Electrification of Space Heating in the Texas Residential Sector

Published: 15 June 2021| Version 1 | DOI: 10.17632/v8mt9d3v6h.1
Philip White,
Eric Wilson,
Michael Webber


These data describe how the energy usage of a large, diverse residential sector would change if all space heating was electrified. Using the actual weather data from 2016 for 17 locations in Texas, thousands of building models representative of the building stock in the residential sector of the Texas electric grid were simulated using the open-source dynamic energy modeling tool, EnergyPlus. Four total scenarios are examined in this study: a base scenario representative of current building stock and three electrification scenarios. In each electrification scenario, building models with fossil fuel heating sources had their heating units replaced with reversible heat pumps (i.e., heat pumps that provide both heating and cooling). The three electrification scenarios reflected the efficiency of the reversible heat pump being installed: standard efficiency, high efficiency, and ultra-high efficiency. Data reflect a residential system peak shifting from summer to winter and reduced summer consumption due to efficiency improvements. 1. Energy Consumption Data ~/hourly_energy_consumption: Hourly energy usage data for each building model was collected from EnergyPlus and multiplied by a scaling factor sized to reflect the actual dimensions of the Texas grid's residential sector. In the case of this study, that scaling factor is 230. The scaled hourly energy usage was summed over all modeled dwellings to give the Texas grid's residential sector hourly energy consumption. Each scenario's hourly consumption data are in a separate csv file in this directory, noted by the filename. 2. Daily Peak Demand ~/daily_peak_demand: The maximum hour of electricity consumption (kWh) for each day is divided by the change in time (one hour) to create an absolute peak hourly demand value (kW) for the day. These values for maximum hourly demand on each day are referred as daily peak demand values in the associated journal article. Each scenario's daily peak demand data are in a separate .csv file in this directory, noted by the filename. 3. Building Stock Details ~/building_stock_details The .csv file "base_scenario_building_stock_data.csv" includes housing information (e.g., insulation details, setpoint data, geometry data) about every building modeled in this study. It also includes annual energy and end-use consumption values for each building. Note: data from approximately 38,000 dwellings of the total 41,000 were used in our study, because some of the locations covered areas not served by the Texas electric grid. The remaining .csv files contain energy consumption data for the buildings that had heating units replaced by heat pumps. The files are organized by electrification scenario.


Steps to reproduce

This study utilized NREL's ResStock GitHub repository to create the building models representative of the electric grid. The specific repository state used in this study can be found at the following URL: https://github.com/NREL/resstock/tree/b2c93f1bcf1dd3496ae985462fcc192e8d86c280 For probability distributions of building stock options, navigate to the ~/project_national/housing_characteristics directory of the GitHub repository state. Specific probability distributions related to consumer diversity are the following: Clothes Dryer.tsv; Clothes Washer Presence.tsv; Clothes Washer.tsv; Cooling Setpoint Has Offset.tsv; Cooling Setpoint Offset Magnitude.tsv; Cooling Setpoint Offset Period.tsv; Cooling Setpoint.tsv; Dishwasher.tsv; Heating Setpoint Has Offset.tsv; Heating Setpoint Offset Magnitude.tsv; Heating Setpoint Offset Period.tsv; Heating Setpoint.tsv; Occupants.tsv. Actual Meteorological Year (AMY) weather files from 2016 for the following 17 locations in Texas were used in the simulations: Abilene, TX; Amarillo, TX; Austin, TX; Brownsville, TX; Corpus Christi, TX; Dallas, TX; El Paso, TX; Houston, TX; Lubbock, TX; Lufkin, TX; Midland, TX; Port Arthur, TX; San Angelo, TX; San Antonio, TX; Victoria, TX; Waco, TX; Wichita Falls, TX. Information about EnergyPlus can be found at the following URL: https://energyplus.net/


National Renewable Energy Laboratory, University of Texas at Austin


Energy Demand, Building Physics Simulation, Building Energy Analysis, Consumer Energy Use