National Library of Energy BETA

Sample records for markal unfccc-global map-annex

  1. UNFCCC-Global Map-Annex 1 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop IncIowa (Utility Company) Jump to:TucsonLLC Jump to:UDIGEFproject(RedirectedGlobal

  2. Market Allocation (MARKAL) Model

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Financing Tool Fits the BillDepartmentSites |Strides to BoostMARket ALlocation (MARKAL) Model

  3. Integrated MARKAL-EFOM System (TIMES) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History View NewTexas: Energy ResourcesOrder at 8, 13 (Vt.InfinifuelInova EnergyIntanFuelMARKAL-EFOM System

  4. MARKet ALlocation (MARKAL) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop Inc Jump to: navigation, search Name: Lyon-Lincoln Electric CoopMAP Jump to:MARKet

  5. MARKet ALlocation (MARKAL) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsource HistoryScenarios Towards 2050 Jump to: navigation,Lyons, Colorado:M38MARKet

  6. Energy Technology Systems Analysis Program (MARKAL) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, AlabamaETEC GmbH JumpEllenville, NewLtd EILEnergy DatadataCentreCo

  7. Appendix A: GPRA08 benefits estimates: NEMS and MARKAL Model Baseline Cases

    SciTech Connect (OSTI)

    None, None

    2009-01-18

    Document summarizes the results of the benefits analysis of EERE’s programs, as described in the FY 2008 Budget Request. EERE estimates benefits for its overall portfolio and nine Research, Development, Demonstration, and Deployment (RD3) programs.

  8. Chapter 5: Long-term benefits analysis of EERE's Programs

    SciTech Connect (OSTI)

    None, None

    2009-01-18

    This chapter provides an overview of the modeling approach used in MARKAL-GPRA05 to evaluate the benefits of EERE R&D programs and technologies. The program benefits reported in this section result from comparisons of each Program Case to the Baseline Case, as modeled in MARKAL-GPRA05.

  9. An Integrated Assessment of the Impacts of Hydrogen Economy on Transportation, Energy Use, and Air Emissions

    E-Print Network [OSTI]

    Yeh, Sonia; Loughlin, Daniel H.; Shay, Carol; Gage, Cynthia

    2007-01-01

    environment,[ Annu. Rev. Energy Environ. , vol. 24, pp. 545–technological learning in the energy-systems markal model,[Int. J. Global Energy Issues, vol. 17, no. 3, pp. 189–213,

  10. EERE Portfolio: Primary Benefits Metrics for FY09

    SciTech Connect (OSTI)

    [EERE

    2011-11-17

    This collection of data tables shows the benefits metrics related to energy security, environmental impacts, and economic impacts for both the entire EERE portfolio of renewable energy technologies as well as the individual technologies. Data are presented for the years 2015, 2020, 2030, and 2050, for both the NEMS and MARKAL models.

  11. Projected Benefits of EERE’s Portfolio – FY 2010

    SciTech Connect (OSTI)

    [EERE

    2011-11-17

    This collection of data tables and charts shows the benefits metrics related to energy security, environmental impacts, and economic impacts for the entire EERE portfolio of renewable energy technologies. Data are presented for the years 2015, 2020, 2030, and 2050, for both the NEMS and MARKAL models.

  12. O.R. Applications Methodological contrasts in costing greenhouse gas

    E-Print Network [OSTI]

    : Optimization and simulation modeling of micro-economic effects in Canada Mark Jaccard a,*, Richard Loulou b , Amit Kanudia b , John Nyboer a , Alison Bailie a , Maryse Labriet b a Energy and Materials Research other input assumptions are the same. MARKAL is a well-known optimization model of the energy

  13. Projected Benefits of EERE's Portfolio - FY 2011

    SciTech Connect (OSTI)

    none,

    2011-11-17

    This collection of data tables and charts shows the benefits metrics related to energy security, environmental impacts, and economic impacts for the entire EERE portfolio of renewable energy technologies. Data are presented for the years 2015, 2020, 2030, and 2050, for both the NEMS and MARKAL models.

  14. Program Benefits of Individual EERE Programs – FY 2010

    SciTech Connect (OSTI)

    none,

    2011-11-01

    This collection of data tables shows the benefits metrics related to energy security, environmental impacts, and economic impacts for individual renewable energy technologies in the EERE portfolio. Data are presented for the years 2015, 2020, 2030, and 2050, for both the NEMS and MARKAL models.

  15. Projected Benefits of Individual EERE Programs (primary and secondary)

    SciTech Connect (OSTI)

    none,

    2011-11-01

    This collection of data tables shows the benefits metrics related to energy security, environmental impacts, and economic impacts for individual renewable energy technologies in the EERE portfolio. Data are presented for the years 2015, 2020, 2030, and 2050, for both the NEMS and MARKAL models.

  16. New York City Energy-Water Integrated Planning: A Pilot Study

    SciTech Connect (OSTI)

    Bhatt,V.; Crosson, K. M.; Horak, W.; Reisman, A.

    2008-12-16

    The New York City Energy-Water Integrated Planning Pilot Study is one of several projects funded by Sandia National Laboratories under the U.S. Department of Energy Energy-Water Nexus Program. These projects are intended to clarify some key issues and research needs identified during the Energy-Water Nexus Roadmapping activities. The objectives of the New York City Pilot Project are twofold: to identify energy-water nexus issues in an established urban area in conjunction with a group of key stakeholders and to define and apply an integrated energy and water decision support tool, as proof-of-concept, to one or more of these issues. During the course of this study, the Brookhaven National Laboratory project team worked very closely with members of a Pilot Project Steering Committee. The Steering Committee members brought a breadth of experience across the energy, water and climate disciplines, and all are well versed in the particular issues faced by an urban environment, and by New York City in particular. The first task was to identify energy-water issues of importance to New York City. This exercise was followed by discussion of the qualities and capabilities that an ideal decision support tool should display to address these issues. The decision was made to start with an existing energy model, the New York City version of the MARKAL model, developed originally at BNL and now used globally by many groups for energy analysis. MARKAL has the virtue of being well-vetted, transparent, and capable of calculating 'material' flows, such as water use by the energy system and energy requirements of water technology. The Steering Committee members defined five scenarios of interest, representing a broad spectrum of New York City energy-water issues. Brookhaven National Laboratory researchers developed a model framework (Water-MARKAL) at the desired level of detail to address the scenarios, and then attempted to gather the New York City-specific information required to analyze the scenarios using Water-MARKAL. This report describes the successes and challenges of defining and demonstrating the decision tool, Water-MARKAL. The issues that the stakeholders perceive for New York City are listed and the difficulties in gathering required information for Water-MARKAL to analyze these issues at the desired level of detail are described.

  17. Chapter 3: FY 2005 benefits estimates

    SciTech Connect (OSTI)

    None, None

    2009-01-18

    The Office of Energy Efficiency and Renewable Energy (EERE) estimates expected benefits for its overall portfolio and for each of its 11 programs. Benefits for the FY 2005 budget request are estimated for the midterm (2010-2025) and long term (2030-2050). Two separate models suited to these periods are employed—NEMS-GPRA05 for the midterm and MARKAL-GPRA05 for the long term.

  18. Chapter 3: FY 2006 benefits estimates

    SciTech Connect (OSTI)

    None, None

    2009-01-18

    The Office of Energy Efficiency and Renewable Energy (EERE) estimates expected benefits for its overall portfolio and for each of its 11 programs. Benefits for the FY 2006 budget request are estimated for the midterm (2010-2025) and long term (2030-2050). Two separate models suited to these periods are employed–NEMS-GPRA06 for the midterm and MARKAL-GPRA06 for the long term.

  19. Market Engagement Overview - 2015 BTO Peer Review | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Financing Tool Fits the BillDepartmentSites |Strides to BoostMARket ALlocation (MARKAL) ModelMarket

  20. Market Transformation Fact Sheet

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Financing Tool Fits the BillDepartmentSites |Strides to BoostMARket ALlocation (MARKAL) ModelMarket

  1. Integrated Model to Access the Global Environment | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History View NewTexas: Energy ResourcesOrder at 8, 13 (Vt.InfinifuelInova EnergyIntanFuelMARKAL-EFOM

  2. Integrated Sensing Systems Inc ISSYS | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History View NewTexas: Energy ResourcesOrder at 8, 13 (Vt.InfinifuelInova EnergyIntanFuelMARKAL-EFOMSensing

  3. Energy development and CO{sub 2} emissions in China

    SciTech Connect (OSTI)

    Xiaolin Xi [Carnegie-Mellon Univ., Pittsburgh, PA (United States)

    1993-03-01

    The objective of this research is to provide a better understanding of future Chinese energy development and CO{sub 2} emissions from burning fossil fuels. This study examines the current Chinese energy system, estimates CO{sub 2} emissions from burning fossil fuels and projects future energy use and resulting CO{sub 2} emissions up to the year of 2050. Based on the results of the study, development strategies are proposed and policy implications are explored. This study first develops a Base scenario projection of the Chinese energy development based upon a sectoral analysis. The Base scenario represents a likely situation of future development, but many alternatives are possible. To explore this range of alternatives, a systematic uncertainty analysis is performed. The Base scenario also represents an extrapolation of current policies and social and economic trends. As such, it is not necessarily the economically optimal future course for Chinese energy development. To explore this issue, an optimization analysis is performed. For further understanding of developing Chinese energy system and reducing CO{sub 2} emissions, a Chinese energy system model with 84 supply and demand technologies has been constructed in MARKAL, a computer LP optimization program for energy systems. Using this model, various technological options and economic aspects of energy development and CO{sub 2} emissions reduction in China during the 1985-2020 period are examined.

  4. Appendix I - GPRA06 solar energy technologies program documentation

    SciTech Connect (OSTI)

    None, None

    2009-01-18

    This appendix provides detailed information on the assumptions and methods employed to estimate the benefits of EERE’s Solar Energy Technologies Program. The benefits analysis for the Solar Program utilized both NEMS and MARKAL as the analytical tools for estimating the Program’s benefits. As will be discussed below, a number of assumptions and structural modifications to the models were made in order to represent the suite of solar technologies funded by the program as accurately as possible (Photovoltaics, Concentrating Solar Power and Solar Water Heating). Many of the assumptions used in the FY06 analysis are the same as or similar to those employed in the FY05 analysis; however, two key changes are important to highlight up-front. First, the AEO2004 analysis used a new set of reference case assumptions with respect to photovoltaic technology cost reductions. The new sets of reference case assumptions are very similar to the Solar Program’s targets for PV. This shift in assumptions necessitated developing a new approach for estimating the baseline (i.e., no program) input assumptions for PV. Second, the FY06 analysis included CSP technology benefits – CSP benefits were not included in the FY05 analysis.

  5. Evaluation of carbon dioxide emission control strategies in New York State

    SciTech Connect (OSTI)

    Morris, S.C.; Lee, J.; Goldstein, G.; Hill, D.

    1992-01-01

    A MARKAL model was developed for the State of New York. It represents the state's energy system as a set of typical technologies for generating, converting, and using energy as it evolves over a 45-year period. NYMARKAL was applied here in demonstration analyses to explore strategies to reduce CO{sub 2} emissions. NYMARKAL was installed at the State Energy Office and in the Offices of the New York Power Pool. Staff members from both organizations and other state agencies were trained in its use. Example scenarios showed that it is more difficult and more expensive to reduce carbon emissions in New York State than in the United States as a whole. Were a common carbon tax instituted, it would have less effect in New York and most carbon emissions reduction would take place elsewhere in the country where it is more cost-effective. Alternatively, were all states required to reduce CO{sub 2} emission an equal percentage (say by 20%), the cost per unit emissions reduction to New York would be much greater than in the rest of the country.

  6. Evaluation of carbon dioxide emission control strategies in New York State. Final report, 1990--1991

    SciTech Connect (OSTI)

    Morris, S.C.; Lee, J.; Goldstein, G.; Hill, D.

    1992-01-01

    A MARKAL model was developed for the State of New York. It represents the state`s energy system as a set of typical technologies for generating, converting, and using energy as it evolves over a 45-year period. NYMARKAL was applied here in demonstration analyses to explore strategies to reduce CO{sub 2} emissions. NYMARKAL was installed at the State Energy Office and in the Offices of the New York Power Pool. Staff members from both organizations and other state agencies were trained in its use. Example scenarios showed that it is more difficult and more expensive to reduce carbon emissions in New York State than in the United States as a whole. Were a common carbon tax instituted, it would have less effect in New York and most carbon emissions reduction would take place elsewhere in the country where it is more cost-effective. Alternatively, were all states required to reduce CO{sub 2} emission an equal percentage (say by 20%), the cost per unit emissions reduction to New York would be much greater than in the rest of the country.

  7. World Biofuels Study

    SciTech Connect (OSTI)

    Alfstad,T.

    2008-10-01

    This report forms part of a project entitled 'World Biofuels Study'. The objective is to study world biofuel markets and to examine the possible contribution that biofuel imports could make to help meet the Renewable Fuel Standard (RFS) of the Energy Independence and Security Act of 2007 (EISA). The study was sponsored by the Biomass Program of the Assistant Secretary for Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy. It is a collaborative effort among the Office of Policy and International Affairs (PI), Department of Energy and Oak Ridge National Laboratory (ORNL), National Renewable Energy Laboratory (NREL) and Brookhaven National Laboratory (BNL). The project consisted of three main components: (1) Assessment of the resource potential for biofuel feedstocks such as sugarcane, grains, soybean, palm oil and lignocellulosic crops and development of supply curves (ORNL). (2) Assessment of the cost and performance of biofuel production technologies (NREL). (3) Scenario-based analysis of world biofuel markets using the ETP global energy model with data developed in the first parts of the study (BNL). This report covers the modeling and analysis part of the project conducted by BNL in cooperation with PI. The Energy Technology Perspectives (ETP) energy system model was used as the analytical tool for this study. ETP is a 15 region global model designed using the MARKAL framework. MARKAL-based models are partial equilibrium models that incorporate a description of the physical energy system and provide a bottom-up approach to study the entire energy system. ETP was updated for this study with biomass resource data and biofuel production technology cost and performance data developed by ORNL and NREL under Tasks 1 and 2 of this project. Many countries around the world are embarking on ambitious biofuel policies through renewable fuel standards and economic incentives. As a result, the global biofuel demand is expected to grow very rapidly over the next two decades, provided policymakers stay the course with their policy goals. This project relied on a scenario-based analysis to study global biofuel markets. Scenarios were designed to evaluate the impact of different policy proposals and market conditions. World biofuel supply for selected scenarios is shown in Figure 1. The reference case total biofuel production increases from 12 billion gallons of ethanol equivalent in 2005 to 54 billion gallons in 2020 and 83 billion gallons in 2030. The scenarios analyzed show volumes ranging from 46 to 64 billion gallons in 2020, and from about 72 to about 100 billion gallons in 2030. The highest production worldwide occurs in the scenario with high feedstock availability combined with high oil prices and more rapid improvements in cellulosic biofuel conversion technologies. The lowest global production is found in the scenario with low feedstock availability, low oil prices and slower technology progress.

  8. World Biofuels Production Potential Understanding the Challenges to Meeting the U.S. Renewable Fuel Standard

    SciTech Connect (OSTI)

    Sastri, B.; Lee, A.

    2008-09-15

    This study by the U.S. Department of Energy (DOE) estimates the worldwide potential to produce biofuels including biofuels for export. It was undertaken to improve our understanding of the potential for imported biofuels to satisfy the requirements of Title II of the 2007 Energy Independence and Security Act (EISA) in the coming decades. Many other countries biofuels production and policies are expanding as rapidly as ours. Therefore, we modeled a detailed and up-to-date representation of the amount of biofuel feedstocks that are being and can be grown, current and future biofuels production capacity, and other factors relevant to the economic competitiveness of worldwide biofuels production, use, and trade. The Oak Ridge National Laboratory (ORNL) identified and prepared feedstock data for countries that were likely to be significant exporters of biofuels to the U.S. The National Renewable Energy Laboratory (NREL) calculated conversion costs by conducting material flow analyses and technology assessments on biofuels technologies. Brookhaven National Laboratory (BNL) integrated the country specific feedstock estimates and conversion costs into the global Energy Technology Perspectives (ETP) MARKAL (MARKet ALlocation) model. The model uses least-cost optimization to project the future state of the global energy system in five year increments. World biofuels production was assessed over the 2010 to 2030 timeframe using scenarios covering a range U.S. policies (tax credits, tariffs, and regulations), as well as oil prices, feedstock availability, and a global CO{sub 2} price. All scenarios include the full implementation of existing U.S. and selected other countries biofuels policies (Table 4). For the U.S., the most important policy is the EISA Title II Renewable Fuel Standard (RFS). It progressively increases the required volumes of renewable fuel used in motor vehicles (Appendix B). The RFS requires 36 billion (B) gallons (gal) per year of renewable fuels by 2022. Within the mandate, amounts of advanced biofuels, including biomass-based diesel and cellulosic biofuels, are required beginning in 2009. Imported renewable fuels are also eligible for the RFS. Another key U.S. policy is the $1.01 per gal tax credit for producers of cellulosic biofuels enacted as part of the 2008 Farm Bill. This credit, along with the DOE's research, development and demonstration (RD&D) programs, are assumed to enable the rapid expansion of U.S. and global cellulosic biofuels production needed for the U.S. to approach the 2022 RFS goal. While the Environmental Protection Agency (EPA) has yet to issue RFS rules to determine which fuels would meet the greenhouse gas (GHG) reduction and land use restrictions specified in EISA, we assume that cellulosic ethanol, biomass-to-liquid fuels (BTL), sugar-derived ethanol, and fatty acid methyl ester biodiesel would all meet the EISA advanced biofuel requirements. We also assume that enough U.S. corn ethanol would meet EISA's biofuel requirements or otherwise be grandfathered under EISA to reach 15 B gal per year.