transfer cases from heron_cases repo

use_cases/2020_12/README.txt

@@ -0,0 +1,34 @@
+
+Cases for M2 HERON Milestone due 2020-12-15.
+
+PI: Paul Talbot
+Project Contact: Cristian Rabiti
+Contributors: Dylan McDowell, James Richards
+
+Data for the load (in many different breakdowns) as well as marginal price
+ and capacity evolution every 5 years for 40 electricity technologies was
+provided by EPRI for 6 different cases using US-REGEN (for 2 states, but we
+focused on IL for this analysis):
+
+Policy: Nominal, CarbonTax, RPS
+Pricing: Default, LNHR
+
+Note Default and Nominal are only different names to help distinguish cases.
+
+The data from EPRI is provided in excel spreadsheets under ./data/from_EPRI.
+To turn these cases into trainable signals, use ./scripts/raw_data_proc.py
+targetting the excel sheet of the case you want to train. This requires the
+python library xlrd to be available (it's on conda).
+
+The ARMA training algorithms are in ./train by case. Any cases must have an
+ARMA trained before they can be run.
+
+To run the cases, run ./run/write_cases.py, which populated the regulated and
+deregulated cases using the dereg_template.xml and reg_template.xml. Then
+navigate into any folder (for which there's trained ARMAs) and run HERON on
+the generated XML file. Then run RAVEN on the generated outer.xml, tweaking
+any cluster run parameters desired first.
+
+The operating branches for this analysis:
+RAVEN: arma_eval_pk on PaulTalbot-INL/raven
+HERON: milestone_changes on PaulTalbot-INL/HERON

use_cases/2020_12/data/extract_cap_marg.py

@@ -0,0 +1,132 @@
+"""
+  Extracts the 5D capacity and marginal costs of components from the
+  slightly-modified EPRI XLSX data
+"""
+
+import os
+from itertools import islice
+
+# conda installs: xlrd, openpyxl
+import numpy as np
+import pandas as pd
+import xarray as xr
+import openpyxl as xl
+
+case_set = False # track whether trimming for the state/strat/price has happened yet
+
+states = ['IL']
+strategies = ['Nominal', 'RPS', 'CarbonTax']
+price_structs = ['Default', 'LNHR']
+years = [2025, 2030, 2035, 2040, 2045, 2050]
+# get from xlsx directly below; components = ['clcl-x1', 'clcl-x2', 'clcl-x3', 'clcl-x4', 'clng-r1', 'clng-r2', 'clng-r3', 'cbcf-x1', 'cbcf-x2', 'cbcf-x3', 'cbcf-x4', 'ngcc-x1', 'ngcc-x2', 'ngcc-x3', 'ngcc-n', 'ngst-x', 'nggt-x1', 'nggt-x2', 'nggt-x3', 'nggt-n', 'ptsg-x', 'othc-x', 'h2cc-n', 'h2cc-ig', 'bioe-r1', 'becs-n', 'h2cc-igcs', 'ngcs-n', 'nucl-n1', 'nucl-n2', 'hydr-x', 'wind-r', 'wind-n3', 'wind-n4', 'wind-n5', 'wind-n6', 'wnos-n1', 'pvft-n4', 'pvsx-n4', 'pvdx-n4']
+
+cwd = os.path.abspath(os.path.join(__file__, '..'))
+filename = os.path.abspath(os.path.join(cwd, 'from_EPRI','Final Illinois Capacity and Costs.xlsx'))
+wb = xl.load_workbook(filename=filename, read_only=True)
+ws = wb['CapCost']
+
+# Add overflow component that's not in the workbook.
+components = [c.value for c in ws['F1':'AX1'][0]] + ['overflow']
+
+indexers = [states, strategies, price_structs, components, years]
+# data_heads = ['capacity', 'marg_cost']
+shape = tuple([len(x) for x in indexers])
+cap_table = np.zeros(shape, dtype=float)
+mgc_table = np.zeros(shape, dtype=float)
+# 1:state, 2:strategy, 3:price, 4:component, 5:year
+
+# load capacities, in GW
+ci = 'B'
+cf = 'AX'
+for sp in range(6):
+  ri = sp * 8 + 1
+  df = pd.read_excel(filename,
+      sheet_name='CapCost',
+      header=0,
+      index_col=[0,1,2,3],
+      usecols=f'{ci}:{cf}',
+      skiprows=ri-1,
+      nrows=6).fillna(0)
+  # Assign overflow component for times when load exceeds stack.
+  df = df.assign(overflow = 1000.0)
+  identifier = df.index[0][:3]
+  t = states.index(identifier[0])
+  s = strategies.index(identifier[1])
+  p = price_structs.index(identifier[2])
+  cap_table[t, s, p, :, :] = df.values.T
+
+# load marginal prices, initially in $/MW
+ci = 'BA'
+cf = 'CT'
+for t, state in enumerate(states):
+  for s, strategy in enumerate(strategies):
+    for p, struct in enumerate(price_structs):
+      if strategy == 'CarbonTax':
+        ri = 1
+      else:
+        ri = 17
+      df = pd.read_excel(filename,
+          sheet_name='CapCost',
+          header=0,
+          index_col=[0],
+          usecols=f'{ci}:{cf}',
+          skiprows=ri-1,
+          nrows=6).fillna(0)
+      # Assign overflow component for times when load exceeds stack.
+      df = df.assign(overflow = 1000.0)
+      table_part = mgc_table[t, s, p, :, :]
+      mgc_table[t, s, p, :, :] = df.values.T * 1000 # $/MW to $/GW
+
+# 1:state, 2:strategy, 3:price, 4:component, 5:year
+coords = {'state': states,
+          'strategy': strategies,
+          'price_struct': price_structs,
+          'component': components,
+          'year': years}
+cap_DA = xr.DataArray(cap_table, coords=coords, dims=list(coords.keys()), name='capacity')
+mgc_DA = xr.DataArray(mgc_table, coords=coords, dims=list(coords.keys()), name='marginal_cost')
+
+# customize nuclear margins
+mgc_DA.loc['IL', :, :, ['nucl-x', 'nucl-n1', 'nucl-n2'], :] *= 1e-10
+# customize VRE margins for PTC
+VREs = ['wind-r','wind-n3','wind-n4','wind-n5','wind-n6','wnos-n1','pvft-n3','pvft-n4','pvsx-n3','pvsx-n4','pvdx-n3','pvdx-n4']
+mgc_DA.loc['IL', :, :, VREs, :] -= 17000
+
+ds = xr.Dataset({'capacity': cap_DA, 'marginal_cost': mgc_DA})
+# interpolate years
+ds = ds.interp(year=np.arange(min(years), max(years)+1))
+ds.to_netcdf(cwd+'/IL_combined.ncdf4')
+print(f'Wrote {cwd+"/IL_combined.ncdf4"}')
+
+
+# collapse for regulated case
+techmap = {
+  'coal': 'clcl-x1|clcl-x2|clcl-x3|clcl-x4|clng-r1|clng-r2|clng-r3|clng-r4|cbcf-x1|cbcf-x2|cbcf-x3|cbcf-x4'.split('|'),
+  'gas': 'ngcc-x1|ngcc-x2|ngcc-x3|ngcc-n|ngst-x|nggt-x1|nggt-x2|nggt-x3|nggt-n|ngcs-n'.split('|'),
+  'petrol': ['ptsg-x', 'overflow'],
+  'other': ['othc-x', 'bioe-r1', 'becs-n'],
+  'h2gen': ['h2cc-n', 'h2cc-ig', 'h2cc-igcs'],
+  'nuclear': ['nucl-n1', 'nucl-n2', 'nucl-x'],
+  'VRE': 'hydr-x|wind-r|wind-n3|wind-n4|wind-n5|wind-n6|wnos-n1|pvft-n4|pvsx-n4|pvdx-n4'.split('|'),
+}
+
+# DEBUG
+## select the subset
+arrays = {}
+comp_ax = list(ds.capacity.dims).index('component')
+for t, (tech, members) in enumerate(techmap.items()):
+  subset = ds.sel(component=members)
+  caps = subset.capacity.sum(dim='component')# .expand_dims(axis=comp_ax, component=[tech])
+  costs = (subset.capacity / caps * subset.marginal_cost).sum(dim='component').expand_dims(axis=comp_ax, component=[tech])
+  caps = caps.expand_dims(axis=comp_ax, component=[tech])
+  new = xr.Dataset({'capacity': caps, 'marginal_cost': costs})
+  if t == 0:
+    truncated = new
+  else:
+    truncated = truncated.merge(new)
+
+truncated.to_netcdf(cwd+'/IL_truncated.ncdf4')
+print(f'Wrote {cwd+"/IL_truncated.ncdf4"}')
+
+print('done')
+

use_cases/2020_12/data/stack_IL_carbontax.csv

@@ -0,0 +1,7 @@
+Year,clcl-x1,clcl-x2,clcl-x3,clcl-x4,clng-r1,clng-r2,clng-r3,clng-r4,cbcf-x1,cbcf-x2,cbcf-x3,cbcf-x4,ngcc-x1,ngcc-x2,ngcc-x3,ngcc-n,ngst-x,nggt-x1,nggt-x2,nggt-x3,nggt-n,ptsg-x,othc-x,h2cc-n,h2cc-ig,bioe-r1,becs-n,h2cc-igcs,ngcs-n,nucl-n1,nucl-n2,hydr-x,wind-r,wind-n3,wind-n4,wind-n5,wind-n6,wnos-n1,pvft-n4,pvsx-n4,pvdx-n4
+2025,59.93,62.62,65.94,71.60,61.62,64.35,67.78,73.62,57.00,59.57,62.72,68.10,44.80,45.36,47.78,44.58,99.07,63.54,68.38,75.99,71.86,215.94,2.06,40.45,40.45,1.20,-60.66,40.45,34.48,8.00,8.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2030,69.21,72.29,76.14,82.68,65.87,68.78,72.46,78.70,65.81,68.75,72.40,78.63,47.85,48.46,51.07,47.54,105.90,67.94,73.13,81.27,76.58,236.65,2.06,39.62,39.62,1.20,-75.10,39.62,33.71,9.30,9.30,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2035,88.36,92.26,97.20,105.57,68.79,71.83,75.67,82.19,84.01,87.73,92.41,100.37,49.95,50.60,53.33,49.58,110.58,70.97,76.39,84.90,79.83,250.58,2.06,37.83,37.83,1.20,-92.67,37.83,30.95,10.80,10.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2040,102.26,106.75,112.49,122.18,77.65,81.06,85.42,92.77,97.21,101.49,106.94,116.15,56.30,57.07,60.19,55.74,124.79,80.15,86.27,95.90,89.67,269.43,2.06,36.59,36.59,1.20,-114.05,36.59,31.87,12.50,12.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2045,119.22,124.44,131.14,142.44,88.80,92.69,97.68,106.10,113.32,118.29,124.66,135.40,64.31,65.21,68.83,63.50,142.68,91.71,98.72,109.74,102.06,280.67,2.06,36.73,36.73,1.20,-140.06,36.73,33.29,14.50,14.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2050,139.96,146.07,153.96,167.23,102.88,107.37,113.17,122.92,133.03,138.85,146.33,158.95,74.41,75.49,79.73,73.30,165.27,106.30,114.44,127.23,117.71,305.07,2.06,36.73,36.73,1.20,-171.70,36.73,35.43,16.80,16.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00

use_cases/2020_12/data/stack_IL_notax.csv

@@ -0,0 +1,7 @@
+Year,clcl-x1,clcl-x2,clcl-x3,clcl-x4,clng-r1,clng-r2,clng-r3,clng-r4,cbcf-x1,cbcf-x2,cbcf-x3,cbcf-x4,ngcc-x1,ngcc-x2,ngcc-x3,ngcc-n,ngst-x,nggt-x1,nggt-x2,nggt-x3,nggt-n,ptsg-x,othc-x,h2cc-n,h2cc-ig,bioe-r1,becs-n,h2cc-igcs,ngcs-n,nucl-n1,nucl-n2,hydr-x,wind-r,wind-n3,wind-n4,wind-n5,wind-n6,wnos-n1,pvft-n4,pvsx-n4,pvdx-n4
+2025,16.07,16.88,17.70,19.19,34.87,36.46,38.37,41.66,15.33,16.12,16.89,18.31,25.60,25.83,27.07,25.97,56.17,35.82,38.53,42.78,42.14,168.17,2.06,44.52,44.52,1.20,6.00,44.52,32.33,8.00,8.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2030,15.84,16.64,17.44,18.91,33.33,34.85,36.68,39.82,15.11,15.89,16.65,18.04,24.50,24.70,25.88,24.90,53.70,34.22,36.81,40.87,40.43,178.52,2.06,45.27,45.27,1.20,6.00,45.27,31.08,9.30,9.30,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2035,23.43,24.56,25.79,27.98,29.20,30.55,32.14,34.89,22.32,23.41,24.58,26.66,21.54,21.69,22.68,22.03,47.07,29.94,32.20,35.74,35.84,179.86,2.06,45.76,45.76,1.20,6.00,45.76,27.75,10.80,10.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2040,23.26,24.38,25.61,27.78,29.49,30.84,32.45,35.22,22.16,23.24,24.40,26.47,21.74,21.89,22.90,22.22,47.53,30.23,32.51,36.09,36.15,183.39,2.06,46.03,46.03,1.20,6.00,46.03,27.98,12.50,12.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2045,23.10,24.22,25.43,27.59,30.20,31.59,33.24,36.08,22.02,23.08,24.24,26.29,22.25,22.42,23.45,22.72,48.68,30.98,33.31,36.98,36.95,175.99,2.06,47.07,47.07,1.20,6.00,47.07,28.56,14.50,14.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2050,23.03,24.14,25.35,27.50,31.59,33.03,34.76,37.73,21.95,23.01,24.16,26.21,23.25,23.43,24.53,23.69,50.90,32.41,34.86,38.70,38.49,177.72,2.06,47.96,47.96,1.20,6.00,47.96,29.68,16.80,16.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00

use_cases/2020_12/data/stack_OH_carbontax.csv

@@ -0,0 +1,7 @@
+Year,clcl-x1,clcl-x2,clcl-x3,clcl-x4,clng-r1,clng-r2,clng-r3,cbcf-x1,cbcf-x2,cbcf-x3,cbcf-x4,ngcc-x1,ngcc-x2,ngcc-x3,ngcc-n,ngst-x,nggt-x1,nggt-x2,nggt-x3,nggt-n,ptsg-x,othc-x,h2cc-n,h2cc-ig,bioe-r1,becs-n,h2cc-igcs,ngcs-n,nucl-n1,nucl-n2,hydr-x,wind-r,wind-n3,wind-n4,wind-n5,wind-n6,wnos-n1,pvft-n4,pvsx-n4,pvdx-n4
+2025,59.96,62.84,63.80,66.91,50.76,53.22,54.04,53.89,56.74,57.57,60.21,33.85,36.97,37.65,38.21,78.67,56.00,61.63,69.26,61.69,216.74,1.59,38.31,38.31,1.78,-60.66,38.31,27.10,8.00,8.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2030,68.84,72.19,73.29,76.83,59.21,62.13,63.07,61.89,65.17,66.12,69.15,39.52,43.21,43.98,44.36,91.82,65.42,72.07,80.97,71.50,237.47,1.59,37.21,37.21,1.78,-75.10,37.21,30.02,9.30,9.30,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2035,85.74,89.98,91.34,95.71,68.36,71.76,72.84,77.42,81.51,82.70,86.49,45.64,49.95,50.84,51.01,106.05,75.62,83.36,93.64,82.12,250.78,1.59,35.00,35.00,1.78,-92.67,35.00,32.61,10.80,10.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2040,99.05,103.99,105.55,110.58,76.85,80.69,81.90,89.42,94.15,95.53,99.90,51.32,56.21,57.20,57.17,119.25,85.07,93.83,105.40,91.96,269.65,1.59,31.28,31.28,1.78,-114.05,31.28,33.53,12.50,12.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2045,115.28,121.09,122.90,128.72,87.52,91.93,93.31,104.07,109.57,111.17,116.27,58.47,64.09,65.20,64.93,135.86,96.97,107.00,120.19,104.35,280.90,1.59,29.01,29.01,1.78,-140.06,29.01,34.95,14.50,14.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2050,135.15,142.01,144.12,150.91,101.00,106.13,107.71,121.99,128.45,130.32,136.30,67.50,74.03,75.30,74.73,156.83,112.00,123.64,138.87,120.00,305.33,1.59,29.01,29.01,1.78,-171.70,29.01,37.09,16.80,16.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00

use_cases/2020_12/data/stack_OH_notax.csv

@@ -0,0 +1,7 @@
+Year,clcl-x1,clcl-x2,clcl-x3,clcl-x4,clng-r1,clng-r2,clng-r3,cbcf-x1,cbcf-x2,cbcf-x3,cbcf-x4,ngcc-x1,ngcc-x2,ngcc-x3,ngcc-n,ngst-x,nggt-x1,nggt-x2,nggt-x3,nggt-n,ptsg-x,othc-x,h2cc-n,h2cc-ig,bioe-r1,becs-n,h2cc-igcs,ngcs-n,nucl-n1,nucl-n2,hydr-x,wind-r,wind-n3,wind-n4,wind-n5,wind-n6,wnos-n1,pvft-n4,pvsx-n4,pvdx-n4
+2025,17.96,18.62,18.94,19.99,25.15,26.26,26.68,15.98,16.83,17.08,17.86,16.71,18.09,18.46,19.60,38.83,27.46,30.03,33.78,31.97,168.92,1.59,43.83,43.83,1.78,6.00,43.83,24.94,8.00,8.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2030,17.74,18.39,18.70,19.74,28.06,29.32,29.79,15.77,16.61,16.85,17.62,18.65,20.23,20.63,21.72,43.35,30.70,33.61,37.81,35.34,179.28,1.59,44.45,44.45,1.78,6.00,44.45,27.39,9.30,9.30,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2035,23.57,24.52,24.92,26.25,30.46,31.85,32.35,21.31,22.44,22.76,23.81,20.26,22.00,22.43,23.46,47.09,33.37,36.57,41.13,38.13,179.99,1.59,45.48,45.48,1.78,6.00,45.48,29.41,10.80,10.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2040,23.41,24.35,24.75,26.07,30.73,32.13,32.64,21.15,22.27,22.60,23.64,20.44,22.20,22.64,23.66,47.51,33.68,36.91,41.51,38.44,183.52,1.59,45.27,45.27,1.78,6.00,45.27,29.64,12.50,12.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2045,23.26,24.19,24.59,25.90,31.42,32.86,33.37,21.01,22.13,22.45,23.48,20.90,22.71,23.15,24.16,48.58,34.44,37.76,42.46,39.24,176.12,1.59,45.83,45.83,1.78,6.00,45.83,30.22,14.50,14.50,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00
+2050,23.19,24.12,24.51,25.82,32.74,34.25,34.79,20.94,22.05,22.38,23.40,21.79,23.68,24.14,25.12,50.64,35.92,39.39,44.29,40.78,177.84,1.59,46.03,46.03,1.78,6.00,46.03,31.34,16.80,16.80,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00

use_cases/2020_12/data/stack_key.csv

@@ -0,0 +1,41 @@
+Designation,Name
+clcl-x1,Existing Coal Class 1
+clcl-x2,Existing Coal Class 2
+clcl-x3,Existing Coal Class 3
+clcl-x4,Existing Coal Class 4
+clng-r1,Coal refit to NG Class 1
+clng-r2,Coal refit to NG Class 2
+clng-r3,Coal refit to NG Class 3
+cbcf-x1,Coal co-burn Biofuel Class 1
+cbcf-x2,Coal co-burn Biofuel Class 2
+cbcf-x3,Coal co-burn Biofuel Class 3
+cbcf-x4,Coal co-burn Biofuel Class 4
+ngcc-x1,NG Combined Cycle Class 1
+ngcc-x2,NG Combined Cycle Class 2
+ngcc-x3,NG Combined Cycle Class 3
+ngcc-n,NG Combined Cycle New
+ngst-x,NG Steam Existing
+nggt-x1,NG Gas Turbine Class 1
+nggt-x2,NG Gas Turbine Class 2
+nggt-x3,NG Gas Turbine Class 3
+nggt-n,NG Gas Turbine New
+ptsg-x,Petroleum Steam Gas Turbine
+othc-x,Existing Biomass/Other
+h2cc-n,Hydrogen Combined Cycle
+h2cc-ig,Integrated Gasification Hydrogen Combined Cycle
+bioe-r1,Bioenergy - Repowered
+becs-n,Bioenergy - New
+h2cc-igcs,Integrated Gasification Hydrogen Combined Cycle w/ Carbon Capture
+ngcs-n,NG Combined Cycle w/ Carbon Capture
+nucl-n1,New Nuclear (Traditional)
+nucl-n2,New Nuclear (SMR)
+hydr-x,Existing Hydropower
+wind-r,Repowered Wind
+wind-n3,Wind (Class 3 Resource)
+wind-n4,Wind (Class 4 Resource)
+wind-n5,Wind (Class 5 Resource)
+wind-n6,Wind (Class 6 Resource)
+wnos-n1,Offshore Wind
+pvft-n4,PV - Fixed Axis (Class 4 Resource)
+pvsx-n4,PV - Single Axis Tracking (Class 4 Resource)
+pvdx-n4,PV - Dual Axis Tracking (Class 4 Resource)

use_cases/2020_12/reports/ARMA.md

@@ -0,0 +1,31 @@
+# ARMA
+
+An AutoRegressiveMovingAverage (ARMA) model was used to train the synthetic power histories
+
+\begin{align}
+x_t = \Sigma_{i=1}^{p} \phi_{i}x_{t-i} + \alpha_{t} + \Sigma_{j=1}^{q} \theta_{j}\alpha_{t-j},
+\end{align}
+
+where x is a vector of dimension $n$, and $\phi_{i}$ and $\theta_{j}$ are both $n$
+by $n$ matrices. When $q=0$, the above is an autoregressive (AR) model of order
+$p$; when $p=0$, the above is a moving average model of order $q$.
+
+Before training the ARMA, a Fourier series is used to detrend the time series.
+
+\begin{align}
+x_t = y_t - \Sigma_{m} \left{a_{m}\sin\left(2 \pi f_{m} t\right) + b_{m}\cos\left(2\pi f_{m} t\right)\right},
+\end{align}
+
+where $1/f_{m}$ is defined as the basis points to detrend on.
+
+## Default
+
+## Default LNHR
+
+## Carbon-Tax
+
+## Carbon-Tax LNHR
+
+## RPS
+
+## RPS LNHR