ReEDS: Electricity demand profiles 2022

This record provides hourly modeled state-level electricity demand profiles for the contiguous United States (U.S.), produced by Evolved Energy Research (EER) using the EnergyPathways model and used in the Regional Energy Deployment System (ReEDS) model.

Additional details are provided in the ReEDS documentation.

Demand assumptions

Demand profiles represent gross end-use load (i.e., distributed generation is not subtracted) in hour-ending format, in units of [MWh/h].

Profiles are provided for nine demand assumptions representing different combinations of electrification and technology advancement. Additional details are provided in the Electrification Futures Study and a related data publication. A separate data release provides related information for a subset of the demand profiles that can be assumed to be flexible in modeling of demand response. The Clean2035 scenarios are described by Denholm et al. (2022).

The ReEDS documentation provides a high-level description of the scenarios.

Temporal resolution

Demand profiles are defined for 41 model years (2010 through 2050); each model year uses the 2012 weather year to define the temperature and calendar days (weekdays / weekends / holidays). All profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00). The final day of the 2012 weather year (December 31) is dropped, such that each year contains 8760 hours.

File structure

Modeled electricity demand profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

import h5py
import pandas as pd
def read_demand_profile(filepath):
"""
Read a demand profile from `filepath` and return a pandas dataframe.
Usage:
`df = read_profile('/path/to/filename.h5')`
"""
encoding = 'utf-8'
with h5py.File(filepath, 'r') as f:
years = list(f.keys())
first_year = years[0]
not_states = ['columns', 'datetime']
states = [i for i in list(f[first_year].keys()) if i not in not_states]
dictout = {}
for year in years:
for state in states:
dictout[year,state] = pd.Series(f[year][state][:])
df = pd.concat(dictout, axis=1, names=('year','state'))
df.index = pd.to_datetime(
pd.Series(f[first_year]['datetime'], name='datetime')
.str.decode(encoding)
)
return df