Add a heating/cooling demand bit

heating_cooling/0_extract_nrel_ts.py

@@ -0,0 +1,118 @@
+# ---------------------------------------
+# Extract ReStock/ComStock data by states
+# ---------------------------------------
+
+# paths must be adjusted
+resstock_dir = "./_geothermal_/data/_NREL_profiles_/ResStock/"
+comstock_dir = "./_geothermal_/data/_NREL_profiles_/ComStock/"
+
+output_dir = "./_heating implementation_/_data processing_/_res_/"
+
+import os
+import pypsa
+
+import geopandas as gpd
+import pandas as pd
+
+# define constants ------------------------------------------------------------
+STATES=["AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", 
+        "FL", "GA", "IA", "ID", "IL", "IN", "KS", "KY", 
+        "LA", "MA", "MD", "ME", "MI", "MN", "MO", "MS", 
+        "MT", "NC", "ND", "NE", "NH", "NJ", "NM", "NV", 
+        "NY", "OH", "OK", "OR", "PA", "RI", "SC", "SD", 
+        "TN", "TX", "UT", "VA", "VT", "WA", "WI", "WV", "WY"]
+
+HEATING_COLS_RESSTOCK = [
+    "out.electricity.heating.energy_consumption.kwh",
+    "out.fuel_oil.heating.energy_consumption.kwh",
+    "out.natural_gas.heating.energy_consumption.kwh",
+    "out.propane.heating.energy_consumption.kwh",
+]
+
+HEATING_COLS_COMSTOCK = [
+    "out.electricity.heating.energy_consumption.kwh",
+    "out.natural_gas.heating.energy_consumption.kwh",
+    "out.other_fuel.heating.energy_consumption.kwh",
+]
+
+COOLING_COLS_RESSTOCK = [
+    "out.electricity.cooling.energy_consumption.kwh",
+    # "out.electricity.cooling_fans_pumps.energy_consumption.kwh"
+]
+
+COOLING_COLS_COMSTOCK = [
+    "out.district_cooling.cooling.energy_consumption.kwh",
+    "out.electricity.cooling.energy_consumption.kwh",
+    "out.other_fuel.cooling.energy_consumption.kwh"
+]
+
+# parameters of the run -------------------------------------------------------
+
+load_type = "heating" # "cooling" or "heating"
+
+output_prefix = "residential"   # "residential" or "commercial"
+
+if (output_prefix == "residential"):
+    data_dir = resstock_dir
+else:
+    data_dir = comstock_dir 
+
+if ((output_prefix == "residential") & (load_type == "heating")):
+        load_type_columns = HEATING_COLS_RESSTOCK
+if ((output_prefix == "commercial") & (load_type == "heating")):
+        load_type_columns = HEATING_COLS_COMSTOCK
+if ((output_prefix == "residential") & (load_type == "cooling")):
+        load_type_columns = COOLING_COLS_RESSTOCK 
+if ((output_prefix == "commercial") & (load_type == "cooling")):
+        load_type_columns = COOLING_COLS_COMSTOCK                      
+
+load_output_suff = "_" + load_type + "_by_states.csv"
+
+# define functions ------------------------------------------------------------
+def extract_state_load(state_path, data_cols=COOLING_COLS_RESSTOCK):
+    state_fls = os.listdir(state_path)
+    load_dfs_list = [None] * len(state_fls)
+
+    for i, fls in enumerate(state_fls):
+        load_df = pd.read_csv(
+            os.path.join(state_fls_path, fls)
+        )
+        load_df["overall_resload"] = load_df[data_cols].sum(axis=1)
+        load_df["heat_consumer"] = fls
+        print(load_df[["upgrade", "in.state", "timestamp", "overall_resload"]].head(2))
+
+        load_dfs_list[i] = load_df[["upgrade", "in.state", "timestamp", "overall_resload"]]
+
+    state_resid_df = pd.concat(load_dfs_list)
+    consolid_profile = (
+        state_resid_df[["timestamp", "in.state", "overall_resload"]]
+        .groupby(["timestamp"])
+        .sum()
+    )
+
+    consolid_profile["in.state"] = consolid_profile["in.state"].str[0:2]
+
+    return consolid_profile 
+
+# run extraction --------------------------------------------------------------
+
+# assuming that heat consumers are same for all the states
+state_fls_path = os.path.join(data_dir, STATES[0])
+state_fls = os.listdir(state_fls_path)
+load_dfs_list = [None] * len(state_fls)
+
+# it's convenient to put all the data into a list
+states = STATES
+dfs_list = [None] * len(states)
+
+for i, state in enumerate(states):
+    print(output_prefix)
+    print(i, state)
+    state_fls_path = os.path.join(data_dir, STATES[i])
+    print(state_fls_path)
+    state_df = extract_state_load(state_fls_path, data_cols=load_type_columns)
+    dfs_list[i] = state_df
+
+state_resid_df = pd.concat(dfs_list)
+
+state_resid_df.to_csv(output_dir + output_prefix + load_output_suff)