NREL/ReEDS
Published February 24, 2026 | Version v1
Dataset Open

ReEDS: Demand response data

Authors/Creators

Description

This record provides hourly demand response shed data for 11 states. The demonstration data used to characterize the resource were obtained from a workflow which includes ResStock, DR-Path (LBNL), and dsgrid. The data reflect the technical potential for residential load shedding in 11 states and were developed using the 2018 weather year. 

Additional details are provided in the ReEDS documentation.

Demand assumptions

The demand response demonstration data represent residential heating and cooling in 11 states. Through this implementation the demand response is effectively added as a new supply curve-based resource, however the DR shed potential is only available during stress periods. 

Temporal resolution

The hourly demand response shed data are defined for model years 2030, 2040, and 2050; for each model year, the 2018 weather year data are provided. Interim model years are linearly interpolated such that shed data exist for each model year between 2025 and 2050. The data are at hourly resolution in U.S. Central Standard Time (UTC–06:00). 

File structure

The following files are used to define the demand response shed resource in ReEDS. To enable the resource set GSw_DRShed = 1 . By default the demonstration data are used to characterize the resource (dr_shedscen = demo_data_January_2025). 

  • dr_shed_hourly_{dr_shedscen}.h5: hourly dr shed resource availability
  • inputs/supply_curve/dr_shed_cost_{dr_shedscen}.csv: supply curve cost [$/MW]. Represents the installation cost for the DR-enabling technology (program implementation or procurement costs are not included) in 2020 dollars. 
  • inputs/plant_characteristics/dr_shed_capcost_scalars_{dr_shedscen}.csv: the supply curve is populated with 2030 cost data, the 2030 values are scaled in the model to reflect additional years through 2050
  • inputs/supply_curve/dr_shed_cap_{dr_shedscen}.csv: supply curve capacity [MW]. Populated with maximum technical potential in 2030 for each ReEDS zone from the hourly shed data
  • inputs/demand_response/dr_shed_capacity_scalar_{dr_shedscen}.csv: the supply curve is populated with 2030 capacity data, the 2030 values are scaled in the model to reflect additional years through 2050
  • inputs/plant_characteristics/dr_shed_vom_{dr_shedscen}.csv: variable operation and maintenance costs for the DR Shed resource [$/MWh]
  • inputs/plant_characteristics/dr_shed_fom_{dr_shedscen}.csv: fixed operation and maintenance costs for the DR Shed resource [$/MWh]
  • inputs/demand_response/dr_shed_avail_scalar.csv: scalar used to represent the response rate of the resource by decrementing the availability. Set to 1 by default. 
  • inputs/plant_characteristics/maxdailycf.csv: defines the maximum daily capacity factor for any technology for which it is specified. Set by default to 4 hours per day for dr _shed (0.167 = 4/24) 

Plot of the supply curves for the demonstration demand response shed heating and cooling resources attached. 

The hourly shed profile is saved as a hierarchical Data Format (HDF5) file. The following Python code snippet can be used to read the dr shed .h5 file into a pandas dataframe:

import pandas as pd
dr_shed_hourly_dataframe = pd.read_hdf('path/to/dr_shed_hourly_{dr_shedscen}.h5

Files

dr shed supply curve.png

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