Merge pull request #87 from staadecker/add_storage

add_storage post process

Author: PatyHidalgo

switch_model/tools/templates/config.yaml

@@ -39,4 +39,12 @@ get_inputs:
   #  ca_policies_scenario_id:
   #  enable_planning_reserves:
   #  generation_plant_technologies_scenario_id:
-  #  variable_o_m_cost_scenario_id:
+  #  variable_o_m_cost_scenario_id:
+# add_storage was used by Martin when studying LDES
+# you likely don't need to use these parameters
+# they won't impact your runs
+# add_storage:
+  # costs_scenario: 0
+  # plants_scenario: 0
+  # constant_scenario: 0
+  # minimums_scenario: 0

switch_model/wecc/get_inputs/post_process_steps/add_storage.py

@@ -0,0 +1,142 @@
+"""
+This post-process step was created by Martin Staadecker
+when studying long duration energy storage. It
+allows adding storage technologies from a Google Sheet to
+the csvs in the inputs folder.
+"""
+import pandas as pd
+
+from switch_model.wecc.get_inputs.register_post_process import register_post_process
+
+
+def fetch_df(tab_name, key, config):
+    """
+    Returns a dataframe from the google sheet and filters it by the key
+    """
+    TAB_NAME_GID = {
+        "constants": 0,
+        "plants": 889129113,
+        "costs": 1401952285,
+        "minimums": 1049456965
+    }
+    SHEET_ID = "1SJrj039T1T95NLTs964VQnsfZgo2QWCo29x2ireVYcU"
+
+    gid = TAB_NAME_GID[tab_name]
+    url = f"https://docs.google.com/spreadsheet/ccc?key={SHEET_ID}&output=csv&gid={gid}"
+
+    df: pd.DataFrame = pd.read_csv(url, index_col=False) \
+        .replace("FALSE", 0) \
+        .replace("TRUE", 1)
+
+    if "description" in df.columns:
+        df = df.drop("description", axis=1)
+
+    if key is not None:
+        df = filer_by_scenario(df, key, config)
+    return df
+
+
+def filer_by_scenario(df, scenario_column, config):
+    """
+    Filters a dataframe by a scenario param
+    """
+    if scenario_column in config:
+        scenario = config[scenario_column]
+    else:
+        scenario = input(f"Which scenario do you want for '{scenario_column}' (default 0) : ")
+        scenario = int(scenario) if scenario != "" else 0
+    df = df[df[scenario_column] == scenario]
+    return df.drop(scenario_column, axis=1)
+
+
+def cross_join(df1, df2):
+    return df1.assign(key=1).merge(
+        df2.assign(key=1),
+        on="key"
+    ).drop("key", axis=1)
+
+
+def add_to_csv(filename, to_add, primary_key=None, append=True):
+    """
+    Used to append a dataframe to an input .csv file
+    """
+    if append:
+        try:
+            df = pd.read_csv(filename, index_col=False)
+            df = pd.concat([df, to_add], ignore_index=True)[df.columns]
+        except FileNotFoundError:
+            df = to_add
+    else:
+        df = to_add
+    # Confirm that primary_key is unique
+    if primary_key is not None:
+        assert len(df[primary_key]) == len(df[primary_key].drop_duplicates())
+    df.to_csv(filename, index=False)
+
+
+def drop_previous_candidate_storage():
+    """
+    Drops all candidate storage from the model
+    """
+    # Get the generation projects
+    STORAGE_TECH = "Battery_Storage"
+
+    gen = pd.read_csv("generation_projects_info.csv", index_col=False)
+    # Find generation projects that are both storage and not predetermined (i.e. candidate)
+    predetermined_gen = pd.read_csv("gen_build_predetermined.csv", index_col=False)["GENERATION_PROJECT"]
+    should_drop = (gen["gen_tech"] == STORAGE_TECH) & ~gen["GENERATION_PROJECT"].isin(predetermined_gen)
+    # Find projects that we should drop (candidate storage)
+    gen_to_drop = gen[should_drop]["GENERATION_PROJECT"]
+    # Verify we're dropping the right amount
+    assert len(gen_to_drop) == 50  # 50 is the number of load zones. we expect one candidate per load zone
+
+    # Drop and write output
+    gen = gen[~should_drop]
+    gen.to_csv("generation_projects_info.csv", index=False)
+
+    # Drop the dropped generation projects from gen_build_costs.csv
+    costs = pd.read_csv("gen_build_costs.csv", index_col=False)
+    costs = costs[~costs["GENERATION_PROJECT"].isin(gen_to_drop)]
+    costs.to_csv("gen_build_costs.csv", index=False)
+
+
+@register_post_process(
+    name="add_storage",
+    msg="Adding storage from Google Sheets",
+    only_with_config=True,
+    priority=1  # Increased priority (default is 2) so that it always runs before replace_plants_in_zone_all.py
+)
+def main(config):
+    # Drop previous candidate storage from inputs
+    drop_previous_candidate_storage()
+
+    # Get the generation storage plants from Google Sheet
+    gen_projects = fetch_df("constants", "constant_scenario", config).set_index("param_name").transpose()
+    gen_projects = cross_join(gen_projects, fetch_df("plants", "plants_scenario", config))
+
+    # Append the storage plants to the inputs
+    add_to_csv("generation_projects_info.csv", gen_projects, primary_key="GENERATION_PROJECT")
+
+    # Create min_per_tech.csv
+    min_projects = fetch_df("minimums", "minimums_scenario", config)
+    add_to_csv("min_per_tech.csv", min_projects, primary_key=["gen_tech", "period"], append=False)
+
+    # Get the plant costs from GSheets and append to costs
+    storage_costs = fetch_df("costs", "costs_scenario", config)
+    add_to_csv("gen_build_costs.csv", storage_costs, primary_key=["GENERATION_PROJECT", "build_year"])
+
+    # Create add_storage_info.csv
+    pd.DataFrame([config]).transpose().to_csv("add_storage_info.csv", header=False)
+
+    # Add the storage types to the graphs
+    gen_type = gen_projects[["gen_tech", "gen_energy_source"]].drop_duplicates()
+    gen_type.columns = ["gen_tech", "energy_source"]
+    gen_type["map_name"] = "default"
+    gen_type["gen_type"] = "Storage"
+    pd.concat([
+        pd.read_csv("graph_tech_types.csv", index_col=False), gen_type
+    ]).to_csv("graph_tech_types.csv", index=False)
+
+
+if __name__ == "__main__":
+    main({})