custom nodes

workflow/rules/preprocess.smk

@@ -164,6 +164,12 @@ rule variable_costs:
     shell:
         'python workflow/scripts/osemosys_global/variablecosts.py 2> {log}'
 
+def demand_custom_csv() -> str:
+    if config["nodes_to_add"]:
+        return "resources/data/custom_nodes/specified_annual_demand.csv"
+    else:
+        return []
+
 rule demand_projections:
     message:
         "Generating demand data..."
@@ -175,10 +181,12 @@ rule demand_projections:
         iamc_urb = "resources/data/iamc_db_URB_Countries.xlsx",
         iamc_missing = "resources/data/iamc_db_POP_GDPppp_URB_Countries_Missing.xlsx",
         td_losses = "resources/data/T&D Losses.xlsx",
-	    ember = "resources/data/ember_yearly_electricity_data.csv"
+	    ember = "resources/data/ember_yearly_electricity_data.csv",
+        custom_nodes = demand_custom_csv()
     params:
         start_year = config['startYear'],
         end_year = config['endYear'],
+        custom_nodes = config["nodes_to_add"]
     output:
         csv_files = 'results/data/SpecifiedAnnualDemand.csv',
     log:

workflow/scripts/osemosys_global/demand/custom.py

@@ -0,0 +1,48 @@
+"""Custom Nodes logic"""
+
+import pandas as pd
+import itertools
+
+
+def _get_custom_demand_expected(
+    nodes: list[str], start_year: int, end_year: int
+) -> pd.DataFrame:
+    """Gets formatted expected custom data"""
+
+    years = range(start_year, end_year + 1)
+
+    df = pd.DataFrame(
+        list(itertools.product(nodes, years)), columns=["CUSTOM_NODE", "YEAR"]
+    )
+    df["REGION"] = "GLOBAL"
+    df["FUEL"] = "ELC" + df["CUSTOM_NODE"] + "02"
+
+    return df
+
+
+def import_custom_demand_data(csv: str) -> pd.DataFrame:
+    """Gets all custom demand data"""
+    return pd.read_csv(csv)
+
+
+def get_custom_demand_data(
+    all_custom: pd.DataFrame, nodes: list[str], start_year: int, end_year: int
+) -> pd.DataFrame:
+    """Gets merged custom demand data"""
+
+    expected = _get_custom_demand_expected(nodes, start_year, end_year)
+
+    df = pd.merge(expected, all_custom, how="left", on=["CUSTOM_NODE", "YEAR"])
+    df = df[["REGION", "FUEL", "YEAR", "VALUE"]]
+
+    return df
+
+
+def merge_default_custom_data(
+    default: pd.DataFrame, custom: pd.DataFrame
+) -> pd.DataFrame:
+    assert default.columns.equals(custom.columns)
+    df = pd.concat([default, custom], ignore_index=True)
+    df["VALUE"] = df["VALUE"].round(2)
+    df = df.drop_duplicates(keep="first", subset=["REGION", "FUEL", "YEAR"])
+    return df

workflow/scripts/osemosys_global/demand/main.py

@@ -12,6 +12,11 @@
 from data import get_historical_urban_pop_wb, get_iamc_data, format_for_writing
 from regression import perform_regression
 from projection import perform_node_projections
+from custom import (
+    get_custom_demand_data,
+    import_custom_demand_data,
+    merge_default_custom_data,
+)
 
 
 def main(
@@ -58,6 +63,8 @@ def main(
         csv = snakemake.output.csv_files
         start_year = snakemake.params.start_year
         end_year = snakemake.params.end_year
+        custom_nodes = snakemake.params.custom_nodes
+        custom_nodes_data = snakemake.input.custom_nodes
     else:
         file_plexos = "resources/data/PLEXOS_World_2015_Gold_V1.1.xlsx"
         file_plexos_demand = "resources/data/All_Demand_UTC_2015.csv"
@@ -72,6 +79,8 @@ def main(
         csv = "SpecifiedAnnualDemand.csv"
         start_year = 2020
         end_year = 2050
+        custom_nodes = ["INDWE", "INDEA", "INDNE", "INDNO", "INDSO"]
+        custom_nodes_data = "resources/data/custom_nodes/specified_annual_demand.csv"
 
     # first bring together original and missing iamc data
     plexos = import_plexos_2015(file_plexos)
@@ -102,4 +111,9 @@ def main(
 
     df = df[df.YEAR.isin(range(start_year, end_year + 1))]
 
+    if custom_nodes:
+        all_custom = import_custom_demand_data(custom_nodes_data)
+        custom = get_custom_demand_data(all_custom, custom_nodes, start_year, end_year)
+        df = merge_default_custom_data(df, custom)
+
     df.to_csv(csv, index=False)