Merge pull request #186 from NREL/update-v2024.7.0

Update to v2024.7.0

.github/workflows/build-docs-external.yml

@@ -26,11 +26,13 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
+          pip install pandas
+          pip install argparse
           pip install sphinx==7.2.6
           pip install myst-parser==2.0.0
           pip install sphinx_rtd_theme==2.0.0
-          pip install pandas
-          pip install argparse
+          pip install sphinxcontrib-bibtex==2.6.2
+
 
       - name: Set variables for public github repo
         run: |

Project.toml

@@ -16,10 +16,10 @@ TimeZones = "f269a46b-ccf7-5d73-abea-4c690281aa53"
 [compat]
 ArgParse = "1.1.4"
 CSV = "0.10.11"
-DataFrames = "1.5.0"
+DataFrames = "1.6.0"
 HDF5 = "0.16.15"
 InlineStrings = "1.4.0"
 JSON = "0.21.4"
-LoggingExtras = "1.0.0"
+LoggingExtras = "1.0.3"
 PRAS = "0.6.3"
-TimeZones = "1.10.0"
+TimeZones = "~1.13.0"

README.md

@@ -3,7 +3,7 @@
 
 This GitHub repository contains the source code for NREL's ReEDS model. The ReEDS model source code is available at no cost from the National Renewable Energy Laboratory. The ReEDS model can be downloaded or cloned from [https://github.com/NREL/ReEDS-2.0](https://github.com/NREL/ReEDS-2.0). 
 
-**For more information about the model and how to get started, see the [open source ReEDS-2.0 Documentation](https://nrel.github.io/ReEDS-2.0)**
+**For more information about the model, see the [open source ReEDS-2.0 Documentation](https://nrel.github.io/ReEDS-2.0)**
 
 A ReEDS training video (based on the 2020 version of ReEDS) is available on the NREL YouTube channel at [https://youtu.be/aGj3Jnspk9M?si=iqCRNn5MbGZc8ZIO](https://youtu.be/aGj3Jnspk9M?si=iqCRNn5MbGZc8ZIO).
 

ReEDS_Augur/diagnostic_plots.py

@@ -191,7 +191,7 @@ def get_inputs(sw):
         os.path.join(sw['casedir'],'inputs_case','resources.csv')
     ).set_index('resource')
     resources['tech'] = (
-        resources.i.map(lambda x: x.split('_')[0])
+        resources.i.map(lambda x: x.split('|')[0])
         .map(lambda x: x if x.startswith('battery') else x.strip('_01234567890*')))
 
     resources['rb'] = resources.r

ReEDS_Augur/prep_data.py

@@ -65,9 +65,8 @@ def main(t, casedir):
     resources = pd.read_csv(os.path.join(inputs_case, 'resources.csv'))
 
     recf = pd.read_hdf(os.path.join(inputs_case, 'recf.h5')).astype(np.float32)
-    recf.columns = recf.columns.map(
-        resources.set_index('resource')[['i','r']].apply(lambda row: tuple(row), axis=1)
-    ).rename(('i','r'))
+    recf.columns = pd.MultiIndex.from_tuples([tuple(x.split('|')) for x in recf.columns],
+                                             names=('i','r'))
 
     techs = gdxreeds['i_subsets'].pivot(columns='i_subtech',index='i',values='Value')
 
@@ -146,7 +145,7 @@ def _devint_storage(dfin):
 
     ### Store generation by (i,r) for capacity_credit.py
     vre_gen_exist = gen_vre_ir.reindex(resources[['i','r']], axis=1).fillna(0).clip(lower=0)
-    vre_gen_exist.columns = resources.resource
+    vre_gen_exist.columns = ['|'.join(c) for c in vre_gen_exist.columns]
     vre_gen_exist.index = h_dt_szn.set_index(['ccseason','year','h','hour']).index
     h5out['vre_gen_exist'] = vre_gen_exist
 
@@ -179,7 +178,7 @@ def intify(v):
         recf.multiply(cf_adj_i, level='i', axis=1)
         .reindex(resources[['i','r']], axis=1)
     )
-    vre_cf_marg.columns = resources.resource
+    vre_cf_marg.columns = ['|'.join(c) for c in vre_cf_marg.columns]
     vre_cf_marg.index = h_dt_szn.set_index(['ccseason','year','h','hour']).index
     h5out['vre_cf_marg'] = vre_cf_marg
 
@@ -256,6 +255,36 @@ def intify(v):
     csvout['energy_cap'] = energy_cap.drop(too_small_storage, errors='ignore')
     csvout['max_cap'] = max_cap.drop(too_small_storage, errors='ignore')
 
+    #%% Strip water tech suffixes from water-dependent technologies
+    ### NOTE: This must be done to make water runs compatible with PRAS, as PRAS is not set 
+    ###       up to ingest generation techs with water tech suffixes, as well as to ensure
+    ###       PRAS is operating the same for runs with/without GSw_WaterMain on.
+
+    # For each dataframe in csvout, check if it has an 'i' index and if it does, use 
+    # i_ctt_wst_link to remove the water suffixes
+    watertech_link = pd.read_csv(
+        os.path.join(casedir,'inputs_case','i_coolingtech_watersource_link.csv'),
+             usecols=['*i','ii']
+        )
+    waterupgrades_link = pd.read_csv(
+        os.path.join(casedir,'inputs_case','i_coolingtech_watersource_upgrades_link.csv'),
+             usecols=['*i','ii']
+        )
+    watertech_link = pd.concat([watertech_link, waterupgrades_link])
+    watertech_link = watertech_link.apply(lambda x: x.str.lower()).set_index('*i').squeeze(1)
+
+    for key in csvout.keys():
+        df = csvout[key]
+        if 'i' in df.index.names:
+            # Strip water tech suffixes from tech names
+            df.rename(index=watertech_link, level='i', inplace=True)
+            # Sum over i,v,t combination duplicates now that water techs have been stripped
+            indices = list(df.index.names)
+            df = df.groupby(df.index).sum()
+            # Reset the index names
+            df.index = pd.MultiIndex.from_tuples(df.index, names=indices)
+            # Rewrite data in csvout with updated data
+            csvout[key] = df.copy()
 
     #%%### Write it
     #%% .csv files