of capacity limitation. Cognitive1972). Visual processing capacity and attentional control.J. (1996). Goodness of CAPACITY LIMIT OF SYMMETRY PERCEPTION

Huang, L Q; Pashler, Harold; Junge, J A

2004-01-01T23:59:59.000Z

216

The Ergodic Capacity of Interference Networks  

E-Print Network (OSTI)

A. Jafar, “The ergodic capacity of interference networks,”Gupta and P. R. Kumar, “The capacity of wireless networks,”cooperation achieves optimal capacity scaling in ad hoc

Jafar, Syed A

2010-01-01T23:59:59.000Z

217

Mapping Individual Variations in Learning Capacity  

E-Print Network (OSTI)

in working memory capacity. Integrative Physiological andVariations in Learning Capacity Eduardo Mercado IIIdifferences in learning capacity are evident in humans and

Mercado III, Eduardo

2011-01-01T23:59:59.000Z

218

Definition: Capacity Emergency | Open Energy Information  

Open Energy Info (EERE)

Emergency Jump to: navigation, search Dictionary.png Capacity Emergency A capacity emergency exists when a Balancing Authority Area's operating capacity, plus firm purchases from...

219

On the use of the parabolic concentration profile assumption for a rotary desiccant dehumidifier  

SciTech Connect

The current work describes a model for a desiccant dehumidifier which uses a parabolic concentration profile assumption to model the diffusion resistance inside the desiccant particle. The relative merits of the parabolic concentration profile model compared with widely utilized rotary desiccant wheel models are discussed. The periodic steady-state parabolic concentration profile model developed is efficient and can accommodate a variety of materials. These features make it an excellent tool for design studies requiring repetitive desiccant wheel simulations. A quartic concentration profile assumption was also investigated which yielded a 2.8 percent average improvement in prediction error over the parabolic model.

Chant, E.E. [Univ. of Turabo, Gurabo (Puerto Rico); Jeter, S.M. [Georgia Inst. of Technology, Atlanta, GA (United States). George W. Woodruff School of Mechanical Engineering

1995-02-01T23:59:59.000Z

220

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

Science Conference Proceedings (OSTI)

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.

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

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Electric Capacity | OpenEI  

Open Energy Info (EERE)

Capacity Capacity Dataset Summary Description The New Zealand Ministry of Economic Development publishes an annual Energy Outlook, which presents projections of New Zealand's future energy supply, demand, prices and greenhouse gas emissions. The principle aim of these projections is to inform the national energy debate. Included here are the model results for electricity and generation capacity. The spreadsheet provides an interactive tool for selecting which model results to view, and which scenarios to evaluate; full model results for each scenario are also included. Source New Zealand Ministry of Economic Development Date Released Unknown Date Updated December 15th, 2010 (3 years ago) Keywords Electric Capacity Electricity Generation New Zealand projections

222

Adaptive capacity and its assessment  

SciTech Connect

This paper reviews the concept of adaptive capacity and various approaches to assessing it, particularly with respect to climate variability and change. I find that adaptive capacity is a relatively under-researched topic within the sustainability science and global change communities, particularly since it is uniquely positioned to improve linkages between vulnerability and resilience research. I identify opportunities for advancing the measurement and characterization of adaptive capacity by combining insights from both vulnerability and resilience frameworks, and I suggest several assessment approaches for possible future development that draw from both frameworks and focus on analyzing the governance, institutions, and management that have helped foster adaptive capacity in light of recent climatic events.

Engle, Nathan L.

2011-04-20T23:59:59.000Z

223

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

SciTech Connect

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.

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

2013-04-01T23:59:59.000Z

224

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

E-Print Network (OSTI)

Biennial 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-emission and efficient gas turbine technology made combined-cycle gas turbine power plants the "resource of choice

225

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

DOE Green Energy (OSTI)

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.

Lai, B.Y.; Poirier, R.N. [Chicago Bridge and Iron Technical Services Co., Plainfield, IL (United States)

1996-08-01T23:59:59.000Z

226

Estimating the Capacity Value of Concentrating Solar Power Plants: A Case Study of the Southwestern United States  

Science Conference Proceedings (OSTI)

We estimate the capacity value of concentrating solar power (CSP) plants without thermal energy storage in the southwestern U.S. Our results show that CSP plants have capacity values that are between 45% and 95% of maximum capacity, depending on their location and configuration. We also examine the sensitivity of the capacity value of CSP to a number of factors and show that capacity factor-based methods can provide reasonable approximations of reliability-based estimates.

Madaeni, S. H.; Sioshansi, R.; Denholm, P.

2012-05-01T23:59:59.000Z

227

Battery Capacity Measurement And Analysis  

E-Print Network (OSTI)

In this paper, we look at different battery capacity models that have been introduced in the literatures. These models describe the battery capacity utilization based on how the battery is discharged by the circuits that consume power. In an attempt to validate these models, we characterize a commercially available lithium coin cell battery through careful measurements of the current and the voltage output of the battery under different load profile applied by a micro sensor node. In the result, we show how the capacity of the battery is affected by the different load profile and provide analysis on whether the conventional battery models are applicable in the real world. One of the most significant finding of our work will show that DC/DC converter plays a significant role in determining the battery capacity, and that the true capacity of the battery may only be found by careful measurements.

Using Lithium Coin; Sung Park; Andreas Savvides; Mani B. Srivastava

2001-01-01T23:59:59.000Z

228

Measuring wind plant capacity value  

DOE Green Energy (OSTI)

Electric utility planners and wind energy researchers pose a common question: What is the capacity value of a wind plant? Tentative answers, which can be phrased in a variety of ways, are based on widely varying definitions and methods of calculation. From the utility`s point of view, a resource that has no capacity value also has a reduced economic value. Utility planners must be able to quantify the capacity value of a wind plant so that investment in conventional generating capacity can be potentially offset by the capacity value of the wind plant. Utility operations personnel must schedule its conventional resources to ensure adequate generation to meet load. Given a choice between two resources, one that can be counted on and the other that can`t, the utility will avoid the risky resource. This choice will be reflected in the price that the utility will pay for the capacity: higher capacity credits result in higher payments. This issue is therefore also important to the other side of the power purchase transaction -- the wind plant developer. Both the utility and the developer must accurately assess the capacity value of wind. This article summarizes and evaluates some common methods of evaluating capacity credit. During the new era of utility deregulation in the United States, it is clear that many changes will occur in both utility planning and operations. However, it is my judgement that the evaluation of capacity credit for wind plants will continue to play an important part in renewable energy development in the future.

Milligan, M.R.

1996-01-01T23:59:59.000Z

229

Estimates of emergency operating capacity in US manufacturing and nonmanufacturing industries - Volume 1: Concepts and Methodology  

SciTech Connect

Development of integrated mobilization preparedness policies requires planning estimates of available productive capacity during national emergency conditions. Such estimates must be developed in a manner to allow evaluation of current trends in capacity and the consideration of uncertainties in various data inputs and in engineering assumptions. This study developed estimates of emergency operating capacity (EOC) for 446 manufacturing industries at the 4-digit Standard Industrial Classification (SIC) level of aggregation and for 24 key nonmanufacturing sectors. This volume lays out the general concepts and methods used to develop the emergency operating estimates. The historical analysis of capacity extends from 1974 through 1986. Some nonmanufacturing industries are included. In addition to mining and utilities, key industries in transportation, communication, and services were analyzed. Physical capacity and efficiency of production were measured. 3 refs., 2 figs., 12 tabs. (JF)

Belzer, D.B. (Pacific Northwest Lab., Richland, WA (USA)); Serot, D.E. (D/E/S Research, Richland, WA (USA)); Kellogg, M.A. (ERCE, Inc., Portland, OR (USA))

1991-03-01T23:59:59.000Z

230

Underground Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

. . Underground Natural Gas Storage Capacity by State, December 31, 1996 (Capacity in Billion Cubic Feet) Table State Interstate Companies Intrastate Companies Independent Companies Total Number of Active Fields Capacity Number of Active Fields Capacity Number of Active Fields Capacity Number of Active Fields Capacity Percent of U.S. Capacity Alabama................. 0 0 1 3 0 0 1 3 0.04 Arkansas ................ 0 0 3 32 0 0 3 32 0.40 California................ 0 0 10 470 0 0 10 470 5.89 Colorado ................ 4 66 5 34 0 0 9 100 1.25 Illinois ..................... 6 259 24 639 0 0 30 898 11.26 Indiana ................... 6 16 22 97 0 0 28 113 1.42 Iowa ....................... 4 270 0 0 0 0 4 270 3.39 Kansas ................... 16 279 2 6 0 0 18 285 3.57 Kentucky ................ 6 167 18 49 0 0 24 216 2.71 Louisiana................ 8 530 4 25 0 0 12 555 6.95 Maryland ................ 1 62

231

COMMUNITY CAPACITY BUILDING THROUGH TECHNOLOGY  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

COMMUNITY CAPACITY BUILDING THROUGH TECHNOLOGY COMMUNITY CAPACITY BUILDING THROUGH TECHNOLOGY Empowering Communities in the Age of E-Government Prepared by Melinda Downing, Environmental Justice Program Manager, U.S. Department of Energy MAR 06 MARCH 2006 Since 1999, the Department of Energy has worked with the National Urban Internet and others to create community capacity through technology.  Empowering Communities in the Age of E-Government Table of Contents Message from the Environmental Justice Program Manager . . . . . . . . 3 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Partnerships. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Process Chart: From Agency to Community. . . . . . . . . . . . . . . . . . . 7 Case Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

232

Reconciling Simulated Moisture Fluxes Resulting from Alternate Hydrologic Model Time Steps and Energy Budget Closure Assumptions  

Science Conference Proceedings (OSTI)

Hydrological model predictions are sensitive to model forcings, input parameters, and the parameterizations of physical processes. Analyses performed for the Variable Infiltration Capacity model show that the resulting moisture fluxes are ...

Ingjerd Haddeland; Dennis P. Lettenmaier; Thomas Skaugen

2006-06-01T23:59:59.000Z

233

Paducah DUF6 Conversion Final EIS - Chapter 4: Environmental Impact Assessment Approach, Assumptions, and Methodology  

NLE Websites -- All DOE Office Websites (Extended Search)

Paducah DUF Paducah DUF 6 Conversion Final EIS 4 ENVIRONMENTAL IMPACT ASSESSMENT APPROACH, ASSUMPTIONS, AND METHODOLOGY This EIS evaluates potential impacts on human health and the natural environment from building and operating a DUF 6 conversion facility at three alternative locations at the Paducah site and for a no action alternative. These impacts might be positive, in that they would improve conditions in the human or natural environment, or negative, in that they would cause a decline in those conditions. This chapter provides an overview of the methods used to estimate the potential impacts associated with the EIS alternatives, summarizes the major assumptions that formed the basis of the evaluation, and provides some background information on human health

234

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

SciTech Connect

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.

Phillip Mills

2012-02-01T23:59:59.000Z

235

EIA-Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2007 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously derived supply curves, initial price paths and international regional supply and demand levels into NEMS. These quantities are not modeled directly in NEMS because NEMS is not an international model. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2007 with the IEO2006 and the STEO some functionality was removed from the IEM. More analyst time was devoted to analyzing price relationships between marker crude oils and refined products. A new exogenous oil supply model, Generate World Oil Balances (GWOB), was also developed to incorporate actual investment occurring in the international oil market through 2015 and resource assumptions through 2030. The GWOB model provides annual country level oil production detail for eight conventional and unconventional oils.

236

GRI baseline projection: Methodology and assumptions 1996 edition. Topical report, January-December 1995  

Science Conference Proceedings (OSTI)

The report documents the methodology employed in producing the 1996 Edition of the GRI Baseline Projection. DRI/McGraw-Hill`s Energy Group (DRI) maintains an energy modeling system for the Gas Research Institute (GRI) that is used to produce an annual projection of the supply and demand for energy by regions in the United States. The 1996 Edition of the GRI Baseline Projection is produced using several different models. The models analyze various pieces of the U.S. energy markets and their solutions are based on a framework of exogenous assumptions provided by GRI. The report describes the integration and solution procedures of the models and the assumptions used to produce the final projection results.

Rhodes, M.R.; Baxter, R.P.; Nottingham, R.P.

1996-04-01T23:59:59.000Z

237

GRI baseline projection: Methodology and assumptions 1995 edition. Topical report, January-December 1994  

SciTech Connect

The report documents the methodology employed in producing the 1995 Edition of the GRI Baseline Projection. DRI/McGraw-Hill`s Energy Group (DRI) maintains an energy modeling system for the Gas Research Institute (GRI) that is used to produce an annual projection of the supply and demand for energy by regions in the United States. The 1995 Edition of the GRI Baseline Projection is produced using several different models. The models analyze various pieces of the U.S. energy markets and their solutions are based on a framework of exogeneous assumptions provided by GRI. The report describes the integration and solution procedures of the models and the assumptions used to produce the final projection results.

Baxter, R.P.; Silveira, T.S.; Harshbarger, S.L.

1995-02-01T23:59:59.000Z

238

High Capacity Immobilized Amine Sorbents  

NLE Websites -- All DOE Office Websites (Extended Search)

Capacity Immobilized Amine Sorbents Capacity Immobilized Amine Sorbents Opportunity The Department of Energy's National Energy Technology Laboratory is seeking licensing partners interested in implementing United States Patent Number 7,288,136 entitled "High Capacity Immobilized Amine Sorbents." Disclosed in this patent is the invention of a method that facilitates the production of low-cost carbon dioxide (CO 2 ) sorbents for use in large-scale gas-solid processes. This method treats an amine to increase the number of secondary amine groups and impregnates the amine in a porous solid support. As a result of this improvement, the method increases CO 2 capture capacity and decreases the cost of using an amine-enriched solid sorbent in CO 2 capture systems. Overview The U.S. Department of Energy has placed a high priority on the separation

239

generation capacity | OpenEI  

Open Energy Info (EERE)

generation capacity generation capacity Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO Electricity electricity market module region generation capacity Data application/vnd.ms-excel icon AEO2011: Electricity Generation Capacity by Electricity Market Module Region and Source- Reference Case (xls, 10.6 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata Average vote Your vote

240

Building Regulatory Capacity for Change  

NLE Websites -- All DOE Office Websites (Extended Search)

Regulatory Capacity for Change PRESENTED BY Sarah Spencer-Workman, LEED AP July 27, 2011 "How to identify and review laws relevant to buildings and find places and opportunities...

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Capacity Markets and Market Stability  

Science Conference Proceedings (OSTI)

The good news is that market stability can be achieved through a combination of longer-term contracts, auctions for far enough in the future to permit new entry, a capacity management system, and a demand curve. The bad news is that if and when stable capacity markets are designed, the markets may seem to be relatively close to where we started - with integrated resource planning. Market ideologues will find this anathema. (author)

Stauffer, Hoff

2006-04-15T23:59:59.000Z

242

Capacity Value of Wind Power  

Science Conference Proceedings (OSTI)

Power systems are planned such that they have adequate generation capacity to meet the load, according to a defined reliability target. The increase in the penetration of wind generation in recent years has led to a number of challenges for the planning and operation of power systems. A key metric for system adequacy is the capacity value of generation. The capacity value of a generator is the contribution that a given generator makes to overall system adequacy. The variable and stochastic nature of wind sets it apart from conventional energy sources. As a result, the modeling of wind generation in the same manner as conventional generation for capacity value calculations is inappropriate. In this paper a preferred method for calculation of the capacity value of wind is described and a discussion of the pertinent issues surrounding it is given. Approximate methods for the calculation are also described with their limitations highlighted. The outcome of recent wind capacity value analyses in Europe and North America are highlighted with a description of open research questions also given.

Keane, Andrew; Milligan, Michael; Dent, Chris; Hasche, Bernhard; DAnnunzio, Claudine; Dragoon, Ken; Holttinen, Hannele; Samaan, Nader A.; Soder, Lennart; O'Malley, Mark J.

2011-05-04T23:59:59.000Z

243

Atmospheric Crude Oil Distillation Operable Capacity  

Gasoline and Diesel Fuel Update (EIA)

(Barrels per Calendar Day) (Barrels per Calendar Day) Data Series: Total Number of Operable Refineries Number of Operating Refineries Number of Idle Refineries Atmospheric Crude Oil Distillation Operable Capacity (B/CD) Atmospheric Crude Oil Distillation Operating Capacity (B/CD) Atmospheric Crude Oil Distillation Idle Capacity (B/CD) Atmospheric Crude Oil Distillation Operable Capacity (B/SD) Atmospheric Crude Oil Distillation Operating Capacity (B/SD) Atmospheric Crude Oil Distillation Idle Capacity (B/SD) Vacuum Distillation Downstream Charge Capacity (B/SD) Thermal Cracking Downstream Charge Capacity (B/SD) Thermal Cracking Total Coking Downstream Charge Capacity (B/SD) Thermal Cracking Delayed Coking Downstream Charge Capacity (B/SD Thermal Cracking Fluid Coking Downstream Charge Capacity (B/SD) Thermal Cracking Visbreaking Downstream Charge Capacity (B/SD) Thermal Cracking Other/Gas Oil Charge Capacity (B/SD) Catalytic Cracking Fresh Feed Charge Capacity (B/SD) Catalytic Cracking Recycle Charge Capacity (B/SD) Catalytic Hydro-Cracking Charge Capacity (B/SD) Catalytic Hydro-Cracking Distillate Charge Capacity (B/SD) Catalytic Hydro-Cracking Gas Oil Charge Capacity (B/SD) Catalytic Hydro-Cracking Residual Charge Capacity (B/SD) Catalytic Reforming Charge Capacity (B/SD) Catalytic Reforming Low Pressure Charge Capacity (B/SD) Catalytic Reforming High Pressure Charge Capacity (B/SD) Catalytic Hydrotreating/Desulfurization Charge Capacity (B/SD) Catalytic Hydrotreating Naphtha/Reformer Feed Charge Cap (B/SD) Catalytic Hydrotreating Gasoline Charge Capacity (B/SD) Catalytic Hydrotreating Heavy Gas Oil Charge Capacity (B/SD) Catalytic Hydrotreating Distillate Charge Capacity (B/SD) Catalytic Hydrotreating Kerosene/Jet Fuel Charge Capacity (B/SD) Catalytic Hydrotreating Diesel Fuel Charge Capacity (B/SD) Catalytic Hydrotreating Other Distillate Charge Capacity (B/SD) Catalytic Hydrotreating Residual/Other Charge Capacity (B/SD) Catalytic Hydrotreating Residual Charge Capacity (B/SD) Catalytic Hydrotreating Other Oils Charge Capacity (B/SD) Fuels Solvent Deasphalting Charge Capacity (B/SD) Catalytic Reforming Downstream Charge Capacity (B/CD) Total Coking Downstream Charge Capacity (B/CD) Catalytic Cracking Fresh Feed Downstream Charge Capacity (B/CD) Catalytic Hydro-Cracking Downstream Charge Capacity (B/CD) Period:

244

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network (OSTI)

A comparison of national energy consumption by fuel typeenergy consumption in homes under differing assumptions, scenarios, and policies. At the national

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

245

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network (OSTI)

G. Koomey. 1994. Residential Appliance Data, Assumptions andunits) Table A 3 : Number of Appliances in Existing Homes (sector, including appliances and heating, ventilation, and

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

246

Optimal Capacity Adjustments for Supply Chain Control  

E-Print Network (OSTI)

Decisions on capacity are often treated separately from those of production and inventory. In most situations, capacity issues are longer-term, so capacity-related decisions are considered strategic and thus not part of ...

Budiman, Benny

247

Building Regulatory Capacity for Change  

NLE Websites -- All DOE Office Websites (Extended Search)

Regulatory Capacity for Regulatory Capacity for Change PRESENTED BY Sarah Spencer-Workman, LEED AP July 27, 2011 "How to identify and review laws relevant to buildings and find places and opportunities that can accept changes that would support building energy objectives" Presentation Highlights Rulemaking Community and Stakeholder Identification To Support Code Changes Engagement: Building Capacity for Change Pay It Forward RULEMAKING : Plan Development and Research of Laws Relevant to Buildings How is it conducted? 'Landscape' Review Key words or phrases to look for Identify "home rule" jurisdictions Update and review cycle built in 'Landscape' Review:

248

production capacity | OpenEI  

Open Energy Info (EERE)

production capacity production capacity Dataset Summary Description No description given. Source Oak Ridge National Laboratory Date Released November 30th, 2009 (4 years ago) Date Updated Unknown Keywords biodiesel ethanol location production capacity transportation Data application/zip icon Biorefineries.zip (zip, 7 MiB) Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Time Period License License Other or unspecified, see optional comment below Comment Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset Average vote Your vote Ease of access Average vote Your vote Overall rating Average vote Your vote Comments Login or register to post comments If you rate this dataset, your published comment will include your rating.

249

installed capacity | OpenEI  

Open Energy Info (EERE)

installed capacity installed capacity Dataset Summary Description Estimates for each of the 50 states and the entire United States show Source Wind Powering America Date Released February 04th, 2010 (4 years ago) Date Updated April 13th, 2011 (3 years ago) Keywords annual generation installed capacity usa wind Data application/vnd.ms-excel icon Wind potential data (xls, 102.4 KiB) Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Time Period License License Other or unspecified, see optional comment below Comment Work of the U.S. Federal Government. Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset Average vote Your vote Ease of access Average vote Your vote Overall rating Average vote Your vote Comments

250

Bases, Assumptions, and Results of the Flowsheet Calculations for the Decision Phase Salt Disposition Alternatives  

SciTech Connect

The High Level Waste (HLW) Salt Disposition Systems Engineering Team was formed on March 13, 1998, and chartered to identify options, evaluate alternatives, and recommend a selected alternative(s) for processing HLW salt to a permitted wasteform. This requirement arises because the existing In-Tank Precipitation process at the Savannah River Site, as currently configured, cannot simultaneously meet the HLW production and Authorization Basis safety requirements. This engineering study was performed in four phases. This document provides the technical bases, assumptions, and results of this engineering study.

Dimenna, R.A.; Jacobs, R.A.; Taylor, G.A.; Durate, O.E.; Paul, P.K.; Elder, H.H.; Pike, J.A.; Fowler, J.R.; Rutland, P.L.; Gregory, M.V.; Smith III, F.G.; Hang, T.; Subosits, S.G.; Campbell, S.G.

2001-03-26T23:59:59.000Z

251

Science with the Square Kilometer Array: Motivation, Key Science Projects, Standards and Assumptions  

E-Print Network (OSTI)

The Square Kilometer Array (SKA) represents the next major, and natural, step in radio astronomical facilities, providing two orders of magnitude increase in collecting area over existing telescopes. In a series of meetings, starting in Groningen, the Netherlands (August 2002) and culminating in a `science retreat' in Leiden (November 2003), the SKA International Science Advisory Committee (ISAC), conceived of, and carried-out, a complete revision of the SKA science case (to appear in New Astronomy Reviews). This preface includes: (i) general introductory material, (ii) summaries of the key science programs, and (iii) a detailed listing of standards and assumptions used in the revised science case.

C. Carilli; S. Rawlings

2004-09-12T23:59:59.000Z

252

Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network (OSTI)

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting. DE-AC03-76SF00098 #12;i ABSTRACT This report details the data, assumptions and methodology for end-use provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which

253

A study of two phases heat transport capacity in a micro heat pipe  

Science Conference Proceedings (OSTI)

Present study modifies Cotter's model by using the dimensionless liquid flow shape factor, K1, to predict the maximum heat transport capacity and to discus the effects of contact angle. The results indicated that as the dimensionless ... Keywords: Cotter's model, contact angle, dimensionless, heat pipe, heat transport capacity, shape factor

Cheng-Hsing Hsu; Kuang-Yuan Kung; Shu-Yu Hu; Ching-Chuan Chang

2009-07-01T23:59:59.000Z

254

Worldwide Energy Efficiency Action through Capacity Building...  

Open Energy Info (EERE)

Worldwide Energy Efficiency Action through Capacity Building and Training (WEACT) Jump to: navigation, search Logo: Worldwide Energy Efficiency Action through Capacity Building and...

255

Working and Net Available Shell Storage Capacity  

U.S. Energy Information Administration (EIA) Indexed Site

and tank farms. Excludes storage capacity of refineries, fuel ethanol plants, and pipelines. 2 Percent exclusive use is that portion of capacity in operation that is for the...

256

High Capacity Hydrogen Storage Nanocomposite - Energy ...  

Energy Storage Advanced Materials High Capacity Hydrogen Storage Nanocomposite Processes to add metal hydrideds to nanocarbon structures to yield high capacity ...

257

Property:Cooling Capacity | Open Energy Information  

Open Energy Info (EERE)

Capacity Jump to: navigation, search This is a property of type Number. Pages using the property "Cooling Capacity" Showing 2 pages using this property. D Distributed Generation...

258

Economic Dispatch of Electric Generation Capacity | Department...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Economic Dispatch of Electric Generation Capacity Economic Dispatch of Electric Generation Capacity A report to congress and the states pursuant to sections 1234 and 1832 of the...

259

California Working Natural Gas Underground Storage Capacity ...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) California Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

260

Resource Adequacy Capacity - Power Marketing - Sierra Nevada...  

NLE Websites -- All DOE Office Websites (Extended Search)

Resource Adequacy Capacity Resource Adequacy Capacity Resource Adequacy Plan - Current Local Resource Adequacy Plan (Word - 175K) - Notice of Proposed Final Resource Adequacy Plan...

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Washington Natural Gas Underground Storage Acquifers Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Acquifers Capacity (Million Cubic Feet) Washington Natural Gas Underground Storage Acquifers Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3...

262

Missouri Natural Gas Underground Storage Acquifers Capacity ...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Acquifers Capacity (Million Cubic Feet) Missouri Natural Gas Underground Storage Acquifers Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3...

263

Mississippi Working Natural Gas Underground Storage Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Mississippi Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

264

Minnesota Natural Gas Underground Storage Acquifers Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Acquifers Capacity (Million Cubic Feet) Minnesota Natural Gas Underground Storage Acquifers Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3...

265

Pennsylvania Working Natural Gas Underground Storage Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Pennsylvania Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

266

Washington Working Natural Gas Underground Storage Capacity ...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Washington Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

267

EIA Crude Oil Distillation Capacity (Table 36)  

U.S. Energy Information Administration (EIA)

(Important Note on Sources of Crude Oil Distillation Capacity Estimates) Table 3.6 World Crude Oil Distillation Capacity, January 1, 1970 - January 1, 2009

268

Comparison of Capacity Value Methods for Photovoltaics in the Western United States  

DOE Green Energy (OSTI)

This report compares different capacity value estimation techniques applied to solar photovoltaics (PV). It compares more robust data and computationally intense reliability-based capacity valuation techniques to simpler approximation techniques at 14 different locations in the western United States. The capacity values at these locations are computed while holding the underlying power system characteristics fixed. This allows the effect of differences in solar availability patterns on the capacity value of PV to be directly ascertained, without differences in the power system confounding the results. Finally, it examines the effects of different PV configurations, including varying the orientation of a fixed-axis system and installing single- and double-axis tracking systems, on the capacity value. The capacity value estimations are done over an eight-year running from 1998 to 2005, and both long-term average capacity values and interannual capacity value differences (due to interannual differences in solar resource availability) are estimated. Overall, under the assumptions used in the analysis, we find that some approximation techniques can yield similar results to reliability-based methods such as effective load carrying capability.

Madaeni, S. H.; Sioshansi, R.; Denholm, P.

2012-07-01T23:59:59.000Z

269

Biennial Assessment of the Fifth Power Plan Interim Report on Fuel Price Assumptions  

E-Print Network (OSTI)

The Fifth Power Plan includes price forecasts for natural gas, oil, and coal. Natural gas prices have by far and natural gas prices have put some pressure on coal prices as well, although they remain lower capacity to deliver the coal and higher oil prices increased the delivery costs as well. Both natural gas

270

Analysis of the effect of packing capacity on pork prices  

E-Print Network (OSTI)

In 1998, pork prices fell to an all time low. Across the industry, concern was expressed for research as to what led to this price crash. Capacity constraints at the packer level have been a key area of concern. This study is an analysis of the effect of capacity constraints on pork prices. Ordinary least squares (OLS) models were run for both live and cutout prices. Capacity constraints were measured three ways: using a binary variable (0,1 dummy) and two continuous variables. One continuous variable was for the number of head slaughtered on the weekend, and the second continuous variable was found by using a ratio of slaughter during the weekends to slaughter during the 5-day workweek ("over-flow" ratio). The continuous variables used to measure capacity constraints were statistically significant explanatory factors in the regressions for hog and pork prices. The capacity constraints were estimated to have a different relationship with the prices at the farm level as compared with packer prices. Increasing capacity constraints is associated with a negative relationship to farm prices, and a positive relationship to packer prices. The measurement used for over-flow ratio, the ratio of weekend slaughter to slaughter during the 5-day workweek, did not generate different results than the continuous variable of weekend slaughter. The estimated coefficients for both continuous variables were more statistically significant than a dummy variable approach for the capacity constraint.

Spivey, Sarah Elizabeth

2000-01-01T23:59:59.000Z

271

Dynamic Deferral of Workload for Capacity Provisioning in Data Centers  

E-Print Network (OSTI)

Recent increase in energy prices has led researchers to find better ways for capacity provisioning in data centers to reduce energy wastage due to the variation in workload. This paper explores the opportunity for cost saving and proposes a novel approach for capacity provisioning under bounded latency requirements for the workload. We investigate how many servers to be kept active and how much workload to be delayed for energy saving while meeting every deadline. We present an offline LP formulation for capacity provisioning by dynamic deferral and give two online algorithms to determine the capacity of the data center and the assignment of workload to servers dynamically. We prove the feasibility of the online algorithms and show that their worst case performance are bounded by a constant factor with respect to the offline formulation. We validate our algorithms on synthetic workload generated from two real HTTP traces and show that they actually perform much better in practice than the worst case, resultin...

Adnan, Muhammad Abdullah; Sugihara, Ryo; Gupta, Rajesh

2011-01-01T23:59:59.000Z

272

The Role Of Modeling Assumptions And Policy Instruments in Evaluating The Global Implications Of U.S. Biofuel Policies  

Science Conference Proceedings (OSTI)

The primary objective of current U.S. biofuel law the Energy Independence and Security Act of 2007 (EISA) is to reduce dependence on imported oil, but the law also requires biofuels to meet carbon emission reduction thresholds relative to petroleum fuels. EISA created a renewable fuel standard with annual targets for U.S. biofuel use that climb gradually from 9 billion gallons per year in 2008 to 36 billion gallons (or about 136 billion liters) of biofuels per year by 2022. The most controversial aspects of the biofuel policy have centered on the global social and environmental implications of its potential land use effects. In particular, there is an ongoing debate about whether indirect land use change (ILUC) make biofuels a net source, rather sink, of carbon emissions. However, estimates of ILUC induced by biofuel production and use can only be inferred through modeling. This paper evaluates how model structure, underlying assumptions, and the representation of policy instruments influence the results of U.S. biofuel policy simulations. The analysis shows that differences in these factors can lead to divergent model estimates of land use and economic effects. Estimates of the net conversion of forests and grasslands induced by U.S. biofuel policy range from 0.09 ha/1000 gallons described in this paper to 0.73 ha/1000 gallons from early studies in the ILUC change debate. We note that several important factors governing LUC change remain to be examined. Challenges that must be addressed to improve global land use change modeling are highlighted.

Oladosu, Gbadebo A [ORNL; Kline, Keith L [ORNL

2010-01-01T23:59:59.000Z

273

Entangling and disentangling capacities of nonlocal maps  

E-Print Network (OSTI)

Entangling and disentangling capacities are the key manifestation of the nonlocal content of a quantum operation. A lot of effort has been put recently into investigating (dis)entangling capacities of unitary operations, but very little is known about capacities of non-unitary operations. Here we investigate (dis)entangling capacities of unital CPTP maps acting on two qubits.

Berry Groisman

2007-04-08T23:59:59.000Z

274

Louisiana Refinery Catalytic Reforming Downstream Charge Capacity ...  

U.S. Energy Information Administration (EIA)

Louisiana Refinery Catalytic Reforming Downstream Charge Capacity as of January 1 (Barrels per Stream Day)

275

Oklahoma Refinery Vacuum Distillation Downstream Charge Capacity ...  

U.S. Energy Information Administration (EIA)

Oklahoma Refinery Vacuum Distillation Downstream Charge Capacity as of January 1 (Barrels per Stream Day)

276

Georgia Refinery Marketable Petroleum Coke Production Capacity ...  

U.S. Energy Information Administration (EIA)

Georgia Refinery Marketable Petroleum Coke Production Capacity as of January 1 (Barrels per Stream Day)

277

Natural gas productive capacity for the lower 48 States, 1980 through 1995  

SciTech Connect

The purpose of this report is to analyze monthly natural gas wellhead productive capacity in the lower 48 States from 1980 through 1992 and project this capacity from 1993 through 1995. For decades, natural gas supplies and productive capacity have been adequate to meet demand. In the 1970`s the capacity surplus was small because of market structure (split between interstate and intrastate), increasing demand, and insufficient drilling. In the early 1980`s, lower demand, together with increased drilling, led to a large surplus capacity as new productive capacity came on line. After 1986, this large surplus began to decline as demand for gas increased, gas prices fell, and gas well completions dropped sharply. In late December 1989, the decline in this surplus, accompanied by exceptionally high demand and temporary weather-related production losses, led to concerns about the adequacy of monthly productive capacity for natural gas. These concerns should have been moderated by the gas system`s performance during the unusually severe winter weather in March 1993 and January 1994. The declining trend in wellhead productive capacity is expected to be reversed in 1994 if natural gas prices and drilling meet or exceed the base case assumption. This study indicates that in the low, base, and high drilling cases, monthly productive capacity should be able to meet normal production demands through 1995 in the lower 48 States (Figure ES1). Exceptionally high peak-day or peak-week production demand might not be met because of physical limitations such as pipeline capacity. Beyond 1995, as the capacity of currently producing wells declines, a sufficient number of wells and/or imports must be added each year in order to ensure an adequate gas supply.

Not Available

1994-07-14T23:59:59.000Z

278

EIA-Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2007 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS25 data.

279

EIA-Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2007 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

280

EIA - Assumptions to the Annual Energy Outlook 2008 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2008 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Module. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2007), (Washington, DC, 2007). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

EIA-Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2007 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new

282

EIA-Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2007 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

283

EIA-Assumptions to the Annual Energy Outlook - National Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2007 National Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

284

EIA-Assumptions to the Annual Energy Outlook - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2007 Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

285

Assumptions to the Annual Energy Outlook 2000-Table 1. Summary of the  

Gasoline and Diesel Fuel Update (EIA)

0 Cases 0 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.7 percent, compared to the reference case growth of 2.2 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.2 percent. Fully Integrated Low World Oil Price World oil prices are $14.90 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated High World Oil Price World oil prices are $28.04 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated Residential: 2000 Technology

286

Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumption to the Annual Energy Outlook Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. Table 50. Crude Oil Technically Recoverable Resources (Billion barrels) Printer Friendly Version Crude Oil Resource Category As of January 1, 2002 Undiscovered 56.02 Onshore 19.33 Northeast 1.47 Gulf Coast 4.76 Midcontinent 1.12 Southwest 3.25 Rocky Moutain 5.73 West Coast 3.00 Offshore 36.69 Deep (>200 meter W.D.) 35.01 Shallow (0-200 meter W.D.) 1.69 Inferred Reserves 49.14 Onshore 37.78 Northeast 0.79 Gulf Coast 0.80 Midcontinent 3.73 Southwest 14.61 Rocky Mountain 9.91 West Coast 7.94

287

EIA - Assumptions to the Annual Energy Outlook 2009 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2009 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.1. Crude Oil Technically Recoverable Resources. Need help, contact the Naitonal Energy Information Center at 202-586-8800. printer-friendly version Table 9.2. Natural Gas Technically Recoverable Resources. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.2. Continued printer-friendly version Table 9.3. Assumed Size and Initial Production year of Major Announced Deepwater Discoveries. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 9.4. Assumed Annual Rates of Technological Progress for Conventional Crude Oil and Natural Gas Sources. Need help, contact the National Energy Information Center at 202-586-8800.

288

Assumptions to the Annual Energy Outlook 2001 - Table 1. Summary of AEO2001  

Gasoline and Diesel Fuel Update (EIA)

1 Cases 1 Cases Case name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully integrated Low Economic Growth Gross domestic product grows at an average annual rate of 2.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated High Economic Growth Gross domestic product grows at an average annual rate of 3.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated Low World Oil Price World oil prices are $15.10 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated High World Oil Price World oil prices are $28.42 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated Residential: 2001 Technology

289

EIA - Assumptions to the Annual Energy Outlook 2010 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2010 Oil and Gas Supply Module Figure 8. Natural Gas Transmission and Distribution Model Regions. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas natural gas exploration and development on a regional basis (Figure 7). The OGSM is organized into 4 submodules: Onshore Lower 48 Oil and Gas Supply Submodule, Offshore Oil and Gas Supply Submodule, Oil Shale Supply submodule, and Alaska Oil and Gas Supply Submodule. A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2010), (Washington, DC, 2010). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural

290

Washington International Renewable Energy Conference 2008 Pledges: Methodology and Assumptions Summary  

Science Conference Proceedings (OSTI)

The 2008 Washington International Renewable Energy Conference (WIREC) was held in Washington, D.C., from March 4-6, 2008, and involved nearly 9,000 people from 125 countries. The event brought together worldwide leaders in renewable energy (RE) from governments, international organizations, nongovernmental organizations, and the private sector to discuss the role that renewables can play in alleviating poverty, growing economies, and passing on a healthy planet to future generations. The conference concluded with more than 140 governments, international organizations, and private-sector representatives pledging to advance the uptake of renewable energy. The U.S. government authorized the National Renewable Energy Laboratory (NREL) to estimate the carbon dioxide (CO2) savings that would result from the pledges made at the 2008 conference. This report describes the methodology and assumptions used by NREL in quantifying the potential CO2 reductions derived from those pledges.

Babiuch, B.; Bilello, D. E.; Cowlin, S. C.; Mann, M.; Wise, A.

2008-08-01T23:59:59.000Z

291

Design assumptions and bases for small D-T-fueled spherical tokamak (ST) fusion core  

SciTech Connect

Recent progress in defining the assumptions and clarifying the bases for a small D-T-fueled ST fusion core are presented. The paper covers several issues in the physics of ST plasmas, the technology of neutral beam injection, the engineering design configuration, and the center leg material under intense neutron irradiation. This progress was driven by the exciting data from pioneering ST experiments, a heightened interest in proof-of-principle experiments at the MA level in plasma current, and the initiation of the first conceptual design study of the small ST fusion core. The needs recently identified for a restructured fusion energy sciences program have provided a timely impetus for examining the subject of this paper. Our results, though preliminary in nature, strengthen the case for the potential realism and attractiveness of the ST approach.

Peng, Yueng Kay Martin [ORNL; Haines, J.R. [Oak Ridge National Laboratory (ORNL)

1996-01-01T23:59:59.000Z

292

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

SciTech Connect

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.

Hommel, S.; Fountain, D.

2012-03-28T23:59:59.000Z

293

OpenEI - Electric Capacity  

Open Energy Info (EERE)

New Zealand Energy New Zealand Energy Outlook (2010): Electricity and Generation Capacity http://en.openei.org/datasets/node/357 The New Zealand Ministry of Economic Development publishes an annual Energy Outlook, which presents projections of New Zealand's future energy supply, demand, prices and greenhouse gas emissions. The principle aim of these projections is to inform the national energy debate. Included here are the model results for electricity and generation capacity. The spreadsheet provides an interactive tool for selecting which model results to view, and which scenarios to evaluate; full model results for each scenario are also included.

License

294

High capacity immobilized amine sorbents  

DOE Patents (OSTI)

A method is provided for making low-cost CO.sub.2 sorbents that can be used in large-scale gas-solid processes. The improved method entails treating an amine to increase the number of secondary amine groups and impregnating the amine in a porous solid support. The method increases the CO.sub.2 capture capacity and decreases the cost of utilizing an amine-enriched solid sorbent in CO.sub.2 capture systems.

Gray, McMahan L. (Pittsburgh, PA); Champagne, Kenneth J. (Fredericktown, PA); Soong, Yee (Monroeville, PA); Filburn, Thomas (Granby, CT)

2007-10-30T23:59:59.000Z

295

Building Energy Software Tools Directory : CHP Capacity Optimizer  

NLE Websites -- All DOE Office Websites (Extended Search)

CHP Capacity Optimizer Back to Tool CHP Capacity Optimizer data entry screen CHP Capacity Optimizer results screen CHP Capacity Optimizer restult map...

296

electricity generating capacity | OpenEI  

Open Energy Info (EERE)

generating capacity generating capacity Dataset Summary Description The New Zealand Ministry of Economic Development publishes energy data including many datasets related to electricity. Included here are three electricity generating capacity datasets: annual operational electricity generation capacity by plant type (1975 - 2009); estimated generating capacity by fuel type for North Island, South Island and New Zealand (2009); and information on generating plants (plant type, name, owner, commissioned date, and capacity), as of December 2009. Source New Zealand Ministry of Economic Development Date Released Unknown Date Updated July 03rd, 2009 (5 years ago) Keywords biomass coal Electric Capacity electricity generating capacity geothermal Hydro Natural Gas wind Data application/vnd.ms-excel icon Operational Electricity Generation Capacity by Plant Type (xls, 42.5 KiB)

297

The Effect of Technological Improvement on Capacity  

E-Print Network (OSTI)

We formulate a model of capacity expansion that is relevant to a service provider for whom the cost of capacity shortages would be considerable but difficult to quantify exactly. Due to demand uncertainty and a lead time for adding capacity, not all shortages are avoidable. In addition, technological innovations will reduce the cost of adding capacity but may not be completely predictable. Analytical expressions for the infinite horizon expansion cost and shortages are optimized numerically. Sensitivity analyses allow us to determine the impact of technological change on the optimal timing and sizes of capacity expansions to account for economies of scale, the time value of money and penalties for insufficient capacity.

Expansion For Uncertain; Dohyun Pak; Nattapol Pornsalnuwat; Sarah M. Ryan

2004-01-01T23:59:59.000Z

298

Shortwave Shape Factor Inversion of Earth Radiation Budget Observations  

Science Conference Proceedings (OSTI)

The shape factor technique is routinely used to invert wide-angle radiometric measurements at satellite altitude to flux at the top of the atmosphere. The derivation of a shortwave shape factor requires assumptions on both the viewed radiation ...

Richard N. Green; G. Louis Smith

1991-02-01T23:59:59.000Z

299

50-year-old assumptions about strength muscled aside | Argonne National  

NLE Websites -- All DOE Office Websites (Extended Search)

C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. To view a larger, downloadable version of the image, click on it. To view a larger, downloadable version of the image, click on it. 50-year-old assumptions about strength muscled aside July 11, 2013 Tweet EmailPrint LEMONT, Ill. - Doctors have a new way of thinking about how to treat heart and skeletal muscle diseases. Body builders have a new way of

300

Standard assumptions and methods for solar heating and cooling systems analysis  

DOE Green Energy (OSTI)

A set of inputs, assumptions, analytical methods, and a reporting format is presented to help compare the results of residential and commercial solar system analyses being performed by different investigators. By the common use of load data, meteorological data, economic parameters, and reporting format, researchers examining, for example, two types of collectors may more easily compare their results. For residential heating and cooling systems, three locations were selected. The weather data chosen to characterize these cities are the Typical Meteorological Year (TMY). A house for each location was defined that is typical of new construction in that locale. Hourly loads for each location were calculated using a computerized load model that interacts with the system specified inputs characterizing each house. Four locations for commercial cooling analyses were selected from among the existing sites for which TMYs were available. A light commercial (nominal 25-ton cooling load) office building was defined and is used in all four locations. Hourly cooling and heating loads were computed for each city and are available on magnetic tape from the Solar Energy Research Insititute (SERI).

Leboeuf, C.M.

1980-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Capacity dynamics of feed-forward, flow-matching networks exposed to random disruptions  

E-Print Network (OSTI)

While lean manufacturing has greatly improved the efficiency of production operations, it has left US enterprises in an increasingly risky environment. Causes of manufacturing disruptions continue to multiply, and today, seemingly minor disruptions can cause cascading sequences of capacity losses. Historically, enterprises have lacked viable tools for addressing operational volatility. As a result, each year US companies forfeit billions of dollars to unpredictable capacity disruptions and insurance premiums. In this dissertation we develop a number of stochastic models that capture the dynamics of capacity disruptions in complex multi-tier flow-matching feed-forward networks (FFN). In particular, we relax basic structural assumptions of FFN, introduce random propagation times, study the impact of inventory buffers on propagation times, and make initial efforts to model random network topology. These stochastic models are central to future methodologies supporting strategic risk management and enterprise network design.

Savachkin, Aliaksei

2005-08-01T23:59:59.000Z

302

U.S. Refining Capacity Utilization  

Reports and Publications (EIA)

This article briefly reviews recent trends in domestic refining capacity utilization and examines in detail the differences in reported crude oil distillation capacities and utilization rates among different classes of refineries.

Tancred Lidderdale

1995-10-01T23:59:59.000Z

303

Definition: Capacity Revenue | Open Energy Information  

Open Energy Info (EERE)

through the competitive capacity market for a capacity credit.1 References SmartGrid.gov 'Description of Benefits' An LikeLike UnlikeLike You like this.Sign Up to see...

304

Empirical Study of Ramp Metering and Capacity  

E-Print Network (OSTI)

Empirical Study of Ramp Metering and Capacity Michael J.EMPIRICAL STUDY OF RAMP METERING AND CAPACITY June 7, 2002Thus, the benefits of metering inflows at this on-ramp seem

Cassidy, Michael J.; Rudjanakanoknad, Jittichai

2002-01-01T23:59:59.000Z

305

On the capacity of bosonic channels  

E-Print Network (OSTI)

The capacity of the bosonic channel with additive Gaussian noise is unknown, but there is a known lower bound that is conjectured to be the capacity. We have quantified the gap that exists between this known achievable ...

Blake, Christopher Graham

2011-01-01T23:59:59.000Z

306

Capacity expansion in contemporary telecommunication networks  

E-Print Network (OSTI)

We study three capacity expansion problems in contemporary long distance telecommunication networks. The first two problems, motivated by a major long distance provider, address capacity expansion in national hybrid long ...

Sivaraman, Raghavendran

2007-01-01T23:59:59.000Z

307

On Working Memory: Its organization and capacity limits  

E-Print Network (OSTI)

64 iii 6.2 Working memory capacity10 1.4 Capacity limits of workingcapacity . . . . . . . . . . . . . . . . . . . . . . . . . .

Lara, Antonio Homero

2010-01-01T23:59:59.000Z

308

Loads, capacity, and failure rate modeling  

SciTech Connect

Both failure rate and load capacity (stress-strength) interferenece methodologies are employed in the reliability analysis at nuclear facilities. Both of the above have been utilized in a heuristic failure rate model in terms of load capacity inference. Analytical solutions are used to demonstrate that infant mortality and random aging failures may be expressed implicity in terms of capacity variability, load variability, and capacity deterioration, and that mode interactions play a role in the formation of the bathtub curve for failure rates.

Lewis, E.E.; Chen, Hsin-Chieh

1994-12-31T23:59:59.000Z

309

Peak Underground Working Natural Gas Storage Capacity  

U.S. Energy Information Administration (EIA)

Peak Working Natural Gas Capacity. Data and Analysis from the Energy Information Administration (U.S. Dept. of Energy)

310

Texas Number and Capacity of Petroleum Refineries  

U.S. Energy Information Administration (EIA)

Atmospheric Crude Oil Distillation Capacity : Operable ... Idle refineries represent refineries where distillation units were completely idle but not ...

311

Colorado Number and Capacity of Petroleum Refineries  

U.S. Energy Information Administration (EIA)

Atmospheric Crude Oil Distillation Capacity : Operable ... Idle refineries represent refineries where distillation units were completely idle but not ...

312

Optimization of the Refrigerant Capacity in Multiphase ...  

Science Conference Proceedings (OSTI)

Symposium, Magnetic Materials for Energy Applications. Presentation Title, Optimization of the Refrigerant Capacity in Multiphase Magnetocaloric Materials.

313

Regional Profiles: Pipeline Capacity and Service  

U.S. Energy Information Administration (EIA)

Regional Profiles: Pipeline Capacity ... large petrochemical and electric utility industries drawn there ... accounts for large electricity load ...

314

Shannon capacity of nonlinear regenerative channels  

E-Print Network (OSTI)

We compute Shannon capacity of nonlinear channels with regenerative elements. Conditions are found under which capacity of such nonlinear channels is higher than the Shannon capacity of the classical linear additive white Gaussian noise channel. We develop a general scheme for designing the proposed channels and apply it to the particular nonlinear sine-mapping. The upper bound for regeneration efficiency is found and the asymptotic behavior of the capacity in the saturation regime is derived.

Sorokina, M A

2013-01-01T23:59:59.000Z

315

Robust Capacity Planning in Semiconductor Manufacturing  

E-Print Network (OSTI)

Oct 3, 2001 ... Abstract: We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing.

316

Capacity of shrinking condensers in the plane  

E-Print Network (OSTI)

We show that the capacity of a class of plane condensers is comparable to the capacity of corresponding "dyadic condensers". As an application, we show that for plane condensers in that class the capacity blows up as the distance between the plates shrinks, but there can be no asymptotic estimate of the blow-up.

Arcozzi, N

2011-01-01T23:59:59.000Z

317

The Compound Capacity of Polar Codes  

E-Print Network (OSTI)

We consider the compound capacity of polar codes under successive cancellation decoding for a collection of binary-input memoryless output-symmetric channels. By deriving a sequence of upper and lower bounds, we show that in general the compound capacity under successive decoding is strictly smaller than the unrestricted compound capacity.

Hassani, S Hamed; Urbanke, Ruediger

2009-01-01T23:59:59.000Z

318

High current capacity electrical connector  

DOE Patents (OSTI)

An electrical connector is provided for coupling high current capacity electrical conductors such as copper busses or the like. The connector is arranged in a "sandwiched" configuration in which a conductor plate contacts the busses along major surfaces thereof clamped between two stainless steel backing plates. The conductor plate is provided with a plurality of contact buttons affixed therein in a spaced array such that the caps of the buttons extend above the conductor plate surface to contact the busses. When clamping bolts provided through openings in the sandwiched arrangement are tightened, Belleville springs provided under the rim of each button cap are compressed and resiliently force the caps into contact with the busses' contacting surfaces to maintain a predetermined electrical contact area provided by the button cap tops. The contact area does not change with changing thermal or mechanical stresses applied to the coupled conductors.

Bettis, Edward S. (Oak Ridge, TN); Watts, Harry L. (Lake City, TN)

1976-01-13T23:59:59.000Z

319

Assessing the Control Systems Capacity for Demand Response in California Industries  

SciTech Connect

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.

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

2012-01-18T23:59:59.000Z

320

Capacity withholding in the Electricity Pool.  

E-Print Network (OSTI)

Electricity generators can raise the price of power by withholding their plant from the market. We discuss two ways in which this could have affected prices in the England and Wales Pool. Withholding low-cost capacity which should be generating will raise energy prices but make the pattern of generation less efficient. This pattern improved significantly after privatisation. Withholding capacity that was not expected to generate would raise the Capacity Payments based on spare capacity. On a multi-year basis, these did not usually exceed “competitive ” levels, the cost of keeping stations open. The evidence for large-scale capacity withholding is weak. Keywords: JEL:

Richard Green; Richard Green

2004-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Property:InstalledCapacity | Open Energy Information  

Open Energy Info (EERE)

InstalledCapacity InstalledCapacity Jump to: navigation, search Property Name InstalledCapacity Property Type Quantity Description Installed Capacity (MW) or also known as Total Generator Nameplate Capacity (Rated Power) Use this property to express potential electric energy generation, such as Nameplate Capacity. The default unit is megawatts (MW). For spatial capacity, use property Volume. Acceptable units (and their conversions) are: 1 MW,MWe,megawatt,Megawatt,MegaWatt,MEGAWATT,megawatts,Megawatt,MegaWatts,MEGAWATT,MEGAWATTS 1000 kW,kWe,KW,kilowatt,KiloWatt,KILOWATT,kilowatts,KiloWatts,KILOWATT,KILOWATTS 1000000 W,We,watt,watts,Watt,Watts,WATT,WATTS 1000000000 mW,milliwatt,milliwatts,MILLIWATT,MILLIWATTS 0.001 GW,gigawatt,gigawatts,Gigawatt,Gigawatts,GigaWatt,GigaWatts,GIGAWATT,GIGAWATTS

322

Optimal entangling capacity of dynamical processes  

SciTech Connect

We investigate the entangling capacity of dynamical operations when provided with local ancilla. A comparison is made between the entangling capacity with and without the assistance of prior entanglement. An analytic solution is found for the log-negativity entangling capacity of two-qubit gates, which equals the entanglement of the Choi matrix isomorphic to the unitary operator. Surprisingly, the availability of prior entanglement does not affect this result, a property we call resource independence of the entangling capacity. We prove several useful upper bounds on the entangling capacity that hold for general qudit dynamical operations and for a whole family of entanglement monotones including log negativity and log robustness. The log-robustness entangling capacity is shown to be resource independent for general dynamics. We provide numerical results supporting a conjecture that the log-negativity entangling capacity is resource independent for all two-qudit unitary operators.

Campbell, Earl T. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom)

2010-10-15T23:59:59.000Z

323

Optimal Entangling Capacity of Dynamical Processes  

E-Print Network (OSTI)

We investigate the entangling capacity of dynamical operations when provided with local ancilla. A comparison is made between the entangling capacity with and without the assistance of prior entanglement. An analytic solution is found for the log-negativity entangling capacity of two-qubit gates, which equals the entanglement of the Choi matrix isomorphic to the unitary operator. Surprisingly, the availability of prior entanglement does not affect this result; a property we call resource independence of the entangling capacity. We prove several useful upper-bounds on the entangling capacity that hold for general qudit dynamical operations, and for a whole family of entanglement monotones including log-negativity and log-robustness. The log-robustness entangling capacity is shown to be resource independent for general dynamics. We provide numerical results supporting a conjecture that the log-negativity entangling capacity is resource independence for all two-qudit unitaries.

Earl T. Campbell

2010-07-08T23:59:59.000Z

324

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

E-Print Network (OSTI)

PRICES The future prices of natural gas, coal, and oil have an important effect on the Council's power, is an important factor when considering the use of natural gas for electricity generation. Price volatility-sized homes. The cost of energy (natural gas, oil, and electricity) is expected to be significantly higher

325

Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network (OSTI)

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting-uses include Heating, Ventilation and Air Conditioning (HVAC). Our analysis uses the modeling framework provided by the HVAC module in the Residential End-Use Energy Planning System (REEPS), which was developed

326

Preliminary Review of Models, Assumptions, and Key Data used in Performance Assessments and Composite Analysis at the Idaho National Laboratory  

SciTech Connect

This document is in response to a request by Ming Zhu, DOE-EM to provide a preliminary review of existing models and data used in completed or soon to be completed Performance Assessments and Composite Analyses (PA/CA) documents, to identify codes, methodologies, main assumptions, and key data sets used.

Arthur S. Rood; Swen O. Magnuson

2009-07-01T23:59:59.000Z

327

A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data  

SciTech Connect

Bayesian networks have attained widespread use in data analysis and decision making. Well studied topics include: efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating Bayesian network probabilities, and structure learning. It is not uncommon for the researcher to assume the structure of the Bayesian network or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted Bayesian networks; this is the contribution of this paper. We key on the definition of (conditional) independence to develop a graphical diagnostic method which indicates if the conditional independence assumptions imposed when one assumes the structure of the Bayesian network are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data with conditional independence assumptions under the model structure. We describe how this theoretical information and the data are presented in a graphical diagnostic tool. We demonstrate the approach through data simulated from Bayesian networks under different conditional independence assumptions. We also apply the diagnostic to a real world data set. The results indicate that our approach is a reasonable way of visualizing and inspecting the conditional independence assumption of a Bayesian network given data.

Walsh, Stephen J.; Whitney, Paul D.

2012-12-14T23:59:59.000Z

328

Assumptions to the Annual Energy Outlook 2000-Table 2. Carbon Emission  

Gasoline and Diesel Fuel Update (EIA)

Carbon Emission Factors Carbon Emission Factors (Kilograms-carbon per million Btu) Fuel Type Carbon Coefficient at Full Combustion Combustion Fraction Adjusted Emissions Factor Petroleum Motor Gasoline 19.33 0.990 19.14 Liquefied Petroleum Gas Used as Fuel 17.20 0.995 17.11 Used as Feedstock 16.87 0.200 3.37 Jet Fuel 19.33 0.990 19.14 Distillate Fuel 19.95 0.990 19.75 Residual Fuel 21.49 0.990 21.28 Asphalt and Road Oil 20.62 0.000 0.00 Lubricants 20.24 0.600 12.14 Petrochemical Feedstocks 19.37 0.200 3.87 Kerosene 19.72 0.990 19.52 Petroleum Coke 27.85 0.500 13.93 Petroleum Still Gas 17.51 0.995 17.42 Other Industrial 20.31 0.990 20.11 Coal Residential and Commercial 25.92 0.990 25.66 Metallurgical 25.55 0.990 25.29 Industrial Other 25.61 0.990 25.39 Electric Utility1 25.74 0.990 24.486 Natural Gas Used as Fuel

329

Assumptions to the Annual Energy Outlook 2001 - Table 2. Carbon Dioxide  

Gasoline and Diesel Fuel Update (EIA)

Carbon Dioxide Emission Factors Carbon Dioxide Emission Factors (Kilograms-carbon equivalent per million Btu) Fuel Type Carbon Dioxide Coefficient at Full Combustion Combustion Fraction Adjusted Emissions Factor Petroleum Motor Gasoline 19.36 0.990 19.17 Liquefied Petroleum Gas Used as Fuel 17.18 0.995 17.09 Used as Feedstock 16.88 0.200 3.38 Jet Fuel 19.33 0.990 19.14 Distillate Fuel 19.95 0.990 19.75 Residual Fuel 21.49 0.990 21.28 Asphalt and Road Oil 20.62 0.000 0.00 Lubricants 20.24 0.600 12.14 Petrochemical Feedstocks 19.37 0.200 3.87 Kerosene 19.72 0.990 19.52 Petroleum Coke 27.85 0.500 13.93 Petroleum Still Gas 17.51 0.995 17.42 Other Industrial 20.31 0.990 20.11 Coal Residential and Commercial 26.00 0.990 25.74 Metallurgical 25.56 0.990 25.30 Industrial Other 25.63 0.990 25.38 Electric Utility1 25.76 0.990 25.50 Natural Gas

330

Property:MeanCapacity | Open Energy Information  

Open Energy Info (EERE)

MeanCapacity MeanCapacity Jump to: navigation, search Property Name MeanCapacity Property Type Quantity Description Mean capacity potential at location based on the USGS 2008 Geothermal Resource Assessment if the United States Use this property to express potential electric energy generation, such as Nameplate Capacity. The default unit is megawatts (MW). For spatial capacity, use property Volume. Acceptable units (and their conversions) are: 1 MW,MWe,megawatt,Megawatt,MegaWatt,MEGAWATT,megawatts,Megawatt,MegaWatts,MEGAWATT,MEGAWATTS 1000 kW,kWe,KW,kilowatt,KiloWatt,KILOWATT,kilowatts,KiloWatts,KILOWATT,KILOWATTS 1000000 W,We,watt,watts,Watt,Watts,WATT,WATTS 1000000000 mW,milliwatt,milliwatts,MILLIWATT,MILLIWATTS 0.001 GW,gigawatt,gigawatts,Gigawatt,Gigawatts,GigaWatt,GigaWatts,GIGAWATT,GIGAWATTS

331

Working and Net Available Shell Storage Capacity  

U.S. Energy Information Administration (EIA) Indexed Site

Working and Net Available Shell Storage Capacity Working and Net Available Shell Storage Capacity With Data for September 2013 | Release Date: November 27, 2013 | Next Release Date: May 29, 2013 Previous Issues Year: September 2013 March 2013 September 2012 March 2012 September 2011 March 2011 September 2010 Go Containing storage capacity data for crude oil, petroleum products, and selected biofuels. The report includes tables detailing working and net available shell storage capacity by type of facility, product, and Petroleum Administration for Defense District (PAD District). Net available shell storage capacity is broken down further to show the percent for exclusive use by facility operators and the percent leased to others. Crude oil storage capacity data are also provided for Cushing, Oklahoma, an

332

Definition: Nameplate Capacity | Open Energy Information  

Open Energy Info (EERE)

Definition Definition Edit with form History Facebook icon Twitter icon » Definition: Nameplate Capacity Jump to: navigation, search Dictionary.png Nameplate Capacity The maximum amount of electric energy that a generator can produce under specific conditions, as rated by the manufacturer. Generator nameplate capacity is expressed in some multiple of watts such as megawatts (MW), as indicated on a nameplate that is physically attached to the generator.[1] View on Wikipedia Wikipedia Definition Also Known As Capacity Related Terms electricity generation, power References ↑ http://www.nrc.gov/reading-rm/basic-ref/glossary/generator-nameplate-capacity.html Retr LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ieved from "http://en.openei.org/w/index.php?title=Definition:Nameplate_Capacity&oldid=480378"

333

Definition: Deferred Generation Capacity Investments | Open Energy  

Open Energy Info (EERE)

Generation Capacity Investments Generation Capacity Investments Utilities and grid operators ensure that generation capacity can serve the maximum amount of load that planning and operations forecasts indicate. The trouble is, this capacity is only required for very short periods each year, when demand peaks. Reducing peak demand and flattening the load curve should reduce the generation capacity required to service load and lead to cheaper electricity for customers.[1] Related Terms load, electricity generation, peak demand, smart grid References ↑ SmartGrid.gov 'Description of Benefits' An inl LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ine Glossary Definition Retrieved from "http://en.openei.org/w/index.php?title=Definition:Deferred_Generation_Capacity_Investments&oldid=50257

334

Installed Geothermal Capacity | Open Energy Information  

Open Energy Info (EERE)

Geothermal Capacity Geothermal Capacity Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Print PDF Installed Geothermal Capacity International Market Map of U.S. Geothermal Power Plants List of U.S. Geothermal Power Plants Throughout the world geothermal energy is looked at as a potential source of renewable base-load power. As of 2005 there was 8,933 MW of installed power capacity within 24 countries. The International Geothermal Association (IGA) reported 55,709 GWh per year of geothermal electricity. The generation from 2005 to 2010 increased to 67,246 GWh, representing a 20% increase in the 5 year period. The IGA has projected that by 2015 the new installed capacity will reach 18,500 MW, nearly 10,000 MW greater than 2005. [1] Countries with the greatest increase in installed capacity (MW) between

335

Property:PlannedCapacity | Open Energy Information  

Open Energy Info (EERE)

PlannedCapacity PlannedCapacity Jump to: navigation, search Property Name PlannedCapacity Property Type Quantity Description The total planned capacity for a given area, region or project. Use this property to express potential electric energy generation, such as Nameplate Capacity. The default unit is megawatts (MW). For spatial capacity, use property Volume. Acceptable units (and their conversions) are: 1 MW,MWe,megawatt,Megawatt,MegaWatt,MEGAWATT,megawatts,Megawatt,MegaWatts,MEGAWATT,MEGAWATTS 1000 kW,kWe,KW,kilowatt,KiloWatt,KILOWATT,kilowatts,KiloWatts,KILOWATT,KILOWATTS 1000000 W,We,watt,watts,Watt,Watts,WATT,WATTS 1000000000 mW,milliwatt,milliwatts,MILLIWATT,MILLIWATTS 0.001 GW,gigawatt,gigawatts,Gigawatt,Gigawatts,GigaWatt,GigaWatts,GIGAWATT,GIGAWATTS 0.000001 TW,terawatt,terawatts,Terawatt,Terawatts,TeraWatt,TeraWatts,TERAWATT,TERAWATTS

336

The quantum capacity with symmetric side channels  

E-Print Network (OSTI)

We present an upper bound for the quantum channel capacity that is both additive and convex. Our bound can be interpreted as the capacity of a channel for high-fidelity quantum communication when assisted by a family of channels that have no capacity on their own. This family of assistance channels, which we call symmetric side channels, consists of all channels mapping symmetrically to their output and environment. The bound seems to be quite tight, and for degradable quantum channels it coincides with the unassisted channel capacity. Using this symmetric side channel capacity, we find new upper bounds on the capacity of the depolarizing channel. We also briefly indicate an analogous notion for distilling entanglement using the same class of (one-way) channels, yielding one of the few entanglement measures that is monotonic under local operations with one-way classical communication (1-LOCC), but not under the more general class of local operations with classical communication (LOCC).

Graeme Smith; John A. Smolin; Andreas Winter

2006-07-05T23:59:59.000Z

337

Channel capacities via $p$-summing norms  

E-Print Network (OSTI)

In this paper we show how \\emph{the metric theory of tensor products} developed by Grothendieck perfectly fits in the study of channel capacities, a central topic in \\emph{Shannon's information theory}. Furthermore, in the last years Shannon's theory has been generalized to the quantum setting to let the \\emph{quantum information theory} step in. In this paper we consider the classical capacity of quantum channels with restricted assisted entanglement. In particular these capacities include the classical capacity and the unlimited entanglement-assisted classical capacity of a quantum channel. To deal with the quantum case we will use the noncommutative version of $p$-summing maps. More precisely, we prove that the (product state) classical capacity of a quantum channel with restricted assisted entanglement can be expressed as the derivative of a completely $p$-summing norm.

Marius Junge; Carlos palazuelos

2013-05-05T23:59:59.000Z

338

Table 8. Capacity and Fresh Feed Input to Selected Downstream ...  

U.S. Energy Information Administration (EIA)

Capacity Inputs CapacityInputs Capacity Inputs Table 8. ... (EIA) Form EIA-820, "Annual Refinery Report." Inputs are from the form EIA-810, "Monthly Refinery Report."

339

Building Energy Software Tools Directory: CHP Capacity Optimizer  

NLE Websites -- All DOE Office Websites (Extended Search)

Related Links CHP Capacity Optimizer CHP Capacity Optimizer logo Selecting the proper installed capacity for cooling, heating, and power (CHP) equipment is critical to the...

340

On the capacity of isolated, curbside bus stops  

E-Print Network (OSTI)

New Jersey. Kohler, U. , 1991. Capacity of transit lanes.Symposium on Highway Capacity, Karlsruhe, Germany. St.Paulo. TRB, 1985. Highway Capacity Manual. Transportation

Gu, Weihua; Li, Yuwei; Cassidy, Michael J.; Griswold, Julia B.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Property:Installed Capacity (MW) | Open Energy Information  

Open Energy Info (EERE)

Capacity (MW) Jump to: navigation, search Property Name Installed Capacity (MW) Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:InstalledCapac...

342

Stochastic binary problems with simple penalties for capacity ...  

E-Print Network (OSTI)

capacity constraints, using simple penalties for capacities violations. In particular, we take a closer look at the knapsack problem with weights and capacity ...

343

Zero-rate feedback can achieve the empirical capacity  

E-Print Network (OSTI)

Achieving the empirical capacity using feedback: MemorylessGaussian feedback capacity,” IEEE Trans. Inf. Theory, vol.14] Y. -H. Kim, “Feedback capacity of stationary Gaussian

Eswaran, Krishnan; Sarwate, A D; Sahai, Anant; Gastpar, M

2010-01-01T23:59:59.000Z

344

Attention capacity and task difficulty in visual search  

E-Print Network (OSTI)

1980). The demonstration of capacity limitation. Cognitiveof automatic detection: Capacity and scanning in visualD. L. (1984). Central capacity limits in consistent mapping

Huang, L Q; Pashler, Harold

2005-01-01T23:59:59.000Z

345

Robust Dynamic Traffic Assignment under Demand and Capacity Uncertainty  

E-Print Network (OSTI)

Assignment under Demand and Capacity Uncertainty ? Giuseppeworst-case sce- nario of demand and capacity con?gurations.uncertain demands and capacities are modeled as unknown-but-

Calafiore, Giuseppe; El Ghaoui, Laurent

2008-01-01T23:59:59.000Z

346

End-to-end asymmetric link capacity estimation  

E-Print Network (OSTI)

A Simple and Accurate Capacity Estimation Technique. InGerla. Accuracy of Link Capacity Es- timates using Passiveto-end asymmetric link capacity estimation Ling-Jyh Chen,

Chen, Ling-Jyh; Sun, Tony; Yang, Guang; Sanadidi, Medy Y; Gerla, Mario

2005-01-01T23:59:59.000Z

347

EEI/DOE Transmission Capacity Report  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

TRANSMISSION CAPACITY: TRANSMISSION CAPACITY: PRESENT STATUS AND FUTURE PROSPECTS Eric Hirst Consulting in Electric-Industry Restructuring Bellingham, Washington June 2004 Prepared for Energy Delivery Group Edison Electric Institute Washington, DC Russell Tucker, Project Manager and Office of Electric Transmission and Distribution U.S. Department of Energy Washington, DC Larry Mansueti, Project Manager ii iii CONTENTS Page SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v LIST OF ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. TRANSMISSION CAPACITY: DATA AND PROJECTIONS . . . . . . . . . . . . . . . . . . . 5 HISTORICAL DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 CURRENT CONDITIONS . . . . . . .

348

Quantum capacity of channel with thermal noise  

E-Print Network (OSTI)

The quantum capacity of thermal noise channel is studied. The extremal input state is obtained at the postulation that the coherent information is convex or concave at its vicinity. When the input energy tends to infinitive, it is verified by perturbation theory that the coherent information reaches its maximum at the product of identical thermal state input. The quantum capacity is obtained for lower noise channel and it is equal the one shot capacity.

Xiao-yu Chen

2006-02-11T23:59:59.000Z

349

Capacity Scaling Laws for Underwater Networks  

E-Print Network (OSTI)

The underwater acoustic channel is characterized by a path loss that depends not only on the transmission distance, but also on the signal frequency. Signals transmitted from one user to another over a distance $l$ are subject to a power loss of $l^{-\\alpha}{a(f)}^{-l}$. Although a terrestrial radio channel can be modeled similarly, the underwater acoustic channel has different characteristics. The spreading factor $\\alpha$, related to the geometry of propagation, has values in the range $1 \\leq \\alpha \\leq 2$. The absorption coefficient $a(f)$ is a rapidly increasing function of frequency: it is three orders of magnitude greater at 100 kHz than at a few Hz. Existing results for capacity of wireless networks correspond to scenarios for which $a(f) = 1$, or a constant greater than one, and $\\alpha \\geq 2$. These results cannot be applied to underwater acoustic networks in which the attenuation varies over the system bandwidth. We use a water-filling argument to assess the minimum transmission power and optimum...

Lucani, Daniel E; Stojanovic, Milica

2009-01-01T23:59:59.000Z

350

Quantum Capacities of Channels with small Environment  

E-Print Network (OSTI)

We investigate the quantum capacity of noisy quantum channels which can be represented by coupling a system to an effectively small environment. A capacity formula is derived for all cases where both system and environment are two-dimensional--including all extremal qubit channels. Similarly, for channels acting on higher dimensional systems we show that the capacity can be determined if the channel arises from a sufficiently small coupling to a qubit environment. Extensions to instances of channels with larger environment are provided and it is shown that bounds on the capacity with unconstrained environment can be obtained from decompositions into channels with small environment.

Michael M. Wolf; David Perez-Garcia

2006-07-11T23:59:59.000Z

351

Share of Conversion Capacity - Energy Information Administration  

U.S. Energy Information Administration (EIA)

In the early to mid 1980’s, Atlantic Basin refiners rapidly expanded their conversion capacity as a consequence of the belief that world crude production would get ...

352

Total Natural Gas Underground Storage Capacity  

Annual Energy Outlook 2012 (EIA)

Gas Capacity Total Number of Existing Fields Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes...

353

Natural gas, renewables dominate electric capacity additions ...  

U.S. Energy Information Administration (EIA)

These appear in a separate EIA survey collecting data on net metering and distributed generation. More capacity was added in the first half of 2012 than was retired.

354

Working and Net Available Shell Storage Capacity  

U.S. Energy Information Administration (EIA)

Containing storage capacity data for crude oil, petroleum products, and selected biofuels. The report includes tables detailing working and net available shell ...

355

When does noise increase the quantum capacity?  

E-Print Network (OSTI)

Superactivation is the property that two channels with zero quantum capacity can be used together to yield positive capacity. Here we demonstrate that this effect exists for a wide class of inequivalent channels, none of which can simulate each other. We also consider the case where one of two zero capacity channels are applied, but the sender is ignorant of which one is applied. We find examples where the greater the entropy of mixing of the channels, the greater the lower bound for the capacity. Finally, we show that the effect of superactivation is rather generic by providing example of superactivation using the depolarizing channel.

Fernando G. S. L. Brandão; Jonathan Oppenheim; Sergii Strelchuk

2011-07-21T23:59:59.000Z

356

,"Texas Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Texas Underground Natural Gas Storage Capacity",11,"Annual",2011,"6301988" ,"Release...

357

An FPTAS for Capacity Constrained Assortment Optimization  

E-Print Network (OSTI)

May 13, 2013 ... In this paper, we consider the capacity constrained version of the assortment optimization problem where each item $i$ has weight $w_i$, and ...

358

,"Nebraska Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Nebraska Underground Natural Gas...

359

,"Kentucky Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Kentucky Underground Natural Gas...

360

,"Wyoming Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Wyoming Underground Natural Gas...

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

,"Minnesota Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Minnesota Underground Natural Gas...

362

,"Maryland Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Maryland Underground Natural Gas...

363

,"Indiana Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Indiana Underground Natural Gas...

364

,"West Virginia Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","West Virginia Underground Natural...

365

,"Michigan Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Michigan Underground Natural Gas...

366

,"California Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","California Underground Natural...

367

,"Natural Gas Depleted Fields Storage Capacity "  

U.S. Energy Information Administration (EIA) Indexed Site

Depleted Fields Storage Capacity " ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Natural...

368

,"Mississippi Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Mississippi Underground Natural...

369

,"Arkansas Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Arkansas Underground Natural Gas...

370

,"Alabama Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Alabama Underground Natural Gas...

371

,"Oregon Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Oregon Underground Natural Gas...

372

,"New York Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New York Underground Natural Gas...

373

,"Missouri Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Missouri Underground Natural Gas...

374

,"Oklahoma Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Oklahoma Underground Natural Gas...

375

,"Washington Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Washington Underground Natural...

376

Refinery Capacity Report - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Energy Information Administration (U.S. Dept. of Energy) ... Tables: 1: Number and Capacity of Operable Petroleum Refineries by PAD District and State as of ...

377

,"Kansas Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Kansas Underground Natural Gas...

378

Quantum Communication With Zero-Capacity Channels  

E-Print Network (OSTI)

Communication over a noisy quantum channel introduces errors in the transmission that must be corrected. A fundamental bound on quantum error correction is the quantum capacity, which quantifies the amount of quantum data that can be protected. We show theoretically that two quantum channels, each with a transmission capacity of zero, can have a nonzero capacity when used together. This unveils a rich structure in the theory of quantum communications, implying that the quantum capacity does not uniquely specify a channel's ability for transmitting quantum information.

Graeme Smith; Jon Yard

2008-07-30T23:59:59.000Z

379

,"Natural Gas Salt Caverns Storage Capacity "  

U.S. Energy Information Administration (EIA) Indexed Site

Salt Caverns Storage Capacity " ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Natural Gas...

380

,"New Mexico Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New Mexico Underground Natural...

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Optimization of Storage vs. Compression Capacity  

NLE Websites -- All DOE Office Websites (Extended Search)

Storage Volume vs. Compression Capacity Amgad Elgowainy Argonne National Laboratory Presentation at CSD Workshop Argonne National Laboratory March 21, 2013 0 5 10 15 20 25 0 100...

382

,"Montana Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Montana Underground Natural Gas...

383

,"Virginia Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Virginia Underground Natural Gas...

384

,"Colorado Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Colorado Underground Natural Gas...

385

,"Utah Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Utah Underground Natural Gas...

386

Increasing water holding capacity for irrigation  

NLE Websites -- All DOE Office Websites (Extended Search)

Increasing water holding capacity for irrigation Reseachers recommend solutions for sediment trapping in irrigation system LANL and SNL leveraged technical expertise to determine...

387

,"Tennessee Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Tennessee Underground Natural Gas...

388

,"Louisiana Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Louisiana Underground Natural Gas...

389

,"Ohio Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Ohio Underground Natural Gas...

390

,"Pennsylvania Underground Natural Gas Storage Capacity"  

U.S. Energy Information Administration (EIA) Indexed Site

Capacity" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Pennsylvania Underground Natural...

391

Total Atmospheric Crude Oil Distillation Capacity Former ...  

U.S. Energy Information Administration (EIA)

Former Corporation/Refiner Total Atmospheric Crude Oil Distillation Capacity (bbl/cd)a New Corporation/Refiner Date of Sale Table 14. Refinery Sales During 2005

392

PAD District 4 Refinery Utilization and Capacity  

U.S. Energy Information Administration (EIA)

Gross Input to Atmospheric Crude Oil Distillation Units: 575: 577: 562: 542: 578: 587: 1985-2013: Operable Capacity (Calendar Day) 625: 625: 630: 630: 630: 630: 1985 ...

393

,"Illinois Natural Gas Underground Storage Capacity (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Illinois Natural Gas Underground Storage Capacity (MMcf)",1,"Monthly","52013" ,"Release...

394

Solar Energy and Capacity Value (Fact Sheet)  

SciTech Connect

This is a one-page, two-sided fact sheet on the capacity of solar power to provide value to utilities and power system operators.

Not Available

2013-09-01T23:59:59.000Z

395

Optimizing Power Factor Correction  

E-Print Network (OSTI)

The optimal investment for power factor correcting capacitors for Kansas Power and Light Company large power contract customers is studied. Since the billing capacity is determined by dividing the real demand by the power factor (the minimum billing capacity is based on 80 percent of the summer peak billing capacity) and the billing capacity is used to determine the number of kilowatt-hours billed at each pricing tier, the power factor affects both the demand and the energy charge. There is almost no information available in the literature concerning recommended power factor corrections for this situation. The general advice commonly given in the past has been that power factor should be corrected to above 0.9 if it is below that value to begin with, but that does not take into account the facts of the situation studied here. Calculations relevant to a commercial consumer of electricity were made for demands of 200, 400, 800, 1,600, 3,200, and 6,400 kW and monthly energy consumption periods of 100, 150, 200, 300, 400, and 500 hours for several capacitor purchase and installation costs. The results are displayed in a series of graphs that enable annual cost savings and payback periods to be readily determined over a range of commonly encountered parameter values. It is found that it is often economically advantageous to correct a power factor to near unity.

Phillips, R. K.; Burmeister, L. C.

1986-06-01T23:59:59.000Z

396

Capacity of Byzantine Consensus with Capacity-Limited Point-to-Point Links  

E-Print Network (OSTI)

We consider the problem of maximizing the throughput of Byzantine consensus, when communication links have finite capacity. Byzantine consensus is a classical problem in distributed computing. In existing literature, the communication links are implicitly assumed to have infinite capacity. The problem changes significantly when the capacity of links is finite. We define the throughput and capacity of consensus, and identify upper bound of achievable consensus throughput. We propose an algorithm that achieves consensus capacity in complete four-node networks with at most 1 failure with arbitrary distribution of link capacities.

Liang, Guanfeng

2011-01-01T23:59:59.000Z

397

Influence of assumptions about household waste composition in waste management LCAs  

SciTech Connect

Highlights: Black-Right-Pointing-Pointer Uncertainty in waste composition of household waste. Black-Right-Pointing-Pointer Systematically changed waste composition in a constructed waste management system. Black-Right-Pointing-Pointer Waste composition important for the results of accounting LCA. Black-Right-Pointing-Pointer Robust results for comparative LCA. - Abstract: This article takes a detailed look at an uncertainty factor in waste management LCA that has not been widely discussed previously, namely the uncertainty in waste composition. Waste composition is influenced by many factors; it can vary from year to year, seasonally, and with location, for example. The data publicly available at a municipal level can be highly aggregated and sometimes incomplete, and performing composition analysis is technically challenging. Uncertainty is therefore always present in waste composition. This article performs uncertainty analysis on a systematically modified waste composition using a constructed waste management system. In addition the environmental impacts of several waste management strategies are compared when applied to five different cities. We thus discuss the effect of uncertainty in both accounting LCA and comparative LCA. We found the waste composition to be important for the total environmental impact of the system, especially for the global warming, nutrient enrichment and human toxicity via water impact categories.

Slagstad, Helene, E-mail: helene.slagstad@ntnu.no [Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, N-7491 Trondheim (Norway); Brattebo, Helge [Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, N-7491 Trondheim (Norway)

2013-01-15T23:59:59.000Z

398

Dynamic Capacity Investment with Two Competing Technologies  

Science Conference Proceedings (OSTI)

With the recent focus on sustainability, firms making adjustments to their production or distribution capacity levels often have the option of investing in newer technologies with lower carbon footprints and/or energy consumption. These more sustainable ... Keywords: dynamic capacity investment, sustainable operations, technology choice

Wenbin Wang, Mark E. Ferguson, Shanshan Hu, Gilvan C. Souza

2013-10-01T23:59:59.000Z

399

Constrained capacity of MIMO Rayleigh fading channels  

E-Print Network (OSTI)

In this thesis channel capacity of a special type of multiple-input multiple-output (MIMO) Rayleigh fading channels is studied, where the transmitters are subject to a finite phase-shift keying (PSK) input alphabet. The constraint on the input alphabet makes an analytical solution for the capacity beyond reach. However we are able to simplify the final expression, which requires a single expectation and thus can be evaluated easily through simulation. To facilitate simulations, analytical expressions are derived for the eigenvalues and eigenvectors of a covariance matrix involved in the simplified capacity expression. The simplified expression is used to provide some good approximations to the capacity at low signal-to-noise ratios (SNRs). Involved in derivation of the capacity is the capacity-achieving input distribution. It is proved that a uniform prior distribution is capacity achieving. We also show that it is the only capacity-achieving distribution for our channel model. On top of that we generalize the uniqueness case for an input distribution to a broader range of channels.

He, Wenyan

2011-05-01T23:59:59.000Z

400

On Quantum Capacity and its Bound  

E-Print Network (OSTI)

The quantum capacity of a pure quantum channel and that of classical-quantum-classical channel are discussed in detail based on the fully quantum mechanical mutual entropy. It is proved that the quantum capacity generalizes the so-called Holevo bound.

Masanori Ohya; Igor V. Volovich

2004-06-29T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Capacity Bounded Grammars and Petri Nets  

E-Print Network (OSTI)

A capacity bounded grammar is a grammar whose derivations are restricted by assigning a bound to the number of every nonterminal symbol in the sentential forms. In the paper the generative power and closure properties of capacity bounded grammars and their Petri net controlled counterparts are investigated.

Stiebe, Ralf; 10.4204/EPTCS.3.18

2009-01-01T23:59:59.000Z

402

Table 6. Operable Crude Oil and Downstream Charge Capacity of ...  

U.S. Energy Information Administration (EIA)

Downstream Charge Capacity Table 6. ... (EIA), Form EIA-820, "Annual Refinery Report." Energy Information Administration, Refinery Capacity 2011 46. Title:

403

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Definitions Definitions Definitions Since 2006, EIA has reported two measures of aggregate capacity, one based on demonstrated peak working gas storage, the other on working gas design capacity. Demonstrated Peak Working Gas Capacity: This measure sums the highest storage inventory level of working gas observed in each facility over the 5-year range from May 2005 to April 2010, as reported by the operator on the Form EIA-191M, "Monthly Underground Gas Storage Report." This data-driven estimate reflects actual operator experience. However, the timing for peaks for different fields need not coincide. Also, actual available maximum capacity for any storage facility may exceed its reported maximum storage level over the last 5 years, and is virtually certain to do so in the case of newly commissioned or expanded facilities. Therefore, this measure provides a conservative indicator of capacity that may understate the amount that can actually be stored.

404

Property:Capacity | Open Energy Information  

Open Energy Info (EERE)

Capacity Capacity Jump to: navigation, search Property Name Capacity Property Type Quantity Description Potential electric energy generation, default units of megawatts. Use this property to express potential electric energy generation, such as Nameplate Capacity. The default unit is megawatts (MW). For spatial capacity, use property Volume. Acceptable units (and their conversions) are: 1 MW,MWe,megawatt,Megawatt,MegaWatt,MEGAWATT,megawatts,Megawatt,MegaWatts,MEGAWATT,MEGAWATTS 1000 kW,kWe,KW,kilowatt,KiloWatt,KILOWATT,kilowatts,KiloWatts,KILOWATT,KILOWATTS 1000000 W,We,watt,watts,Watt,Watts,WATT,WATTS 1000000000 mW,milliwatt,milliwatts,MILLIWATT,MILLIWATTS 0.001 GW,gigawatt,gigawatts,Gigawatt,Gigawatts,GigaWatt,GigaWatts,GIGAWATT,GIGAWATTS 0.000001 TW,terawatt,terawatts,Terawatt,Terawatts,TeraWatt,TeraWatts,TERAWATT,TERAWATTS

405

Planned Geothermal Capacity | Open Energy Information  

Open Energy Info (EERE)

Planned Geothermal Capacity Planned Geothermal Capacity Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Print PDF Planned Geothermal Capacity This article is a stub. You can help OpenEI by expanding it. General List of Development Projects Map of Development Projects Planned Geothermal Capacity in the U.S. is reported by the Geothermal Energy Association via their Annual U.S. Geothermal Power Production and Development Report (April 2011). Related Pages: GEA Development Phases Geothermal Development Projects Add.png Add a new Geothermal Project Please be sure the project does not already exist in the list below before adding - perhaps under a different name. Technique Developer Phase Project Type Capacity Estimate (MW) Location Geothermal Area Geothermal Region GEA Report

406

Property:GeneratingCapacity | Open Energy Information  

Open Energy Info (EERE)

GeneratingCapacity GeneratingCapacity Jump to: navigation, search Property Name GeneratingCapacity Property Type Quantity Use this property to express potential electric energy generation, such as Nameplate Capacity. The default unit is megawatts (MW). For spatial capacity, use property Volume. Acceptable units (and their conversions) are: 1 MW,MWe,megawatt,Megawatt,MegaWatt,MEGAWATT,megawatts,Megawatt,MegaWatts,MEGAWATT,MEGAWATTS 1000 kW,kWe,KW,kilowatt,KiloWatt,KILOWATT,kilowatts,KiloWatts,KILOWATT,KILOWATTS 1000000 W,We,watt,watts,Watt,Watts,WATT,WATTS 1000000000 mW,milliwatt,milliwatts,MILLIWATT,MILLIWATTS 0.001 GW,gigawatt,gigawatts,Gigawatt,Gigawatts,GigaWatt,GigaWatts,GIGAWATT,GIGAWATTS 0.000001 TW,terawatt,terawatts,Terawatt,Terawatts,TeraWatt,TeraWatts,TERAWATT,TERAWATTS

407

Definition: Deferred Distribution Capacity Investments | Open Energy  

Open Energy Info (EERE)

Deferred Distribution Capacity Investments Deferred Distribution Capacity Investments Jump to: navigation, search Dictionary.png Deferred Distribution Capacity Investments As with the transmission system, reducing the load and stress on distribution elements increases asset utilization and reduces the potential need for upgrades. Closer monitoring and load management on distribution feeders could potentially extend the time before upgrades or capacity additions are required.[1] Related Terms load, transmission lines, transmission line, sustainability References ↑ SmartGrid.gov 'Description of Benefits' An inl LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ine Glossary Definition Retrieved from "http://en.openei.org/w/index.php?title=Definition:Deferred_Distribution_Capacity_Investments&oldid=502613

408

Effective Capacity Analysis of Cognitive Radio Channels for Quality of Service Provisioning  

E-Print Network (OSTI)

In this paper, cognitive transmission under quality of service (QoS) constraints is studied. In the cognitive radio channel model, it is assumed that the secondary transmitter sends the data at two different average power levels, depending on the activity of the primary users, which is determined by channel sensing performed by the secondary users. A state-transition model is constructed for this cognitive transmission channel. Statistical limitations on the buffer lengths are imposed to take into account the QoS constraints. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. This analysis is conducted for fixed-power/fixed-rate, fixed-power/variable-rate, and variable-power/variable-rate transmission schemes under different assumptions on the availability of channel side information (CSI) at the transmitter. The impact upon the effective capacity of several system parameters, including channel sensing duration, detect...

Akin, Sami

2009-01-01T23:59:59.000Z

409

Flawed Assumptions, Models and Decision Making: Misconceptions Concerning Human Elements in Complex System  

SciTech Connect

The history of high consequence accidents is rich with events wherein the actions, or inaction, of humans was critical to the sequence of events preceding the accident. Moreover, it has been reported that human error may contribute to 80% of accidents, if not more (dougherty and Fragola, 1988). Within the safety community, this reality is widely recognized and there is a substantially greater awareness of the human contribution to system safety today than has ever existed in the past. Despite these facts, and some measurable reduction in accident rates, when accidents do occur, there is a common lament. No matter how hard we try, we continue to have accidents. Accompanying this lament, there is often bewilderment expressed in statements such as, ''There's no explanation for why he/she did what they did''. It is believed that these statements are a symptom of inadequacies in how they think about humans and their role within technological systems. In particular, while there has never been a greater awareness of human factors, conceptual models of human involvement in engineered systems are often incomplete and in some cases, inaccurate.

FORSYTHE,JAMES C.; WENNER,CAREN A.

1999-11-03T23:59:59.000Z

410

wind power capacity | OpenEI  

Open Energy Info (EERE)

capacity capacity Dataset Summary Description These estimates are derived from a composite of high resolution wind resource datasets modeled for specific countries with low resolution data originating from the National Centers for Environmental Prediction (United States) and the National Center for Atmospheric Research (United States) as processed for use in the IMAGE model. The high resolution datasets were produced by the National Renewable Energy Laboratory (United States), Risø DTU National Laboratory (Denmark), the National Institute for Space Research (Brazil), and the Canadian Wind Energy Association. The data repr Source National Renewable Energy Laboratory Date Released Unknown Date Updated Unknown Keywords area capacity clean energy international

411

Preparing Guyana's REDD+ Participation: Developing Capacities for  

Open Energy Info (EERE)

Guyana's REDD+ Participation: Developing Capacities for Guyana's REDD+ Participation: Developing Capacities for Monitoring, Reporting and Verification Jump to: navigation, search Name Preparing Guyana's REDD+ Participation: Developing Capacities for Monitoring, Reporting and Verification Agency/Company /Organization Guyana Forestry Commission, The Government of Norway Sector Land Focus Area Forestry Topics Implementation, Policies/deployment programs, Background analysis Resource Type Workshop, Guide/manual Website http://unfccc.int/files/method Country Guyana UN Region Latin America and the Caribbean References Preparing Guyana's REDD+ Participation[1] Overview "In this context, the overall goal of the activities reported here are to develop a road map for the establishment of a MRV system for REDD+

412

Information Capacity of Energy Harvesting Sensor Nodes  

E-Print Network (OSTI)

Sensor nodes with energy harvesting sources are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting 'green communication'. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over an AWGN channel and show that the capacity achieving energy management policy is the same as the throughput optimal policy. We also obtain the capacity for the system with energy inefficiencies in storage and an achievable rate when energy conserving sleep-wake modes are supported.

Rajesh, R

2010-01-01T23:59:59.000Z

413

On channels with finite Holevo capacity  

E-Print Network (OSTI)

We consider a nontrivial class of infinite dimensional quantum channels characterized by finiteness of the Holevo capacity. Some general properties of channels of this class are described. In particular, a special sufficient condition of existence of an optimal measure is obtained and examples of channels with no optimal measure are constructed. It is shown that each channel with finite Holevo capacity has a natural extension to the set of all positive normalized functionals on the algebra of all bounded operators. General properties of such an extension are described. The class of infinite dimensional channels, for which the Holevo capacity can be explicitly determined, is considered.

M. E. Shirokov

2006-02-07T23:59:59.000Z

414

Illinois Underground Natural Gas Storage Capacity  

U.S. Energy Information Administration (EIA) Indexed Site

Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2006 2007 2008 2009 2010 2011 View History Total Storage Capacity 984,768 980,691...

415

Heat Capacity as A Witness of Entanglement  

E-Print Network (OSTI)

We demonstrate that the presence of entanglement in macroscopic bodies (e.g. solids) in thermodynamical equilibrium could be revealed by measuring heat-capacity. The idea is that if the system were in a separable state, then for certain Hamiltonians heat capacity would not tend asymptotically to zero as the temperature approaches absolute zero. Since this would contradict the third law of thermodynamics, one concludes that the system must contain entanglement. The separable bounds are obtained by minimization of the heat capacity over separable states and using its universal low-temperature behavior. Our results open up a possibility to use standard experimental techniques of solid state physics -- namely, heat capacity measurements -- to detect entanglement in macroscopic samples.

Marcin Wiesniak; Vlatko Vedral; Caslav Brukner

2005-08-26T23:59:59.000Z

416

renewable energy generating capacity | OpenEI  

Open Energy Info (EERE)

energy generating capacity energy generating capacity Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 16, and contains only the reference case. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO generation renewable energy renewable energy generating capacity Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generating Capacity and Generation- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata

417

U.S. Refinery Utilization and Capacity  

U.S. Energy Information Administration (EIA) Indexed Site

2007 2008 2009 2010 2011 2012 View History Gross Input to Atmospheric Crude Oil Distillation Units 15,450 15,027 14,659 15,177 15,289 15,362 1985-2012 Operable Capacity (Calendar...

418

California Interstate Natural Gas Pipeline Capacity Levels ...  

U.S. Energy Information Administration (EIA)

PG&E Gas Transmission - NW Tuscarora Pipeline (Malin OR) 110 Mmcf/d 2,080 Mmcf/d Total Interstate Pipeline Capacity into California 7,435 Mmcf/d Net Natural Gas ...

419

,"California Natural Gas Underground Storage Capacity (MMcf)...  

U.S. Energy Information Administration (EIA) Indexed Site

,,"(202) 586-8800",,,"10312013 6:21:10 PM" "Back to Contents","Data 1: California Natural Gas Underground Storage Capacity (MMcf)" "Sourcekey","N5290CA2"...

420

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Note: 1) 'Demonstrated Peak Working Gas Capacity' is the sum of the highest storage inventory level of working gas observed in each facility over the prior 5-year period as...

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Internal Markets for Supply Chain Capacity Allocation  

E-Print Network (OSTI)

This paper explores the possibility of solving supply chain capacity allocation problems using internal markets among employees of the same company. Unlike earlier forms of transfer pricing, IT now makes it easier for such ...

McAdams, David

2005-07-08T23:59:59.000Z

422

Optimal capacity adjustment for supply chain control  

E-Print Network (OSTI)

This research attempts to answer the questions involving the time and size of capacity adjustments for better supply chain management. The objective of this research is to analytically determine simple structures to adjust ...

Budiman, Benny S., 1969-

2004-01-01T23:59:59.000Z

423

Feedback Capacity of the Compound Channel  

E-Print Network (OSTI)

In this work, we find the capacity of a compound finite-state channel (FSC) with time-invariant deterministic feedback. We consider the use of fixed length block codes over the compound channel. Our achievability result ...

Shrader, Brooke E.

424

Capacity-Speed Relationships in Prefrontal Cortex  

E-Print Network (OSTI)

Working memory (WM) capacity and WM processing speed are simple cognitive measures that underlie human performance in complex processes such as reasoning and language comprehension. These cognitive measures have shown to ...

Prabhakaran, Vivek

425

Minimal capacity points and the Lowest eigenfunctions  

E-Print Network (OSTI)

We introduce the concept of the point of minimal capacity of the domain, and observe a connection between this point and the lowest eigenfunction of a Laplacian on this domain, in one special case.

Mark Levi; Jia Pan

2011-04-04T23:59:59.000Z

426

Lattice Heat Capacity of Mesoscopic Nanostructures  

E-Print Network (OSTI)

We present a rigorous full quantum mechanical model for the lattice heat capacity of mesoscopic nanostructures in various dimensions. Model can be applied to arbitrary nanostructures with known vibrational spectrum in zero, one, two, or three dimensions. The limiting case of infinitely sized multi-dimensional materials are also found, which are in agreement with well-known results. As examples, we obtain the heat capacity of fullerenes.

Gharekhanlou, B; Vafai, A

2010-01-01T23:59:59.000Z

427

Measuring the capacity impacts of demand response  

Science Conference Proceedings (OSTI)

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)

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

2009-07-15T23:59:59.000Z

428

Technical Assessment Guide -- Generation Capacity Addition Topics  

Science Conference Proceedings (OSTI)

This report discusses the challenges facing the power industry with regard to capacity addition. These challenges include technological and regulatory risks, life cycle management, and material and labor escalation forecast. The report also examines the market trends for CT and CTCC, as this technology has become a reliable technology for capacity addition, and provides the cost data for various switchyard configurations. These topics have been addressed in past TAG reports and the content in this ...

2013-03-06T23:59:59.000Z

429

Heat capacity in weakly correlated liquids  

Science Conference Proceedings (OSTI)

Previously unavailable numerical data related to the heat capacity in two- and three-dimensional liquid Yukawa systems are obtained by means of fluctuation theory. The relations between thermal conductivity and diffusion constants are numerically studied and discussed. New approximation for heat capacity dependence on non-ideality parameter for weakly correlated systems of particles is proposed. Comparison of the obtained results to the existing theoretical and numerical data is discussed.

Khrustalyov, Yu. V.; Vaulina, O. S. [Joint Institute for High Temperatures RAS, 125412, Izhorskaya St., 13 bld.2, Moscow (Russian Federation); Moscow Institute of Physics and Technology, 117303, Kerchenskaya St., 1A bld.1, Moscow (Russian Federation); Koss, X. G. [Joint Institute for High Temperatures RAS, 125412, Izhorskaya St., 13 bld.2, Moscow (Russian Federation)

2012-12-15T23:59:59.000Z

430

EPRI Increased Transmission Capacity Workshop Proceedings  

Science Conference Proceedings (OSTI)

This report documents the proceedings of EPRI's Increased Overhead Transmission Capacity Workshop. The workshop was held on September 20, 2011 at the offices of the American Transmission Company in Waukesha, Wisconsin. Participants included members of the EPRI Increased Overhead Transmission Capacity Task Force. The workshop was a joint effort of two EPRI research projects: (1) Increased Power Flow Guidebook and Ratings for Overhead Lines, and (2) Impact of High Temperature Operation on Conductor Systems...

2011-11-30T23:59:59.000Z

431

Capacity Value of Wind Power - Summary  

Science Conference Proceedings (OSTI)

Power systems are planned such that they have adequate generation capacity to meet the load, according to a defined reliability target. The increase in the penetration of wind generation in recent years has led to a number of challenges for the planning and operation of power systems. A key metric for generation system adequacy is the capacity value of generation. The capacity value of a generator is the contribution that a given generator makes to generation system aequacy. The variable and stochastic nature of wind sets it apart from conventional energy sources. As a result, the modeling of wind generation in the same manner as conventional generation for capacity value calculations is inappropriate. In this paper a preferred method for calculation of the capacity value of wind is described and a discussion of the pertinent issues surrounding it is given. Approximate methods for the calculation are also described with their limitations highlighted. The outcome of recent wind capacity value analyses in Europe and North America, along with some new analysis, are highlighted with a discussion of relevant issues also given.

O'Malley, M.; Milligan, M.; Holttinen, H.; Dent, C.; Keane, A.

2010-01-01T23:59:59.000Z

432

Building research capacity in Education: evidence from recent initiatives in England, Scotland and Wales.  

E-Print Network (OSTI)

Building research capacity in Education: evidence from recent initiatives in England, Scotland and Wales. Zoe Fowler, Adela Baird, Stephen Baron, Susan M.B.Davies, Richard Procter and Jane Salisbury. Corresponding author: Zoe Fowler, Independent... of expertise as they move between unrelated research projects (Fowler and Procter, 2008). Typically, short-term funded research projects have little opportunity to invest in building research capacity. These factors affect professional identifies, and the ways...

Fowler, Zoe Louise; Baird, Adela; Davies, Susan M B; Procter, Richard; Baron, Stephen; Salisbury, Jane

2009-01-01T23:59:59.000Z

433

Capacity Value of PV and Wind Generation in the NV Energy System  

Science Conference Proceedings (OSTI)

Calculation of photovoltaic (PV) and wind power capacity values is important for estimating additional load that can be served by new PV or wind installations in the electrical power system. It also is the basis for assigning capacity credit payments in systems with markets. Because of variability in solar and wind resources, PV and wind generation contribute to power system resource adequacy differently from conventional generation. Many different approaches to calculating PV and wind generation capacity values have been used by utilities and transmission operators. Using the NV Energy system as a study case, this report applies peak-period capacity factor (PPCF) and effective load carrying capability (ELCC) methods to calculate capacity values for renewable energy sources. We show the connection between the PPCF and ELCC methods in the process of deriving a simplified approach that approximates the ELCC method. This simplified approach does not require generation fleet data and provides the theoretical basis for a quick check on capacity value results of PV and wind generation. The diminishing return of capacity benefit as renewable generation increases is conveniently explained using the simplified capacity value approach.

Lu, Shuai; Diao, Ruisheng; Samaan, Nader A.; Etingov, Pavel V.

2012-09-01T23:59:59.000Z

434

Heat capacity and compactness of denatured proteins  

E-Print Network (OSTI)

One of the striking results of protein thermodynamics is that the heat capacity change upon denaturation is large and positive. This change is generally ascribed to the exposure of non-polar groups to water on denaturation, in analogy to the large heat capacity change for the transfer of small non-polar molecules from hydrocarbons to water. Calculations of the heat capacity based on the exposed surface area of the completely unfolded denatured state give good agreement with experimental data. This result is difficult to reconcile with evidence that the heat denatured state in the absence of denaturants is reasonably compact. In this work, sample conformations for the denatured state of truncated CI2 are obtained by use of an effective energy function for proteins in solution. The energy function gives denatured conformations that are compact with radii of gyration that are slightly larger than that of the native state. The model is used to estimate the heat capacity, as well as that of the native state, at 300 and 350 K via finite enthalpy differences. The calculations show that the heat capacity of denaturation can have large positive contributions from non-covalent intraprotein interactions because these interactions change more with temperature in non-native conformations than in the native state. Including this contribution, which has been neglected in empirical surface area models, leads to heat capacities of unfolding for compact denatured states that are consistent with the experimental heat capacity data. Estimates of the stability curve of CI2 made with the effective energy function support the present model. # 1999 Elsevier Science B.V. All rights reserved.

Themis Lazaridis; Martin Karplus

1999-01-01T23:59:59.000Z

435

Can Bounded Rationality Explain Excess Capacity? ?  

E-Print Network (OSTI)

Excess capacity is observed in many markets especially those where a substantial initial investment is required. The theoretical literature often explains this feature by strategic attempts to deter entry or to limit new entrants ’ market shares but the empirical evidence for such a rationale is mixed. Moreover, excess capacity has also been observed in experimental studies on capacityconstrained games where there is no entry (and therefore no entry-deterrence motive). This paper explores experimentally another rationale for excess capacity: rather than (in addition to) being a threat to (potential) entrants, excess capacity held by incumbents may constitute a valuable option to reap extra gains from competition with an inexperienced entrant, if he turns out to makes a mistake. In our experimental design we used the level of experience (the number of periods played) as a proxy for the level of rationality and matched subjects with different levels of experience. We find evidence of excess capacity decreasing with opponent’s experience. ? This paper is a sustantially revised version of a chapter of Le Coq and Sturluson’s 2003 Stockholm School of Economics Ph.D. thesis. It was before circulated as "Does Opponent’s experience matter?". The authors would like to thank Tore Ellingsen for his insightful comments in the project’s infancy, Urs Fischbacher for allowig us tousethez-TreesoftwareandHans-TheoNorman for technical help. We thank also seminar participants at the IIOC 2004 (Chicago), EARIE 2003 (Lausanne), SAET 2003 (Rhodos) for helpful comments. We gratefully acknowledge financial

Chloélecoq Jon; Thor Sturluson

2006-01-01T23:59:59.000Z

436

U.S. Fuel Ethanol Plant Production Capacity  

U.S. Energy Information Administration (EIA)

U.S. Nameplate Fuel Ethanol Plant Production Capacity as of January 1, 2013 PAD District: Number of Plants: 2013 Nameplate Capacity: 2012 Nameplate Capacity

437

On the capacity of network coding for random networks  

E-Print Network (OSTI)

d ) , the network NC coding capacity C s;t ; ;t > (1 0 )8, AUGUST 2005 On the Capacity of Network Coding for Randomthat the network coding capacity concentrates around the

Ramamoorthy, A; Shi, J; Wesel, R D

2005-01-01T23:59:59.000Z

438

Zero-error capacity of a quantum channel  

E-Print Network (OSTI)

We define the quantum zero-error capacity, a new kind of classical capacity of a noisy quantum channel. Moreover, the necessary requirement for which a quantum channel has zero-error capacity greater than zero is also given.

Rex A. C. Medeiros; Francisco M. de Assis

2004-03-26T23:59:59.000Z

439

Statistical Approaches and Assumptions  

Science Conference Proceedings (OSTI)

... during the PCR amplification process – This is highly affected by DNA quantity and quality ... PCR inhibitors present in the sample may reduce PCR ...

2012-10-16T23:59:59.000Z

440

Africa - CCS capacity building | Open Energy Information  

Open Energy Info (EERE)

Africa - CCS capacity building Africa - CCS capacity building Jump to: navigation, search Name Africa - CCS capacity building Agency/Company /Organization Energy Research Centre of the Netherlands Partner EECG Consultants, the University of Maputo, the Desert Research Foundation Namibia and the South Africa New Energy Research Institute Sector Energy Focus Area Conventional Energy Resource Type Training materials Website http://www.ccs-africa.org/ Program Start 2010 Program End 2011 Country Botswana, Mozambique, Namibia UN Region "Sub-Saharan Africa" is not in the list of possible values (Eastern Africa, Middle Africa, Northern Africa, Southern Africa, Western Africa, Caribbean, Central America, South America, Northern America, Central Asia, Eastern Asia, Southern Asia, South-Eastern Asia, Western Asia, Eastern Europe, Northern Europe, Southern Europe, Western Europe, Australia and New Zealand, Melanesia, Micronesia, Polynesia, Latin America and the Caribbean) for this property.

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

DOE mixed waste treatment capacity analysis  

SciTech Connect

This initial DOE-wide analysis compares the reported national capacity for treatment of mixed wastes with the calculated need for treatment capacity based on both a full treatment of mixed low-level and transuranic wastes to the Land Disposal Restrictions and on treatment of transuranic wastes to the WIPP waste acceptance criteria. The status of treatment capacity is reported based on a fifty-element matrix of radiation-handling requirements and functional treatment technology categories. The report defines the classifications for the assessment, describes the models used for the calculations, provides results from the analysis, and includes appendices of the waste treatment facilities data and the waste stream data used in the analysis.

Ross, W.A.; Wehrman, R.R.; Young, J.R.; Shaver, S.R.

1994-06-01T23:59:59.000Z

442

Heat capacity at the glass transition  

E-Print Network (OSTI)

A fundamental problem of glass transition is to explain the jump of heat capacity at the glass transition temperature $T_g$ without asserting the existence of a distinct solid glass phase. This problem is also common to other disordered systems, including spin glasses. We propose that if $T_g$ is defined as the temperature at which the liquid stops relaxing at the experimental time scale, the jump of heat capacity at $T_g$ follows as a necessary consequence due to the change of system's elastic, vibrational and thermal properties. In this picture, we discuss time-dependent effects of glass transition, and identify three distinct regimes of relaxation. Our approach explains widely observed logarithmic increase of $T_g$ with the quench rate and the correlation of heat capacity jump with liquid fragility.

Kostya Trachenko; Vadim Brazhkin

2010-02-10T23:59:59.000Z

443

Definition: Capacity Benefit Margin | Open Energy Information  

Open Energy Info (EERE)

Benefit Margin Benefit Margin Jump to: navigation, search Dictionary.png Capacity Benefit Margin The amount of firm transmission transfer capability preserved by the transmission provider for Load- Serving Entities (LSEs), whose loads are located on that Transmission Service Provider's system, to enable access by the LSEs to generation from interconnected systems to meet generation reliability requirements. Preservation of CBM for an LSE allows that entity to reduce its installed generating capacity below that which may otherwise have been necessary without interconnections to meet its generation reliability requirements. The transmission transfer capability preserved as CBM is intended to be used by the LSE only in times of emergency generation deficiencies.[1] Related Terms

444

Correlation between thermal expansion and heat capacity  

E-Print Network (OSTI)

Theoretically predicted linear correlation between the volume coefficient of thermal expansion and the thermal heat capacity was investigated for highly symmetrical atomic arrangements. Normalizing the data of these thermodynamic parameters to the Debye temperature gives practically identical curves from zero Kelvin to the Debye temperature. This result is consistent with the predicted linear correlation. At temperatures higher than the Debye temperature the normalized values of the thermal expansion are always higher than the normalized value of the heat capacity. The detected correlation has significant computational advantage since it allows calculating the volume coefficient of thermal expansion from one experimental data by using the Debye function.

Jozsef Garai

2004-04-25T23:59:59.000Z

445

"Table A7. Shell Storage Capacity of Selected Petroleum Products by Census"  

U.S. Energy Information Administration (EIA) Indexed Site

Shell Storage Capacity of Selected Petroleum Products by Census" Shell Storage Capacity of Selected Petroleum Products by Census" " Region, Industry Group, and Selected Industries, 1991" " (Estimates in Thousand Barrels)" " "," "," "," "," ","Other","RSE" "SIC"," ","Motor","Residual"," ","Distillate","Row" "Code(a)","Industry Groups and Industry","Gasoline","Fuel Oil","Diesel","Fuel Oil","Factors" ,,"Total United States" ,"RSE Column Factors:",1,0.9,1,1.1 , 20,"Food and Kindred Products",38,1448,306,531,12.1 2011," Meat Packing Plants",1,229,40,13,13.2

446

Modeling the Capacity and Emissions Impacts of Reduced Electricity...  

NLE Websites -- All DOE Office Websites (Extended Search)

Modeling the Capacity and Emissions Impacts of Reduced Electricity Demand Title Modeling the Capacity and Emissions Impacts of Reduced Electricity Demand Publication Type Report...

447

Changing World Product Markets and Potential Refining Capacity ...  

U.S. Energy Information Administration (EIA)

Energy Information Administration ... Asia Demand growth, product mix, trade Price Signals for Capacity Changes Capacity ... 150 AZ Clean Fuels FCC/RCC Coking ...

448

Indonesia-ECN Capacity building for energy policy formulation...  

Open Energy Info (EERE)

ECN Capacity building for energy policy formulation and implementation of sustainable energy projects Jump to: navigation, search Name CASINDO: Capacity development and...

449

GIZ-Developing Climate Policy Capacity within the South African...  

Open Energy Info (EERE)

Policy Capacity within the South African Department of Environmental Affairs (DEA) Jump to: navigation, search Name South Africa - Developing Climate Policy Capacity within DEA...

450

Changing World Product Markets and Potential Refining Capacity Increases  

Reports and Publications (EIA)

The presentation explores potential refinery capacity increases over the next 5 years in various world regions, based on changing demand patterns, changing price incentives, and capacity expansion announcements.

Information Center

2006-03-20T23:59:59.000Z

451

Assessing the Control Systems Capacity for Demand Response in...  

NLE Websites -- All DOE Office Websites (Extended Search)

the Control Systems Capacity for Demand Response in California Industries Title Assessing the Control Systems Capacity for Demand Response in California Industries Publication Type...

452

EIA - Reference Case Projections for Electricity Capacity and...  

Gasoline and Diesel Fuel Update (EIA)

for Electricity Capacity and Generation by Fuel (2003-2030) International Energy Outlook 2006 Reference Case Projections for Electricity Capacity and Generation by Fuel Data Tables...

453

Dynamics of the Oil Transition: Modeling Capacity, Costs, and Emissions  

E-Print Network (OSTI)

extra-heavy oil and shale have zero Resource- Cost), whileof the Oil Transition: Modeling Capacity, Costs, andof the oil transition: modeling capacity, costs, and

Brandt, Adam R.; Farrell, Alexander E.

2008-01-01T23:59:59.000Z

454

Texas Natural Gas Underground Storage Capacity (Million Cubic...  

U.S. Energy Information Administration (EIA) Indexed Site

View History: Monthly Annual Download Data (XLS File) Texas Natural Gas Underground Storage Capacity (Million Cubic Feet) Texas Natural Gas Underground Storage Capacity (Million...

455

Indonesia-Enhancing Capacity for Low Emission Development Strategies...  

Open Energy Info (EERE)

Indonesia-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Indonesia-Enhancing Capacity for Low Emission Development Strategies...

456

Indonesia-Strengthening Planning Capacity for Low Carbon Growth...  

Open Energy Info (EERE)

Indonesia-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Jump to: navigation, search Name Indonesia-Strengthening Planning Capacity for Low Carbon Growth...

457

Working crude oil storage capacity at Cushing, Oklahoma rises ...  

U.S. Energy Information Administration (EIA)

Greenhouse gas data, ... as reported in EIA's recently released report on Working and Net Available Shell Storage Capacity. Utilization of working storage capacity ...

458

India-Vulnerability Assessment and Enhancing Adaptive Capacities...  

Open Energy Info (EERE)

Vulnerability Assessment and Enhancing Adaptive Capacities to Climate Change Jump to: navigation, search Name India-Vulnerability Assessment and Enhancing Adaptive Capacities to...

459

Solar Energy and Capacity Value (Fact Sheet), NREL (National...  

NLE Websites -- All DOE Office Websites (Extended Search)

Solar Energy and Capacity Value e Solar Energy Can Provide Valuable Capacity to Utilities and Power System Operators Solar photovoltaic (PV) systems and concentrating solar power...

460

"Assessment of the Adequacy of Natural Gas Pipeline Capacity...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

"Assessment of the Adequacy of Natural Gas Pipeline Capacity in the Northeast United States" Report Now Available "Assessment of the Adequacy of Natural Gas Pipeline Capacity in...

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Assessment of the Adequacy of Natural Gas Pipeline Capacity in...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Assessment of the Adequacy of Natural Gas Pipeline Capacity in the Northeast United States - November 2013 Assessment of the Adequacy of Natural Gas Pipeline Capacity in the...

462

Gulf Coast (PADD 3) Shell Storage Capacity at Operable Refineries  

U.S. Energy Information Administration (EIA)

Propane/Propylene: 4,376: 3,520: 3,565-----1982-2013: ... Notes: Shell storage capacity is the design capacity of the tank. See Definitions, Sources, ...

463

Estimates of Peak Underground Working Gas Storage Capacity in the ...  

U.S. Energy Information Administration (EIA)

Estimates of Peak Underground Working Gas Storage Capacity in the United States, 2009 Update The aggregate peak capacity for U.S. underground natural gas storage is ...

464

Lower 48 States Total Natural Gas Underground Storage Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

data. Release Date: 9302013 Next Release Date: 10312013 Referring Pages: Total Natural Gas Underground Storage Capacity Lower 48 States Underground Natural Gas Storage Capacity...

465

Stochastic binary problems with simple penalties for capacity ...  

E-Print Network (OSTI)

Mar 24, 2009 ... Abstract: This paper studies stochastic programs with first-stage binary variables and capacity constraints, using simple penalties for capacities ...

466

Indiana Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Indiana Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

467

Wyoming Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Wyoming Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

468

Louisiana Natural Gas Count of Underground Storage Capacity ...  

U.S. Energy Information Administration (EIA) Indexed Site

Count of Underground Storage Capacity (Number of Elements) Louisiana Natural Gas Count of Underground Storage Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3...

469

Louisiana Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Louisiana Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

470

Virginia Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Virginia Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

471

On the complexity of maximizing the minimum Shannon capacity in ...  

E-Print Network (OSTI)

capacity in wireless networks by joint channel assignment and power allocation ... tal Shannon capacity of any mobile user in the system. The corresponding.

472

New Mexico Working Natural Gas Underground Storage Capacity ...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) New Mexico Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

473

Washington Natural Gas Count of Underground Storage Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Count of Underground Storage Capacity (Number of Elements) Washington Natural Gas Count of Underground Storage Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3...

474

Iowa Natural Gas Underground Storage Acquifers Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Acquifers Capacity (Million Cubic Feet) Iowa Natural Gas Underground Storage Acquifers Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

475

Illinois Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Illinois Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

476

New York Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) New York Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

477

Y-12 builds capacity to meet nuclear testing schedule - Or: ...  

NLE Websites -- All DOE Office Websites (Extended Search)

builds capacity to meet nuclear testing schedule - Or: Increasing capacity to meet nuclear testing schedule (title as it appeared in The Oak Ridger) The continuing high volume...

478

Maryland Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Maryland Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

479

Oklahoma Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Oklahoma Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

480

Alabama Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Alabama Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

Note: This page contains sample records for the topic "assumptions capacity factors" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Kansas Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Kansas Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

482

Utah Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Utah Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3...

483

Tennessee Natural Gas Count of Underground Storage Capacity ...  

U.S. Energy Information Administration (EIA) Indexed Site

Count of Underground Storage Capacity (Number of Elements) Tennessee Natural Gas Count of Underground Storage Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3...

484

Maryland Natural Gas Underground Storage Depleted Fields Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Depleted Fields Capacity (Million Cubic Feet) Maryland Natural Gas Underground Storage Depleted Fields Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

485

Missouri Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Missouri Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

486

Oregon Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Oregon Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

487

DOE Issues Enforcement Guidance on Large-Capacity Clothes Washer...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Enforcement Guidance on Large-Capacity Clothes Washer Waivers and the Waiver Process DOE Issues Enforcement Guidance on Large-Capacity Clothes Washer Waivers and the Waiver Process...

488

Tennessee Natural Gas Underground Storage Depleted Fields Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Depleted Fields Capacity (Million Cubic Feet) Tennessee Natural Gas Underground Storage Depleted Fields Capacity (Million Cubic Feet) Decade Year-0 Year-1...

489

Colorado Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Colorado Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

490

Montana Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Montana Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

491

Minnesota Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Minnesota Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

492

Arkansas Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Arkansas Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

493

Minnesota Natural Gas Count of Underground Storage Capacity ...  

U.S. Energy Information Administration (EIA) Indexed Site

Count of Underground Storage Capacity (Number of Elements) Minnesota Natural Gas Count of Underground Storage Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3...

494

Iowa Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Iowa Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3...

495

Nebraska Natural Gas Underground Storage Depleted Fields Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Depleted Fields Capacity (Million Cubic Feet) Nebraska Natural Gas Underground Storage Depleted Fields Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

496

Nebraska Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Nebraska Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

497

California Natural Gas Count of Underground Storage Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Count of Underground Storage Capacity (Number of Elements) California Natural Gas Count of Underground Storage Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3...

498

Texas Working Natural Gas Underground Storage Capacity (Million...  

U.S. Energy Information Administration (EIA) Indexed Site

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Texas Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3...

499

Arkansas Natural Gas Underground Storage Depleted Fields Capacity...  

U.S. Energy Information Administration (EIA) Indexed Site

Underground Storage Depleted Fields Capacity (Million Cubic Feet) Arkansas Natural Gas Underground Storage Depleted Fields Capacity (Million Cubic Feet) Decade Year-0 Year-1 Year-2...

500

Capacity and Energy Payments to Small Power Producers and Cogenerators...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Capacity and Energy Payments to Small Power Producers and Cogenerators Under PURPA Docket (Georgia) Capacity and Energy Payments to Small Power Producers and Cogenerators Under...