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U.S. Building-Sector Energy Efficiency Potential

Description: This paper presents an estimate of the potential for energy efficiency improvements in the U.S. building sector by 2030. The analysis uses the Energy Information Administration's AEO 2007 Reference Case as a business-as-usual (BAU) scenario, and applies percentage savings estimates by end use drawn from several prior efficiency potential studies. These prior studies include the U.S. Department of Energy's Scenarios for a Clean Energy Future (CEF) study and a recent study of natural gas savings potential in New York state. For a few end uses for which savings estimates are not readily available, the LBNL study team compiled technical data to estimate savings percentages and costs of conserved energy. The analysis shows that for electricity use in buildings, approximately one-third of the BAU consumption can be saved at a cost of conserved energy of 2.7 cents/kWh (all values in 2007 dollars), while for natural gas approximately the same percentage savings is possible at a cost of between 2.5 and 6.9 $/million Btu. This cost-effective level of savings results in national annual energy bill savings in 2030 of nearly $170 billion. To achieve these savings, the cumulative capital investment needed between 2010 and 2030 is about $440 billion, which translates to a 2-1/2 year simple payback period, or savings over the life of the measures that are nearly 3.5 times larger than the investment required (i.e., a benefit-cost ratio of 3.5).
Date: September 30, 2008
Creator: Brown, Rich; Borgeson, Sam; Koomey, Jon & Biermayer, Peter
Partner: UNT Libraries Government Documents Department

Towards a Very Low Energy Building Stock: Modeling the U.S. Commercial Building Sector to Support Policy and Innovation Planning

Description: This paper describes the origin, structure and continuing development of a model of time varying energy consumption in the US commercial building stock. The model is based on a flexible structure that disaggregates the stock into various categories (e.g. by building type, climate, vintage and life-cycle stage) and assigns attributes to each of these (e.g. floor area and energy use intensity by fuel type and end use), based on historical data and user-defined scenarios for future projections. In addition to supporting the interactive exploration of building stock dynamics, the model has been used to study the likely outcomes of specific policy and innovation scenarios targeting very low future energy consumption in the building stock. Model use has highlighted the scale of the challenge of meeting targets stated by various government and professional bodies, and the importance of considering both new construction and existing buildings.
Date: July 1, 2009
Creator: Coffey, Brian; Borgeson, Sam; Selkowitz, Stephen; Apte, Josh; Mathew, Paul & Haves, Philip
Partner: UNT Libraries Government Documents Department

Japan's Long-term Energy Demand and Supply Scenario to 2050 - Estimation for the Potential of Massive CO2 Mitigation

Description: In this analysis, the authors projected Japan's energy demand/supply and energy-related CO{sub 2} emissions to 2050. Their analysis of various scenarios indicated that Japan's CO{sub 2} emissions in 2050 could be potentially reduced by 26-58% from the current level (FY 2005). These results suggest that Japan could set a CO{sub 2} emission reduction target for 2050 at between 30% and 60%. In order to reduce CO{sub 2} emissions by 60% in 2050 from the present level, Japan will have to strongly promote energy conservation at the same pace as an annual rate of 1.9% after the oil crises (to cut primary energy demand per GDP (TPES/GDP) in 2050 by 60% from 2005) and expand the share of non-fossil energy sources in total primary energy supply in 2050 to 50% (to reduce CO{sub 2} emissions per primary energy demand (CO{sub 2}/TPES) in 2050 by 40% from 2005). Concerning power generation mix in 2050, nuclear power will account for 60%, solar and other renewable energy sources for 20%, hydro power for 10% and fossil-fired generation for 10%, indicating substantial shift away from fossil fuel in electric power supply. Among the mitigation measures in the case of reducing CO{sub 2} emissions by 60% in 2050, energy conservation will make the greatest contribution to the emission reduction, being followed by solar power, nuclear power and other renewable energy sources. In order to realize this massive CO{sub 2} abatement, however, Japan will have to overcome technological and economic challenges including the large-scale deployment of nuclear power and renewable technologies.
Date: September 1, 2009
Creator: Komiyama, Ryoichi; Marnay, Chris; Stadler, Michael; Lai, Judy; Borgeson, Sam; Coffey, Brian et al.
Partner: UNT Libraries Government Documents Department

A Buildings Module for the Stochastic Energy Deployment System

Description: The U.S. Department of Energy (USDOE) is building a new long-range (to 2050) forecasting model for use in budgetary and management applications called the Stochastic Energy Deployment System (SEDS), which explicitly incorporates uncertainty through its development within the Analytica(R) platform of Lumina Decision Systems. SEDS is designed to be a fast running (a few minutes), user-friendly model that analysts can readily run and modify in its entirety through a visual programming interface. Lawrence Berkeley National Laboratory is responsible for implementing the SEDS Buildings Module. The initial Lite version of the module is complete and integrated with a shared code library for modeling demand-side technology choice developed by the National Renewable Energy Laboratory (NREL) and Lumina. The module covers both commercial and residential buildings at the U.S. national level using an econometric forecast of floorspace requirement and a model of building stock turnover as the basis for forecasting overall demand for building services. Although the module is fundamentally an engineering-economic model with technology adoption decisions based on cost and energy performance characteristics of competing technologies, it differs from standard energy forecasting models by including considerations of passive building systems, interactions between technologies (such as internal heat gains), and on-site power generation.
Date: May 15, 2008
Creator: Lacommare, Kristina S H; Marnay, Chris; Stadler, Michael; Borgeson, Sam; Coffey, Brian; Komiyama, Ryoichi et al.
Partner: UNT Libraries Government Documents Department