• Energy Research

Overview and Intended Use Cases These scenarios establish a range of futures for U.S. buildings sector energy use and CO2 emissions to 2050 using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO). Scout benchmark scenario data are suitable for the following example use cases: setting high-level policy goals for the U.S. buildings sector to 2050 (e.g., X% building CO2 emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050); exploring the effects of key dynamics driving U.S. buildings sector energy and CO2 emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market); determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO2 emissions reductions to 2050 under a given set of assumptions; and/or identifying the energy and emissions impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio. Scenario Summary A total of 8 scenarios explore the effects of changes across both the demand- and supply-side of building energy use on annual U.S. building energy use and CO2 emissions from 2022–2050. Scenarios are organized into three groups representing low, moderate, and best-case potentials for building decarbonization, respectively. Individual scenarios are distinguished by four input dimensions: market-available technology performance range (EE): the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”; load electrification (EL): the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment; early retrofits (R): the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and power grid (P): the annual average CO2 emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2022–2050), resolved by grid region. Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Summary_Execution” XLSX. Results data are reported as an annual time series (2022–2050) at both a national and regional (EMM grid region) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information. What's New in This Version This set of benchmark scenarios carries forward elements of past versions of this dataset (previously titled “Scout Core Measures Scenario Analysis” and summarized in this paper) while also streamlining the scenario design and reflecting updated policy ambitions regarding deployment of building efficiency, flexibility, and electrification as well as power grid evolution. Three scenarios in the current dataset map back to past scenarios: Scenario 2.1: EE1.P1 -> Scenario 6: HR 1T-2T-3T Scenario 2.2: EE1.ELe1a.P1 -> Scenario 7: HR 1T-2T-3T FS0 Scenario 2.3: EE1.ELe1b.P1 -> Scenario 8: HR 1T-2T-3T FS20 The following scenario features are new in this dataset: Measures in the “best available” tier are deployed with load flexibility features that are based on a previous study of the U.S. building-grid resource. Past versions reflected only efficiency and electrification measures. The effects of progressively raising the market-available technology performance “floor” are explored by including reference case technologies in the measure competition and assuming codes/standards remove these technologies from the market-available mix beginning in a certain year. Past versions only explored the effects of a higher technology “ceiling”. Increasing ambitions for the top “Prospective” tier of measure performance are reflected. Past versions mapped much of this measure tier to the 2016 BTO MYPP. Electrification is explored via both endogenous and exogenous model settings, where the former is based on Scout’s economic measure competition models and the latter is based on fuel switching scenarios developed by Guidehouse for the BTO E3 Initiative. Past versions only explored endogenous electrification. Inefficient electrification is explored (past versions did not explore inefficient electrification). In such cases, consumers switch fossil-based heating and water heating equipment to a mix of electric resistance and heat pump technologies, with the mix determined by AEO 2021 Reference Case sales share data for these technologies. The effects of early retrofitting behavior are isolated by running all but one scenario without early retrofits. Past versions assumed a 1% early retrofit rate. More aggressive grid scenarios are explored using Brattle’s GridSIM model. Two scenarios are included—an 80% decarbonized grid by 2050 and 100% decarbonized grid by 2035. Past versions used the AEO 2018 “$25 carbon allowance fee” side case, which reached ~73% carbon-free electricity generation (including nuclear) by 2050.

Abstract This paper addresses the challenge of incorporating occupant behavior into building performance simulation models used during the design process—that is, before the actual occupants are known. It proposes the use of synthetic population data, an approach that is novel in building performance modeling although common in urban planning and public health. A simpler approach embodied in the ASHRAE Fundamentals volume is to report standard distributions of values for behavioral variables, assuming that parameters vary independently of one another when in fact many co-vary or are interdependent. An alternative approach calibrates models of occupant behavior against actual occupants in specific existing buildings, but this raises questions of transferability. Needed is a database of “generic” occupants that designers can use prospectively during the design process. This paper documents a process of combining disparate field studies of commercial buildings into a larger occupant behavior database and generating a statistically similar synthetic data set that can be shared without compromising confidentiality requirements associated with field studies. The synthetic data set successfully incorporates much of the covariance structure of the underlying field data and supports multivariate modeling. Its scope and structure necessarily serve the needs of the associated modeling framework. Cooperative and systematic sharing of data by field researchers is crucial for building large enough data sets to serve as a behaviorally-robust basis for building design.

Overview and Intended Use Cases These scenarios establish a range of futures for U.S. buildings sector energy use and CO2 emissions to 2050 using Scout, a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO). Scout benchmark scenario data are suitable for the following example use cases: Setting high-level policy goals for U.S. buildings sector energy use, electricity demand, and CO2 emissions over both the near- and long-term (e.g., X% building CO2 emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050); Exploring the effects of key deployment dynamics driving U.S. buildings sector energy and CO2 emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; improving market penetration of commercially available technologies; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market); Determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO2 emissions reductions and/or changes in total consumption by fuel type to 2050 under a given set of assumptions; Identifying the energy and CO2 impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio; and/or Exploring the total cost of deploying different portfolios of building energy efficiency and end-use electrification measures, as well as the total consumer energy cost savings potential of those portfolios. Scenario Summary A total of 5 scenarios explore total building energy use, CO2 emissions, and technology and energy costs from 2024–2050 under varying levels of demand-side deployment of building efficiency and electrification measures and parallel decarbonization of buildings’ electricity supply. Narrative descriptions of these scenarios are as follows: Stated Policies: Existing policies and regulations (mainly IRA for buildings) lead to modestly accelerated deployment of HPs/HPWHs but not other efficiency measures in the buildings sector. The power sector decarbonizes consistent with a “Mid-case (with tax credit phaseout)” scenario. Mid: Policy makers rely mostly on market-based instruments to moderately increase deployment of efficient technology and fuel switching to heat pumps. The power sector decarbonizes consistent with a “Mid-case with 95% Decarbonization by 2050 (without tax credit phaseout)” scenario. High: Policy makers use both regulations and market-based instruments to dramatically accelerate deployment of high efficiency technologies and fuel switching to heat pumps, though building technologies with breakthrough increases in performance at low cost do not materialize on the market. The power sector decarbonizes consistent with a “Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)” scenario. Breakthrough: Research and innovation breakthroughs lead to market availability of cost-effective, high-performance building technologies by 2030; these, coupled with accelerated deployment of high efficiency technologies and fuel switching to heat pumps, lead to aggressive buildings sector transformation. The power sector decarbonizes consistent with a “Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)” scenario. Inefficient Electrification Sensitivity: Policy makers use regulations and market-based instruments to encourage fuel switching but do not include provisions that require switching to efficient heat pumps, resulting in a substantial amount of switching to inefficient electric resistance heating and water heating technologies. The power sector decarbonizes consistent with a “Mid-case (with tax credit phaseout)” scenario. The key input dimensions that are varied to produce the above range of scenarios are as follows: Market-available technology performance range: the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”; Load electrification rate and efficiency: the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment; Early retrofits: the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and Power grid decarbonization: the annual average CO2 emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2024–2050), resolved by grid region. Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Execution” XLSX. Results data are reported as an annual time series (2024–2050) at both a national and regional (EMM grid region) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information. What's New in This Version Note: v6.1 updates the file ./Results/Results_Summary.xlsx to reflect the latest scenario runs. Please disregard the outdated version of this file that was posted in v6. This set of benchmark scenarios provides an update to Version 5 of the Scout Benchmark Scenarios (June 2023) using the same scenario definitions but an updated set of baseline and measure input data alongside several minor methodological changes. The following scenario features are new in this dataset: Reference case data and energy use projections updated to AEO 2023, including updates to energy and stock and technology cost, performance, and lifetime data; updated site-source energy conversions, CO2 emissions intensities, and energy prices; and revised peak and take period definitions that are consistent with 2023 EMM projections. Integration of federal and state cost incentives from AEO 2023 (see AEO2023 Issues in Focus: Inflation Reduction Act Cases in the AEO2023 for details); these incentives reduce the initial cost of upgrades for applicable measures. Revised method for allocating end use electricity baselines in AEO from census divisions to EMM regions and states by using End Use Load Profiles (EULP) data. EULP data now also underpin updated, EMM-resolved hourly load baseline shapes. Retail price projections for grid scenarios are updated to match those produced by NREL under the Department of Energy’s DECARB Initiative (these are similar to but differ in slight ways from NREL’s Standard Scenarios). Three scenarios are included: Stated Policies: includes moderate estimates for inputs such as technology costs, fuel prices, and demand growth with no nascent technologies and electric sector policies that match current federal laws and regulations (including IRA & BIL); achieves an 88% reduction in building site electricity emissions intensity (Mt CO2/quad site) from 2005 levels by 2050. Mid: consistent with Stated Policies except achieves 97% reduction in building site electricity emissions intensity from 2005 levels by 2050. High: includes low demand growth projections with advanced inputs for technology costs and allowance of transmission expansion between regions (without limitations based on historical build rates); federal policies are consistent with implemented laws (including IRA & BIL); building electricity is fully decarbonized after 2035. The previous version of the benchmark datasets used retail price data from EIA’s Annual Energy Outlook scenarios. In contrast to Version 5, measures in the “best available” measure tier are not deployed with load flexibility features. 

Load shedding enjoys increasing popularity as a way to reduce power consumption in buildings during hours of peak demand on the electricity grid. This practice has well known cost saving and reliability benefits for the grid, and the contracts utilities sign with their “interruptible” customers often pass on substantial electricity cost savings to participants. Less well-studied are the impacts of load shedding on building occupants, hence this study investigates those impacts on occupant comfort and adaptive behaviors. It documents experience in two office buildings located near Philadelphia (USA) that vary in terms of controllability and the set of adaptive actions available to occupants. An agent-based model (ABM) framework generalizes the case-study insights in a “what-if” format to support operational decision making by building managers and tenants. The framework, implemented in EnergyPlus and NetLogo, simulates occupants that have heterogeneous thermal and lighting preferences. The simulated occupants pursue local adaptive actions such as adjusting clothing or using portable fans when central building controls are not responsive, and experience organizational constraints, including a corporate dress code and miscommunication with building managers. The model predicts occupant decisions to act fairly well but has limited ability to predict which specific adaptive actions occupants will select.

Buildings are energy-intensive and a primary source of US end-use sector carbon emissions. Although building emissions today are 25% below their 2005 peak, far deeper reductions are needed to reach the US 2050 net-zero emissions goal. However, plausible decarbonization pathways that consider both buildings and their interactions with the power grid remain poorly understood. Here, we couple detailed modeling of building energy use and the grid to quantify building decarbonization potential and associated grid impacts. We find up to a 91% reduction in building CO2 emissions from 2005 levels by 2050 using a portfolio of building efficiency, demand flexibility, and electrification measures alongside rapid grid decarbonization. Building efficiency and flexibility could generate up to $107 billion in annual power system cost savings by 2050, offsetting over a third of the incremental cost of full grid decarbonization. Our results underscore multiple benefits of demand-side solutions for deep decarbonization of US buildings. Demand-side measure deployment is assessed with the Scout model (https://scout-bto.readthedocs.io/en/latest/) relative to the EIA Annual Energy Outlook 2021 Reference Case forecast (https://www.eia.gov/outlooks/archive/aeo21/), which includes projections for both new and existing building stock and largely carries forward historical trends in building technology adoption and energy consumption. Annual electricity emissions factors and hourly power system costs are projected by the GridSIM model (https://www.brattle.com/practices/electricity-wholesale-markets-planning/electricity-market-modeling/gridsim/) under different grid decarbonization scenarios. These projections are multiplied by Scout projections of annual building electricity demand and hourly system load impacts through 2050 to assess electricity CO2 emissions and power system cost reductions across the full measure portfolio. Measure installed cost data from Scout are used to estimate the total incremental costs of deploying the measure portfolio. Full-portfolio reductions in CO2 emissions from on-site combustion of fossil fuels are assessed by coupling Scout projections of annual building fossil fuel demand through 2050 with EIA fossil fuel emissions intensities. Files in this record: - "ONE-EARTH-D-22-00509 Model Runs.xlsx" (detailed instructions on how to reproduce Scout model results) - "ONE-EARTH-D-22-00509 Figure Data.xlsx" (data for all key paper figures) - "ONE-EARTH-D-22-00509 Table Data.xlsx" (all paper Table data) - "Measure_Sets.zip" (all Scout measures to run analysis as instructed in "ONE-EARTH-D-22-00509 Model Runs.xlsx") - "Raw_Results.zip" (all raw Scout results, GridSIM hourly cost and emissions data) - "Postprocess_Data.zip" (AEO/GridSIM Reference Case totals, electricity emissions intensity ratios)