Flexible start year option & tech classes addition

cases.csv

@@ -2,8 +2,7 @@
 timetype,Sequential or Intertemporal,seq; int; win,seq,
 ignore,"0=run this case, 1=ignore this case",0; 1,0,
 AWS,Switch for when running on AWS,0; 1,0,
-yearset,"_-delimited list of years to model OR bash-formatted year ranges (start..end..step). For example, 2010_2015_2020..2035..3_2040..2050..5 means: 2010, 2015, then 2020 through 2035 in 3-year steps, then 2040 through 2050 in 5-year steps. startyear will be prepended if it is not included. Truncated at endyear.",N/A,2010_2015_2020..2050..3,
-startyear,First year to be modeled,N/A,2010,
+yearset,"_-delimited list of years to model OR bash-formatted year ranges (start..end..step). For example, 2010_2015_2020..2035..3_2040..2050..5 means: 2010, 2015, then 2020 through 2035 in 3-year steps, then 2040 through 2050 in 5-year steps. startyear will be the first year in the list. Truncated at endyear.",N/A,2010_2015_2020..2050..3,
 endyear,Last year to be modeled,N/A,2050,
 windows_suffix,Set of windows that are modeled,default; step5; step10; 2100,default,
 solver,Solver for GAMS to use,CPLEX; gurobi,CPLEX,

docs/sources.csv

@@ -117,14 +117,7 @@ RelativeFilePath,RelativeFolderPath,FileName_new,FileExtension,Description_new,I
 /inputs/capacity_exogenous/cappayments.csv,/inputs/capacity_exogenous,cappayments,.csv,,,,,,
 /inputs/capacity_exogenous/cappayments_ba.csv,/inputs/capacity_exogenous,cappayments_ba,.csv,,,,,,
 /inputs/capacity_exogenous/demonstration_plants.csv,/inputs/capacity_exogenous,demonstration_plants,.csv,Nuclear-smr demonstration plants; active when GSw_NuclearDemo=1,"t,r,i,coolingwatertech,ctt,wst,value",,See 'notes' column in the file and https://www.energy.gov/oced/advanced-reactor-demonstration-projects-0,Prescribed capacity,MW
-/inputs/capacity_exogenous/exog_cap_geohydro_allkm_reference.csv,/inputs/capacity_exogenous,exog_cap_geohydro_allkm_reference,.csv,,,,,,
-/inputs/capacity_exogenous/exog_cap_geohydro_reference.csv,/inputs/capacity_exogenous,exog_cap_geohydro_reference,.csv,,,,,,
-/inputs/capacity_exogenous/exog_cap_upv_limited.csv,/inputs/capacity_exogenous,exog_cap_upv_limited,.csv,,,,,,
-/inputs/capacity_exogenous/exog_cap_upv_open.csv,/inputs/capacity_exogenous,exog_cap_upv_open,.csv,,,,,,
-/inputs/capacity_exogenous/exog_cap_upv_reference.csv,/inputs/capacity_exogenous,exog_cap_upv_reference,.csv,,,,,,
-/inputs/capacity_exogenous/exog_cap_wind-ons_limited.csv,/inputs/capacity_exogenous,exog_cap_wind-ons_limited,.csv,,,,,,
-/inputs/capacity_exogenous/exog_cap_wind-ons_open.csv,/inputs/capacity_exogenous,exog_cap_wind-ons_open,.csv,,,,,,
-/inputs/capacity_exogenous/exog_cap_wind-ons_reference.csv,/inputs/capacity_exogenous,exog_cap_wind-ons_reference,.csv,,,,,,
+/inputs/supply_curve/classification_geothermal.csv,/inputs/capacity_exogenous,classification_geothermal,.csv,Mean tempurature range of geothermal facilities classes defined based on reV resource supply curves and taken from Table 6 of the model documentation https://reeds-model.github.io/ReEDS/model_documentation.html#technical-resource-potential ,"class,access_case,min_reV_mean_temp,max_reV_mean_temp",,,Use to define prescribed capacity class of rsc technology,degree celsius
 /inputs/capacity_exogenous/interconnection_queues.csv,/inputs/capacity_exogenous,interconnection_queues,.csv,,,,,,
 /inputs/capacity_exogenous/prescribed_builds_wind-ofs_meshed_limited.csv,/inputs/capacity_exogenous,prescribed_builds_wind-ofs_meshed_limited,.csv,,,,,,
 /inputs/capacity_exogenous/prescribed_builds_wind-ofs_meshed_open.csv,/inputs/capacity_exogenous,prescribed_builds_wind-ofs_meshed_open,.csv,,,,,,
@@ -532,10 +525,8 @@ RelativeFilePath,RelativeFolderPath,FileName_new,FileExtension,Description_new,I
 /inputs/sets/orcat.csv,/inputs/sets,orcat,.csv,set of operating reserve categories,,,,GAMS set,
 /inputs/sets/ortype.csv,/inputs/sets,ortype,.csv,set of types of operating reserve constraints,,,,GAMS set,
 /inputs/sets/p.csv,/inputs/sets,p,.csv,set of products produced,,,,GAMS set,
-/inputs/sets/pcat.csv,/inputs/sets,pcat,.csv,set of prescribed technology categories,,,,GAMS set,
 /inputs/sets/plantcat.csv,/inputs/sets,plantcat,.csv,set of categories for plant characteristics,,,,GAMS set,
 /inputs/sets/prepost.csv,/inputs/sets,prepost,.csv,,,,,GAMS set,
-/inputs/sets/prescriptivelink0.csv,/inputs/sets,prescriptivelink0,.csv,initial set of prescribed categories and their technologies - used in assigning prescribed builds,,,,GAMS set,
 /inputs/sets/pvb_agg.csv,/inputs/sets,pvb_agg,.csv,crosswalk between hybrid pv+battery configurations and technology options,,,,GAMS set,
 /inputs/sets/pvb_config.csv,/inputs/sets,pvb_config,.csv,set of hybrid pv+battery configurations,,,,GAMS set,
 /inputs/sets/quarter.csv,/inputs/sets,quarter,.csv,,,,,GAMS set,

docs/sources_documentation.md

@@ -2702,11 +2702,6 @@
     - **Description:** set of products produced
 ---
 
-  - [pcat.csv](/inputs/sets/pcat.csv)
-    - **File Type:** GAMS set
-    - **Description:** set of prescribed technology categories
----
-
   - [plantcat.csv](/inputs/sets/plantcat.csv)
     - **File Type:** GAMS set
     - **Description:** set of categories for plant characteristics
@@ -2716,11 +2711,6 @@
     - **File Type:** GAMS set
 ---
 
-  - [prescriptivelink0.csv](/inputs/sets/prescriptivelink0.csv)
-    - **File Type:** GAMS set
-    - **Description:** initial set of prescribed categories and their technologies - used in assigning prescribed builds
----
-
   - [pvb_agg.csv](/inputs/sets/pvb_agg.csv)
     - **File Type:** GAMS set
     - **Description:** crosswalk between hybrid pv+battery configurations and technology options

hourlize/README.md

@@ -72,13 +72,13 @@ Follow setup details in the [Running hourlize](#running-hourlize) section.
 
 Hourlize relies on a set of columns being in the reV supply curve. In some cases hourlize can fill in missing columns hourlize in a pre-processing step, but in others these missing columns can cause hourlize to fall. A list of required columns can be found in `hourlize/inputs/resource/rev_sc_columns.csv`
 
-
 ### 4. Synchronize shared directories
 
 By default hourlize will copy the required files in your ReEDS repository (`copy_to_reeds=True`). You can also copy the hourlize outputs back to the shared repository by setting `copy_to_shared=True`.
 
 Note that hourlize copies to only one of the shared locations (either the HPC or nrelnas01, depending on where you are running it), so even with `copy_to_shared=True` you'll want to sync up the two shared folders. When starting from the HPC, be sure to open up permissions to the supply curve outputs you've just created (e.g., `chmod -R 777 UPV/2023_06_06_Update`). Then, from your local computer, use WinSCP or rsync to copy the files from Kestrel to nrelnas01:
 
+
 ```bash
 rsync -aPu [username]@kestrel.nlr.gov://projects/shared-projects-reeds/reeds/Supply_Curve_Data/UPV/2023_11_02_LandCover /Volumes/ReEDS/Supply_Curve_Data/UPV
 ```
@@ -168,9 +168,8 @@ The main inputs to hourlize are reV outputs for a given reV scenario:
 By default, the outputs will be dumped to a subdirectory named `results` within `hourlize/out/[casename]`. In addition, with `copy_to_reeds` set to true (as is default), we'll copy the results to the ReEDS repo containing this hourlize directory, and with`copy_to_shared` set to true (not default), we'll copy to the shared drive (see Shared Drive Locations below).
 
 * `supplycurve_{tech}.csv`: A supply curve with rows for each site and columns for region, class, available capacity, and costs. E.g. see `inputs/supply_curve/wind-ons_supply_curve-reference_ba.csv` (within ReEDS repo)
-* `{tech}_exog_cap.csv`: Exogenous (built pre-2010) capacity with columns for region, site and year. This is not capacity builds in each year, but rather cumulative capacity of each existing site over time. E.g. see `inputs/capacity_exogenous/wind-ons_exog_cap_reference_ba.csv` (within ReEDS repo)
-* `{tech}_prescribed_builds.csv`: Capacity prescribed builds (2010 - present) with columns for region, year, capacity. This is the installed capacity in each year rather than cumulative capacity over time. E.g. see `inputs/capacity_exogenous/wind-ons_prescribed_builds_reference_ba.csv` (within ReEDS repo)
 * `{tech}_.h5`: Hourly capacity factor profiles for each region/class. See `inputs/profiles_cf/cf_wind-ons_reference_ba.h5` within the ReEDS repository as an example. These files include datasets with column names (class|region) and an index with datetime and timezone information.
+* `classification_{tech}.csv`: Tech classes based on capacity factors.
 
 ### Shared Drive Locations
 

hourlize/resource.py

@@ -776,46 +776,6 @@ def save_sc_outputs(
     df_sc.to_csv(os.path.join(outpath, 'results', tech + '_supply_curve_raw.csv'), index=False)
     #Round now to prevent infeasibility in model because existing (pre-2010 + prescribed) capacity is slightly higher than supply curve capacity
     df_sc[['capacity','existing_capacity']] = df_sc[['capacity','existing_capacity']].round(decimals)
-    if existing_sites:
-        # bincol = ['sc_point_gid'] if exog_bin else []
-        df_exist = df_sc[df_sc['existing_capacity'] > 0].copy()
-        #Exogenous (pre-start-year) capacity output
-        df_exog = df_exist[df_exist['online_year'] < start_year].copy()
-        # Aggregate existing capacity to (i,rs,t)
-        if not df_exog.empty:
-            df_exog = df_exog[[profile_id_col, 'class','retire_year','existing_capacity']].copy()
-            max_exog_ret_year = df_exog['retire_year'].max()
-            ret_year_ls = list(range(start_year,max_exog_ret_year + 1))
-            df_exog = df_exog.pivot_table(index=[profile_id_col,'class'], columns='retire_year', values='existing_capacity')
-            # Make a column for every year until the largest retirement year
-            df_exog = df_exog.reindex(columns=ret_year_ls).fillna(method='bfill', axis='columns')
-            df_exog = pd.melt(
-                df_exog.reset_index(), id_vars= [profile_id_col,'class'],
-                value_vars=ret_year_ls, var_name='year', value_name='capacity')
-            df_exog = df_exog[df_exog['capacity'].notnull()].copy()
-            if(tech == 'egs' or tech == 'geohydro'):
-                df_exog['tech'] = tech + '_allkm_' + df_exog['class'].astype(str)
-            else:
-                df_exog['tech'] = tech + '_' + df_exog['class'].astype(str)
-            df_exog = df_exog[[profile_id_col,'tech','year','capacity']].copy()
-            df_exog = df_exog.groupby(['tech','year',profile_id_col], sort=False, as_index=False).sum()
-            df_exog = df_exog.sort_values(['year',profile_id_col]).round(decimals)
-            df_exog.to_csv(os.path.join(outpath, 'results', tech + '_exog_cap.csv'), index=False)
-        #Prescribed capacity output
-        if tech in ['wind-ons', 'wind-ofs']:
-            df_pre = df_exist[df_exist['online_year'] >= start_year].copy()
-            if not df_pre.empty:
-                df_pre = df_pre[[profile_id_col,'online_year','existing_capacity']].copy()
-                df_pre = df_pre.rename(columns={'online_year':'year', 'existing_capacity':'capacity'})
-                df_pre = df_pre.groupby([profile_id_col,'year'], sort=False, as_index =False).sum()
-                df_pre['capacity'] =  df_pre['capacity'].round(decimals)
-                df_pre = df_pre.sort_values(['year',profile_id_col])
-                df_pre.to_csv(os.path.join(outpath, 'results', tech + '_prescribed_builds.csv'), index=False)
-        #Reduce supply curve based on exogenous (pre-start-year) capacity
-        if subtract_exog:
-            criteria = (df_sc['online_year'] > 0) & (df_sc['online_year'] < start_year)
-            df_sc.loc[criteria, 'capacity'] = (
-                df_sc.loc[criteria, 'capacity'] - df_sc.loc[criteria, 'existing_capacity'])
 
     cfcol = 'capacity_factor_ac' if 'capacity_factor_ac' in df_sc else 'mean_cf'
     df_sc_out = (
@@ -865,27 +825,6 @@ def copy_outputs(
             os.path.join(inputspath, 'supply_curve', f'supplycurve_{tech}-{access_case}.csv')
         )
 
-        #Prescribed builds and exogenous capacity (if they exist)
-        try:
-            shutil.copy2(
-                os.path.join(resultspath, f'{tech}_prescribed_builds.csv'),
-                os.path.join(
-                    inputspath, 'capacity_exogenous',
-                    f'prescribed_builds_{techlabel}_{access_case}.csv',
-                )
-            )
-        except Exception:
-            print('WARNING: No prescribed builds')
-
-        try:
-            df = pd.read_csv(os.path.join(resultspath,f'{tech}_exog_cap.csv'))
-            df.rename(columns={df.columns[0]: '*'+str(df.columns[0])}, inplace=True)
-            df.to_csv(
-                os.path.join(inputspath,'capacity_exogenous',f'exog_cap_{tech}_{access_case}.csv'),
-                index=False
-            )
-        except Exception:
-            print('WARNING: No exogenous capacity')
 
         ## Metadata
         # rev configs