bus mapping to variables and input info (resource siting etc.)

gtep/demo_pull_vars_for_bus.py

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+import pandas as pd
+from pathlib import Path
+
+# Net Capacity Factor || CAPEX         || Construction Financing Cost || Overnight Capital Cost || fixed operation etc. || variable operation etc. || fuel costs($/MWh) (fuel costs won't exist for all types, can be assumed 0 in that case)
+# Summart_CF          || Summary_CAPEX || Scrape                      || Scrape                 || Scrape               || Scrape                  || Summary_Fuel
+
+# pull the bus gen mappigs
+bus_data_df = pd.read_csv("Bus_data_gen_mappings.csv")
+
+# read pricing data
+pricing_data_book_names = [
+    "Land-Based Wind",
+    "Solar - CSP",
+    "Natural Gas_FE",
+    "Coal_FE",
+    "Biopower",
+    "Nuclear",
+    "Geothermal",
+    "Solar - Utility PV",
+]
+pricing_data_sheet = "./gtep/2022 v3 Annual Technology Baseline Workbook Mid-year update 2-15-2023 Clean.xlsx"
+pricing_data_sheet_path = Path(pricing_data_sheet)
+pricing_dict = {}
+for this_book_name in pricing_data_book_names:
+    pricing_dict[this_book_name] = pd.read_excel(
+        pricing_data_sheet_path, this_book_name
+    )
+
+# now show a demo of how you can use the bus data df to pull data of interest
+
+# example: get Land-Basd Wind CAPEX for all counties using the Moderate value for all years
+# CAPEX ($/kW)
+
+# define the gen type
+gen_type = "Land-Based Wind"
+var_of_interest = "CAPEX ($/kW)"
+subvar_of_interest = "Moderate"
+
+demo_df = pricing_dict[gen_type][
+    (pricing_dict[gen_type]["Key1"] == var_of_interest)
+    & (pricing_dict[gen_type]["Key3"] == subvar_of_interest)
+]
+
+# bus_data_df['Land-Based Wind']
+
+bus_cutdown_df = bus_data_df[["Bus Name", gen_type]]
+the_thing_we_want = bus_cutdown_df["Land-Based Wind"].iloc[0]
+the_row_that_has_the_prices = demo_df[demo_df["Key2"] == the_thing_we_want]
+bus_capex_df = pd.merge(bus_cutdown_df, demo_df, left_on=gen_type, right_on="Key2")
+
+pass