Add multi-metrics for RA

cases.csv

@@ -261,14 +261,17 @@ GSw_PRM_NetImportLimit,Turn on (1) or off (0) the eq_firm_transfer_limit constra
 GSw_PRM_NetImportLimitScen,/-delimited list of {year}_{max percent import OR 'hist' for historical percent OR 'histmax' for max historical percent across all regions} values to use in eq_firm_transfer_limit,N/A,2031_hist/2050_100,
 GSw_PRM_scenario,"column of inputs/reserves/prm_annual.csv from which to draw the PRM (""nerc"" means the annual values from the 2024 NERC LTRA; ""static"" means the 2024 values from the same source); or a float (like 0.12) which gets applied as a fraction to all regions in all years (0.12 means 12%)",(static|nerc|ramp2025_20by50|^\d*\.?\d+$),0.12,
 GSw_PRM_StressIncrement,How many stress periods to add per iteration,int,2,
-GSw_PRM_StressIterateMax,Max number of times to iterate on a given solve year to achieve GSw_PRM_StressThreshold when using stress periods (0 means don't iterate; 1 means 1 extra ReEDS run; etc),int,5,
+GSw_PRM_StressIterateMax,"Max number of times to iterate on a given solve year to achieve adequacy stress levels (e.g., GSw_PRM_StressThresholdNEUE) when using stress periods (0 means don't iterate; 1 means 1 extra ReEDS run; etc)",int,5,
 GSw_PRM_StressLoadAggMethod,How to aggregate load for stress periods within the chunks specified by GSw_HourlyChunkLengthStress (only used if GSw_HourlyChunkAggMethod=mean; otherwise GSw_HourlyChunkAggMethod is used),mean; max,max,
 GSw_PRM_StressModel,Model used to identify stress periods: pras or a string starting with 'user' which specifies a file at inputs/temporal/stressperiods_{GSw_PRM_StressModel}.csv,N/A,pras,
 GSw_PRM_StressOutages,Whether to apply the availability factor (forced + scheduled outages) during stress periods,0; 1,1,
 GSw_PRM_StressSeedLoadLevel,Region hierarchy level at which to include peak coincident load days as seeded stress periods (or False to ignore peaks),false; False; FALSE; r; nercr; transreg; transgrp; cendiv; st; interconnect; country; usda_region; ccreg,transgrp,
 GSw_PRM_StressSeedMinRElevel,Region hierarchy level at which to include minimum wind and solar capacity factor days as seeded stress periods (or False to ignore min-wind/solar CF days),false; False; FALSE; r; nercr; transreg; transgrp; cendiv; st; interconnect; country; usda_region; ccreg,interconnect,
 GSw_PRM_StressStorageCutoff,"How to select ""shoulder"" stress periods giving storage time to recharge before/after high-unserved-energy periods. Two-part switch separated by _. The first argument is ""EUE"" or ""capacity"" or ""absolute"". If ""EUE"" the second argument specifies a PRAS storage headspace / EUE threshold [fraction]; if ""cap"" it specifies a headspace / storage capacity threshold [fraction]; if ""abs"" it specifies absolute number of periods before/after [integer]. Turned off if set to ""off"".",N/A,EUE_0.1,
-GSw_PRM_StressThreshold,/-delimited list of annual NEUE level [ppm] above which to re-solve the latest model year with new stress periods; formulated as HierarchyLevel_NEUEppm_StressMetric_PeriodAggMethod where HierarchyLevel is a column in hierarchy.csv; NEUEppm is normalized expected unserved energy in parts per million; StressMetric is EUE or NEUE (only used in period selection); PeriodAggMethod is 'sum' or 'max' over the hours in each period (only used in period selection) (see README.md for detailed notes),N/A,transgrp_1_EUE_sum,
+GSw_PRM_StressThresholdMetrics,/-delimited list of metrics for identifying stress periods,N/A,NEUE,
+GSw_PRM_StressThresholdLOLE,LOLE threshold above which to re-solve the latest model year with new stress periods; formulated as HierarchyLevel_nLOLE_StressMetric_PeriodAggMethod where HierarchyLevel is a column in hierarchy.csv; nLOLE is number of loss of load events; StressMetric is LOLE; PeriodAggMethod is 'sum' or 'max' over the hours in each period (only used in period selection) (see README.md for detailed notes),N/A,transgrp_36_LOLE_sum,
+GSw_PRM_StressThresholdEUE,/Annual EUE level [MWh] threshold above which to re-solve the latest model year with new stress periods; formulated as HierarchyLevel_EUE_StressMetric_PeriodAggMethod where HierarchyLevel is a column in hierarchy.csv; EUE is expected unserved energy; StressMetric EUE (only used in period selection); PeriodAggMethod is 'sum' or 'max' over the hours in each period (only used in period selection) (see README.md for detailed notes),N/A,transgrp_10000_EUE_sum,
+GSw_PRM_StressThresholdNEUE,/Annual NEUE level [ppm] threshold above which to re-solve the latest model year with new stress periods; formulated as HierarchyLevel_NEUEppm_StressMetric_PeriodAggMethod where HierarchyLevel is a column in hierarchy.csv; NEUEppm is normalized expected unserved energy in parts per million; StressMetric is EUE or NEUE (only used in period selection); PeriodAggMethod is 'sum' or 'max' over the hours in each period (only used in period selection) (see README.md for detailed notes),N/A,transgrp_1_NEUE_sum,
 GSw_PRM_UpdateFraction,Fraction to add to the PRM if a region fails RA threshold (only used if GSw_PRM_UpdateMethod = 1),float,0.02,
 GSw_PRM_UpdateMethod,Option to update PRM: (0) no update; (1) static update set by GSw_PRM_UpdateFraction; (2) dynamic update informed by PRAS; (3) dynamic update but only after all new stress periods have been added,0; 1; 2; 3,0,
 GSw_PRMTRADE_level,hierarchy level within which to allow PRM trading,r; nercr; transreg; transgrp; cendiv; st; interconnect; country; usda_region,country,

postprocessing/compare_cases.py

@@ -1436,7 +1436,7 @@ def two_bars(dfplot, basecase, colors, ax, col=0, ypad=0.02):
 
 #%%### SCOE, NEUE
 try:
-    neue_threshold = float(sw.GSw_PRM_StressThreshold.split('_')[1])
+    neue_threshold = float(sw.GSw_PRM_StressThresholdNEUE.split('_')[1])
     width = 9 + len(cases)*0.5
     plt.close()
     f,ax = plt.subplots(
@@ -1549,9 +1549,9 @@ def two_bars(dfplot, basecase, colors, ax, col=0, ypad=0.02):
 
 #%% Regional NEUE
 try:
-    neue_threshold = float(sw.GSw_PRM_StressThreshold.split('_')[1])
+    neue_threshold = float(sw.GSw_PRM_StressThresholdNEUE.split('_')[1])
     dfmap = reeds.io.get_dfmap(cases[basecase])
-    level = sw.GSw_PRM_StressThreshold.split('_')[0]
+    level = sw.GSw_PRM_StressThresholdNEUE.split('_')[0]
     regions = dfmap[level].loc[hierarchy[basecase][level].unique()].bounds.minx.sort_values().index
     _nrows, _ncols, _coords = reeds.plots.get_coordinates(regions, ncols=6)
     labelcoords = {
@@ -2500,7 +2500,10 @@ def two_bars(dfplot, basecase, colors, ax, col=0, ypad=0.02):
 
 
 #%%### Copy some premade single-case plots
-level = dictin_sw[basecase]['GSw_PRM_StressThreshold'].split('_')[0]
+# Use first stress metric level
+## TODO: add a check for choosing level if there are multiple stress metrics
+stress_metrics = dictin_sw[basecase]['GSw_PRM_StressThresholdMetrics'].split('/')
+level = dictin_sw[basecase][f'GSw_PRM_StressThreshold{stress_metrics[0]}'].split('_')[0]
 wide = 1 if len(hierarchy[basecase]['transreg'].unique()) > 6 else 0
 metrics = [
     'cap',

postprocessing/single_case_plots.py

@@ -701,7 +701,9 @@
         print(traceback.format_exc())
 
     try:
-        level, threshold, _, metric = sw['GSw_PRM_StressThreshold'].split('/')[0].split('_')
+        _first_metric = sw['GSw_PRM_StressThresholdMetrics'].split('/')[0].upper()
+        _parts = sw[f'GSw_PRM_StressThreshold{_first_metric}'].split('_')
+        level, threshold, metric = _parts[0], _parts[1], _parts[2]
         plt.close()
         f,ax = reedsplots.plot_stressperiod_evolution(
             case=case, level=level, metric=metric)

reeds/reedsplots.py

@@ -484,7 +484,7 @@ def plot_diff(
     ymax = max(ax[0].get_ylim()[1], ax[1].get_ylim()[1])
     ymin = min(ax[0].get_ylim()[0], ax[1].get_ylim()[0])
     if val == 'NEUE (ppm)':
-        neue_threshold = float(sw.GSw_PRM_StressThreshold.split('_')[1])
+        neue_threshold = float(sw.GSw_PRM_StressThresholdNEUE.split('_')[1])
         ymax = max(ymax, 10, neue_threshold*1.05)
         ax[0].axhline(neue_threshold, c='C7', ls='--', lw=0.75)
         ax[1].axhline(neue_threshold, c='C7', ls='--', lw=0.75)
@@ -4137,7 +4137,9 @@ def plot_stressperiod_evolution(
     """Plot NEUE by year and stress period iteration"""
     ### Parse inputs
     sw = reeds.io.get_switches(case)
-    _level, _threshold, _, _metric = sw['GSw_PRM_StressThreshold'].split('/')[0].split('_')
+    _first_metric = sw['GSw_PRM_StressThresholdMetrics'].split('/')[0].upper()
+    _parts = sw[f'GSw_PRM_StressThreshold{_first_metric}'].split('_')
+    _level, _threshold, _metric = _parts[0], _parts[1], _parts[2]
     level = _level if level is None else level
     threshold = float(_threshold) if threshold is None else threshold
     metric = _metric if metric is None else metric
@@ -4261,8 +4263,9 @@ def plot_neue_bylevel(
     norm = {'sum':1, 'max':1e-4}
     ylabel = {'sum': 'Sum of NEUE [ppm]', 'max':'Max NEUE [%]'}
     thresholds = {
-        i.split('_')[0]: float(i.split('_')[1])
-        for i in sw.GSw_PRM_StressThreshold.split('/')
+        sw[f'GSw_PRM_StressThreshold{m.upper()}'].split('_')[0]:
+        float(sw[f'GSw_PRM_StressThreshold{m.upper()}'].split('_')[1])
+        for m in sw.GSw_PRM_StressThresholdMetrics.split('/')
     }
     ### Plot it
     plt.close()
@@ -4326,8 +4329,8 @@ def map_neue(
     neue = reeds.io.read_output(case, f'neue_{year}i{_iteration}.csv')
     neue = neue.loc[neue.metric==metric].set_index(['level','region']).NEUE_ppm
     sw = reeds.io.get_switches(case)
-    neue_threshold = float(sw.GSw_PRM_StressThreshold.split('_')[1])
-    neue_threshold_level = sw.GSw_PRM_StressThreshold.split('_')[0]
+    neue_threshold = float(sw.GSw_PRM_StressThresholdNEUE.split('_')[1])
+    neue_threshold_level = sw.GSw_PRM_StressThresholdNEUE.split('_')[0]
 
     ### Set up plot
     levels = ['interconnect','nercr','transreg','transgrp','st','r']
@@ -6195,7 +6198,8 @@ def map_stressors(
         dfmap[k] = v.to_crs(crs)
 
     ### Derived inputs
-    criterion = sw.GSw_PRM_StressThreshold.split('/')[0]
+    _first_metric = sw.GSw_PRM_StressThresholdMetrics.split('/')[0].upper()
+    criterion = sw[f'GSw_PRM_StressThreshold{_first_metric}']
     level = criterion.split('_')[0]
     regions = hierarchy[level].unique()
     region2rs = {
@@ -6656,7 +6660,8 @@ def map_prm(case, tmin=2023, cmap=cmocean.cm.rain, scale=3, fontsize=7, vmax=Non
 
     ### Plot it
     nrows, ncols, coords = plots.get_coordinates(year2iteration.index, aspect=1.8)
-    level = sw.GSw_PRM_StressThreshold.split('_')[0]
+    _first_metric = sw.GSw_PRM_StressThresholdMetrics.split('/')[0].upper()
+    level = sw[f'GSw_PRM_StressThreshold{_first_metric}'].split('_')[0]
     plt.close()
     f,ax = plt.subplots(
         nrows, ncols, figsize=(ncols*scale, nrows*scale*0.8), sharex=True, sharey=True,

reeds/resource_adequacy/run_pras.jl

@@ -204,9 +204,9 @@ function run_pras(pras_system_path::String, args::Dict)
     results = Dict{String,Any}(zip(keys(resultspec), results_tuple))
 
     #%% Print some results for the entire modeled region to show it worked
-    @info "$(PRAS.LOLE(results["short"])) event-h"
-    @info "$(PRAS.EUE(results["short"])) MWh"
-    @info "NEUE = $(1e6 * PRAS.EUE(results["short"]).eue.estimate / sum(sys.regions.load)) ppm"
+    @info "$(PRAS.LOLE(results["short"]))"
+    @info "$(PRAS.EUE(results["short"]))"
+    @info "$(PRAS.NEUE(results["short"]))"
 
     ## Filter out DC regions used for VSC HVDC transmission
     regions = [r for r in sys.regions.names if !(occursin("|", r))]