Remove supply curve cost clipping

hourlize/write_site_parameters.py

@@ -8,6 +8,23 @@
 import reeds
 
 
+def check_negative_values(df, cost_cols):
+    neg_vals_all = {}
+    for cc in cost_cols.values():
+        if cc in df.columns:    
+            neg_vals = sum(df[cc] < 0)
+            if neg_vals:
+                neg_vals_all[cc] = neg_vals
+
+    if neg_vals_all:
+        message = "\n".join(
+            f"{cc} -> {count}" for cc, count in neg_vals_all.items()
+        )
+        raise ValueError(
+            f"The following cost columns have negative values:\n{message}\n"
+            "Check reV data to confirm these."
+        ) 
+
 #%%### Fixed inputs
 if reeds.io.hpc:
     remotepath = '/kfs2/shared-projects/reeds'
@@ -116,8 +133,23 @@
 _diff = len(outcols) - dfland.shape[1]
 assert _diff == 0, len(_diff)
 
+# drop ac suffix for cost columns
+cost_cols = {
+    'cost_spur_usd_per_mw_ac': 'cost_spur_usd_per_mw',
+    'cost_poi_usd_per_mw_ac': 'cost_poi_usd_per_mw',
+    'cost_reinforcement_usd_per_mw_ac': 'cost_reinforcement_usd_per_mw',
+    'cost_total_trans_usd_per_mw_ac': 'cost_total_trans_usd_per_mw',
+    'cost_export_usd_per_mw_ac': 'cost_export_usd_per_mw'
+}
+dfland = dfland.rename(columns=cost_cols)
+
+# subset to outcols 
 dfland = dfland[list(outcols.keys())].astype(outcols)
 
+# check for negative values
+check_negative_values(dfland, cost_cols)
+
+
 #%% Write it
 drop = ['trans_gid', 'trans_type']
 drop = []
@@ -188,6 +220,14 @@
 columns_meshed = {'Zone_ReEDS':'ba'}
 
 #%% Make combined dataframe
+for offshoretype in ['radial', 'meshed']:
+    cost_cols_sub = {
+        k: v for k, v in cost_cols.items()
+        if v not in dictin[f'offshore_{offshoretype}'].columns
+    }
+    dictin[f'offshore_{offshoretype}'] = dictin[f'offshore_{offshoretype}'].rename(columns=cost_cols_sub)
+    check_negative_values(dictin[f'offshore_{offshoretype}'], cost_cols)
+
 dfwrite = dictin['offshore_radial'][columns_same].copy()
 for col in columns_different:
     for offshoretype in ['radial', 'meshed']:

reeds/input_processing/writesupplycurves.py

@@ -30,8 +30,6 @@
 numbins_other = 5
 ### Rounding precision
 decimals = 7
-### spur_cutoff [$/MW]: Cutoff for spur line costs; clip cost for sites with larger costs
-spur_cutoff = 1e7
 
 # %% ===========================================================================
 ### --- FUNCTIONS ---
@@ -89,7 +87,6 @@ def agg_supplycurve(
     numbins_tech,
     bin_method='equal_cap_cut',
     bin_col='supply_curve_cost_per_mw',
-    spur_cutoff=1e7,
     deflate=None,
 ):
     """
@@ -129,8 +126,6 @@ def agg_supplycurve(
         )
     ### Aggregate it
     dfout = dfin.groupby(index_cols).agg(aggs)
-    ### Clip negative costs and costs above cutoff
-    dfout.supply_curve_cost_per_mw = dfout.supply_curve_cost_per_mw.clip(lower=0, upper=spur_cutoff)
 
     return dfin, dfout
 
@@ -226,7 +221,6 @@ def main(
             scpath=os.path.join(inputs_case,f'supplycurve_wind-{s}.csv'),
             inputs_case=inputs_case,
             numbins_tech=numbins[f'wind-{s}'],
-            spur_cutoff=spur_cutoff,
             deflate=deflate,
         )
 
@@ -328,7 +322,6 @@ def main(
         scpath=os.path.join(inputs_case, 'supplycurve_upv.csv'),
         inputs_case=inputs_case,
         numbins_tech=numbins['upv'],
-        spur_cutoff=spur_cutoff,
         deflate=deflate,
     )
 
@@ -411,7 +404,6 @@ def main(
             scpath=os.path.join(inputs_case, 'supplycurve_csp.csv'),
             inputs_case=inputs_case,
             numbins_tech=numbins['csp'],
-            spur_cutoff=spur_cutoff,
             deflate=deflate,
         )
 
@@ -492,7 +484,6 @@ def main(
                 ),
                 numbins_tech=numbins[s],
                 inputs_case=inputs_case,
-                spur_cutoff=spur_cutoff,
                 deflate=deflate
             )
             spurout_list.append(