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1

Capacity factors and solar job creation  

Science Journals Connector (OSTI)

We discuss two main job creation statistics often used by solar advocates to support increased solar deployment. Whilst overall solar technologies have a tendency to be labor-intensive, we find that the jobs per gigawatt hour statistic is relatively mis-leading as it has a tendency to reward technologies that have a low capacity factor. Ultimately the lower the capacity factor the more amplified the solar job creation number.

Matt Croucher

2011-01-01T23:59:59.000Z

2

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

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

3

Models of transcription factor binding: Sensitivity of activation functions to model assumptions  

E-Print Network [OSTI]

on statistical physics, a Markov-chain model and a computational simulation. Comparison of these models suggests for cooperativity. The simulation model suggests that direct interactions between TFs are unlikely to be the main in this contribution, the assumption of the cell being a well stirred reactor makes a qualitative difference

Kent, University of

4

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 . . . . . . . . . . . . . . . . . .

5

Key Assumptions Policy Issues  

E-Print Network [OSTI]

11/13/2014 1 Key Assumptions and Policy Issues RAAC Steering Committee November 17, 2014 Portland Supply Limitations 8 Withi h B l i8. Within-hour Balancing 9. Capacity and Energy Values for Wind/Solar t b it d d li d· Thermal: must be sited and licensed · Wind/solar: must be sited and licensed · EE

6

The harmonization of Canadian and U.S. window programs and standards. Impact on U-factor and SHGC of differences in simulation styles and assumptions  

SciTech Connect (OSTI)

The thermal performance of a window is currently characterized by the window`s thermal transmittance (U-factor) and its solar heat gain coefficient. The National Fenestration Rating Council (NFRC) has established a system for rating the thermal performance of windows. the U-factor is determined through computer simulation and validated with physical tests. The solar heat gain coefficient is determined for homogeneous products through computer simulation. Test methods exist for measuring solar heat gain through more complex products, although there is currently no standard. Under the NFRC`s rating program, a window must be simulated using the Window 4.1 and Frame 3.1 computer programs. There is some debate as to how accurately these computer programs simulate actual windows. This report addresses the differences in simulation styles and assumptions and what impact these differences have on the U-factor and solar heat gain coefficient. Section 2.0 covers center-of-glass modeling, section 3.0 covers spacer modeling, section 4.0 covers frame modeling, and section 5.0 concludes by weight the relative importance of the assumptions discussed. The focus of this research is on U-factor. For a more detailed study of solar heat gain coefficients refer to Wright (1995). This report also addresses the efficacy of various techniques, such as increasing glazing gap width or applying low-emittance coatings to interior glazing surfaces, at reducing total window U-factors.

NONE

1995-05-31T23:59:59.000Z

7

Regulatory Factors and Capacity-Expansion Planning in Global Chemical Supply Chains  

Science Journals Connector (OSTI)

In what follows, we first extensively review the existing work on capacity-expansion planning to highlight the scarcity of literature considering regulatory factors. ... The model determines new processes, expansion plans, and shutdown policies to maximize the net present value of a project given the forecasts of prices and demands of the chemicals over a long planning horizon. ... Using the sales forecast from the marketing division, the multinational company wishes to develop an optimum, strategic, and global capacity-expansion plan over a planning horizon of T fiscal years or periods (t = 1, 2, ..., T). ...

Hong-Choon Oh; I. A. Karimi

2004-05-28T23:59:59.000Z

8

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

9

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

10

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).

11

Preliminary Assumptions for Natural Gas Peaking  

E-Print Network [OSTI]

Preliminary Assumptions for Natural Gas Peaking Technologies Gillian Charles and Steve Simmons GRAC, Reciprocating Engines Next steps 2 #12;Definitions Baseload Energy: power generated (or conserved) across a period of time to serve system demands for electricity Peaking Capacity: capability of power generating

12

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).

13

Factors predicting the capacity of Los Angeles city-region recreation programs to promote energy expenditure  

Science Journals Connector (OSTI)

Abstract An audit of recreation programs with moderate or higher levels of physical activity (PA) in Los Angeles area cities (N=82) was conducted using internet, telephone, and survey methods. Metabolic Equivalents (METs) were used to code programs? physical activity intensity. MET-hours per recreation program was associated with required age for enrollment, percent of residents >64 years of age, and fiscal capacity of cities. Capacity to promote energy expenditure may depend on targeted age groups, age of population, and municipal fiscal capacity. Cities with lower fiscal capacity might offer those higher MET-hour activities which require less specialized equipment and seek outside funding to offer higher MET programs.

Kim D. Reynolds; Nicholas Dahmann; Jennifer Wolch; Pascale Joassart-Marcelli; Genevieve Dunton; Diana Rudulph; Joshua Newell; Jennifer Thayer; Michael Jerrett

2014-01-01T23:59:59.000Z

14

On the exergetic capacity factor of a wind solar power generation system  

Science Journals Connector (OSTI)

In the recent years, exergy analysis has become a very important tool in the evaluation of systems' efficiency. It aims on minimizing the energy related-system losses and therefore maximizing energy savings and helps society substantially to move towards sustainable development and cleaner production. In this paper, a detailed exergetic analysis aiming to identify the overall Exergetic Capacity Factor (ExCF) for a wind solar power generation system was done. ExCF, as a new parameter, can be used for better classification and evaluation of renewable energy sources (RES). All the energy and exergy characteristics of wind and solar energy were examined in order to identify the variables that affect the power output of the hybrid system. A validated open source PV optimization tool was also included in the analysis, It was shown that parameters as e.g. air density or tracking losses, low irradiation losses play a crucial role in identifying the real and net wind and solar power output while planning new renewable energy projects and in fact do play a significant role on the wind solar plant's overall exergetic efficiency. In specific, it was found that air density varies from site to site influencing productivity. A difference of 6.2% on the productivity because of the air density was calculated. The wind and solar potential around a mountainous area were studied and presented based on field measurements and simulations. Since the number and the size of RES projects, over the last few years, are continually increasing, and new areas are required, the basic idea behind this research, was not only to introduce ExCF, as a new evaluation index for RES, but also to investigate the combined use of wind and solar energy under the same area and the benefits coming out of this combination.

G. Xydis

2013-01-01T23:59:59.000Z

15

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).

16

Nuclear Factor of Activated T-cell Activity Is Associated with Metastatic Capacity in Colon Cancer  

Science Journals Connector (OSTI)

...gene expression datasets and applied expression...module predicted nuclear factor of activated...human microarray datasets, representing data...further predicted the nuclear factor of activated...signaling driving nuclear transport where...gene expression datasets were downloaded...

Manish K. Tripathi; Natasha G. Deane; Jing Zhu; Hanbing An; Shinji Mima; Xiaojing Wang; Sekhar Padmanabhan; Zhiao Shi; Naresh Prodduturi; Kristen K. Ciombor; Xi Chen; M. Kay Washington; Bing Zhang; and R. Daniel Beauchamp

2014-12-01T23:59:59.000Z

17

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.

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  

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. . . . . . . . . . . . .

20

Section 25: Future State Assumptions  

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

the Compliance Certification Application (CCA), Chapter 6.0, Section 6.2 and Appendices SCR and MASS (U.S. DOE 1996). Many of these future state assumptions were derived from the...

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

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

23

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

24

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).

25

Idaho National Engineering Laboratory installation roadmap assumptions document. Revision 1  

SciTech Connect (OSTI)

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

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

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.

28

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.

29

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.

30

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).

31

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.

32

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).

33

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

34

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

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 - 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

37

WINDExchange: Wind Potential Capacity  

Wind Powering America (EERE)

area with a gross capacity factor1 of 35% and higher, which may be suitable for wind energy development. AWS Truepower LLC produced the wind resource data with a spatial...

38

Climate Action Planning Tool Formulas and Assumptions  

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

39

TABLE 1. Nuclear Reactor, State, Type, Net Capacity, Generation...  

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

TABLE 1. Nuclear Reactor, State, Type, Net Capacity, Generation, and Capacity Factor " "PlantReactor Name","Generator ID","State","Type","2009 Summer Capacity"," 2010 Annual...

40

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.

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 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.

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

Preliminary Assumptions for Natural Gas Peaking  

E-Print Network [OSTI]

Preliminary Assumptions for Natural Gas Peaking Technologies Gillian Charles GRAC 2/27/14 #12;Today Vernon, WA PSE Klamath Generation Peakers June 2002 (2) 54 MW P&W FT8 Twin- pac 95 MW Klamath, OR IPP; winter-only PPA w/ PSE Dave Gates Generating Station Jan 2011 (3) P&W SWIFTPAC 150 MW Anaconda, MT North

45

Empirically Revisiting the Test Independence Assumption  

E-Print Network [OSTI]

Empirically Revisiting the Test Independence Assumption Sai Zhang, Darioush Jalali, Jochen Wuttke}@cs.washington.edu ABSTRACT In a test suite, all the test cases should be independent: no test should affect any other test's result, and running the tests in any order should produce the same test results. Techniques such as test

Ernst, Michael

46

Assumptions to the Annual Energy Outlook 2013  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

set using a discount rate of 10 percent. The model limits the annual builds to one two-train facility a year, with total annual export capacity of 400 billion cubic feet. The...

47

Fundamentals of Capacity Control  

Science Journals Connector (OSTI)

Whereas capacity planning determines in advance the capacities required to implement a production program, capacity control determines the actual capacities implemented shortly beforehand. The capacity control...

Prof. Dr.-Ing. habil. Hermann Ldding

2013-01-01T23:59:59.000Z

48

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

49

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

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

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.

52

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

53

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),

54

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.

55

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.

56

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.

57

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

58

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 Energy’s Office of Policy and International Affairs, other DOE offices, and other government agencies.

59

Manufacturing Energy and Carbon Footprint Definitions and Assumptions, October 2012  

Broader source: Energy.gov [DOE]

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

60

Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and  

Alternative Fuels and Advanced Vehicles Data Center [Office of Energy Efficiency and Renewable Energy (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...

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 - 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

62

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

63

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

64

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.

65

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).

66

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).

67

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).

68

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).

69

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

70

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).

71

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).

72

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.

73

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.

74

2010 Manufacturing Energy and Carbon Footprints: Definitions and Assumptions  

Broader source: Energy.gov [DOE]

This 13-page document provides definitions and assumptions used in the Manufacturing Energy and Carbon Footprints (MECS 2010)

75

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.

76

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.

77

Economies of size and other factors influencing costs and returns on major U.S. crop farms with implications for debt repayment capacity  

E-Print Network [OSTI]

ECONOFIIES OF STEP AND OTHER PACTOP+ INFIUENCII1C COSTS AND RETUPoNS ON ILK. IOR U. S. CROP FARMS NITH IMPLICATIONS FOR DEBT REPAYMENT CAPACITY A Thesis Sandra Kay McDonald Submitted to the Graduate Col. lege of Texas A&M University.... S. Crop Farms with Implications For Debt Repayment Capacity (August 1978) Sandra Kay McDonald, B. S. , Texas A&M University Chairman of Advisory Committee: Peter J. Barry This study analyzes several classification variables, including , co...

McDonald, Sandra Kay

1978-01-01T23:59:59.000Z

78

Capacity Markets for Electricity  

E-Print Network [OSTI]

ternative Approaches for Power Capacity Markets, Papers andprof id=pjoskow. Capacity Markets for Electricity [13]Utility Commission- Capacity Market Questions, available at

Creti, Anna; Fabra, Natalia

2004-01-01T23:59:59.000Z

79

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).

80

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.

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

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?

82

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).

83

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

84

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.

85

Monitoring Infrastructure Capacity Monitoring Infrastructure Capacity  

E-Print Network [OSTI]

Levinson, D. (2000) Monitoring Infrastructure Capacity p. 165-181 in Land Market Monitoring for Smart Urban) task. Monitoring infrastructure capacity is at least as complex as monitoring urban land markets Levinson, D. (2000) Monitoring Infrastructure Capacity p. 165-181 in Land Market Monitoring for Smart Urban

Levinson, David M.

86

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

87

MONITORED GEOLOGIC REPOSITORY LIFE CYCLE COST ESTIMATE ASSUMPTIONS DOCUMENT  

SciTech Connect (OSTI)

The purpose of this assumptions document is to provide general scope, strategy, technical basis, schedule and cost assumptions for the Monitored Geologic Repository (MGR) life cycle cost (LCC) estimate and schedule update incorporating information from the Viability Assessment (VA) , License Application Design Selection (LADS), 1999 Update to the Total System Life Cycle Cost (TSLCC) estimate and from other related and updated information. This document is intended to generally follow the assumptions outlined in the previous MGR cost estimates and as further prescribed by DOE guidance.

R.E. Sweeney

2001-02-08T23:59:59.000Z

88

DOE Transmission Capacity Report | Department of Energy  

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

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

89

Refinery Capacity Report  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

Report --- Full report in PDF (1 MB) XLS --- Refinery Capacity Data by individual refinery as of January 1, 2006 Tables 1 Number and Capacity of Operable Petroleum...

90

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

91

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

92

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

93

Capacity Value of Concentrating Solar Power Plants  

SciTech Connect (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

94

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

95

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

96

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

97

COMPARING ALASKA'S OIL PRODUCTION TAXES: INCENTIVES AND ASSUMPTIONS1  

E-Print Network [OSTI]

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

Pantaleone, Jim

98

Reasoning by Assumption: Formalisation and Analysis of Human Reasoning Traces  

E-Print Network [OSTI]

for the traces acquired in experiments undertaken. 1 Introduction Practical reasoning processes are often not limited to single reasoning steps, but extend to traces or trajectories of a number of interrelated by assumption'. This (non-deductive) practical reasoning pattern in- volves a number of interrelated reasoning

Treur, Jan

99

ORISE: Capacity Building  

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

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  

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

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  

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

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

109

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

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

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.

113

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

114

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

115

EIA - Electricity Generating Capacity  

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

Electricity Generating Capacity Release Date: January 3, 2013 | Next Release: August 2013 Year Existing Units by Energy Source Unit Additions Unit Retirements 2011 XLS XLS XLS 2010...

116

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

117

Diversion assumptions for high-powered research reactors  

SciTech Connect (OSTI)

This study deals with diversion assumptions for high-powered research reactors -- specifically, MTR fuel; pool- or tank-type research reactors with light-water moderator; and water, beryllium, or graphite reflectors, and which have a power level of 25 MW(t) or more. The objective is to provide assistance to the IAEA in documentation of criteria and inspection observables related to undeclared plutonium production in the reactors described above, including: criteria for undeclared plutonium production, necessary design information for implementation of these criteria, verification guidelines including neutron physics and heat transfer, and safeguards measures to facilitate the detection of undeclared plutonium production at large research reactors.

Binford, F.T.

1984-01-01T23:59:59.000Z

118

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

119

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

120

Nanofluid heat capacities  

Science Journals Connector (OSTI)

Significant increases in the heat capacity of heat transfer fluids are needed not only to reduce the costs of liquid heating and cooling processes but also to bring clean energy producing technologies like concentrating solar power (CSP) to price parity with conventional energy generation. It has been postulated that nanofluids could have higher heat capacities than conventional fluids. In this work nano- and micron-sized particles were added to five base fluids (poly-? olefin mineral oil ethylene glycol a mixture of water and ethylene glycol and calcium nitrate tetrahydrate) and the resulting heat capacities were measured and compared with those of the neat base fluids and the weighted average of the heat capacities of the components. The particles used were inert metals and metal oxides that did not undergo any phase transitions over the temperature range studied. In the nanofluids studied here we found no increase in heat capacity upon the addition of the particles larger than the experimental error.

Anne K. Starace; Judith C. Gomez; Jun Wang; Sulolit Pradhan; Greg C. Glatzmaier

2011-01-01T23:59:59.000Z

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.


121

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 Energy’s Office of Policy, other DOE offices, and other government agencies.

122

The contour method cutting assumption: error minimization and correction  

SciTech Connect (OSTI)

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

Prime, Michael B [Los Alamos National Laboratory; Kastengren, Alan L [ANL

2010-01-01T23:59:59.000Z

123

Manufacturing Energy and Carbon Footprint Definitions and Assumptions...  

Energy Savers [EERE]

The age of the boiler, boiler size, maintenance practices, and fuel type are important factors. Power generation losses vary depending on whether cogeneration is employed (systems...

124

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

125

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

126

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.

127

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.

128

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).

129

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

130

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.

131

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.

132

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.

133

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

134

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

135

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

136

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

137

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 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.14

138

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.

139

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

140

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,

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

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

142

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

143

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

144

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 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.1

145

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

146

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)

147

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 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.1

148

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.

149

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

150

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 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 [1].

151

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

152

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

153

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

154

Assessing the Control Systems Capacity for Demand Response in California  

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

155

Panama Canal capacity analysis  

SciTech Connect (OSTI)

Predicting the transit capacities of the various Panama Canal alternatives required analyzing data on present Canal operations, adapting and extending an existing computer simulation model, performing simulation runs for each of the alternatives, and using the simulation model outputs to develop capacity estimates. These activities are summarized in this paper. A more complete account may be found in the project final report (TAMS 1993). Some of the material in this paper also appeared in a previously published paper (Rosselli, Bronzini, and Weekly 1994).

Bronzini, M.S. [Oak Ridge National Lab., Knoxville, TN (United States). Center for Transportation Analysis

1995-04-27T23:59:59.000Z

156

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.

157

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

SciTech Connect (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

158

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.

159

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

160

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.

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

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.

162

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

163

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,

164

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,

165

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

166

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,

167

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

168

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

169

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

170

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

171

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

172

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.

173

Photovoltaics effective capacity: Interim final report 2  

SciTech Connect (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

174

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.

175

Capacity of steganographic channels  

Science Journals Connector (OSTI)

An information-theoretic approach is used to determine the amount of information that may be safely transferred over a steganographic channel with a passive adversary. A steganographic channel, or stego-channel is a pair consisting of the channel transition ... Keywords: information spectrum, information theory, steganalysis, steganographic capacity, steganography, stego-channel

Jeremiah J. Harmsen; William A. Pearlman

2005-08-01T23:59:59.000Z

176

On the self-similarity assumption in dynamic models for large eddy simulations  

E-Print Network [OSTI]

that the present formulation of the DP is usually incompatible with its under- lying self-similarity assumption SSAOn the self-similarity assumption in dynamic models for large eddy simulations Daniele Carati eddy simulations and their underlying self-similarity assumption is discussed. The interpretation

Van Den Eijnden, Eric

177

Estimation of capacity credit for wind power in Libya  

Science Journals Connector (OSTI)

This paper presents the results of a study that evaluated the wind potential at the central region of the Libyan coast and estimated the capacity credit of wind power in the national network. Several sites were investigated to choose the most suitable sites for wind farm establishment. Different sizes of Wind Energy Converter Systems (WECSs) were selected to estimate the wind potential. The sizes were selected to satisfy present and future market development as well as to satisfy technical, economic, and environmental aspects. Wind data from three meteorological stations in the proposed region were used in assessing the wind potential. The wind potential was estimated according to the characteristics of the sites and power curves of the WECSs, and considering certain assumptions. The results showed that the capacity credit varied from about 20% to 50%, depending on penetration levels of wind power, for the assumptions made in this study.

Wedad B. El-Osta; Mohamed Ali Ekhlat; Amal S. Yagoub; Yousef Khalifa; E. Borass

2005-01-01T23:59:59.000Z

178

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

179

Capacity Value of Solar Power  

SciTech Connect (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

180

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......................................

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

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

182

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).

183

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

184

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).

185

Genetic Regulation of Intrinsic Endurance Exercise Capacity in Mice  

E-Print Network [OSTI]

been reported across cross-section, twin, and family studies. This variation is evidence of a genetic component to the phenotype of endurance exercise capacity: however, the genetic factors responsible for explaining this variation are undefined...

Courtney, Sean M.

2013-07-26T23:59:59.000Z

186

State and National Wind Resource Potential at Various Capacity...  

Wind Powering America (EERE)

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...

187

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

SciTech Connect (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

188

OIKOS 101: 499504, 2003 Do seedlings in gaps interact? A field test of assumptions in ESS  

E-Print Network [OSTI]

OIKOS 101: 499­504, 2003 Do seedlings in gaps interact? A field test of assumptions in ESS seed seedlings in gaps interact? A field test of assumptions in ESS seed size models. ­ Oikos 101: 499­504. ESS for the occupancy of `safe sites' or vegetation gaps. If mortality rates are high and/or frequency-independent, ESS

Silvertown, Jonathan

189

Granular Matter 4(3) (2002) How good is the equipartition assumption for the transport  

E-Print Network [OSTI]

Granular Matter 4(3) (2002) How good is the equipartition assumption for the transport properties of a granular mixture? Meheboob Alam (1) , Stefan Luding (1;2) ? Abstract Kinetic-theory, with the assumption of equipar- tition of granular energy, suggests that the pressure and viscosity of a granular mixture vary

Luding, Stefan

190

Impact of assumption of log-normal distribution on monthly rainfall estimation from TMI  

E-Print Network [OSTI]

The log-normal assumption for the distribution of the rain rates used for the estimation of monthly rain totals proposed in Wilheit et al 1991 was examined. Since the log-normal assumption was originally used for the SSM/I, it is now necessary to re...

Lee, Dong Heon

2012-06-07T23:59:59.000Z

191

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

SciTech Connect (OSTI)

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

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

2014-04-01T23:59:59.000Z

192

First mideast capacity planned  

SciTech Connect (OSTI)

Kuwait catalyst Co.`s (KCC) plans to build a hydrodesulfurization (HDS) catalysts plant in Kuwait will mark the startup of the first refining catalysts production in the Persian Gulf region. KCC, owned by a conglomerate of Kuwait companies and governmental agencies, has licensed catalyst manufacturing technology from Japan Energy in a deal estimated at more than 7 billion ($62 million). Plant design will be based on technology from Orient Catalyst, Japan Energy`s catalysts division. Construction is expected to begin in January 1997 for production startup by January 1998. A source close to the deal says the new plant will eventually reach a capacity of 5,000 m.t./year of HDS catalysts to supply most of Kuwait`s estimated 3,500-m.t./year demand, driven primarily by Kuwait National Petroleum refineries. KCC also expects to supply demand from other catalyst consumers in the region. Alumina supply will be acquired on the open market. KCC will take all production from the plant and will be responsible for marketing.

Fattah, H.

1996-11-06T23:59:59.000Z

193

Moldy Assumptions  

E-Print Network [OSTI]

sustainability movements. 2 Despite these noble intentions, using human responsibility as a base for architecture

Heully, Gustave Paul

2012-01-01T23:59:59.000Z

194

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

195

Adaptive capacity and its assessment  

SciTech Connect (OSTI)

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

196

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

E-Print Network [OSTI]

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

Webster, Tom; Benedek, Corinne; Bauman, Fred

2008-01-01T23:59:59.000Z

197

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

Science Journals Connector (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

198

Behavioral Assumptions Underlying California Residential Sector Energy Efficiency Programs (2009 CIEE Report)  

Broader source: Energy.gov [DOE]

This paper examines the behavioral assumptions that underlie Californias residential sector energy efficiency programs and recommends improvements that will help to advance the states ambitious greenhouse gas reduction goals.

199

Length measurement of a moving rod by a single observer without assumptions concerning its magnitude  

E-Print Network [OSTI]

We extend the results presented by Weinstein concerning the measurement of the length of a moving rod by a single observer, without making assumptions concerning the distance between the moving rod and the observer who measures its length.

Bernhard Rothenstein; Ioan Damian

2005-07-03T23:59:59.000Z

200

Assumptions about the U.S., the EU, NATO, and their Impact on the Transatlantic Agenda  

Science Journals Connector (OSTI)

I propose in this paper to discuss, from an American perspective, the assumptions and assertions that influence the way that I look at foreign policy events at the end of this decade. I will conclude with a fe...

Stanley Sloan

2000-01-01T23:59:59.000Z

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

Coping with rivals absorptive capacity in innovation activities  

Science Journals Connector (OSTI)

Abstract Two factors jointly determine the likelihood of a firm?s competitors obtaining information on its intangible assets and using it to damage the firm?s innovation performance. Those factors are the absorptive capacity of the rival firm and the appropriability regime of the innovating firm. However, the precise roles of the two factors in affecting performance outcomes are not well documented. Furthermore, we lack knowledge of the interplay between an appropriability regime and absorptive capacity, although they clearly have the capacity to exert positive and negative effects both on each other and on innovativeness. This study presents findings derived from theoretical discussion and an empirical examination of 155 firms that suggest that while competitors absorptive capacity does not play a direct negative or positive role on the innovation performance of a firm, an appropriability regime exerts a strong positive influence. Nevertheless, high rival absorptive capacity is not without importance, since the significant interaction effects suggest that a strong appropriability regime has positive effects on innovation performance especially in the context of a rival having high absorptive capacity.

Pia Hurmelinna-Laukkanen; Heidi Olander

2014-01-01T23:59:59.000Z

202

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

203

COMMUNITY CAPACITY BUILDING THROUGH TECHNOLOGY  

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

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

204

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:

205

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

206

High Capacity Immobilized Amine Sorbents  

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

207

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

SciTech Connect (OSTI)

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

208

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

209

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

SciTech Connect (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

210

California Working Natural Gas Underground Storage Capacity ...  

Gasoline and Diesel Fuel Update (EIA)

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) California Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Year Jan Feb Mar Apr May Jun...

211

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...

212

Economic Dispatch of Electric Generation Capacity | Department...  

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

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...

213

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.

214

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

215

Hybrid Zero-capacity Channels  

E-Print Network [OSTI]

There are only two known kinds of zero-capacity channels. The first kind produces entangled states that have positive partial transpose, and the second one - states that are cloneable. We consider the family of 'hybrid' quantum channels, which lies in the intersection of the above classes of channels and investigate its properties. It gives rise to the first explicit examples of the channels, which create bound entangled states that have the property of being cloneable to the arbitrary finite number of parties. Hybrid channels provide the first example of highly cloneable binding entanglement channels, for which known superactivation protocols must fail - superactivation is the effect where two channels each with zero quantum capacity having positive capacity when used together. We give two methods to construct a hybrid channel from any binding entanglement channel. We also find the low-dimensional counterparts of hybrid states - bipartite qubit states which are extendible and possess two-way key.

Sergii Strelchuk; Jonathan Oppenheim

2012-07-04T23:59:59.000Z

216

Building Regulatory Capacity for Change  

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

217

Campus Recreation at Sonoma State University RELEASE OF LIABILITY -PROMISE NOT TO SUE ASSUMPTION OF  

E-Print Network [OSTI]

Campus Recreation at Sonoma State University RELEASE OF LIABILITY - PROMISE NOT TO SUE ASSUMPTION OF RISK - AGREEMENT TO PAY CLAIMS PERMISSION TO USE VISUAL LIKENESS Activities: a) USE OF SSU RECREATION RECREATION PROGRAMS. Effective Locations and Time Periods: a) RECREATION CENTER: DURING HOURS OF OPERATION

Ravikumar, B.

218

Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric IRT  

E-Print Network [OSTI]

Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric IRT Brian of the monotonicity conditions discussed in Section 4. #12;Abstract In recent years, as cognitive theories of learning" on student achievement relative to theory-driven lists of examinee skills, beliefs and other cognitive

Junker, Brian

219

Draft -F. Nicoud 1 About the zero Mach number assumption in  

E-Print Network [OSTI]

Draft - F. Nicoud 1 About the zero Mach number assumption in the calculation of thermoacoustic as the the flame forcing ('Rayleigh') term. Besides, the net effect of the non zero Mach number terms the frequency of oscillation and growth rate are modified when the Mach number is not zero. It is demonstrated

Nicoud, Franck

220

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

SciTech Connect (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

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

Capacity Allocation with Competitive Retailers Masabumi Furuhata  

E-Print Network [OSTI]

to uncertainty of market demands, costly capacity construction and time consuming capacity expansion. This makes the market to be unstable and malfunc- tioning. Such a problem is known as the capacity allocation investigate the properties of capacity allocation mechanisms for the markets where a sin- gle supplier

Zhang, Dongmo

222

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

SciTech Connect (OSTI)

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

223

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.

224

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

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

225

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

SciTech Connect (OSTI)

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

226

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

227

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

228

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)

229

Work Capacity, Thermal Responses and Lung Function: United Kingdom Studies in the I.B.P.  

Science Journals Connector (OSTI)

...1976 research-article Work Capacity, Thermal Responses and Lung Function: United Kingdom...and water, as shown by studies in the Sudan and Tanzania. Lung function of some seven...factors was examined. Work capacity, thermal responses and lung function: united kingdom...

1976-01-01T23:59:59.000Z

230

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

E-Print Network [OSTI]

of distributed PV capacity by the year 2016.8 In response to the California Solar Initiative, a component,7 However, California has established the Go Solar California program with a goal of installing 3,000 MW of the Go Solar California program, 158 MW were installed in 2008 alone.9 The reasonableness of the CEC

231

[working paper] Regional Economic Capacity, Economic Shocks,  

E-Print Network [OSTI]

1 [working paper] Regional Economic Capacity, Economic Shocks, and Economic that makes them more likely to resist economic shocks or to recover quickly from of resilience capacity developed by Foster (2012) is related to economic resilience

Sekhon, Jasjeet S.

232

Fair capacity sharing of multiple aperiodic servers  

E-Print Network [OSTI]

For handling multiple aperiodic tasks with different temporal requirements, multiple aperiodic servers are used. Since capacity is partitioned statically among the multiple servers, they suffer from heavy capacity exhaustions. Bernat and Burns...

Melapudi, Vinod Reddy

2002-01-01T23:59:59.000Z

233

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

SciTech Connect (OSTI)

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

234

Diversion assumptions for high-powered research reactors. ISPO C-50 Phase 1  

SciTech Connect (OSTI)

This study deals with diversion assumptions for high-powered research reactors -- specifically, MTR fuel; pool- or tank-type research reactors with light-water moderator; and water, beryllium, or graphite reflectors, and which have a power level of 25 MW(t) or more. The objective is to provide assistance to the IAEA in documentation of criteria and inspection observables related to undeclared plutonium production in the reactors described above, including: criteria for undeclared plutonium production, necessary design information for implementation of these criteria, verification guidelines including neutron physics and heat transfer, and safeguards measures to facilitate the detection of undeclared plutonium production at large research reactors.

Binford, F.T.

1984-01-01T23:59:59.000Z

235

Can Science and Technology Capacity be Measured?  

E-Print Network [OSTI]

The ability of a nation to participate in the global knowledge economy depends to some extent on its capacities in science and technology. In an effort to assess the capacity of different countries in science and technology, this article updates a classification scheme developed by RAND to measure science and technology capacity for 150 countries of the world.

Wagner, Caroline S; Dutta, Arindum

2015-01-01T23:59:59.000Z

236

Internal Markets for Supply Chain Capacity Allocation  

E-Print Network [OSTI]

Internal Markets for Supply Chain Capacity Allocation David McAdams and Thomas W. Malone Sloan David McAdams & Thomas Malone #12;Internal Markets for Supply Chain Capacity Allocation David Mc ("internal markets") to help allocate manufacturing capacity and determine the prices, delivery dates

237

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

238

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

239

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

240

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

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

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.

242

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

243

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

244

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

245

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

246

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.

247

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

248

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

249

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.

250

Design and Evaluation of Novel High Capacity Cathode Materials...  

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

High Capacity Cathodes Vehicle Technologies Office Merit Review 2014: Design and Evaluation of High Capacity Cathodes Design and Evaluation of Novel High Capacity Cathode Materials...

251

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

252

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

253

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

254

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

255

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

256

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"

257

EEI/DOE Transmission Capacity Report  

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

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 . . . . . . .

258

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

259

Controlling the bullwhip with transport capacity constraints  

Science Journals Connector (OSTI)

The bullwhip effect can be costly to companies in terms of capacity-on costs and stock-out costs. This paper examines the possibilities for controlling the bullwhip effect with transport capacity management in the supply chain. The goal is to examine how inventories and service levels react to transport capacity constraints in a simulated supply chain that is prone to the bullwhip effect. By controlling the transport capacities, the companies may be able to reduce the impacts of demand amplification and inventory variations. Thus, there may be significant practical implications of the findings for logistics managers in today's volatile business environments.

Jouni Juntunen; Jari Juga

2009-01-01T23:59:59.000Z

260

,"California Underground Natural Gas Storage Capacity"  

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

Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","California Underground Natural Gas Storage Capacity",12,"Annual",2013,"6301988" ,"Release...

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

Increasing the Capacity of Existing Power Lines  

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

works with Idaho Power engineers to train system operators in the use of weather station data and software tools to generate transmission capacity operat- ing limits. The ability...

262

Generation capacity expansion in restructured energy markets.  

E-Print Network [OSTI]

??With a significant number of states in the U.S. and countries around the world trading electricity in restructured markets, a sizeable proportion of capacity expansion (more)

Nanduri, Vishnuteja

2009-01-01T23:59:59.000Z

263

Increasing water holding capacity for irrigation  

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

264

Property:USGSMeanCapacity | Open Energy Information  

Open Energy Info (EERE)

Resource Assessment of the United States. Use this property to express potential electric energy generation, such as Nameplate Capacity. The default unit is megawatts (MW). For...

265

Solar Energy and Capacity Value (Fact Sheet)  

SciTech Connect (OSTI)

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

266

,"New York Underground Natural Gas Storage Capacity"  

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

Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New York Underground Natural Gas Storage Capacity",11,"Annual",2013,"6301988" ,"Release...

267

WINDExchange: U.S. Installed Wind Capacity  

Wind Powering America (EERE)

The animation shows the progress of installed wind capacity between 1999 and 2013. The Energy Department's annual Wind Technologies Market Report provides information about wind...

268

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

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

269

Special relativity as the limit of an Aristotelian universal friction theory under Reye's assumption  

E-Print Network [OSTI]

This work explores a classical mechanical theory under two further assumptions: (a) there is a universal dry friction force (Aristotelian mechanics), and (b) the variation of the mass of a body due to wear is proportional to the work done by the friction force on the body (Reye's hypothesis). It is shown that mass depends on velocity as in Special Relativity, and that the velocity is constant for a particular characteristic value. In the limit of vanishing friction the theory satisfies a relativity principle as bodies do not decelerate and, therefore, the absolute frame becomes unobservable. However, the limit theory is not Newtonian mechanics, with its Galilei group symmetry, but rather Special Relativity. This result suggests to regard Special Relativity as the limit of a theory presenting universal friction and exchange of mass-energy with a reservoir (vacuum). Thus, quite surprisingly, Special Relativity follows from the absolute space (ether) concept and could have been discovered following studies of Aristotelian mechanics and friction. We end the work confronting the full theory with observations. It predicts the Hubble law through tired light, and hence it is incompatible with supernova light curves unless both mechanisms of tired light (locally) and universe expansion (non-locally) are at work. It also nicely accounts for some challenging numerical coincidences involving phenomena under low acceleration.

E. Minguzzi

2014-11-28T23:59:59.000Z

270

Representation of the Solar Capacity Value in the ReEDS Capacity...  

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

Representation of the Solar Capacity Value in the ReEDS Capacity Expansion Model Preprint Ben Sigrin, Patrick Sullivan, Eduardo Ibanez, and Robert Margolis Presented at the 40th...

271

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

272

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

273

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

274

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

275

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

276

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.

277

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

SciTech Connect (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.

2014-03-21T23:59:59.000Z

278

Hanford Waste Vitrification Plant capacity increase options  

SciTech Connect (OSTI)

Studies are being conducted by the Hanford Waste Vitrification Plant (HWVP) Project on ways to increase the waste processing capacity within the current Vitrification Building structural design. The Phase 1 study on remote systems concepts identification and extent of capacity increase was completed. The study concluded that the HWVP capacity could be increased to four times the current capacity with minor design adjustments to the fixed facility design, and the required design changes would not impact the current footprint of the vitrification building. A further increase in production capacity may be achievable but would require some technology development, verification testing, and a more systematic and extensive engineering evaluation. The primary changes included a single advance melter with a higher capacity, new evaporative feed tank, offgas quench collection tank, ejector venturi scrubbers, and additional inner canister closure station,a smear test station, a new close- coupled analytical facility, waste hold capacity of 400,000 gallon, the ability to concentrate out-of-plant HWVP feed to 90 g/L waste oxide concentration, and limited changes to the current base slab construction package.

Larson, D.E.

1996-04-01T23:59:59.000Z

279

DSM of Newton type for solving operator equations F(u) = f with minimal smoothness assumptions on F  

Science Journals Connector (OSTI)

This paper is a review of the authors' results on the Dynamical Systems Method (DSM) for solving operator equation (*) F(u) = f. It is assumed that (*) is solvable. The novel feature of the results is the minimal assumption on the smoothness of F. It is assumed that F is continuously Frechet differentiable, but no smoothness assumptions on F?(u) are imposed. The DSM for solving equation (*) is developed. Under weak assumptions global existence of the solution u(t) is proved, the existence of u(?) is established, and the relation F(u(?)) = f is obtained. The DSM is developed for a stable solution of equation (*) when noisy data f? are given, ''f ? f?'' ? ?.

N.S. Hoang; A.G. Ramm

2010-01-01T23:59:59.000Z

280

Representation of the Solar Capacity Value in the ReEDS Capacity Expansion Model: Preprint  

SciTech Connect (OSTI)

An important emerging issue is the estimation of renewables' contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to resource variability. Reliability-based methods, particularly, effective load-carrying capacity (ELCC), are considered to be the most robust techniques for addressing this resource variability. The Regional Energy Deployment System (ReEDS) capacity expansion model and other long-term electricity capacity planning models require an approach to estimating CV for generalized PV and system configurations with low computational and data requirements. In this paper we validate treatment of solar photovoltaic (PV) capacity value by ReEDS capacity expansion model by comparing model results to literature for a range of energy penetration levels. Results from the ReEDS model are found to compare well with both comparisons--despite not being resolved at an hourly scale.

Sigrin, B.; Sullivan, P.; Ibanez, E.; Margolis, R.

2014-08-01T23:59:59.000Z

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.


281

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+

282

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

283

Ethylene capacity tops 77 million mty  

SciTech Connect (OSTI)

World ethylene production capacity is 77.8 million metric tons/year (mty). This total represents an increase of more than 6 million mty, or almost 9%, over last year`s survey. The biggest reason for the large change is more information about plants in the CIS. Also responsible for the increase in capacity is the start-up of several large ethylene plants during the past year. The paper discusses construction of ethylene plants, feedstocks, prices, new capacity, price outlook, and problems in Europe`s ethylene market.

Rhodes, A.K.; Knott, D.

1995-04-17T23:59:59.000Z

284

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

285

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

286

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

287

U.S. Refinery Utilization and Capacity  

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

2008 2009 2010 2011 2012 2013 View History Gross Input to Atmospheric Crude Oil Distillation Units 15,027 14,659 15,177 15,289 15,373 15,724 1985-2013 Operable Capacity (Calendar...

288

Information capacity of a single photon  

Science Journals Connector (OSTI)

Quantum states of light are the obvious choice for communicating quantum information. To date, encoding information into the polarization states of single photons has been widely used as these states form a natural closed two-state qubit. However, photons are able to encode much morein principle, infiniteinformation via the continuous spatiotemporal degrees of freedom. Here we consider the information capacity of an optical quantum channel, such as an optical fiber, where a spectrally encoded single photon is the means of communication. We use the Holevo bound to calculate an upper bound on the channel capacity, and relate this to the spectral encoding basis and the spectral properties of the channel. Further, we derive analytic bounds on the capacity of such channels, and, in the case of a symmetric two-state encoding, calculate the exact capacity of the corresponding channel.

Peter P. Rohde; Joseph F. Fitzsimons; Alexei Gilchrist

2013-08-09T23:59:59.000Z

289

Information capacity of holograms in photorefractive crystals  

Science Journals Connector (OSTI)

From a single measurement of the signal-to-noise ratio of the image reconstructed from a hologram it is possible to estimate the information capacity of superimposed holograms and to...

Miridonov, S V; Kamshilin, A A; Khomenko, A V; Tentori, D

1994-01-01T23:59:59.000Z

290

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

291

Tripling the capacity of wireless communications using  

E-Print Network [OSTI]

channels of electric-®eld polarization for wireless communication. In order to make our statements more................................................................. Tripling the capacity of wireless .............................................................................................................................................. Wireless communications are a fundamental part of modern information infrastructure. But wireless bandwidth

292

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

293

Measuring the capacity impacts of demand response  

SciTech Connect (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

294

"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

295

Older People With Dementia Cared for Mostly at Home Study challenges assumption that most patients die in nursing homes  

E-Print Network [OSTI]

Older People With Dementia Cared for Mostly at Home Study challenges assumption that most patients die in nursing homes -- Robert Preidt FRIDAY, May 11 (HealthDay News) -- Many elderly people with dementia live and die at home rather than in nursing homes, a new study has found. The findings challenge

Belogay, Eugene A.

296

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

297

Influence of assumptions about household waste composition in waste management LCAs  

SciTech Connect (OSTI)

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

298

DECENTRALIZING SEMICONDUCTOR CAPACITY PLANNING VIA INTERNAL MARKET COORDINATION  

E-Print Network [OSTI]

1 DECENTRALIZING SEMICONDUCTOR CAPACITY PLANNING VIA INTERNAL MARKET COORDINATION SULEYMAN KARABUK semiconductor manufacturer: marketing managers reserve capacity from manufacturing based on product demands, while attempting to maximize profit; manufacturing managers allocate capacity to competing marketing

Wu, David

299

Increasing the Capacity of Existing Power Lines | Department...  

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

Increasing the Capacity of Existing Power Lines Increasing the Capacity of Existing Power Lines The capacity of the grid has been largely unchanged for decades and needs to expand...

300

Solvation thermodynamics and heat capacity of polar and charged solutes in water  

SciTech Connect (OSTI)

The solvation thermodynamics and in particular the solvation heat capacity of polar and charged solutes in water is studied using atomistic molecular dynamics simulations. As ionic solutes we consider a F{sup -} and a Na{sup +} ion, as an example for a polar molecule with vanishing net charge we take a SPC/E water molecule. The partial charges of all three solutes are varied in a wide range by a scaling factor. Using a recently introduced method for the accurate determination of the solvation free energy of polar solutes, we determine the free energy, entropy, enthalpy, and heat capacity of the three different solutes as a function of temperature and partial solute charge. We find that the sum of the solvation heat capacities of the Na{sup +} and F{sup -} ions is negative, in agreement with experimental observations, but our results uncover a pronounced difference in the heat capacity between positively and negatively charged groups. While the solvation heat capacity {Delta}C{sub p} stays positive and even increases slightly upon charging the Na{sup +} ion, it decreases upon charging the F{sup -} ion and becomes negative beyond an ion charge of q=-0.3e. On the other hand, the heat capacity of the overall charge-neutral polar solute derived from a SPC/E water molecule is positive for all charge scaling factors considered by us. This means that the heat capacity of a wide class of polar solutes with vanishing net charge is positive. The common ascription of negative heat capacities to polar chemical groups might arise from the neglect of non-additive interaction effects between polar and apolar groups. The reason behind this non-additivity is suggested to be related to the second solvation shell that significantly affects the solvation thermodynamics and due to its large spatial extent induces quite long-ranged interactions between solvated molecular parts and groups.

Sedlmeier, Felix; Netz, Roland R. [Fachbereich Physik, Freie Universitaet Berlin, 14195 Berlin (Germany)

2013-03-21T23:59:59.000Z

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

Representation of Solar Capacity Value in the ReEDS Capacity Expansion Model  

SciTech Connect (OSTI)

An important issue for electricity system operators is the estimation of renewables' capacity contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to the resource variability. Reliability-based methods, particularly assessment of the Effective Load-Carrying Capacity, are considered to be the most robust and widely-accepted techniques for addressing this resource variability. This report compares estimates of solar PV capacity value by the Regional Energy Deployment System (ReEDS) capacity expansion model against two sources. The first comparison is against values published by utilities or other entities for known electrical systems at existing solar penetration levels. The second comparison is against a time-series ELCC simulation tool for high renewable penetration scenarios in the Western Interconnection. Results from the ReEDS model are found to compare well with both comparisons, despite being resolved at a super-hourly temporal resolution. Two results are relevant for other capacity-based models that use a super-hourly resolution to model solar capacity value. First, solar capacity value should not be parameterized as a static value, but must decay with increasing penetration. This is because -- for an afternoon-peaking system -- as solar penetration increases, the system's peak net load shifts to later in the day -- when solar output is lower. Second, long-term planning models should determine system adequacy requirements in each time period in order to approximate LOLP calculations. Within the ReEDS model we resolve these issues by using a capacity value estimate that varies by time-slice. Within each time period the net load and shadow price on ReEDS's planning reserve constraint signals the relative importance of additional firm capacity.

Sigrin, B.; Sullivan, P.; Ibanez, E.; Margolis, R.

2014-03-01T23:59:59.000Z

302

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

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Colorado Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Year Jan Feb Mar Apr May Jun...

303

Expanded Capacity Microwave-Cleaned Diesel Particulate Filter...  

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

Expanded Capacity Microwave-Cleaned Diesel Particulate Filter Expanded Capacity Microwave-Cleaned Diesel Particulate Filter 2002 DEER Conference Presentation: Industrial Ceramic...

304

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

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

"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...

305

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

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

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...

306

Los Alamos Neutron Science Center gets capacity boost  

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

Neutron Science Center capacity boost Los Alamos Neutron Science Center gets capacity boost The facility can simulate the effects of hundreds or thousands of years of...

307

Working and Net Available Shell Storage Capacity as of September...  

Gasoline and Diesel Fuel Update (EIA)

capacity and also allows for tracking seasonal shifts in petroleum product usage of tanks and underground storage. Using the new storage capacity data, it will be possible to...

308

Expansion of Novolyte Capacity for Lithium Ion Electrolyte Production...  

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

15eswise2012p.pdf More Documents & Publications Expansion of Novolyte Capacity for Lithium Ion Electrolyte Production Expansion of Novolyte Capacity for Lithium Ion Electrolyte...

309

Expansion of Novolyte Capacity for Lithium Ion Electrolyte Production...  

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

15eswise2011p.pdf More Documents & Publications Expansion of Novolyte Capacity for Lithium Ion Electrolyte Production Expansion of Novolyte Capacity for Lithium Ion Electrolyte...

310

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

Open Energy Info (EERE)

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

311

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

Open Energy Info (EERE)

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

312

Study Finds 54 Gigawatts of Offshore Wind Capacity Technically...  

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

Study Finds 54 Gigawatts of Offshore Wind Capacity Technically Possible by 2030 Study Finds 54 Gigawatts of Offshore Wind Capacity Technically Possible by 2030 September 11, 2014 -...

313

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...

314

National CHP Roadmap: Doubling Combined Heat and Power Capacity...  

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

National CHP Roadmap: Doubling Combined Heat and Power Capacity in the United States by 2010, March 2001 National CHP Roadmap: Doubling Combined Heat and Power Capacity in the...

315

High-capacity hydrogen storage in lithium and sodium amidoboranes...  

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

capacity hydrogen storage in lithium and sodium amidoboranes. High-capacity hydrogen storage in lithium and sodium amidoboranes. Abstract: A substantial effort worldwide has been...

316

Solid-State Hydrogen Storage: Storage Capacity,Thermodynamics...  

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

Hydrogen Storage: Storage Capacity,Thermodynamics and Kinetics. Solid-State Hydrogen Storage: Storage Capacity,Thermodynamics and Kinetics. Abstract: Solid-state reversible...

317

Development of High-Capacity Cathode Materials with Integrated...  

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

Development of High-Capacity Cathode Materials with Integrated Structures Development of High-Capacity Cathode Materials with Integrated Structures 2013 DOE Hydrogen and Fuel Cells...

318

Design and Evaluation of Novel High Capacity Cathode Materials...  

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

Design and Evaluation of Novel High Capacity Cathode Materials Design and Evaluation of Novel High Capacity Cathode Materials 2009 DOE Hydrogen Program and Vehicle Technologies...

319

Development of high-capacity cathode materials with integrated...  

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

Development of high-capacity cathode materials with integrated structures Development of high-capacity cathode materials with integrated structures 2009 DOE Hydrogen Program and...

320

Design and Evaluation of Novel High Capacity Cathode Materials...  

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

and Evaluation of Novel High Capacity Cathode Materials Design and Evaluation of Novel High Capacity Cathode Materials 2011 DOE Hydrogen and Fuel Cells Program, and Vehicle...

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

,,,,,"Capacity MW",,,,,"Number of Meters",,,,,"Energy Sold Back...  

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

Other",,,"All Technologies" ,,,,,"Capacity MW",,,,,"Number of Meters",,,,,"Energy Sold Back MWh",,,,,"Capacity MW",,,,,"Number of Meters",,,,,"Energy Sold Back...

322

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.

323

DOE mixed waste treatment capacity analysis  

SciTech Connect (OSTI)

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

324

Ethical receptive capacity and teaching business ethics  

Science Journals Connector (OSTI)

In this study, we proposed the ethical receptive capacity (ERC) perspective on teaching business ethics. The ERC perspective was developed on two premises: the separation of personal moral values and professional ethics, and the path dependent nature of professional ethics, such that individuals in the early stage of their profession have higher ERC (i.e., individuals' capacity to receive ethical contents) and thus are more receptive to new ethical contents prescribed to them. The experimental results in this study supported the ERC perspective, suggesting that business ethics education should be introduced to students as early as possible in their business programme.

Chanchai Tangpong; Michael D. Michalisin; Jin Li

2012-01-01T23:59:59.000Z

325

The effect of rain on freeway capacity  

E-Print Network [OSTI]

. The procedure used was basically a process of selection and processing of data from historical records. The facility used as a source of traific information was t' he Gulf Freeway in Houston, Texas, and rs. infall records were obtained from the Weather... to separate acceptable data, and the accepted capacity figures were related to the weather condition of wet or dry which prevs. iled on the relevant occs. sion. The results showed that rain does have a significant effect on freevray capacity which is very...

Jones, Edward Roy

2012-06-07T23:59:59.000Z

326

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

327

Int. J. Spray and Comb. Dynamics -Accepted for publication 1 About the zero Mach number assumption in  

E-Print Network [OSTI]

as much as the the flame forcing ('Rayleigh') term. Besides, the net effect of the non zero Mach numberInt. J. Spray and Comb. Dynamics - Accepted for publication 1 About the zero Mach number assumption in the calculation of thermoacoustic instabilities By F. N I C O U D1 AND K. W I E C Z O R E K1,2 1 University

Paris-Sud XI, Université de

328

Methods to infer transmission risk factors in complex outbreak data  

Science Journals Connector (OSTI)

...to infer transmission risk factors in complex outbreak...update may affect the risk of infection of all other...This assumption seems acceptable for a relatively wide...longer for accuracy to be acceptable (figure 4). When 25...infectious diseases and the risk factors affecting transmission...

2012-01-01T23:59:59.000Z

329

Power, Capacity, and Efficiency of Pumps  

Science Journals Connector (OSTI)

Power, Capacity, and Efficiency of Pumps ... p. motor through a 40-foot head, friction head included, efficiency of the pump being 50 per cent, join the 40 (column A ) with the 50 per cent (column E ) and locate the intersection with column C . ...

W. F. SCHAPHORST

1940-08-10T23:59:59.000Z

330

Building Environmental Health Capacity in Allegheny County  

E-Print Network [OSTI]

Building Environmental Health Capacity in Allegheny County: Environmental Indicators Outcomes standard Air Quality Computer Systems Days exceeding ozone standard Air Quality Computer Systems Attainment of the annual PM-2.5 standard (Fine particulates) Air Quality Computer Systems Annual PM-2.5 level Air Quality

331

PROJECT REPORT HVAC EQUIPMENT DEMOGRAPHICS AND CAPACITY  

E-Print Network [OSTI]

PROJECT REPORT HVAC EQUIPMENT DEMOGRAPHICS AND CAPACITY ANALYSIS TOOLS APPLICABLE TO MULTI Commercial HVAC Design Process 12 5.0 Conclusion 18 6.0 References 19 TABLE OF CONTENTS SECTIONS #12;MULTI performance by collectively improving the enve- lope, lighting and HVAC systems. The primary goals of the UC

California at Davis, University of

332

Fagatele Bay National Marine Sanctuary GIS Capacity  

E-Print Network [OSTI]

Report, configuration notes American Samoa Spatial Data Infrastructure Maps GIS Data CDs Operating System, a number of issues regarding map projections and datums were resolved allowing GIS users to processFagatele Bay National Marine Sanctuary GIS Capacity Binder Index Background 2 Hardware, Software

Wright, Dawn Jeannine

333

CSEM WP 124 Capacity Markets for Electricity  

E-Print Network [OSTI]

CSEM WP 124 Capacity Markets for Electricity Anna Creti, LEEERNA, University of Toulouse for Electricity Anna Creti LEEERNA, University of Toulouse Natalia Fabra Universidad Carlos III de Madrid February 2004 Abstract The creation of electricity markets has raised the fundamental question as to whether

California at Berkeley. University of

334

Capacity Building in Wind Energy for PICs  

E-Print Network [OSTI]

1 Capacity Building in Wind Energy for PICs Presentation of the project Regional Workshop Suva hydropower is relatively important (Papua New Guinea, Fiji and Samoa · The traditional use of wind energy has indicates that significant wind energy potential exists. · A monitoring project showed that in Rarotonga

335

Partial energies fluctuations and negative heat capacities  

E-Print Network [OSTI]

We proceed to a critical examination of the method used in nuclear fragmentation to exhibit signals of negative heat capacity. We show that this method leads to unsatisfactory results when applied to a simple and well controlled model. Discrepancies are due to incomplete evaluation of potential energies.

Xavier Campi; H. Krivine; E. Plagnol; N. Sator

2004-08-03T23:59:59.000Z

336

Wireless Network Capacity Management: A Real Options Approach  

E-Print Network [OSTI]

capacity, market price of risk, investment timing option 1 Introduction Wireless networks are now regarded

Forsyth, Peter A.

337

Prevalence of Temperature Dependent Heat Capacity Changes in Protein-DNA Interactions  

SciTech Connect (OSTI)

A large, negative {Delta}Cp of DNA binding is a thermodynamic property of the majority of sequence-specific DNA-protein interactions, and a common, but not universal property of non-sequence-specific DNA binding. In a recent study of the binding of Taq polymerase to DNA, we showed that both the full-length polymerase and its 'Klentaq' large fragment bind to primed-template DNA with significant negative heat capacities. Herein, we have extended this analysis by analyzing this data for temperature-variable heat capacity effects ({Delta}{Delta}Cp), and have similarly analyzed an additional 47 protein-DNA binding pairs from the scientific literature. Over half of the systems examined can be easily fit to a function that includes a {Delta}{Delta}Cp parameter. Of these, 90% display negative {Delta}{Delta}Cp values, with the result that the {Delta}Cp of DNA binding will become more negative with rising temperature. The results of this collective analysis have potentially significant consequences for current quantitative theories relating {Delta}Cp values to changes in accessible surface area, which rely on the assumption of temperature invariance of the {Delta}Cp of binding. Solution structural data for Klentaq polymerase demonstrate that the observed heat capacity effects are not the result of a coupled folding event.

Liu, C.-C.; Richard, A.J.; Kausiki, D.; LiCata, V.J.

2009-05-19T23:59:59.000Z

338

The Effect of Temperature on Capacity and Power in Cycled Lithium Ion Batteries  

SciTech Connect (OSTI)

The Idaho National Laboratory (INL) tested six Saft America HP-12 (Generation 2000), 12-Ah lithium ion cells to evaluate cycle life performance as a power assist vehicle battery. The cells were tested to investigate the effects of temperature on capacity and power fade. Test results showed that five of the six cells were able to meet the Power Assist Power and Energy Goals at the beginning of test and after 300,000 cycles using a Battery Size Factor of 44.3 cells. The initial Static Capacity tests showed that the capacities of the cells were stable for three discharges and had an average of 16.4 Ah. All the cells met the Self-Discharge goal, but failed to meet the Cold Cranking goal. As is typical for lithium ion cells, both power and capacity were diminished during the low-temperature Thermal Performance test and increased during the high-temperature Thermal Performance test. Capacity faded as expected over the course of 300,000 life cycles and showed a weak inverse relationship to increasing temperature. Power fade was mostly a result of cycling while temperature had a minor effect compared to cycle life testing. Consequently, temperature had very little effect on capacity and power fade for the proprietary G4 chemistry.

Jeffrey R. Belt

2005-03-01T23:59:59.000Z

339

Sorption-capacity limited retardation of radionuclides transport in water-saturated packing materials  

SciTech Connect (OSTI)

Radionuclides breakthrough times as calculated through constant retardation factors obtained in dilute solutions are non-conservative. The constant retardation approach regards the solid as having infinite sorption capacity throughout the solid. However, as the solid becomes locally saturated, such as in the proximity of the waste form-packing materials interface, it will exhibit no retardation properties, and transport will take place as if the radionuclides were locally non-reactive. The magnitude of the effect of finite sorption capacity of the packing materials on radionuclide transport is discussed with reference to high-level waste package performance. An example based on literature sorption data indicates that the breakthrough time may be overpredicted by orders of magnitude using a constant retardation factor as compared to using the entire sorption isotherm to obtain a concentration-dependent retardation factor. 8 references, 3 figures, 3 tables.

Pescatore, C.; Sullivan, T.

1984-01-01T23:59:59.000Z

340

Mechanism of antioxidant capacity assays and the CUPRAC (cupric ion reducing antioxidant capacity) assay  

Science Journals Connector (OSTI)

We report on the application of a simple and versatile antioxidant capacity assay for dietary polyphenols, vitamin C and vitamin E utilizing the copper(II)-neocuproine (Cu(II)-Nc) reagent as the chromogenic ox...

Re?at Apak; Kubilay Gl; Mustafa zyrek; Saliha Esin elik

2008-04-01T23:59:59.000Z

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

Working and Net Available Shell Storage Capacity  

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

Working and Net Available Shell Working and Net Available Shell Storage Capacity November 2013 With Data as of September 30, 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Working and Net Available Shell Storage Capacity as of September 30, 2013 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

342

Underground Natural Gas Working Storage Capacity - Methodology  

Gasoline and Diesel Fuel Update (EIA)

Summary Prices Exploration & Reserves Production Imports/Exports Pipelines Storage Consumption All Natural Gas Data Reports Analysis & Projections Most Requested Consumption Exploration & Reserves Imports/Exports & Pipelines Prices Production Projections Storage All Reports ‹ See All Natural Gas Reports Underground Natural Gas Working Storage Capacity With Data for November 2012 | Release Date: July 24, 2013 | Next Release Date: Spring 2014 Previous Issues Year: 2013 2012 2011 2010 2009 2008 2007 2006 Go Methodology Demonstrated Peak Working Gas Capacity Estimates: Estimates are based on aggregation of the noncoincident peak levels of working gas inventories at individual storage fields as reported monthly over a 60-month period ending in November 2012 on Form EIA-191, "Monthly Natural Gas Underground Storage

343

Kuwait pressing toward preinvasion oil production capacity  

SciTech Connect (OSTI)

Oil field reconstruction is shifting focus in Kuwait as the country races toward prewar production capacity of 2 million b/d. Oil flow last month reached 1.7 million b/d, thanks largely to a massive workover program that has accomplished about as much as it can. By midyear, most of the 19 rigs in Kuwait will be drilling rather than working over wells vandalized by retreating Iraqi troops in February 1991. Seventeen gathering centers are at work, with capacities totaling 2.4 million b/d, according to state-owned Kuwait Oil Co. (KOC). This article describes current work, the production infrastructure, facilities strategy, oil recovery, well repairs, a horizontal pilot project, the drilling program, the constant reminders of war, and heightened tensions.

Tippee, B.

1993-03-15T23:59:59.000Z

344

Working and Net Available Shell Storage Capacity  

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

Net Available Shell Storage Capacity by PAD District as of September 30, 2013 Net Available Shell Storage Capacity by PAD District as of September 30, 2013 (Thousand Barrels) Commodity In Operation Idle 1 In Operation Idle 1 In Operation Idle 1 In Operation Idle 1 In Operation Idle 1 In Operation Idle 1 Refineries Crude Oil 17,334 831 21,870 1,721 86,629 3,468 4,655 174 39,839 1,230 170,327 7,424 Fuel Ethanol 174 - 175 1 289 - 134 - 92 - 864 1 Natural Gas Plant Liquids and Liquefied Refinery Gases 2 1,267 23 11,599 382 28,865 78 641 19 2,412 23 44,784 525 Propane/Propylene (dedicated)

345

Working and Net Available Shell Storage Capacity  

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

Working Storage Capacity by PAD District as of September 30, 2013 Working Storage Capacity by PAD District as of September 30, 2013 (Thousand Barrels) Commodity 1 2 3 4 5 U.S. Total Ending Stocks Utilization Rate 1 Refineries Crude Oil 15,154 17,952 72,858 4,109 35,324 145,397 90,778 62% Fuel Ethanol 151 142 257 114 79 743 482 65% Natural Gas Plant Liquids and Liquefied Refinery Gases 2 1,149 10,996 24,902 581 2,219 39,847 19,539 49% Propane/Propylene (dedicated) 3 405 3,710 3,886 54 199 8,254 4,104 NA Motor Gasoline (incl. Motor Gasoline Blending Components)

346

Calculations of Heat-Capacities of Adsorbates  

E-Print Network [OSTI]

PHYSICAL REVIEW B VOLUME 14, NUMBER 7 1 OCTOBER 1976 Calculations of heat capacities of adsorbates W. R. Lawrence and R. E. Allen Department of Physics, Texas A& M University, College Station, Texas 77843 (Received 2 September 1975) The phonon... the substrate has a perfect (100) surface and the adsorbate goes down as a solid monolayer in registry with the substrate. The quasiharmonic approximation was used, and the results for Ne adsorbates were considerably different from those obtained...

LAWRENCE, WR; Allen, Roland E.

1976-01-01T23:59:59.000Z

347

Transportation radiological risk assessment for the programmatic environmental impact statement: An overview of methodologies, assumptions, and input parameters  

SciTech Connect (OSTI)

The U.S. Department of Energy is considering a broad range of alternatives for the future configuration of radioactive waste management at its network of facilities. Because the transportation of radioactive waste is an integral component of the management alternatives being considered, the estimated human health risks associated with both routine and accident transportation conditions must be assessed to allow a complete appraisal of the alternatives. This paper provides an overview of the technical approach being used to assess the radiological risks from the transportation of radioactive wastes. The approach presented employs the RADTRAN 4 computer code to estimate the collective population risk during routine and accident transportation conditions. Supplemental analyses are conducted using the RISKIND computer code to address areas of specific concern to individuals or population subgroups. RISKIND is used for estimating routine doses to maximally exposed individuals and for assessing the consequences of the most severe credible transportation accidents. The transportation risk assessment is designed to ensure -- through uniform and judicious selection of models, data, and assumptions -- that relative comparisons of risk among the various alternatives are meaningful. This is accomplished by uniformly applying common input parameters and assumptions to each waste type for all alternatives. The approach presented can be applied to all radioactive waste types and provides a consistent and comprehensive evaluation of transportation-related risk.

Monette, F.; Biwer, B.; LePoire, D.; Chen, S.Y.

1994-02-01T23:59:59.000Z

348

EPA-GHG Inventory Capacity Building | Open Energy Information  

Open Energy Info (EERE)

EPA-GHG Inventory Capacity Building EPA-GHG Inventory Capacity Building Jump to: navigation, search Tool Summary Name: US EPA GHG inventory Capacity Building Agency/Company /Organization: United States Environmental Protection Agency Sector: Energy, Land Topics: GHG inventory, Background analysis Resource Type: Training materials, Lessons learned/best practices References: US EPA GHG inventory Capacity Building[1] Logo: US EPA GHG inventory Capacity Building "Developing greenhouse gas inventories is an important first step to managing emissions. U.S. EPA's approach for building capacity to develop GHG inventories is based on the following lessons learned from working alongside developing country experts: Technical expertise for GHG inventories already exists in developing countries.

349

Stochastic volatility models with persistent latent factors: theory and its applications to asset prices  

E-Print Network [OSTI]

consider the nonlinear nonstationary state-space model given by yt = radicalbig f(xt,?) ut, xt+1 = ?xt +vt+1,(2.1) where I make the following assumptions: Assumption 1: The volatility function is given by (2.2) f(xt,?) = + ?1+exp(??(x t ??)) , where ? = (.... Assumption 2: (xt) is a scalar latent volatility factor and |?| ? 1, I describe the volatility factor, (xt) explicitly in the transition equation because I am interested in the linkage between it and macro economic fundamentals. I assume that this volatility...

Lee, Hyoung Il

2008-10-10T23:59:59.000Z

350

Capacity computations of right-turn-on-red using the Highway Capacity Manual  

SciTech Connect (OSTI)

Right-turn-on-red (RTOR) is a traffic control strategy at signalized intersections that allows vehicles to turn right during red phases provided they do not impede the vehicles and pedestrians in green phases. RTOR is primarily a delay and energy conservation measure. Several studies that examined the impact of RTOR on vehicular delays have shown the potential of reducing fuel consumption by about 5 percent on urban streets. The reduction of delay and fuel consumption is related to extra capacity because RTOR allows vehicles to pass through an intersection in red phases. The extra capacity can be significant if an exclusive right-turn lane is provided. The 1985 {ital Highway Capacity Manual} (HCM) provides a powerful technique for evaluating how well an intersection will operate. This technique, however, is less successful in dealing with intersections where RTOR movement is permitted because it requires the analyst to supply RTOR volumes. This situation has led to a need for a formula to compute RTOR capacity. This paper proposes a method to calculate this capacity.

Luh, J.Z. (Langan Engineering Associates, NJ (US)); Lu, Y.J. (Concordia Univ., Loyola Campus, Montreal, PQ (Canada))

1990-04-01T23:59:59.000Z

351

1992 Annual Capacity Report. Revision 1  

SciTech Connect (OSTI)

The Standard Contract for Disposal of Spent Nuclear Fuel and/or High-Level Radioactive Waste (10 CFR Part 961) requires the Department of Energy (DOE) to issue an Annual Capacity Report (ACR) for planning purposes. This report is the fifth in the series published by DOE. In May 1993, DOE published the 1992 Acceptance Priority Ranking (APR) that established the order in which DOE will allocate projected acceptance capacity. As required by the Standard Contract, the acceptance priority ranking is based on the date the spent nuclear fuel (SNF) was permanently discharged, with the owners of the oldest SNF, on an industry-wide basis, given the highest priority. The 1992 ACR applies the projected waste acceptance rates in Table 2.1 to the 1992 APR, resulting in individual allocations for the owners and generators of the SNF. These allocations are listed in detail in the Appendix, and summarized in Table 3.1. The projected waste acceptance rates for SNF presented in Table 2.1 are nominal and assume a site for a Monitored Retrievable Storage (MRS) facility will be obtained; the facility will initiate operations in 1998; and the statutory linkages between the MRS facility and the repository set forth in the Nuclear Waste Policy Act of 1982, as amended (NWPA), will be modified. During the first ten years following projected commencement of Civilian Radioactive Waste Management System (CRWMS) operation, the total quantity of SNF that could be accepted is projected to be 8,200 metric tons of uranium (MTU). This is consistent with the storage capacity licensing conditions imposed on an MRS facility by the NWPA. The annual acceptance rates provide an approximation of the system throughput and are subject to change as the program progresses.

Not Available

1993-05-01T23:59:59.000Z

352

Parametric study of relay seismic capacity  

Science Journals Connector (OSTI)

An evaluation of the existing relay test data base at Brookhaven National Laboratory (BNL) has indicated that the seismic capacity of a relay may depend on various parameters related to the design or the input motion. In order to investigate the effect of these parameters on the seismic fragility level, BNL has conducted a relay test program. Establishing the correlation between the single frequency fragility test input and the corresponding multifrequency response spectrum (TRS) is also an objective of this test program. The testing has been performed at Wyle Laboratories. This paper discusses the methodology used for testing and presents a brief summary of important test results.

K. Bandyopadhyay; C. Hofmayer

1992-01-01T23:59:59.000Z

353

LEDS Capacity Building and Training Inventory | Open Energy Information  

Open Energy Info (EERE)

LEDS Capacity Building and Training Inventory LEDS Capacity Building and Training Inventory Jump to: navigation, search Home | About | Inventory | Partnerships | Capacity Building | Webinars | Reports | Events | News | List Serve LEDS Capacity Building and Training Activities and Resources Upcoming Capacity Building Events CLEAN shares capacity building activity information to encourage technical institutions to better coordinate efforts and avoid duplication of effort. If you are aware of an upcoming LEDS-related training or capacity building event please add it to the calendar below. Add Capacity Building or Training Event Webinars Title Developer Biopower Tool Webinar National Renewable Energy Laboratory United States Department of Energy Centro de Energías Renovables (CER) CESC-Webinar: Building an Innovation and Entrepreneurship Driven Economy: How Policies Can Foster Risk Capital Investment in Renewable Energy Clean Energy Solutions Center

354

Natural Gas Productive Capacity for the Lower-48 States  

Gasoline and Diesel Fuel Update (EIA)

for the Lower-48 States for the Lower-48 States 6/4/01 Click here to start Table of Contents Natural Gas Productive Capacity for the Lower-48 States Natural Gas Productive Capacity for the Lower-48 States Natural Gas Productive Capacity for the Lower-48 States - Summary - Natural Gas Productive Capacity for the Lower-48 States - Summary - PPT Slide Natural Gas Productive Capacity for the Lower-48 States - Summary - Natural Gas Productive Capacity for the Lower-48 States - Methodology - Natural Gas Productive Capacity for the Lower-48 States - Methodology - Natural Gas Productive Capacity for the Lower-48 States - Methodology - PPT Slide PPT Slide PPT Slide PPT Slide PPT Slide PPT Slide PPT Slide PPT Slide PPT Slide PPT Slide PPT Slide Other Areas PPT Slide PPT Slide PPT Slide

355

U.S. Fuel Ethanol Plant Production Capacity  

Gasoline and Diesel Fuel Update (EIA)

U.S. Fuel Ethanol Plant Production Capacity U.S. Fuel Ethanol Plant Production Capacity Release Date: May 20, 2013 | Next Release Date: May 2014 Previous Issues Year: 2013 2012 2011 Go Notice: Changes to Petroleum Supply Survey Forms for 2013 This is the third release of U.S. Energy Information Administration data on fuel ethanol production capacity. EIA first reported fuel ethanol production capacities as of January 1, 2011 on November 29, 2011. This new report contains production capacity data for all operating U.S. fuel ethanol production plants as of January 1, 2013. U.S. Nameplate Fuel Ethanol Plant Production Capacity as of January 1, 2013 PAD District Number of Plants 2013 Nameplate Capacity 2012 Nameplate Capacity (MMgal/year) (mb/d) (MMgal/year) (mb/d) PADD 1 4 360 23 316 21

356

A reduction theorem for capacity of positive maps  

E-Print Network [OSTI]

We prove a reduction theorem for capacity of positive maps of finite dimensional C*-algebras, thus reducing the computation of capacity to the case when the image of a nonscalar projection is never a projection.

Erling Stormer

2005-10-06T23:59:59.000Z

357

Evaluation of capacity release transactions in the natural gas industry  

E-Print Network [OSTI]

The purpose of this thesis is to analyze capacity release transactions in the natural gas industry and to state some preliminary conclusions about how the capacity release market is functioning. Given FERC's attempt to ...

Lautzenhiser, Stephen

1994-01-01T23:59:59.000Z

358

Storage and capacity rights markets in the natural gas industry  

E-Print Network [OSTI]

This dissertation presents a different approach at looking at market power in capacity rights markets that goes beyond the functional aspects of capacity rights markets as access to transportation services. In particular, ...

Paz-Galindo, Luis A.

1999-01-01T23:59:59.000Z

359

Economics and Design of Capacity Markets for the Power Sector  

Science Journals Connector (OSTI)

Capacity markets are a means to assure resource adequacy. The need for a capacity market stems from several market failures the most prominent of which is the absence of a robust demand-side. Limited demand response

Peter Cramton; Axel Ockenfels

2012-06-01T23:59:59.000Z

360

Development of high-capacity cathode materials with integrated...  

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

to improve rate performance * Optimize composition (Li- and Mn composition) and synthesis conditions * Evaluation of electrochemical properties (capacity, cycling performance...

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

Weak locking capacity of quantum channels can be much larger than private capacity  

E-Print Network [OSTI]

We show that it is possible for the so-called weak locking capacity of a quantum channel [Guha et al., PRX 4:011016, 2014] to be much larger than its private capacity. Both reflect different ways of capturing the notion of reliable communication via a quantum system while leaking almost no information to an eavesdropper; the difference is that the latter imposes an intrinsically quantum security criterion whereas the former requires only a weaker, classical condition. The channels for which this separation is most straightforward to establish are the complementary channels of classical-quantum (cq-)channels, and hence a subclass of Hadamard channels. We also prove that certain symmetric channels (related to photon number splitting) have positive weak locking capacity in the presence of a vanishingly small pre-shared secret, whereas their private capacity is zero. These findings are powerful illustrations of the difference between two apparently natural notions of privacy in quantum systems, relevant also to quantum key distribution (QKD): the older, naive one based on accessible information, contrasting with the new, composable one embracing the quantum nature of the eavesdropper's information. Assuming an additivity conjecture for constrained minimum output Renyi entropies, the techniques of the first part demonstrate a single-letter formula for the weak locking capacity of complements to cq-channels, coinciding with a general upper bound of Guha et al. for these channels. Furthermore, still assuming this additivity conjecture, this upper bound is given an operational interpretation for general channels as the maximum weak locking capacity of the channel activated by a suitable noiseless channel.

Andreas Winter

2014-03-25T23:59:59.000Z

362

Theory of Molecular Machines. I. Channel Capacity of Molecular Machines  

E-Print Network [OSTI]

Theory of Molecular Machines. I. Channel Capacity of Molecular Machines running title: Channel Capacity of Molecular Machines Thomas D. Schneider version = 5.76 of ccmm.tex 2004 Feb 3 Version 5.67 was submitted 1990 December 5 Schneider, T. D. (1991). Theory of molecular machines. I. Channel capacity

Schneider, Thomas D.

363

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Methodology Methodology Methodology Demonstrated Peak Working Gas Capacity Estimates: Estimates are based on aggregation of the noncoincident peak levels of working gas inventories at individual storage fields as reported monthly over a 60-month period ending in April 2010 on Form EIA-191M, "Monthly Natural Gas Underground Storage Report." The months of measurement for the peak storage volumes by facilities may differ; i.e., the months do not necessarily coincide. As such, the noncoincident peak for any region is at least as big as any monthly volume in the historical record. Data from Form EIA-191M, "Monthly Natural Gas Underground Storage Report," are collected from storage operators on a field-level basis. Operators can report field-level data either on a per reservoir basis or on an aggregated reservoir basis. It is possible that if all operators reported on a per reservoir basis that the demonstrated peak working gas capacity would be larger. Additionally, these data reflect inventory levels as of the last day of the report month, and a facility may have reached a higher inventory on a different day of the report month, which would not be recorded on Form EIA-191M.

364

Electrical Generating Capacities of Geothermal Slim Holes  

SciTech Connect (OSTI)

Theoretical calculations are presented to estimate the electrical generating capacity of the hot fluids discharged from individual geothermal wells using small wellhead generating equipment over a wide range of reservoir and operating conditions. The purpose is to appraise the possibility of employing slim holes (instead of conventional production-size wells) to power such generators for remote off-grid applications such as rural electrification in developing countries. Frequently, the generating capacity desired is less than one megawatt, and can be as low as 100 kilowatts; if slim holes can be usefully employed, overall project costs will be significantly reduced. This report presents the final results of the study. Both self-discharging wells and wells equipped with downhole pumps (either of the ''lineshaft'' or the ''submersible'' type) are examined. Several power plant designs are considered, including conventional single-flash backpressure and condensing steam turbines, binary plants, double-flash steam plants, and steam turbine/binary hybrid designs. Well inside diameters from 75 mm to 300 mm are considered; well depths vary from 300 to 1200 meters. Reservoir temperatures from 100 C to 240 C are examined, as are a variety of reservoir pressures and CO2 contents and well productivity index values.

Pritchett, J.W.

1998-10-01T23:59:59.000Z

365

Thermal capacity of composite floor slabs  

Science Journals Connector (OSTI)

AbstractObjective Thermal building simulation tools take account of the thermal capacity of the walls and floors by a one-dimensional characterization. The objective was to obtain thermal equivalent parameters for ribbed or composite slab elements that can be input into one-dimensional models. Method Transient finite element calculations (FEM) were used to establish the heat transfer to and from composite floors using four deck profiles and for daily heating cycles in compartments with defined heat gains and operating conditions. Results The performance of composite slabs was compared to a concrete flat slab for a typical office in the UK and Germany. It was shown that a deep ribbed slab generates a maximum heat flux of 30.5W/m2 for a 5C temperature variation about the mean, and that the daily heat absorbed by a typical composite slab was 220Wh/m2 floor area. Conclusions Using the thermal capacity of the ribbed floor slabs, the comfort conditions defined in terms of the number of hours over 25C are acceptable for many classes of offices. Practical implications Thermally equivalent properties of ribbed slabs can be used in conventional software to predict the thermal performance.

B. Doering; C. Kendrick; R.M. Lawson

2013-01-01T23:59:59.000Z

366

Natural Gas Underground Storage Capacity (Summary)  

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

Total Working Gas Capacity Total Number of Existing Fields Period: Monthly Annual Total Working Gas Capacity Total Number of Existing Fields Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History U.S. 9,072,508 9,104,181 9,111,242 9,117,296 9,132,250 9,171,017 1989-2013 Alaska 83,592 83,592 83,592 83,592 83,592 83,592 2013-2013 Lower 48 States 8,988,916 9,020,589 9,027,650 9,033,704 9,048,658 9,087,425 2012-2013 Alabama 35,400 35,400 35,400 35,400 35,400 35,400 2002-2013 Arkansas 21,853 21,853 21,853 21,853 21,853 21,853 2002-2013 California 592,711 592,711 592,711 599,711 599,711 599,711 2002-2013 Colorado 122,086 122,086 122,086 122,086 122,086 122,086 2002-2013

367

Douglas Factors  

Broader source: Energy.gov [DOE]

The Merit Systems Protection Board in its landmark decision, Douglas vs. Veterans Administration, 5 MSPR 280, established criteria that supervisors must consider in determining an appropriate penalty to impose for an act of employee misconduct. These twelve factors are commonly referred to as Douglas Factors and have been incorporated into the Federal Aviation Administration (FAA) Personnel Management System and various FAA Labor Agreements.

368

Multi-region capacity planning model with contracts of varying duration under uncertainty : a satellite capacity acquisition case study  

E-Print Network [OSTI]

This paper highlights the issues associated with and presents a modeling framework for long-term capacity planning problems constrained in a similar fashion to satellite capacity acquisition. Although ambiguities exist, ...

Lydiard, John M., IV

2014-01-01T23:59:59.000Z

369

IAEA Planning and Economic Studies Section (PESS) Capacity Building | Open  

Open Energy Info (EERE)

IAEA Planning and Economic Studies Section (PESS) Capacity Building IAEA Planning and Economic Studies Section (PESS) Capacity Building Jump to: navigation, search Tool Summary Name: IAEA Planning and Economic Studies Section (PESS) Capacity Building Agency/Company /Organization: International Atomic Energy Agency Sector: Energy Focus Area: Non-renewable Energy, Energy Efficiency, Renewable Energy Topics: Pathways analysis Resource Type: Software/modeling tools, Training materials References: IAEA PESS capacity building[1] Logo: IAEA Planning and Economic Studies Section (PESS) Capacity Building "PESS offers assistance to Member States, particularly from developing regions, to improve their energy system analysis & planning capabilities. Assistance can include: transferring modern planning methods, tools and databanks

370

UNDP-Low Emission Capacity Building Programme | Open Energy Information  

Open Energy Info (EERE)

Programme Programme Jump to: navigation, search Logo: UNDP-Low Emission Capacity Building Programme Name UNDP-Low Emission Capacity Building Programme Agency/Company /Organization United Nations Development Programme (UNDP), European Union Sector Climate, Energy, Land, Water Topics Low emission development planning Resource Type Training materials Website http://www.undp.org/climatestr References UNDP-Low Emission Capacity Building Programme[1] UNDP-Low Emission Capacity Building Programme Screenshot "This collaborative programme aims to strengthen technical and institutional capacities at the country level, while at the same time facilitating inclusion and coordination of the public and private sector in national initiatives addressing climate change. It does so by utilizing the

371

EIA - Appendix H - Reference Case Projections for Electricity Capacity and  

Gasoline and Diesel Fuel Update (EIA)

for Electricity Capacity and Generation by Fuel Tables (2006-2030) for Electricity Capacity and Generation by Fuel Tables (2006-2030) International Energy Outlook 2009 Reference Case Projections for Electricity Capacity and Generation by Fuel Tables (2006-2030) Formats Data Table Titles (1 to 18 complete) Reference Case Projections for Electricity Capacity and Generation by Fuel Tables. Need help, contact the National Energy Information Center at 202-586-8800. Reference Case Projections for Electricity Capacity and Generation by Fuel Tables. Need help, contact the National Energy Information Center at 202-586-8800. Table H1 World Total Installed Generating Capacity by Region and Country Table H1. World Total Installed Generating Capacity by Region and Country. Need help, contact the National Energy Information Center at 202-586-8800.

372

EIA - Appendix H - Reference Case Projections for Electricity Capacity and  

Gasoline and Diesel Fuel Update (EIA)

Reference Case Projections for Electricity Capacity and Generation by Fuel Tables (2005-2030) Reference Case Projections for Electricity Capacity and Generation by Fuel Tables (2005-2030) International Energy Outlook 2008 Reference Case Projections for Electricity Capacity and Generation by Fuel Tables (2005-2030) Formats Data Table Titles (1 to 12 complete) Reference Case Projections for Electricity Capacity and Generation by Fuel Data Tables. Need help, contact the National Energy Information Center at 202-586-8800. Reference Case Projections for Electricity Capacity and Generation by Fuel Data Tables. Need help, contact the National Energy Information Center at 202-586-8800. Table H1 World Total Installed Generating Capacity by Region and Country Table H1. World Total Installed Generating Capacity by Region and Country. Need help, contact the National Energy Information Center at 202-586-8800.

373

EIA - Appendix H - Reference Case Projections for Electricity Capacity and  

Gasoline and Diesel Fuel Update (EIA)

for Electricity Capacity and Generation by Fuel Tables (2007-2035) for Electricity Capacity and Generation by Fuel Tables (2007-2035) International Energy Outlook 2010 Reference Case Projections for Electricity Capacity and Generation by Fuel Tables (2007-2035) Formats Data Table Titles (1 to 18 complete) Reference Case Projections for Electricity Capacity and Generation by Fuel Tables. Need help, contact the National Energy Information Center at 202-586-8800. Appendix H. Reference Case Projections for Electricity Capacity and Generation by Fuel Tables. Need help, contact the National Energy Information Center at 202-586-8800. Table H1 World Total Installed Generating Capacity by Region and Country Table H1. World Total Installed Generating Capacity by Region and Country. Need help, contact the National Energy Information Center at 202-586-8800.

374

EPA-GHG Inventory Capacity Building | Open Energy Information  

Open Energy Info (EERE)

EPA-GHG Inventory Capacity Building EPA-GHG Inventory Capacity Building (Redirected from US EPA GHG Inventory Capacity Building) Jump to: navigation, search Tool Summary Name: US EPA GHG inventory Capacity Building Agency/Company /Organization: United States Environmental Protection Agency Sector: Energy, Land Topics: GHG inventory, Background analysis Resource Type: Training materials, Lessons learned/best practices References: US EPA GHG inventory Capacity Building[1] Logo: US EPA GHG inventory Capacity Building "Developing greenhouse gas inventories is an important first step to managing emissions. U.S. EPA's approach for building capacity to develop GHG inventories is based on the following lessons learned from working alongside developing country experts: Technical expertise for GHG inventories already exists in developing

375

Maryland Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 64,000 64,000 64,000 64,000 64,000 64,000 1988-2012 Salt Caverns

376

Ohio Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 572,477 572,477 580,380 580,380 580,380 577,944 1988-2012

377

Texas Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 690,678 740,477 766,768 783,579 812,394 831,190 1988-2012

378

Kentucky Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 220,359 220,359 220,368 221,751 221,751 221,751 1988-2012

379

Oregon Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 29,415 29,415 29,565 29,565 29,565 28,750 1989-2012 Salt Caverns

380

Michigan Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 1,060,558 1,062,339 1,069,405 1,069,898 1,075,472 1,078,979

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

Tennessee Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 1,200 1,200 1,200 0 1998-2012 Salt Caverns 0 1999-2012

382

Alabama Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 19,300 26,900 26,900 32,900 35,400 35,400 1995-2012 Salt Caverns

383

Wyoming Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 114,067 111,167 111,120 111,120 106,764 124,937 1988-2012

384

Indiana Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 114,294 114,937 114,274 111,271 111,313 110,749 1988-2012

385

Louisiana Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 588,711 615,858 651,968 670,880 690,295 699,646 1988-2012

386

Montana Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 374,201 374,201 376,301 376,301 376,301 376,301 1988-2012

387

Virginia Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 9,560 6,200 9,500 9,500 9,500 9,500 1998-2012 Salt Caverns

388

Mississippi Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 166,909 187,251 210,128 235,638 240,241 289,416 1988-2012

389

Pennsylvania Underground Natural Gas Storage Capacity  

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

Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Alabama Arkansas California Colorado Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Michigan Minnesota Mississippi Missouri Montana Nebraska New Mexico New York Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History Total Storage Capacity 759,365 759,153 776,964 776,822 776,845 774,309 1988-2012

390

The NASA CSTI High Capacity Power Program  

SciTech Connect (OSTI)

The SP-100 program was established in 1983 by DOD, DOE, and NASA as a joint program to develop the technology necessary for space nuclear power systems for military and civil applications. During 1986 and 1987, the NASA Advanced Technology Program was responsible for maintaining the momentum of promising technology advancement efforts started during Phase I of SP-100 and to strengthen, in key areas, the chances for successful development and growth capability of space nuclear reactor power systems for future space applications. In 1988, the NASA Advanced Technology Program was incorporated into NASA`s new Civil Space Technology Initiative (CSTI). The CSTI program was established to provide the foundation for technology development in automation and robotics, information, propulsion, and power. The CSTI High Capacity Power Program builds on the technology efforts of the SP-100 program, incorporates the previous NASA advanced technology project, and provides a bridge to the NASA exploration technology programs. The elements of CSTI high capacity power development include conversion systems - Stirling and thermoelectric, thermal management, power management, system diagnostics, and environmental interactions. Technology advancement in all areas, including materials, is required to provide the growth capability, high reliability and 7 to 10 years lifetime demanded for future space nuclear power systems. The overall program will develop and demonstrate the technology base required to provide a wide range of modular power systems while minimizing the impact of day/night operation as well as attitudes and distance from the Sun. Significant accomplishments in all of the program elements will be discussed, along with revised goals and project timelines recently developed.

Winter, J.M.

1994-09-01T23:59:59.000Z

391

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

E-Print Network [OSTI]

volume) of the equipment (AHAM 1991, ARI1991, G A M A 1992).Energy Factors (SWEFs), (AHAM 1991). b. 1990 RECS (EIAdata for their members (AHAM 1991, ARI1991, G A M A 1992).

Johnson, F.X.

2010-01-01T23:59:59.000Z

392

E-Print Network 3.0 - affecting energy capacity Sample Search...  

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

reserves provided by the block with capacity... , which, in turn, impacts the capacity markets, be they energy or ancillary services markets, is adequacy... capacity ofsellers'...

393

FAO-Capacity Development on Climate Change | Open Energy Information  

Open Energy Info (EERE)

FAO-Capacity Development on Climate Change FAO-Capacity Development on Climate Change Jump to: navigation, search Tool Summary LAUNCH TOOL Name: FAO-Capacity Development on Climate Change Agency/Company /Organization: Food and Agriculture Organization of the United Nations Sector: Land, Climate Focus Area: Forestry, Agriculture Resource Type: Training materials, Lessons learned/best practices, Case studies/examples Website: www.fao.org/climatechange/learning/en/ Cost: Free FAO-Capacity Development on Climate Change Screenshot References: FAO-Capacity Development on Climate Change[1] Logo: FAO-Capacity Development on Climate Change This portal provides a one-stop window for Member States, partners, UN staff and other development actors to access FAO climate change learning resources to facilitate experience-sharing.

394

Property:PotentialEGSGeothermalCapacity | Open Energy Information  

Open Energy Info (EERE)

PotentialEGSGeothermalCapacity PotentialEGSGeothermalCapacity Jump to: navigation, search Property Name PotentialEGSGeothermalCapacity Property Type Quantity Description The nameplate capacity technical potential from EGS Geothermal for a particular place. 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

395

Capacity Building Project with Howard University | Department of Energy  

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

Capacity Building Project with Howard University Capacity Building Project with Howard University Capacity Building Project with Howard University The purpose of this initiative is to build community capacity for public participation in environmental and energy decision making. The target communities are those impacted by U.S. Department of Energy (DOE) facilities and in Washington, DC, the DOE Headquarters host community. The primary focus is on environmental justice communities-low-income and minority communities. Capacity Building Project with Howard University More Documents & Publications National Conference of Black Mayors, Inc. Capacity Building Project with Howard University The State of Environmental Justice in America 2010 Conference Environmental Justice at the U.S. Department of Energy - A Decade of

396

Microsoft Word - GasCapacityReport3-17.doc  

Gasoline and Diesel Fuel Update (EIA)

for the Lower-48 States Executive Summary This analysis examines the availability of effective productive capacity to meet the projected wellhead demand for natural gas through 2003. Effective productive capacity is defined as the maximum production available from natural gas wells considering limitations of the production, gathering, and transportation systems. Surplus or unutilized capacity is the difference between the effective productive capacity and the actual production. This report contains projections of natural gas effective productive capacity in the Lower-48 States for 2003 and is based on prices and production forecasts in EIA's February 2003 Short Term Energy Outlook (STEO). The analysis projects an average surplus capacity of 5.6 Bcf/d in 2003 under STEO Base

397

Property:PotentialOffshoreWindCapacity | Open Energy Information  

Open Energy Info (EERE)

PotentialOffshoreWindCapacity PotentialOffshoreWindCapacity Jump to: navigation, search Property Name PotentialOffshoreWindCapacity Property Type Quantity Description The nameplate capacity technical potential from Offshore Wind for a particular place. 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

398

Property:PotentialGeothermalHydrothermalCapacity | Open Energy Information  

Open Energy Info (EERE)

PotentialGeothermalHydrothermalCapacity PotentialGeothermalHydrothermalCapacity Jump to: navigation, search Property Name PotentialGeothermalHydrothermalCapacity Property Type Quantity Description The nameplate capacity technical potential from Geothermal Hydrothermal for a particular place. 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

399

Property:PotentialHydropowerCapacity | Open Energy Information  

Open Energy Info (EERE)

PotentialHydropowerCapacity PotentialHydropowerCapacity Jump to: navigation, search Property Name PotentialHydropowerCapacity Property Type Quantity Description The nameplate capacity technical potential from Hydropower for a particular place. 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

400

Property:PotentialBiopowerGaseousCapacity | Open Energy Information  

Open Energy Info (EERE)

PotentialBiopowerGaseousCapacity PotentialBiopowerGaseousCapacity Jump to: navigation, search Property Name PotentialBiopowerGaseousCapacity Property Type Quantity Description The nameplate capacity technical potential from gaseous biopower for a particular place. 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

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

ISO New England Forward Capacity Market (Rhode Island) | Department of  

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

ISO New England Forward Capacity Market (Rhode Island) ISO New England Forward Capacity Market (Rhode Island) ISO New England Forward Capacity Market (Rhode Island) < Back Eligibility Developer Industrial State/Provincial Govt Savings Category Alternative Fuel Vehicles Hydrogen & Fuel Cells Buying & Making Electricity Water Home Weatherization Solar Wind Program Info State Rhode Island Program Type Generating Facility Rate-Making Under the Forward Capacity Market (FCM), ISO New England projects the capacity needs of the region's power system three years in advance and then holds an annual auction to purchase the power resources that will satisfy those future regional requirements. Resources that clear in the auction are obligated to provide power or curtail demand when called upon by the ISO. The Forward Capacity Market was developed by ISO New England, the six New

402

Spain Installed Wind Capacity Website | Open Energy Information  

Open Energy Info (EERE)

Spain Installed Wind Capacity Website Spain Installed Wind Capacity Website Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Spain Installed Wind Capacity Website Focus Area: Renewable Energy Topics: Market Analysis Website: www.gwec.net/index.php?id=131 Equivalent URI: cleanenergysolutions.org/content/spain-installed-wind-capacity-website Language: English Policies: Regulations Regulations: Feed-in Tariffs This website presents an overview of total installed wind energy capacity in Spain per year from 2000 to 2010. The page also presents the main market developments from 2010; a policy summary; a discussion of the revision in feed-in tariffs in 2010; and a future market outlook. References Retrieved from "http://en.openei.org/w/index.php?title=Spain_Installed_Wind_Capacity_Website&oldid=514562"

403

Property:PotentialOnshoreWindCapacity | Open Energy Information  

Open Energy Info (EERE)

PotentialOnshoreWindCapacity PotentialOnshoreWindCapacity Jump to: navigation, search Property Name PotentialOnshoreWindCapacity Property Type Quantity Description The nameplate capacity technical potential from Onshore Wind for a particular place. 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

404

Worldwide Energy Efficiency Action through Capacity Building and Training  

Open Energy Info (EERE)

Worldwide Energy Efficiency Action through Capacity Building and Training Worldwide Energy Efficiency Action through Capacity Building and Training (WEACT) Jump to: navigation, search Logo: Worldwide Energy Efficiency Action through Capacity Building and Training (WEACT) Name Worldwide Energy Efficiency Action through Capacity Building and Training (WEACT) Agency/Company /Organization National Renewable Energy Laboratory, The International Partnership for Energy Efficiency Cooperation Sector Energy Focus Area Energy Efficiency Topics Background analysis Resource Type Training materials Website http://www.nrel.gov/ce/ipeec/w Country Mexico, India UN Region Northern America References Worldwide Energy Efficiency Action through Capacity Building and Training (WEACT)[1] Abstract Included are training materials for the Worldwide Energy Efficiency Action through Capacity Building & Training (WEACT) Workshop in Mexico City, 28-30 September 2010.

405

Property:PotentialBiopowerSolidCapacity | Open Energy Information  

Open Energy Info (EERE)

PotentialBiopowerSolidCapacity PotentialBiopowerSolidCapacity Jump to: navigation, search Property Name PotentialBiopowerSolidCapacity Property Type Quantity Description The nameplate capacity technical potential from solid biopower for a particular place. 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

406

GIZ-Best Practices in Capacity Building Approaches | Open Energy  

Open Energy Info (EERE)

GIZ-Best Practices in Capacity Building Approaches GIZ-Best Practices in Capacity Building Approaches Jump to: navigation, search Tool Summary LAUNCH TOOL Name: GIZ-Best Practices in Capacity Building Approaches: Recommendations for the Design of a Long -Term Capacity Building Strategy for the Wind and Solar Sectors by the MEF Working Group Agency/Company /Organization: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH Sector: Energy, Climate Focus Area: Solar, Wind Resource Type: Publications, Training materials, Lessons learned/best practices Website: prod-http-80-800498448.us-east-1.elb.amazonaws.com/w/images/8/80/Best_ Cost: Free GIZ-Best Practices in Capacity Building Approaches: Recommendations for the Design of a Long -Term Capacity Building Strategy for the Wind and Solar Sectors by the MEF Working Group Screenshot

407

U.S. Refinery Utilization and Capacity  

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

Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History Gross Input to Atmospheric Crude Oil Distillation Units 15,283 15,709 16,327 16,490 16,306 16,162 1985-2013 Operable Capacity (Calendar Day) 17,814 17,815 17,815 17,815 17,815 17,818 1985-2013 Operating 17,005 17,228 17,239 17,450 17,439 17,623 1985-2013 Idle 809 587 576 365 376 195 1985-2013 Operable Utilization Rate (%) 85.8 88.2 91.7 92.6 91.5 90.7 1985-2013 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Totals may not equal sum of components due to independent rounding. See Definitions, Sources, and Notes link above for more information on this table. Release Date: 11/27/2013

408

Temperature, Energy, and Heat Capacity of Asymptotically Anti-De Sitter Black Holes  

E-Print Network [OSTI]

We investigate the thermodynamical properties of black holes in (3+1) and (2+1) dimensional Einstein gravity with a negative cosmological constant. In each case, the thermodynamic internal energy is computed for a finite spatial region that contains the black hole. The temperature at the boundary of this region is defined by differentiating the energy with respect to entropy, and is equal to the product of the surface gravity (divided by~$2\\pi$) and the Tolman redshift factor for temperature in a stationary gravitational field. We also compute the thermodynamic surface pressure and, in the case of the (2+1) black hole, show that the chemical potential conjugate to angular momentum is equal to the proper angular velocity of the black hole with respect to observers who are at rest in the stationary time slices. In (3+1) dimensions, a calculation of the heat capacity reveals the existence of a thermodynamically stable black hole solution and a negative heat capacity instanton. This result holds in the limit that the spatial boundary tends to infinity only if the comological constant is negative; if the cosmological constant vanishes, the stable black hole solution is lost. In (2+1) dimensions, a calculation of the heat capacity reveals the existence of a thermodynamically stable black hole solution, but no negative heat capacity instanton.

J. D. Brown; J. Creighton; R. B. Mann

1994-05-03T23:59:59.000Z

409

Ukraine-Capacity Building for Low Carbon Growth | Open Energy...  

Open Energy Info (EERE)

Jump to: navigation, search Name UNDP-Capacity Building for Low Carbon Growth in Ukraine AgencyCompany Organization United Nations Development Programme Sector Energy,...

410

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

Open Energy Info (EERE)

Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) AgencyCompany Organization United States Agency for International Development, United States Environmental...

411

Information capacity and resolution in an optical system  

Science Journals Connector (OSTI)

The concept of invariance of information capacity is discussed and applied to the resolution of an optical system. Methods of obtaining superresolution in microscopy are discussed, and...

Cox, I J; Sheppard, C J R

1986-01-01T23:59:59.000Z

412

Design and Evaluation of Novel High Capacity Cathode Materials  

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

Design and Evaluation of Novel High Capacity Cathode Materials Christopher Johnson and Michael Thackeray Chemical Sciences and Engineering Division, Argonne Annual Merit Review DOE...

413

Open versus closed loop capacity equilibria in electricity markets ...  

E-Print Network [OSTI]

May 7, 2012 ... Abstract: We consider two game-theoretic models of the generation capacity expansion problem in liberalized electricity markets. The first is an...

S. Wogrin

2012-05-07T23:59:59.000Z

414

John S. Wright Forestry Center Room Sizes, Capacities, and Rates  

E-Print Network [OSTI]

Appendix 1 John S. Wright Forestry Center Room Sizes, Capacities, and Rates Room College the Wright Center contact: Marlene Mann, Administrative Assistant Forestry and Natural Resources Voice: 765

415

Africa Adaptation Programme: Capacity Building Experiences-Improving...  

Open Energy Info (EERE)

Data and Information Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Africa Adaptation Programme: Capacity Building Experiences-Improving Access, Understanding...

416

Wave equation prediction of pile bearing capacity  

E-Print Network [OSTI]

as the number of blows required by the pile driving hammer to produce a unit penetration of the pile into the soil. The more common nomenclature is driving resistance or blow count, and these terms will be used interchangeably throughout 10 this work.... The units most often used are blows per inch or blows per foot. In general, the relationship between static soil resistance and dynamic driving resistance is nonlinear. Of the many factors which govern this relationship, those which are considered...

Bartoskewitz, Richard Edward

1970-01-01T23:59:59.000Z

417

Lyapunov-Based Distributed Control of the Safety Factor Profile in a Tokamak Plasma  

E-Print Network [OSTI]

Lyapunov-Based Distributed Control of the Safety Factor Profile in a Tokamak Plasma Federico safety-factor profile in a tokamak plasma. Using relevant physical models and simplifying assumptions within a tokamak plasma is a key issue to achieve (and maintain) in a safe manner high

Boyer, Edmond

418

Dynamic Long-Term Modelling of Generation Capacity Investment and Capacity Margins  

E-Print Network [OSTI]

is the capital expenditure vector for the project with ??x?1i=0 Mxi = 1. For simplicity, the expenditure schedule uses a lagged 3Which in the case of natural gas match quite well with available future prices from ICE Futures Europe (out to 2017) but are arguably... capacity I(t), which is a parallel cascade of the four technology categories. Each single category is defined by a Delay Differential Equation (DDE): dIx dt = ? (?j ,?j)??x ?j?(t? ?j ? ?x)? ? (?j ,?j)??x ?j?(t? ?j ? ?x ? ?x), (1) where ?(t) is the Dirac...

Eager, Dan; Hobbs, Benjamin; Bialek, Janusz

2012-04-25T23:59:59.000Z

419

Building Partnership Capacity and Sustainability in Financially Challenging Times  

E-Print Network [OSTI]

Building Partnership Capacity and Sustainability in Financially Challenging Times Introduction educational inequality. Partnership Question From the outset, the core objective was to design a sustainable that by focusing on capacity building and sustainability from the beginning, it is possible to build a partnership

420

Capacity of a UMTS system for aeronautical communications  

Science Journals Connector (OSTI)

Current Air Traffic Management and Air Traffic Control systems will experience a demand increase in the following years due to the large number of operating aircrafts. As a consequence, new solution must be studied to overcome this capacity limitation ... Keywords: ATC, ATM, ENR, SDR, TMA, UMTS, W-CDMA, air traffic, capacity

Miguel Calvo Ramn; Ramn Martnez Rodrguez-Osorio; Bazil Taha Ahmed; Juan Jos Iglesias Jimnez

2007-07-01T23:59:59.000Z

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.


421

Prediction methods for capacity of drag anchors in clayey soils  

E-Print Network [OSTI]

A drag anchor is a marine foundation element, which is penetrated into the seabed by dragging in order to generate a required capacity. The holding capacity of a drag anchor in a particular soil condition is developed by soil resistance acting...

Yoon, Yeo Hoon

2002-01-01T23:59:59.000Z

422

THE MAXIMUM CAPACITY OF A LINE PLAN IS INAPPROXIMABLE  

E-Print Network [OSTI]

THE MAXIMUM CAPACITY OF A LINE PLAN IS INAPPROXIMABLE CHRISTINA PUHL AND SEBASTIAN STILLER Abstract a network, upper arc-capacities and a line pool. E-mail: puhl@math.tu-berlin.de, stiller of the European Commission under contract no. FP6-021235-2. 1 #12;2 CHRISTINA PUHL AND SEBASTIAN STILLER We

Nabben, Reinhard

423

Optimal Demand Response Capacity of Automatic Lighting Control  

E-Print Network [OSTI]

1 Optimal Demand Response Capacity of Automatic Lighting Control Seyed Ataollah Raziei and Hamed-mails: razieis1@udayton.edu and hamed@ee.ucr.edu Abstract--Demand response programs seek to ad- just the normal prior studies have extensively studied the capacity of offering demand response in buildings

Mohsenian-Rad, Hamed

424

A dynamic programming approach for the airport capacity allocation problem  

Science Journals Connector (OSTI)

......between air traffic demand and system capacity...IMA Journal of Management Mathematics 14...traffic flow management model. In this...considered traffic demand and capacity...the left-hand side are the number...traffic flow management. ADYNAMIC PROGRAMMING...and the current demand. The state of......

Paolo Dell'Olmo; Guglielmo Lulli

2003-07-01T23:59:59.000Z

425

Software-Defined Networking Based Capacity Sharing in Hybrid Networks  

E-Print Network [OSTI]

Software-Defined Networking Based Capacity Sharing in Hybrid Networks Mateus A. S. Santos and Bruno proposes a novel approach to capacity sharing in hybrid networked environments, i.e., environments that consist of infrastructure-based as well as infrastructure- less networks. The proposed framework is based

Turletti, Thierry

426

Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing  

E-Print Network [OSTI]

and EC2 spot market. Furthermore, we formulate the optimal capacity segmentation strategy as a MarkovTowards Optimal Capacity Segmentation with Hybrid Cloud Pricing Wei Wang, Baochun Li, and Ben Liang markets with different service guarantees. For example, Amazon EC2 prices virtual instances under three

Li, Baochun

427

Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing  

E-Print Network [OSTI]

between periodic auctions and EC2 spot market. Furthermore, we formulate the optimal capacity segmentationTowards Optimal Capacity Segmentation with Hybrid Cloud Pricing Wei Wang, Baochun Li, and Ben Liang priced in multiple markets with different service guarantees. For example, Amazon EC2 prices virtual

Li, Baochun

428

Mechanism Design for Capacity Allocation with Price Competition  

E-Print Network [OSTI]

. This paper examines the problem of mechanism design for capacity allocation in two connected markets whereMechanism Design for Capacity Allocation with Price Competition Masabumi Furuhata Intelligent-users in price competition. We consider the problems of how allocation mechanisms in the upstream market de

Zhang, Dongmo

429

Capacity expansion analysis in a chemical plant using linear programming  

Science Journals Connector (OSTI)

An analysis of the fuel additive production process of a US mid-western chemical manufacturer is described. Material balance constraints for each potential bottleneck of the manufacturing process are included as part of a linear programming model. Several capacity expansion scenarios are evaluated. The optimal way of modifying and expanding manufacturing capacity to meet forecast demand is determined.

Kenneth H. Myers; Reuven R. Levary

1996-01-01T23:59:59.000Z

430

Table 1. U.S. Biodiesel Production Capacity and Production  

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

Biodiesel Production Capacity and Production Biodiesel Production Capacity and Production (million gallons) Period 2011 January 2,114 35 February 2,104 40 March 2,081 60 April 2,101 71 May 2,064 77 June 2,069 81

431

State and National Wind Resource Potential 30 Percent Capacity Factor at 80 Meters  

Wind Powering America (EERE)

Note - 50% exclusions are not cumulative. If an area is non-ridgecrest forest on FS land, it is just excluded at the 50% level one time. Note - 50% exclusions are not cumulative. If an area is non-ridgecrest forest on FS land, it is just excluded at the 50% level one time. 1) Exclude areas of slope > 20% Derived from 90 m national elevation dataset. 6) 100% exclude 3 km surrounding criteria 2-5 (except water) Merged datasets and buffer 3 km 5) 100% exclusion of airfields, urban, wetland and water areas. USGS North America Land Use Land Cover (LULC), version 2.0, 1993; ESRI airports and airfields (2006); U.S. Census Urbanized Areas (2000 and 2003) 10) 50% exclusion of non-ridgecrest forest Ridge-crest areas defined using a terrain definition script, overlaid with USGS LULC data screened for the forest categories. Other Criteria 8) 50% exclusion of remaining Dept. of Defense lands except

432

Nuclear Factor of Activated T-cell Activity Is Associated with Metastatic Capacity in Colon Cancer  

Science Journals Connector (OSTI)

...21st Avenue South, MCN D4316, Nashville, TN 37232-2730. Phone: 615-322-2363...constitute a straightforward expression signature to identify colon cancers with high risk of...in patients with colorectal carcinoma. To capture the molecular underpinnings for...

Manish K. Tripathi; Natasha G. Deane; Jing Zhu; Hanbing An; Shinji Mima; Xiaojing Wang; Sekhar Padmanabhan; Zhiao Shi; Naresh Prodduturi; Kristen K. Ciombor; Xi Chen; M. Kay Washington; Bing Zhang; R. Daniel Beauchamp

2014-12-01T23:59:59.000Z

433

Nitrification Capacities of Texas Soil Types and Factors which Affect Nitrification.  

E-Print Network [OSTI]

, the test was repeated n-ith the use of normal nitric acid. Basicity is expressed in terms of percentage of calcium carbonate. The acid was neutralized chiefly by calcium derived from calcium carbonate, if present, from calcium in the base exchange... to neutralize the nitric and sulphuric acids pro- duced, or of both. .4 few soils needed additions of available phos- phates for high nitrification (35) and there was a small percentage left which still did not nitrify the ammonium sulphate completely...

Sterges, A. J.; Fraps, G S. (George Stronach)

1947-01-01T23:59:59.000Z

434

Assess public and private sector capacity to support initiatives | Open  

Open Energy Info (EERE)

public and private sector capacity to support initiatives public and private sector capacity to support initiatives Jump to: navigation, search Stage 2 LEDS Home Introduction to Framework Assess current country plans, policies, practices, and capacities Develop_BAU Stage 4: Prioritizing and Planning for Actions Begin execution of implementation plans 1.0. Organizing the LEDS Process 1.1. Institutional Structure for LEDS 1.2. Workplan to Develop the LEDS 1.3. Roles and responsibilities to develop LEDS 2.1. Assess current country plans, policies, practices, and capacities 2.2. Compile lessons learned and good practices from ongoing and previous sustainable development efforts in the country 2.3. Assess public and private sector capacity to support initiatives 2.4. Assess and improve the national GHG inventory and other

435

Underground Natural Gas Working Storage Capacity - Energy Information  

Gasoline and Diesel Fuel Update (EIA)

Underground Natural Gas Working Storage Capacity Underground Natural Gas Working Storage Capacity With Data for November 2012 | Release Date: July 24, 2013 | Next Release Date: Spring 2014 Previous Issues Year: 2013 2012 2011 2010 2009 2008 2007 2006 Go Overview Natural gas working storage capacity increased by about 2 percent in the Lower 48 states between November 2011 and November 2012. The U.S. Energy Information Administration (EIA) has two measures of working gas storage capacity, and both increased by similar amounts: Demonstrated maximum volume increased 1.8 percent to 4,265 billion cubic feet (Bcf) Design capacity increased 2.0 percent to 4,575 Bcf Maximum demonstrated working gas volume is an operational measure of the highest level of working gas reported at each storage facility at any time

436

Building MRV Standards and Capacity in Key Countries | Open Energy  

Open Energy Info (EERE)

MRV Standards and Capacity in Key Countries MRV Standards and Capacity in Key Countries Jump to: navigation, search Name Building MRV Standards and Capacity in Key Countries Agency/Company /Organization World Resources Institute (WRI) Sector Climate Focus Area Renewable Energy Topics Implementation Website http://www.wri.org/topics/mrv Program Start 2011 Program End 2014 Country Brazil, Colombia, Ethiopia, India, South Africa, Thailand South America, South America, Eastern Africa, Southern Asia, Southern Africa, South-Eastern Asia References World Resources Institute (WRI)[1] Program Overview Developing countries will be required to measure, report, and verify (MRV) mitigation actions according to international guidelines, but few have the capacity to do so. The goal of this project is to build the capacity of a

437

Property:GrossProdCapacity | Open Energy Information  

Open Energy Info (EERE)

GrossProdCapacity GrossProdCapacity Jump to: navigation, search Property Name GrossProdCapacity Property Type Quantity Description Sum of the property AvgAnnlGrossOpCpcty for all Energy Generation Facilities with properties: Sector: Geothermal Energy InGeothermalResourceArea: set to the the variable vName of the Geothermal Resource Area 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

438

Assess current country plans, policies, practices, and capacities | Open  

Open Energy Info (EERE)

Assess current country plans, policies, practices, and capacities Assess current country plans, policies, practices, and capacities Jump to: navigation, search Stage 2 LEDS Home Introduction to Framework Assess current country plans, policies, practices, and capacities Develop_BAU Stage 4: Prioritizing and Planning for Actions Begin execution of implementation plans 1.0. Organizing the LEDS Process 1.1. Institutional Structure for LEDS 1.2. Workplan to Develop the LEDS 1.3. Roles and responsibilities to develop LEDS 2.1. Assess current country plans, policies, practices, and capacities 2.2. Compile lessons learned and good practices from ongoing and previous sustainable development efforts in the country 2.3. Assess public and private sector capacity to support initiatives 2.4. Assess and improve the national GHG inventory and other

439

AEO2011: Electricity Generating Capacity | OpenEI  

Open Energy Info (EERE)

Generating Capacity 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 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

440

Property:Plants with Unknown Planned Capacity | Open Energy Information  

Open Energy Info (EERE)

Plants with Unknown Planned Capacity Plants with Unknown Planned Capacity Jump to: navigation, search Property Name Plants with Unknown Planned Capacity Property Type String Description Number of plants with unknown planned capacity per GEA Pages using the property "Plants with Unknown Planned Capacity" Showing 21 pages using this property. A Alaska Geothermal Region + 1 + C Cascades Geothermal Region + 2 + Central Nevada Seismic Zone Geothermal Region + 9 + G Gulf of California Rift Zone Geothermal Region + 4 + H Hawaii Geothermal Region + 0 + Holocene Magmatic Geothermal Region + 0 + I Idaho Batholith Geothermal Region + 1 + N Northern Basin and Range Geothermal Region + 11 + Northern Rockies Geothermal Region + 0 + Northwest Basin and Range Geothermal Region + 9 + R Rio Grande Rift Geothermal Region + 1 +

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

India-Vulnerability Assessment and Enhancing Adaptive Capacities to Climate  

Open Energy Info (EERE)

Vulnerability Assessment and Enhancing Adaptive Capacities to Climate Vulnerability Assessment and Enhancing Adaptive Capacities to Climate Change Jump to: navigation, search Name India-Vulnerability Assessment and Enhancing Adaptive Capacities to Climate Change Agency/Company /Organization Swiss Agency for Development and Cooperation Sector Energy, Land, Water Focus Area Agriculture Topics Co-benefits assessment, Background analysis Resource Type Lessons learned/best practices Website http://www.intercooperation.or Country India Southern Asia References India-Vulnerability Assessment and Enhancing Adaptive Capacities to Climate Change[1] India-Vulnerability Assessment and Enhancing Adaptive Capacities to Climate Change Screenshot Contents 1 Introduction [1] 2 Community-based Institutions [2] 3 Pasture Land Development [3]

442

Property:Device Nameplate Capacity (MW) | Open Energy Information  

Open Energy Info (EERE)

Nameplate Capacity (MW) Nameplate Capacity (MW) Jump to: navigation, search Property Name Device Nameplate Capacity (MW) Property Type String Pages using the property "Device Nameplate Capacity (MW)" Showing 25 pages using this property. (previous 25) (next 25) M MHK Projects/40MW Lewis project + 0 8MW 1MW Farms of multiple machines will be deployed with installed capacity of circa 20MW + MHK Projects/Algiers Light Project + 40 kW + MHK Projects/Anconia Point Project + 40 kW + MHK Projects/Ashley Point Project + 40 kW + MHK Projects/Avondale Bend Project + 40 kW + MHK Projects/Bar Field Bend + 40 kW + MHK Projects/Barfield Point + 40 kW + MHK Projects/Bayou Latenache + 40 kW + MHK Projects/BioSTREAM Pilot Plant + 250kW pilot 1MW commercial scale + MHK Projects/Bondurant Chute + 40 kW +

443

Property:NetProdCapacity | Open Energy Information  

Open Energy Info (EERE)

NetProdCapacity NetProdCapacity Jump to: navigation, search Property Name NetProdCapacity Property Type Quantity Description Sum of the property SummerPeakNetCpcty for all Energy Generation Facilities with properties: Sector: Geothermal Energy InGeothermalResourceArea: set to the the variable vName of the Geothermal Resource Area 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

444

Influence of Surface Structure on the Capacity and Irreversible Capacity Loss of Sn-Based Anodes for Lithium Ion Batteries  

Science Journals Connector (OSTI)

(1-5) Numerous solar and wind power energy plants have been invested in to exploit sustainable and renewable energy. ... These materials demonstrate discharge capacities on the order of 1000 mAh/(g Sn), which is consistent with the alloying capacity limit of 4.4 Li atoms per Sn atom, or 991 mAh/(g Sn). ...

Li Li; Xuan Liu; Shulan Wang; Wenzhi Zhao

2014-05-19T23:59:59.000Z

445

Social Logics in Development of Institutional Capacity The Case of Capacity Development for the Clean Development Mechanism in Uganda  

E-Print Network [OSTI]

for the Clean Development Mechanism in Uganda Karen Holm Olsen International Development Studies Department in Uganda 2002-2006. The study finds that the politics of institutional change processes are largely ignored of Institutional Capacity The case of Capacity Development for the CDM in Uganda The 15th International Climate

446

Factorized soft graviton theorems at loop level  

E-Print Network [OSTI]

We analyze the low-energy behavior of scattering amplitudes involving gravitons at loop level in four dimensions. The single-graviton soft limit is controlled by soft operators which have been argued to separate into a factorized piece and a non-factorizing infrared divergent contribution. In this note we show that the soft operators responsible for the factorized contributions are strongly constrained by gauge and Poincare invariance under the assumption of a local structure. We show that the leading and subleading orders in the soft-momentum expansion can not receive radiative corrections. The first radiative correction occurs for the sub-subleading soft graviton operator and is one-loop exact. It depends on only two undetermined coefficients which should reflect the field content of the theory under consideration.

Broedel, Johannes; Plefka, Jan; Rosso, Matteo

2014-01-01T23:59:59.000Z

447

Enhancing Capacity for Low Emission Development Strategies (EC-LEDS)  

Open Energy Info (EERE)

Enhancing Capacity for Low Emission Development Strategies Program Enhancing Capacity for Low Emission Development Strategies Program Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2014 Country Albania, Bangladesh, Cambodia, Colombia, Costa Rica, Gabon, Georgia, Guatemala, Indonesia, Jamaica, Kazakhstan, Kenya, Republic of Macedonia, Malawi, Malaysia, Mexico, Moldova, Peru, Philippines, Serbia, South Africa, Thailand, Ukraine, Vietnam, Zambia UN Region Southern Asia References Enhancing Capacity for Low Emission Development Strategies Program[1]

448

Renewable energy capacity and generation | OpenEI  

Open Energy Info (EERE)

21 21 Varnish cache server Browse Upload data GDR 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2142281521 Varnish cache server Renewable energy capacity and generation 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. The dataset uses gigawatts. The data is broken down into electric power capacity and generation. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Renewable energy capacity and generation Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generating Capacity and Generation- Reference Case (xls, 118.9 KiB)

449

Guatemala-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Guatemala-Enhancing Capacity for Low Emission Development Strategies Guatemala-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Guatemala-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Program Start 2010 Program End 2016 Country Guatemala Central America References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a U.S. Government initiative to support developing countries' efforts to

450

Africa Adaptation Programme: Capacity Building Experiences-Improving  

Open Energy Info (EERE)

Africa Adaptation Programme: Capacity Building Experiences-Improving Africa Adaptation Programme: Capacity Building Experiences-Improving Access, Understanding and Application of Climate Data and Information Jump to: navigation, search Tool Summary Name: Africa Adaptation Programme: Capacity Building Experiences-Improving Access, Understanding and Application of Climate Data and Information Agency/Company /Organization: United Nations Development Programme (UNDP) Sector: Climate, Energy Topics: Adaptation, Co-benefits assessment, - Energy Access Resource Type: Dataset, Lessons learned/best practices Website: www.undp.org/environment/library.shtml Cost: Free UN Region: Eastern Africa, Middle Africa, Northern Africa, Southern Africa, Western Africa Language: English Africa Adaptation Programme: Capacity Building Experiences-Improving Access, Understanding and Application of Climate Data and Information Screenshot

451

Ukraine-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Ukraine-Enhancing Capacity for Low Emission Development Strategies Ukraine-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Ukraine-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Ukraine Eastern Europe References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a

452

Property:Project Installed Capacity (MW) | Open Energy Information  

Open Energy Info (EERE)

Installed Capacity (MW) Installed Capacity (MW) Jump to: navigation, search Property Name Project Installed Capacity (MW) Property Type String Pages using the property "Project Installed Capacity (MW)" Showing 25 pages using this property. (previous 25) (next 25) M MHK Projects/40MW Lewis project + 0 + MHK Projects/ADM 5 + 1 + MHK Projects/AWS II + 1 + MHK Projects/Admirality Inlet Tidal Energy Project + 22 + MHK Projects/Agucadoura + 2 + MHK Projects/Alaska 18 + 10 + MHK Projects/Alaska 36 + 10 + MHK Projects/Algiers Cutoff Project + 16 + MHK Projects/Algiers Light Project + 0 + MHK Projects/Anconia Point Project + 0 + MHK Projects/Ashley Point Project + 0 + MHK Projects/Astoria Tidal Energy + 300 + MHK Projects/Avondale Bend Project + 0 + MHK Projects/Bar Field Bend + 0 +

453

Property:EZFeed/ExpectedCapacity | Open Energy Information  

Open Energy Info (EERE)

ExpectedCapacity ExpectedCapacity Jump to: navigation, search Property Name EZFeed/ExpectedCapacity Property Type String Description EZFeed Expected Capacity property Subproperties This property has the following 6081 subproperties: 2 2003 Climate Change Fuel Cell Buy-Down Program (Federal) 3 30% Business Tax Credit for Solar (Vermont) 4 401 Certification (Vermont) A AEP (Central and North) - CitySmart Program (Texas) AEP (Central and North) - Residential Energy Efficiency Programs (Texas) AEP (Central and SWEPCO) - Coolsaver A/C Tune Up (Texas) AEP (Central, North and SWEPCO) - Commercial Solutions Program (Texas) AEP (SWEPCO) - Residential Energy Efficiency Programs (Texas) AEP Appalachian Power - Commercial and Industrial Rebate Programs (West Virginia) AEP Appalachian Power - Residential Home Retrofit Program (West Virginia)

454

AEOP2011:Electricity Generation Capacity by Electricity Market Module  

Open Energy Info (EERE)

AEOP2011:Electricity Generation Capacity by Electricity Market Module AEOP2011:Electricity Generation Capacity by Electricity Market Module Region and Source 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 97, and contains only the reference case. The dataset uses billion kilowatthours. The data is broken down into Texas regional entity, Florida reliability coordinating council, Midwest reliability council and Northeast power coordination council. 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)

455

Property:Technology Nameplate Capacity (MW) | Open Energy Information  

Open Energy Info (EERE)

Nameplate Capacity (MW) Nameplate Capacity (MW) Jump to: navigation, search Property Name Technology Nameplate Capacity (MW) Property Type String Pages using the property "Technology Nameplate Capacity (MW)" Showing 25 pages using this property. (previous 25) (next 25) M MHK Technologies/Aegir Dynamo + 100kW built and tested with 45kW 200kW and 1 4MW designs in development + MHK Technologies/AirWEC + 5kW + MHK Technologies/Aquantis + Proprietary + MHK Technologies/Atlantis AN 150 + 0 15 + MHK Technologies/Atlantis AR 1000 + 1 + MHK Technologies/Atlantis AS 400 + 0 4 + MHK Technologies/Bluetec + 1 + MHK Technologies/Current Power + from 10 kW and up + MHK Technologies/CurrentStar + 1 + MHK Technologies/Deep Green + 500 kW + MHK Technologies/Deep water capable hydrokinetic turbine + 30MW +

456

Costa Rica-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Costa Rica-Enhancing Capacity for Low Emission Development Strategies Costa Rica-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Costa Rica-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Costa Rica Central America References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a

457

Cambodia-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Cambodia-Enhancing Capacity for Low Emission Development Strategies Cambodia-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Cambodia-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Cambodia South-Eastern Asia References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a

458

Philippines-Strengthening Planning Capacity for Low Carbon Growth in  

Open Energy Info (EERE)

Philippines-Strengthening Planning Capacity for Low Carbon Growth in Philippines-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Jump to: navigation, search Name Philippines-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Agency/Company /Organization Asian Development Bank Partner Japan, United Kingdom Sector Climate, Energy Focus Area Non-renewable Energy, Buildings, Economic Development, Energy Efficiency, Greenhouse Gas, Grid Assessment and Integration, People and Policy, Transportation Topics Baseline projection, GHG inventory, Low emission development planning, Market analysis, Pathways analysis, Policies/deployment programs Program Start 2011 Program End 2013 Country Philippines South-Eastern Asia References Strengthening Planning Capacity for Low Carbon Growth in Developing Asia[1]

459

Philippines-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Philippines-Enhancing Capacity for Low Emission Development Strategies Philippines-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Philippines-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Philippines South-Eastern Asia References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a

460

Property:EZFeed/InstalledCapacity | Open Energy Information  

Open Energy Info (EERE)

InstalledCapacity InstalledCapacity Jump to: navigation, search Property Name EZFeed/InstalledCapacity Property Type String Description EZFeed Installed Capacity property Subproperties This property has the following 6079 subproperties: 2 2003 Climate Change Fuel Cell Buy-Down Program (Federal) 3 30% Business Tax Credit for Solar (Vermont) 4 401 Certification (Vermont) A AEP (Central and North) - CitySmart Program (Texas) AEP (Central and North) - Residential Energy Efficiency Programs (Texas) AEP (Central and SWEPCO) - Coolsaver A/C Tune Up (Texas) AEP (Central, North and SWEPCO) - Commercial Solutions Program (Texas) AEP (SWEPCO) - Residential Energy Efficiency Programs (Texas) AEP Appalachian Power - Commercial and Industrial Rebate Programs (West Virginia) AEP Appalachian Power - Residential Home Retrofit Program (West Virginia)

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

Strengthening Planning Capacity for Low Carbon Growth in Developing Asia  

Open Energy Info (EERE)

Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Strengthening Planning Capacity for Low Carbon Growth in Developing Asia - Thailand Jump to: navigation, search Name Thailand-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Agency/Company /Organization Asian Development Bank Partner Japan, United Kingdom Sector Climate, Energy Focus Area Non-renewable Energy, Buildings, Economic Development, Energy Efficiency, Greenhouse Gas, Grid Assessment and Integration, People and Policy, Transportation Topics Baseline projection, GHG inventory, Low emission development planning, Market analysis, Pathways analysis, Policies/deployment programs Program Start 2011 Program End 2013 Country Thailand South-Eastern Asia References Strengthening Planning Capacity for Low Carbon Growth in Developing Asia[1]

462

Albania-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Albania-Enhancing Capacity for Low Emission Development Strategies Albania-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Albania-Enhancing Capacity for Low Emission Development Strategies Program Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Albania UN Region Southern Asia References Enhancing Capacity for Low Emission Development Strategies Program[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet

463

Indonesia-Strengthening Planning Capacity for Low Carbon Growth in  

Open Energy Info (EERE)

Indonesia-Strengthening Planning Capacity for Low Carbon Growth in Indonesia-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Jump to: navigation, search Name Indonesia-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Agency/Company /Organization Asian Development Bank Partner Japan, United Kingdom Sector Climate, Energy Focus Area Non-renewable Energy, Buildings, Economic Development, Energy Efficiency, Greenhouse Gas, Grid Assessment and Integration, People and Policy, Transportation Topics Baseline projection, GHG inventory, Low emission development planning, Market analysis, Pathways analysis, Policies/deployment programs Program Start 2011 Program End 2013 Country Indonesia South-Eastern Asia References Strengthening Planning Capacity for Low Carbon Growth in Developing Asia[1]

464

Indonesia-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Indonesia-Enhancing Capacity for Low Emission Development Strategies Indonesia-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Indonesia-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Indonesia South-Eastern Asia References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a

465

Malaysia-Strengthening Planning Capacity for Low Carbon Growth in  

Open Energy Info (EERE)

Malaysia-Strengthening Planning Capacity for Low Carbon Growth in Malaysia-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Jump to: navigation, search Name Malaysia-Strengthening Planning Capacity for Low Carbon Growth in Developing Asia Agency/Company /Organization Asian Development Bank Partner Japan, United Kingdom Sector Climate, Energy Focus Area Non-renewable Energy, Buildings, Economic Development, Energy Efficiency, Greenhouse Gas, Grid Assessment and Integration, People and Policy, Transportation Topics Baseline projection, GHG inventory, Low emission development planning, Market analysis, Pathways analysis, Policies/deployment programs Program Start 2011 Program End 2013 Country Malaysia South-Eastern Asia References Strengthening Planning Capacity for Low Carbon Growth in Developing Asia[1]

466

Moldova-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Moldova-Enhancing Capacity for Low Emission Development Strategies Moldova-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Moldova-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Moldova Eastern Europe References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a

467

Building REDD Capacity in Developing Countries | Open Energy Information  

Open Energy Info (EERE)

Building REDD Capacity in Developing Countries Building REDD Capacity in Developing Countries Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Building REDD Capacity in Developing Countries Agency/Company /Organization: International Institute for Sustainable Development (IISD) Sector: Land Focus Area: Forestry Topics: Policies/deployment programs Resource Type: Workshop, Lessons learned/best practices Website: www.iisd.org/climate/land_use/redd/ Country: Kenya, Vietnam Eastern Africa, South-Eastern Asia References: IISD Building REDD Capacity in Developing Countries[1] Background "To provide developing countries with this support, IISD has partnered with the Alternatives to Slash and Burn Partnership for the Tropical Forest Margins, World Agroforesty Centre (ASB-ICRAF), to deliver a series of

468

Structural Capacity of Light Gauge Steel Storage Rack Uprights.  

E-Print Network [OSTI]

??Master of Engineering (Research)%%%This report investigates the down-aisle buckling load capacity of steel storage rack uprights. The effects of discrete torsional restraints provided by the (more)

Koen, Damien Joseph

2008-01-01T23:59:59.000Z

469

SEISMIC CAPACITY OF THREADED, BRAZED AND GROOVED PIPE JOINTS  

Broader source: Energy.gov [DOE]

Seismic Capacity of Threaded, Brazed and Grooved Pipe Joints Brent Gutierrez, PhD, PE George Antaki, PE, F.ASME DOE NPH Conference October 25-26, 2011

470

Nitrogen expander cycles for large capacity liquefaction of natural gas  

SciTech Connect (OSTI)

Thermodynamic study is performed on nitrogen expander cycles for large capacity liquefaction of natural gas. In order to substantially increase the capacity, a Brayton refrigeration cycle with nitrogen expander was recently added to the cold end of the reputable propane pre-cooled mixed-refrigerant (C3-MR) process. Similar modifications with a nitrogen expander cycle are extensively investigated on a variety of cycle configurations. The existing and modified cycles are simulated with commercial process software (Aspen HYSYS) based on selected specifications. The results are compared in terms of thermodynamic efficiency, liquefaction capacity, and estimated size of heat exchangers. The combination of C3-MR with partial regeneration and pre-cooling of nitrogen expander cycle is recommended to have a great potential for high efficiency and large capacity.

Chang, Ho-Myung; Park, Jae Hoon; Gwak, Kyung Hyun [Hong Ik University, Department of Mechanical Engineering, Seoul, 121-791 (Korea, Republic of); Choe, Kun Hyung [Korea Gas Corporation, Incheon, 406-130 (Korea, Republic of)

2014-01-29T23:59:59.000Z

471

Why Are We Talking About Capacity Markets? (Presentation)  

SciTech Connect (OSTI)

Capacity markets represent a new and novel way to achieve greater economic use of variable generation assets such as wind and solar, and this concept is discussed in this presentation.

Milligan, M.

2011-06-01T23:59:59.000Z

472

Capacity planning and change management in an aerospace overhaul cell  

E-Print Network [OSTI]

Purpose - This thesis analyzes the transformation of the Small Components Cell in Pratt & Whitney's aftermarket division through lean manufacturing techniques. The thesis focuses on use of a labor capacity planning model, ...

Walker, David, M.B.A. Massachusetts Institute of Technology

2013-01-01T23:59:59.000Z

473

Design and Evaluation of Novel High Capacity Cathode Materials...  

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

the reaction is, in turn, intercalated into the negative electrode (i.e. graphite, graphene composites, intermetallics, Si-C composites, high-capacity TiO 2 (B bronze), TiO 2...

474

On the Capacity of Hybrid Wireless Networks with Opportunistic Routing  

Science Journals Connector (OSTI)

This paper studies the capacity of hybrid wireless networks with opportunistic routing (OR). ... algorithm to exploit high speed data transmissions in infrastructure network through base stations. We then develop...

Tan Le; Yong Liu

2009-01-01T23:59:59.000Z

475

MIMO capacity convergence in frequency-selective channels  

E-Print Network [OSTI]

The dependence of multi-antenna capacity on bandwidth is characterized empirically for narrowband, wideband and ultrawideband indoor channels using spatial and polar arrays. It is shown that both the mean and the outage ...

Malik, Wasim Q.

476

Creative agencies : a model for building community capacity  

E-Print Network [OSTI]

This research investigates how existing initiatives based in artistic and non-artistic disciplines build indigenous capacity for leadership in disenfranchised communities through the application of the creative process. ...

Ramaccia, Elizabeth M. (Elizabeth Marie)

2011-01-01T23:59:59.000Z

477

Spare Capacity (2003) and Peak Production in World Oil  

Science Journals Connector (OSTI)

Reliable estimates of minimum spare capacity for world oil production can be obtained by comparing production ... before and following the collapse of the Iraqi oil industry in March 2003. Spare production was .....

Alfred J. Cavallo

2004-03-01T23:59:59.000Z

478

Design and Evaluation of Novel High Capacity Cathode Materials...  

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

17johnson2011p.pdf More Documents & Publications Design and Evaluation of Novel High Capacity Cathode Materials Lithium Source For High Performance Li-ion Cells Lithium Source...

479

Solid-state hydrogen storage: Storage capacity, thermodynamics, and kinetics  

Science Journals Connector (OSTI)

Solid-state reversible hydrogen storage systems hold great promise for onboard applications. ... key criteria for a successful solid-state reversible storage material are high storage capacity, suitable thermodyn...

William Osborn; Tippawan Markmaitree; Leon L. Shaw; Ruiming Ren; Jianzhi Hu

2009-04-01T23:59:59.000Z

480

Capacity planning and admission control policies for intensive care units  

E-Print Network [OSTI]

Poor management of the patient flow in intensive care units (ICUs) causes service rejections and presents significant challenges from the standpoint of capacity planning and management in ICUs. This thesis reports on the ...

Chaiwanon, Wongsakorn

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.


481

Limits to the representation capacity of imaging in random media  

Science Journals Connector (OSTI)

The information capacity of an image in the atmosphere, ocean, or biological media does not grow indefinitely with increasing light power but has well defined limits. Here, the exact...

Belmonte, Aniceto

2013-01-01T23:59:59.000Z

482

,"New York Natural Gas Underground Storage Capacity (MMcf)"  

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

,,"(202) 586-8800",,,"1162014 3:07:28 PM" "Back to Contents","Data 1: New York Natural Gas Underground Storage Capacity (MMcf)" "Sourcekey","N5290NY2"...

483

,"New York Natural Gas Underground Storage Capacity (MMcf)"  

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

,,"(202) 586-8800",,,"1162014 3:07:27 PM" "Back to Contents","Data 1: New York Natural Gas Underground Storage Capacity (MMcf)" "Sourcekey","N5290NY2"...

484

High capacity stabilized complex hydrides for hydrogen storage  

DOE Patents [OSTI]

Complex hydrides based on Al(BH.sub.4).sub.3 are stabilized by the presence of one or more additional metal elements or organic adducts to provide high capacity hydrogen storage material.

Zidan, Ragaiy; Mohtadi, Rana F; Fewox, Christopher; Sivasubramanian, Premkumar

2014-11-11T23:59:59.000Z

485

Kazakhstan-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Kazakhstan-Enhancing Capacity for Low Emission Development Strategies Kazakhstan-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Kazakhstan-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy, Land Topics Low emission development planning, -LEDS Program Start 2010 Program End 2016 Country Kazakhstan Central Asia References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview "Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) is a

486

Enhancing Capacity for Low Emission Development Strategies (EC-LEDS):  

Open Energy Info (EERE)

Enhancing Capacity for Low Emission Development Strategies (EC-LEDS): Enhancing Capacity for Low Emission Development Strategies (EC-LEDS): Distributed Generation Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Enhancing Capacity for Low Emission Development Strategies (EC-LEDS): Distributed Generation Agency/Company /Organization: National Renewable Energy Laboratory Sector: Energy Topics: Low emission development planning, -LEDS Resource Type: Webinar Website: eeredev.nrel.gov/_proofs/video/2013_EC-LEDS/ Cost: Free References: Enhancing Capacity for Low Emission Development Strategies (EC-LEDS): Distributed Generation[1] Overview A webinar on distributed generation, presented by the National Renewable Energy Laboratory, with funding from the U.S. Agency for International Development. This webinar covers the basics of distributed generation, with an emphasis

487

Capacity of a Nonlinear Optical Channel With Finite Memory  

Science Journals Connector (OSTI)

The channel capacity of a nonlinear, dispersive fiber-optic link is revisited. To this end, the popular Gaussian noise (GN) model is extended with a parameter to account for the finite...

Agrell, Erik; Alvarado, Alex; Durisi, Giuseppe; Karlsson, Magnus

2014-01-01T23:59:59.000Z

488

Creative capacity building in post-conflict Uganda  

E-Print Network [OSTI]

Creative Capacity Building (CCB) is a methodology that emphasizes the ability of people living in poverty to create livelihood technologies, i.e., machines and tools that increase income, improve health and safety, decrease ...

Taha, Kofi A. (Kofi Abdul Malik)

2011-01-01T23:59:59.000Z

489

7-117 The claim of a heat pump designer regarding the COP of the heat pump is to be evaluated. Assumptions The heat pump operates steadily.  

E-Print Network [OSTI]

7-47 7-117 The claim of a heat pump designer regarding the COP of the heat pump is to be evaluated. Assumptions The heat pump operates steadily. HP Wnet,in QH QL TL TH Analysis The maximum heat pump coefficient of performance would occur if the heat pump were completely reversible, 5.7 K026K300 K300 COP maxHP, LH H TT

Bahrami, Majid

490

Review of technical justification of assumptions and methods used by the Environmental Protection Agency for estimating risks avoided by implementing MCLs for radionuclides  

SciTech Connect (OSTI)

The Environmental Protection Agency (EPA) has proposed regulations for allowable levels of radioactive material in drinking water (40 CFR Part 141, 56 FR 33050, July 18, 1991). This review examined the assumptions and methods used by EPA in calculating risks that would be avoided by implementing the proposed Maximum Contaminant Levels for uranium, radium, and radon. Proposed limits on gross alpha and beta-gamma emitters were not included in this review.

Morris, S.C.; Rowe, M.D.; Holtzman, S.; Meinhold, A.F.

1992-11-01T23:59:59.000Z

491

Heritability and localization of genes regulating individual variation of apoptosis capacity  

Science Journals Connector (OSTI)

...Heritability of apoptosis capacity and linkage to chromosomal...individual variation of apoptosis capacity were estimated using a variance...Linkage Analysis Routines (SOLAR). Two key findings emerged...proportion of variation in apoptosis capacity among individuals is due to...

Bao-Li Chang; Sarah D. Isaacs; Matthew J. Loza; Kathy E. Wiley; Amy Tolin; Elizabeth M. Gillanders; Wennuan Liu; Tao Li; Jishan Sun; Tamara Adams; Siqun L. Zheng; Patrick C. Walsh; Jeffrey M. Trent; William B. Isaacs; and Jianfeng Xu

2005-05-01T23:59:59.000Z

492

U.S. Geothermal Energy Capacity Grew 6% in 2009 | Department...  

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

Energy Capacity Grew 6% in 2009 U.S. Geothermal Energy Capacity Grew 6% in 2009 February 10, 2010 - 3:02pm Addthis Photo of a Geothermal photo plant. Geothermal energy capacity...

493

A review on environmental factors regulating arsenic methylation in humans  

SciTech Connect (OSTI)

Subjects exposed to arsenic show significant inter-individual variation in urinary patterns of arsenic metabolites but insignificant day-to-day intra-individual variation. The inter-individual variation in arsenic methylation can be partly responsible for the variation in susceptibility to arsenic toxicity. Wide inter-ethnic variation and family correlation in urinary arsenic profile suggest a genetic effect on arsenic metabolism. In this paper the environmental factors affecting arsenic metabolism are reviewed. Methylation capacity might reduce with increasing dosage of arsenic exposure. Furthermore, women, especially at pregnancy, have better methylation capacity than their men counterparts, probably due to the effect of estrogen. Children might have better methylation capacity than adults and age shows inconsistent relevance in adults. Smoking and alcohol consumption might be associated with a poorer methylation capacity. Nutritional status is important in the methylation capacity and folate may facilitate the methylation and excretion of arsenic. Besides, general health conditions and medications might influence the arsenic methylation capacity; and technical problems can cause biased estimates. The consumption of seafood, seaweed, rice and other food with high arsenic contents and the extent of cooking and arsenic-containing water used in food preparation may also interfere with the presentation of the urinary arsenic profile. Future studies are necessary to clarify the effects of the various arsenic metabolites including the trivalent methylated forms on the development of arsenic-induced human diseases with the consideration of the effects of confounding factors and the interactions with other effect modifiers.

Tseng, C.-H. [National Taiwan University College of Medicine, Taipei, Taiwan (China); Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan (China); Department of Medical Research and Development, National Taiwan University Hospital Yun-Lin Branch, Yun-Lin, Taiwan (China); School of Public Health, Taipei Medical University, Taipei, Taiwan (China); Division of Environmental Health and Occupational Medicine of the National Health Research Institutes, Taipei, Taiwan (China)], E-mail: ccktsh@ms6.hinet.net

2009-03-15T23:59:59.000Z

494

Year/PAD District Cokers Catalytic Crackers Hydrocrackers Capacity  

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

Cokers Catalytic Crackers Hydrocrackers Capacity Inputs Capacity Inputs Capacity Inputs Table 8. Capacity and Fresh Feed Input to Selected Downstream Units at U.S. Refineries, 2011 - 2013 (Barrels per Calendar Day) Reformers Capacity Inputs 2011 2,396,787 5,794,214 1,687,745 2,093,849 4,952,455 1,466,627 2,570,970 3,346,457 93,700 673,300 41,500 37,932 490,729 18,030 PADD I 188,389 266,950 373,897 1,176,972 254,000 350,063 1,017,616 223,751 PADD II 664,852 812,244 1,318,440 2,933,842 841,285 1,183,318 2,570,348 744,638 PADD III 1,243,427 1,629,967 80,350 185,800 28,200 63,362 158,192 18,214 PADD IV 96,649 120,190 530,400 824,300 522,760 459,175 715,570 461,995 PADD V 377,652 517,106 2012 2,499,293 5,611,191 1,706,540 2,173,336 4,901,284 1,528,708 2,614,571 3,246,874 74,900 489,300 20,000

495

Nano-scale Composite Hetero-structures: Novel High Capacity Reversible...  

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

Nano-scale Composite Hetero-structures: Novel High Capacity Reversible Anodes for Lithium-ion Batteries Nano-scale Composite Hetero-structures: Novel High Capacity Reversible...

496

Los Alamos Neutron Science Center gets capacity boost  

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

Neutron Science Center capacity boost Neutron Science Center capacity boost Los Alamos Neutron Science Center gets capacity boost The facility can simulate the effects of hundreds or thousands of years of cosmic-ray-induced neutrons in a single hour. December 2, 2010 Los Alamos National Laboratory sits on top of a once-remote mesa in northern New Mexico with the Jemez mountains as a backdrop to research and innovation covering multi-disciplines from bioscience, sustainable energy sources, to plasma physics and new materials. Los Alamos National Laboratory sits on top of a once-remote mesa in northern New Mexico with the Jemez mountains as a backdrop to research and innovation covering multi-disciplines from bioscience, sustainable energy sources, to plasma physics and new materials. Contact

497

Property:Geothermal/CapacityMwt | Open Energy Information  

Open Energy Info (EERE)

CapacityMwt CapacityMwt Jump to: navigation, search This is a property of type Number. Pages using the property "Geothermal/CapacityMwt" Showing 25 pages using this property. (previous 25) (next 25) 4 4 UR Guest Ranch Pool & Spa Low Temperature Geothermal Facility + 0.2 + A Ace Development Aquaculture Low Temperature Geothermal Facility + 3 + Agua Calientes Trailer Park Space Heating Low Temperature Geothermal Facility + 1.5 + Alive Polarity's Murrietta Hot Spring Pool & Spa Low Temperature Geothermal Facility + 0.3 + Americulture Aquaculture Low Temperature Geothermal Facility + 0.7 + Aq Dryers Agricultural Drying Low Temperature Geothermal Facility + 0.88 + Aqua Caliente County Park Pool & Spa Low Temperature Geothermal Facility + 0.09 +

498

Bangladesh-Enhancing Capacity for Low Emission Development Strategies  

Open Energy Info (EERE)

Bangladesh-Enhancing Capacity for Low Emission Development Strategies Bangladesh-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Jump to: navigation, search Name Bangladesh-Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) Agency/Company /Organization United States Agency for International Development, United States Environmental Protection Agency, United States Department of Energy, United States Department of Agriculture, United States Department of State Sector Climate, Energy Focus Area Renewable Energy, Wind Topics Low emission development planning, -LEDS, Resource assessment, Technology characterizations Country Bangladesh Southern Asia References EC-LEDS[1] Contents 1 Overview 2 Framework 3 Lessons Learned and Good Practices 4 Progress and Outcomes 5 Fact Sheet 6 References Overview

499

Indonesia-ECN Capacity building for energy policy formulation and  

Open Energy Info (EERE)

ECN Capacity building for energy policy formulation and ECN Capacity building for energy policy formulation and implementation of sustainable energy projects Jump to: navigation, search Name CASINDO: Capacity development and strengthening for energy policy formulation and implementation of Sustainable energy projects in Indonesia Agency/Company /Organization Energy Research Centre of the Netherlands Sector Energy Focus Area Energy Efficiency Topics Policies/deployment programs Resource Type Software/modeling tools, Workshop, Publications, Guide/manual, Training materials Website http://www.ecn.nl/en/ Program Start 2009 Program End 2011 Country Indonesia South-Eastern Asia References ECN Policy Studies[1] CASINDO website[2] A key component of the political and economic reforms that are currently being implemented in Indonesia is the devolution of responsibilities for

500

Capacity and Energy Payments to Small Power Producers and Cogenerators  

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

Capacity and Energy Payments to Small Power Producers and 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 PURPA Docket (Georgia) < Back Eligibility Commercial Developer Fuel Distributor General Public/Consumer Industrial Installer/Contractor Investor-Owned Utility Municipal/Public Utility Retail Supplier Rural Electric Cooperative Systems Integrator Utility Savings Category Alternative Fuel Vehicles Hydrogen & Fuel Cells Buying & Making Electricity Water Home Weatherization Solar Wind Program Info State Georgia Program Type Green Power Purchasing Renewables Portfolio Standards and Goals Docket No. 4822 was enacted by the Georgia Public Service Commission in accordance with The Public Utility Regulatory Policies Act of 1978 (PURPA)