Load packages¶
import cerf
Install package data¶
NOTE: The package data will require approximately 195 MB of storage.
Ensure that you have installed the package data using:
cerf.install_package_data()
Conduct a run with CERF¶
We will be exploring the main functionality of the cerf package
using our our example data which is meant for illustrative purposes only.
cerf runs using a single YAML configuration file that contains
project and technology-specific settings, an electricity capacity
expansion plan, and LMP zones pricing data which is described in detail
in the docs here. Expansion plans
and technology data are generally generated by models such as GCAM
which capture multi-sector dynamics that represent alternate futures
based on scenario assumptions for socioeconomics, radiative forcing,
etc. The cerf package also comes equipped with power plant siting suitability
data at a 1-km resolution over the CONUS, publically available data from EIA and
HIFLD for transmission and pipeline infrastructure, and generic 8760 locational
marginal pricing similar to what you could model using your prefered grid
operations model.
Run cerf to site power plants in an expansion plan for a single year for the CONUS¶
# sample year
yr = 2030
# load the sample configuration file path for the target year
config_file = cerf.config_file(yr)
# run the configuration for the target year and return a data frame
result_df = cerf.run(config_file, write_output=False)
2021-08-24 11:44:03,734 - root - INFO - Starting CERF model 2021-08-24 11:44:03,956 - root - INFO - Staging data... 2021-08-24 11:44:04,168 - root - INFO - Using 'zones_raster_file': /Users/d3y010/repos/github/cerf/cerf/data/lmp_zones_1km.img 2021-08-24 11:44:04,203 - root - INFO - Processing locational marginal pricing (LMP) 2021-08-24 11:44:04,203 - root - INFO - Using LMP from default illustrative package data: /Users/d3y010/repos/github/cerf/cerf/data/illustrative_lmp_8760-per-zone_dollars-per-mwh.zip 2021-08-24 11:44:13,798 - root - INFO - Calculating interconnection costs (IC) 2021-08-24 11:44:13,798 - root - INFO - Using default substation costs from file: /Users/d3y010/repos/github/cerf/cerf/data/costs_per_kv_substation.yml 2021-08-24 11:44:13,802 - root - INFO - Using default substation file: /Users/d3y010/repos/github/cerf/cerf/data/hifld_substations_conus_albers.zip 2021-08-24 11:44:21,669 - root - INFO - Using default gas pipeline file: /Users/d3y010/repos/github/cerf/cerf/data/eia_natural_gas_pipelines_conus_albers.zip 2021-08-24 11:44:22,938 - root - INFO - Using gas pipeline costs from default file: /Users/d3y010/repos/github/cerf/cerf/data/costs_gas_pipeline.yml 2021-08-24 11:44:29,649 - root - INFO - Calculating net operational cost (NOV) 2021-08-24 11:44:30,783 - root - INFO - Calculating net locational cost (NLC) 2021-08-24 11:44:31,325 - root - INFO - Building suitability array 2021-08-24 11:44:31,749 - root - INFO - Using suitability file for 'biomass_conv_wo_ccs': /Users/d3y010/repos/github/cerf/cerf/data/suitability_biomass.sdat 2021-08-24 11:44:31,789 - root - INFO - Using suitability file for 'biomass_igcc_wo_ccs': /Users/d3y010/repos/github/cerf/cerf/data/suitability_biomass_igcc.sdat 2021-08-24 11:44:31,825 - root - INFO - Using suitability file for 'coal_conv_pul_wo_ccs': /Users/d3y010/repos/github/cerf/cerf/data/suitability_coal.sdat 2021-08-24 11:44:31,852 - root - INFO - Using suitability file for 'gas_cc_wo_ccs': /Users/d3y010/repos/github/cerf/cerf/data/suitability_gas_cc.sdat 2021-08-24 11:44:31,881 - root - INFO - Using suitability file for 'gas_cc_w_ccs': /Users/d3y010/repos/github/cerf/cerf/data/suitability_gas_cc_ccs.sdat 2021-08-24 11:44:31,908 - root - INFO - Using suitability file for 'nuclear_gen_ii': /Users/d3y010/repos/github/cerf/cerf/data/suitability_nuclear.sdat 2021-08-24 11:44:31,935 - root - INFO - Using suitability file for 'nuclear_gen_iii': /Users/d3y010/repos/github/cerf/cerf/data/suitability_nuclear.sdat 2021-08-24 11:44:31,963 - root - INFO - Using suitability file for 'oil_ct_wo_ccs': /Users/d3y010/repos/github/cerf/cerf/data/suitability_oil_baseload.sdat 2021-08-24 11:44:31,989 - root - INFO - Using suitability file for 'solar_csp': /Users/d3y010/repos/github/cerf/cerf/data/suitability_solar.sdat 2021-08-24 11:44:32,015 - root - INFO - Using suitability file for 'solar_pv_non_dist': /Users/d3y010/repos/github/cerf/cerf/data/suitability_solar.sdat 2021-08-24 11:44:32,042 - root - INFO - Using suitability file for 'wind_onshore': /Users/d3y010/repos/github/cerf/cerf/data/suitability_wind.sdat 2021-08-24 11:44:32,069 - root - INFO - Staged data in 28.1118519 seconds 2021-08-24 11:44:33,086 - root - INFO - Processed alabama in 1.0166159 seconds 2021-08-24 11:44:34,387 - root - INFO - Processed arizona in 1.3004961 seconds 2021-08-24 11:44:34,920 - root - INFO - Processed arkansas in 0.531682 seconds 2021-08-24 11:44:39,114 - root - INFO - Processed california in 4.19344 seconds 2021-08-24 11:44:39,809 - root - INFO - Processed colorado in 0.6944351 seconds 2021-08-24 11:44:39,956 - root - INFO - Processed connecticut in 0.1455598 seconds 2021-08-24 11:44:40,040 - root - INFO - Processed delaware in 0.083498 seconds 2021-08-24 11:44:40,114 - root - INFO - Processed district_of_columbia in 0.0735919 seconds 2021-08-24 11:44:41,768 - root - INFO - Processed florida in 1.6528592 seconds 2021-08-24 11:44:42,681 - root - INFO - Processed georgia in 0.9123399 seconds 2021-08-24 11:44:43,257 - root - INFO - Processed idaho in 0.5753481 seconds 2021-08-24 11:44:44,108 - root - INFO - Processed illinois in 0.8507626 seconds 2021-08-24 11:44:44,496 - root - INFO - Processed indiana in 0.3873999 seconds 2021-08-24 11:44:45,108 - root - INFO - Processed iowa in 0.610285 seconds 2021-08-24 11:44:45,625 - root - INFO - Processed kansas in 0.5162001 seconds 2021-08-24 11:44:46,092 - root - INFO - Processed kentucky in 0.46629 seconds 2021-08-24 11:44:46,929 - root - INFO - Processed louisiana in 0.8366458 seconds 2021-08-24 11:44:47,207 - root - INFO - Processed maine in 0.276648 seconds 2021-08-24 11:44:47,427 - root - INFO - Processed maryland in 0.2200139 seconds 2021-08-24 11:44:47,603 - root - INFO - Processed massachusetts in 0.1754241 seconds 2021-08-24 11:44:48,601 - root - INFO - Processed michigan in 0.9966919 seconds 2021-08-24 11:44:49,403 - root - INFO - Processed minnesota in 0.8013589 seconds 2021-08-24 11:44:50,051 - root - INFO - Processed mississippi in 0.6468239 seconds 2021-08-24 11:44:50,769 - root - INFO - Processed missouri in 0.7176321 seconds 2021-08-24 11:44:51,607 - root - INFO - Processed montana in 0.83709 seconds 2021-08-24 11:44:52,010 - root - INFO - Processed nebraska in 0.4028261 seconds 2021-08-24 11:44:53,065 - root - INFO - Processed nevada in 1.0537767 seconds 2021-08-24 11:44:53,235 - root - INFO - Processed new_hampshire in 0.169647 seconds 2021-08-24 11:44:53,436 - root - INFO - Processed new_jersey in 0.1997998 seconds 2021-08-24 11:44:54,095 - root - INFO - Processed new_mexico in 0.659168 seconds 2021-08-24 11:44:55,054 - root - INFO - Processed new_york in 0.9580491 seconds 2021-08-24 11:44:55,747 - root - INFO - Processed north_carolina in 0.6923168 seconds 2021-08-24 11:44:56,122 - root - INFO - Processed north_dakota in 0.3739688 seconds 2021-08-24 11:44:56,679 - root - INFO - Processed ohio in 0.5559881 seconds 2021-08-24 11:44:57,555 - root - INFO - Processed oklahoma in 0.8756032 seconds 2021-08-24 11:44:58,328 - root - INFO - Processed oregon in 0.7727861 seconds 2021-08-24 11:44:59,067 - root - INFO - Processed pennsylvania in 0.7384419 seconds 2021-08-24 11:44:59,145 - root - INFO - Processed rhode_island in 0.0770929 seconds 2021-08-24 11:44:59,585 - root - INFO - Processed south_carolina in 0.438771 seconds 2021-08-24 11:44:59,922 - root - INFO - Processed south_dakota in 0.3369012 seconds 2021-08-24 11:45:00,338 - root - INFO - Processed tennessee in 0.4145651 seconds 2021-08-24 11:45:12,257 - root - INFO - Processed texas in 11.9182241 seconds 2021-08-24 11:45:12,893 - root - INFO - Processed utah in 0.6355641 seconds 2021-08-24 11:45:13,009 - root - INFO - Processed vermont in 0.1157441 seconds 2021-08-24 11:45:13,649 - root - INFO - Processed virginia in 0.63941 seconds 2021-08-24 11:45:14,111 - root - INFO - Processed washington in 0.4608808 seconds 2021-08-24 11:45:14,389 - root - INFO - Processed west_virginia in 0.2771189 seconds 2021-08-24 11:45:14,896 - root - INFO - Processed wisconsin in 0.5063529 seconds 2021-08-24 11:45:15,473 - root - INFO - Processed wyoming in 0.5768282 seconds 2021-08-24 11:45:15,474 - root - INFO - All regions processed in 43.4045889 seconds. 2021-08-24 11:45:15,475 - root - INFO - Aggregating outputs... 2021-08-24 11:45:15,861 - root - INFO - CERF model run completed in 72.1272678 seconds
Results are returned as a Pandas DataFrame
Each record is a sited power plant having a geographic location and
other siting attributes. Reminder: cerf uses the
USA_Contiguous_Albers_Equal_Area_Conic projected coordinate
reference system in its CONUS example data, so the xcoord and
ycoord are relative to that projection.
result_df.head()
Run cerf to site power plants in an expansion plan for multiple years for the CONUS¶
This exercise demonstrates how to inherit sites from a previous year’s
results and keep them in the mix if they have not yet reached
retirement. If this is done in cerf, users should ensure that their
expansion plan is only for new vintage each timestep.
import cerf
# process year 2010, 2030, and 2050
for index, yr in enumerate([2010, 2030, 2050]):
print(f"\nProcessing year: {yr}")
# load the sample configuration file path for the target year
config_file = cerf.config_file(yr)
# do not intialize the run with previously sited data if it is the first time step
if index == 0:
result_df = cerf.run(config_file, write_output=False)
else:
result_df = cerf.run(config_file, write_output=False, initialize_site_data=result_df)
Explore the results that account for retirement
Since we inherited the each year, and we are only siting new vintage per
year, we see power plants from multiple technlogies until they reach
their retirement age. We can narrow in on biomass power plants in
Virginia to see this:
result_df.loc[(result_df['region_name'] == 'virginia') & (result_df['tech_id'] == 9)]
Plot the output
cerf.plot_siting(result_df)
