clean up unused data

mgarrouste · Oct 4, 2023 · 1cc8fb0 · 1cc8fb0
1 parent ca4c8e2
commit 1cc8fb0
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Showing 6 changed files with 7 additions and 170 deletions.
diff --git a/use_cases/LWR_FT_2023/data/ENC_DF.csv b/use_cases/LWR_FT_2023/data/ENC_DF.csv
diff --git a/use_cases/LWR_FT_2023/data/ENC_JF.csv b/use_cases/LWR_FT_2023/data/ENC_JF.csv
diff --git a/use_cases/LWR_FT_2023/data/ENC_MG.csv b/use_cases/LWR_FT_2023/data/ENC_MG.csv
diff --git a/use_cases/LWR_FT_2023/data/data_cleaner.ipynb b/use_cases/LWR_FT_2023/data/data_cleaner.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The goal of this notebook is to pre-process electricity market data to use it for ARMA training"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

diff --git a/...ses/LWR_FT_2023/data/plot_braidwood.ipynb → ...ata/plot_braidwood_hist_elec_prices.ipynb b/...ses/LWR_FT_2023/data/plot_braidwood.ipynb → ...ata/plot_braidwood_hist_elec_prices.ipynb
diff --git a/use_cases/LWR_FT_2023/run/functions.py b/use_cases/LWR_FT_2023/run/functions.py
@@ -118,35 +118,6 @@ def find_lower_nearest_idx(array, value):
       idx = i
   return idx
 
-def co2_supply_curve_comb(data,meta):
-  """
-    Determines the cost of CO2 as a function of the quantity asked for, 
-    For HTSE and FT combined component cases
-    Based on preliminary data from D. Wendt analysis for Braidwood NPP
-    @ In, data, dict, request for data
-    @ In, meta, dict, state information
-    @ Out, data, dict, filled data
-    @ In, meta, dict, state information
-  """
-  co2_cost = 0
-  comp_cap = meta['HERON']['RAVEN_vars']['htse_ft_capacity'] #MWe (negative value)
-  elec_to_h2_rate = 25.13 #kg-H2/MWe
-  h2_to_co2_rate = 6.58/1.06 #kg-Co2/kg-h2
-  comp_cap = np.abs(comp_cap)
-  co2_demand_year = 365*24*comp_cap*elec_to_h2_rate*h2_to_co2_rate #(kg-CO2/year) neg
-  # Get the data for the NPP
-  labels = meta['HERON']['Case'].get_labels()
-  location = labels['location']
-  location_path = '../data/'+str(location)+'_co2.csv'
-  df = pd.read_csv(os.path.join(os.path.dirname(__file__), location_path))
-  cost_data = df.iloc[:,-1].to_numpy()
-  co2_demand_data = df.iloc[:,-2].to_numpy()
-  diff = np.absolute(co2_demand_data-co2_demand_year)
-  idx = np.argmin(diff)
-  co2_cost = cost_data[idx]
-  data = {'reference_price': -co2_cost*co2_demand_year}
-  return data, meta
-
 def co2_supply_curve(data, meta):
   """
     Determines the cost of CO2 as a function of the quantity asked for, 
@@ -174,33 +145,6 @@ def co2_supply_curve(data, meta):
   data = {'reference_price': -co2_cost*co2_demand_year}
   return data, meta 
 
-def co2_supply_curve_test(data, meta):
-  """
-    Determines the cost of CO2 as a function of the quantity asked for, 
-    Based on data from D. Wendt analysis on CO2 feedstock
-    @ In, data, dict, request for data
-    @ In, meta, dict, state information
-    @ Out, data, dict, filled data
-    @ In, meta, dict, state information
-  """
-  co2_cost = 0
-  ft_cap = meta['HERON']['RAVEN_vars']['ft_capacity'] #kg-H2
-  h2_rate = 1.06
-  co2_rate = 6.58
-  co2_demand_year = 365*24*np.abs(ft_cap)*co2_rate/h2_rate #(kg/year)
-  # Get the data for the NPP
-  labels = meta['HERON']['Case'].get_labels()
-  location = labels['location']
-  location_path = '../data/'+str(location)+'_co2.csv'
-  df = pd.read_csv(os.path.join(os.path.dirname(__file__), location_path))
-  cost_data = df.iloc[:,-1].to_numpy()
-  co2_demand_data = df.iloc[:,-2].to_numpy()
-  diff = np.absolute(co2_demand_data-co2_demand_year)
-  idx = np.argmin(diff)
-  co2_cost = cost_data[idx]
-  data = {'reference_price': -co2_cost*co2_demand_year}
-  return data, meta 
-
 def jet_fuel_price(data, meta):
   """
     Determines the price of jet fuel given the year of the simulation