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Thursday, November 19, 2020 | History

2 edition of Sensivity case results and assumptions for the annual energy outlook 1986 found in the catalog.

Sensivity case results and assumptions for the annual energy outlook 1986

Sensivity case results and assumptions for the annual energy outlook 1986

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Published by Office of Energy Markets and End Use, Energy Information Administration in Washington, DC .
Written in English

    Subjects:
  • Power resources -- United States -- Forecasting.,
  • Fuel -- United States -- Forecasting.

  • Edition Notes

    Other titlesService report ... sensitivity case results and assumptions for the annual energy outlook 1986.
    SeriesService report, EIA service report -- SR/EAFD/87-06.
    ContributionsUnited States. Office of Energy Markets and End Use.
    The Physical Object
    FormatMicroform
    Paginationiii, 32 p.
    Number of Pages32
    ID Numbers
    Open LibraryOL14686463M

    the MAR assumption. We begin by revisiting the results of Angrist et al. () regarding changes across Decennial Censuses in the quantile speci c returns to schooling. Weekly earnings informa-tion is missing for roughly a quarter of the observations in their study, suggesting the results may be sensitive to small deviations from ignorability. An annual supplement analyzes previous forecast errors, compares recent projections by other forecasters, and discusses current topics of the short-term energy markets (see Short-Term Energy Outlook: Annual Supplement, DOE/EIA). The principal users of the Outlook are managers and energy analysts in private industry and government.


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Sensivity case results and assumptions for the annual energy outlook 1986 Download PDF EPUB FB2

Get this from a library. Sensitivity case results and assumptions for the annual energy outlook [United States. Office of Energy Markets and End Use.;]. Sensivity case results and assumptions for the annual energy outlook 1986 book for the Annual energy outlook (DLC) (OCoLC) Microfiche version, Assumptions for the Annual energy outlook (DLC)sn (OCoLC) Material Type: Document, Government publication, National government publication, Internet resource: Document Type: Internet Resource, Computer File, Journal / Magazine.

a key assumption underlying the projections, and it provides a detailed discussion of the sensitivity of results across. Annual Energy Outlook (AEO) cases. The outlook for domestic crude oil and natural gas production is highly sensitive to resource and technology Size: KB.

Assessing Assumptions •In randomized experiments, both assumptions are valid. •In observational studies, they are not guaranteed, in fact, most time they are at the best only approximately true. •It is important to assess the plausibility of these assumptions; when this is not feasible, one should check how sensitive (or robust) the results.

Three alternative assumptions are specified for each of these two factors, with the reference case using the midlevel assumption for each. Economic Growth - In the reference case, real GDP grows at an average annual rate of percent from throughsupported by a percent per year growth in productivity in nonfarm business and a 1.

Regarding the National Energy Modeling System (NEMS) used by the U.S. Energy Information Administration (EIA) to produce the Annual Energy Outlook (e.g., EIA, a), the EIA notes that “[b]ecause of the complexity of NEMS, and the relatively high cost of the proprietary software, NEMS is not widely used outside of the Department of Energy”.

()) or considered the “worst case” bounds on population moments that result when all assumptions regarding the missing data process are abandoned (Manski (), Manski ()). Neither approach has garnered much popularity.1 It is typically quite difficult to find variables that shift the probability of nonresponse but are uncorrelated.

generate the projections in the Annual Energy Outlook [1] (AEO), 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. generate the projections in the Annual Energy Outlook [] (AEO), 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.

The AEO is now on a biennial schedule with a full report every other year. Assumptions to the Annual Energy Outlook Table Generating Capacity Types Represented In the Electricity Market Module capa~'~.

",Y:', ExIsting coal sleam plants t High Sulfur Pulverized Coa1 with Wet Flue Gas Desulfurization Advanced Coal -Integrated Coal Gasification Combined Cyde Advanced Coal with carbon sequestration. Annual Energy Outlook scenarios are projections out to the year and these results are extrapolated to for use in the ReEDS model.

A separate supply curve was developed for each year to represent changes in projected supply and demand interactions as estimated in the multiple Annual Energy Outlook scenarios. The natural gas price scenarios are based on the Annual Energy Outlook High and Low Oil and Gas Resource cases (EIA ), and reflect prices that are 44% above or 15% below central-case assumptions in based on the US Energy Information Administration’s (EIA’s) Annual Energy Outlook.

The results of our analysis using the E3 model to evaluate carbon taxes, cap-and-trade programs, clean energy Considering a Carbon Tax–Gasoline Tax Swap: Projected Energy-Related US CO 2 Emissions Reductions under the MARKET CHOICE Act.

The analyses and market overview presented herein are necessarily based on assumptions with respect to conditions which may exist or events which may occur in the future. Please appreciate that actual future results may differ, perhaps materially, from those indicated.

Toward a Clean Energy Future: A Strategic Outlook NYSERDA. Annual energy generation from all natural gas plants does not exceed Annual Energy Outlook Technical Report. van Heerden R and Senalta M Formal Comments on the Integration Resource Plan Update: Assumptions, Base Case Results, and Observations Technical Report C SIR-EC-ESPO-REP-DOEA Rev   in the Annual Energy Outlook [1] (AEO), 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.

Often this assumption is not realistic, and re-searchers are concerned about the robustness of their results to departures from it. One approach (e.g., Charles Manski, ) is to entirely drop the exogeneity assumption and investigate what can be learned about treatment effects without it.

With unbounded outcomes, and in the ab. 1 and Central) and for two technology development time horizons (Current and Future). Table 1 shows H2A input model assumptions for the technology development year2 (either year or ), the H 2 production plant start date (either year or ),3 H 2 production rates (in units of kilograms (kg) of H 2 per day), and plant lifetime (in years) for the four cases.

The results indicate that annual energy and peak design loads are more sensitive to internal loads, window system, temperature set-points, and HVAC equipment efficiency. Demanuele et al. [22] used differential sensitivity analysis to determine the key factors affecting the total energy use in a.

The range between the base-case and the low case was used to define a high case value of equal range from the base-case, at kWh per GB transferred. Overall embodied energy and CO 2 (e) intensity across the core and access network is estimated at MJ and 30 g CO 2 (e) per GB transferred, again based on literature values utilized in the.

In the Annual Energy Outlook (AEO) Reference case, the share of renewables in the U.S. electricity generation mix increases from 19% in to 38% in Most of the growth in renewable electricity generation is attributed to wind and solar, which.

The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and scientists. Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis.

ii Energy Information Administration / Annual Energy Outlook Preface Projections in AEO are not statements of what will happen but of what might happen, given the assumptions and methodologies used.

The projections are business-as-usual trend estimates, given known technology and technological and demographic trends. Annual Energy Outlook With Projections to December based on results of EIA’s National Energy Modeling System (NEMS).

This report begins with a summary of the reference case, followed by a discussion of the legislative assumptions and evolving legislative and regulatory issues. “Issues in Focus” discusses emerging energy.

A sensitivity test version of the latest TAG data book, containing the updated OBR projections and revised fleet assumptions, has been made available to. energy efficiency and a slow and extended economic recovery 7 0 20 40 60 80 U.S.

primary energy consumption quadrillion Btu Source: EIA, Annual Energy Outlook History Projections 36% 32% 20% 19% 26% 8% 8% 9% 1% 28% 11% 2% Shares of total U.S. energy. The results show that nationwide, roof racks are responsible for ‰ of light duty vehicle fuel consumption incorresponding to million gallons of gasoline per year.

Sensitivity analyses show that results are most sensitive to the fraction of vehicles with installed roof racks but carrying no equipment. Assessment of (b) can be used to identify observations that are outliers in the observed-data distribution or that may be driving weakly identified parts of an MNAR model (Molenberghs and Kenward, ).

This chapter focuses on (c), sensitivity to assumptions about the missing data mechanism. However, CNG vehicle ownership costs could be higher than those of the gasoline high-efficiency ICEV if, for example, fuel prices from the Low Oil Price Scenario in the Annual Energy Outlook are used.

The impacts of other variables are examined in the sensitivity analysis in the supplementary information. The Annual Energy Outlook (AEO97) presents midterm forecasts of energy supply, demand, and prices through prepared by the Energy Information Administration (EIA).

These projections are based on results of EIA`s National Energy Modeling System (NEMS). This report begins with a summary of the reference case, followed by a discussion of the legislative assumptions and evolving.

Overall, our data source for this analysis was the EIA Annual Energy Outlook (AEO). The base case values for the projected LDV travel demand (VMT) and future annual sales were all taken from the AEO. Also, the projected base case EV sales and fleet stock from AEO and VISION were used in modeling the fleet turnover [16, 54].

Sensitivity analysis examines how changes in the assumptions of an economic model affect its predictions. By definition, an economic model is a simplified mathematical representation of a complex interaction of economic variables, and as such is built upon certain assumptions.

These assumptions, which include the structural specification of the model and the values of its. Results –Existing Homes, Per Home Base Case Annual NG Emissions tCO2e/yr Type of home: Existing Homes Electrification scenario: Scenario A: Full Electric Heat Scenario B: Hybrid ASHP + gas Scenario C: Hybrid CC-ASHP + gas Capital Costs (delta vs NG Base Case) $4, $5, $9, Annual Energy Costs (delta vs NG Base Case) $/yr $/yr.

The Benefits and Costs of the Clean Air Act fron to CHAPTER 6 – ECOLOGICAL AND OTHER WELFARE BENEFITS Overview of Approach Qualitative Characterization of Effects Distribution of Air Pollutants in Sensitive Ecosystems of the United States Quantified Results: National Estimates Uncertainty in Ecological and Other Welfare Benefits   Results are most sensitive to vehicle sales prices, fuel prices, and technological developments relating to range and charging technologies.

Battery cost projections based upon the US Energy Information Administrations “Annual Energy Outlook” reference case From these assumptions, the annual ICE car O&M costs were calculated by car.

Nilsson, L.J. () Energy intensity trends in 31 industrial and developing countries –, Energy—The International Journal– Google Scholar Powell, W.W. () Expanding the scope of institutional analysis, in W.W.

Powell, P.J. Di Maggio (eds.), The New Institutionalism in Organizational Analysis, University of. A rental farm will be used as a representative case study for other poultry producers.

Sensitivity analysis will be used to determine potential profitability when input costs and point of sale values are varied.

Access quality crowd-sourced study materials tagged to courses at universities all over the world and get homework help from our tutors when you need it. This case excludes effects of tax reform, tax credits, technology-specific tariffs, and changing interest rates over time.

The R&D + Market LCOE case adds to these the financial assumptions (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform, tax credits, and tariffs. The study and results were documented in Section of the Transmission Plan.

The study was provided on an information-only basis and the results are dependent on the assumptions made in the study. The methodology, assumptions, and results of the study are set out in this section.

Initial Base Case in Analysis. A sensitivity analysis was performed to determine how variation of oil and gas prices affects the Net Present Value of sustainable energy technologies. The economic analysis was developed to determine if variations in fuel prices would alter a recommendation in a prior study of sustainable energy technology.•In light of Q1 differential outlook, we are planning a base spending platform of $65MM in H1 and the ability to toggle between $55 and $95MM for H2 •Anticipate delivering self-funded Cardium light oil growth of 10 percent or more; percent for the full portfolio in the base case.

Examine sensitivity of key learnings to assumptions on future natural gas prices F. Low Technology Costs Explore changes in cost and portfolio composition under assumptions of lower costs for solar, wind and energy storage G. California % RPS Explore implicationsof California clean energy policy on.