Merge pull request #42 from Open-ISP/template-energy-policy

Author: emilyzzhao

src/ispypsa/iasr_table_caching/local_cache.py

@@ -53,11 +53,25 @@
     "technology_specific_lcfs",
 ] + _GENERATOR_PROPERTY_TABLES
 
+_POLICY_REQUIRED_TABLES = [
+    "vic_renewable_target_trajectory",
+    "qld_renewable_target_trajectory",
+    "powering_australia_plan_trajectory",
+    "capacity_investment_scheme_renewable_trajectory",
+    "capacity_investment_scheme_storage_trajectory",
+    "nsw_roadmap_storage_trajectory",
+    "vic_storage_target_trajectory",
+    "vic_offshore_wind_target_trajectory",
+    "nsw_roadmap_renewable_trajectory",
+    "tas_renewable_target_trajectory",
+]
+
 REQUIRED_TABLES = (
     _NETWORK_REQUIRED_TABLES
     + _GENERATORS_STORAGE_REQUIRED_SUMMARY_TABLES
     + _GENERATORS_REQUIRED_PROPERTY_TABLES
     + _NEW_ENTRANTS_COST_TABLES
+    + _POLICY_REQUIRED_TABLES
 )
 
 

src/ispypsa/templater/create_template.py

@@ -5,6 +5,9 @@
 from ispypsa.templater.dynamic_generator_properties import (
     _template_generator_dynamic_properties,
 )
+from ispypsa.templater.energy_policy_targets import (
+    _template_energy_policy_targets,
+)
 from ispypsa.templater.flow_paths import (
     _template_regional_interconnectors,
     _template_sub_regional_flow_paths,
@@ -148,6 +151,10 @@ def create_ispypsa_inputs_template(
 
     template.update(dynamic_generator_property_templates)
 
+    energy_policy_targets = _template_energy_policy_targets(iasr_tables, scenario)
+
+    template.update(energy_policy_targets)
+
     return template
 
 

src/ispypsa/templater/energy_policy_targets.py

@@ -0,0 +1,241 @@
+import logging
+import re
+from pathlib import Path
+
+import pandas as pd
+
+from .mappings import _TEMPLATE_RENEWABLE_ENERGY_TARGET_MAP
+
+
+def _template_energy_policy_targets(
+    iasr_tables: dict[str : pd.DataFrame], scenario: str
+) -> dict[str, pd.DataFrame]:
+    """Creates ISPyPSA templates for energy policy targets including NEM-wide and state-level policies.
+
+    Args:
+        iasr_tables: Dict of tables from the IASR workbook that have been parsed using
+            `isp-workbook-parser`.
+        scenario: Scenario obtained from the model configuration
+
+    Returns:
+        `dict[pd.DataFrame]`: Templates for renewable share targets, powering australia share targets (by scenario)
+            renewable generation targets, and technology capacity targets
+    """
+    logging.info("Creating templates for energy policy targets")
+
+    # Create templates for energy policy targets
+    renewable_share_targets = _template_renewable_share_targets(iasr_tables)
+
+    power_aus_plan = iasr_tables["powering_australia_plan_trajectory"]
+    power_aus_plan = _template_powering_australia_plan(power_aus_plan, scenario)
+
+    renewable_generation_targets = _template_renewable_generation_targets(iasr_tables)
+
+    technology_capacity_targets = _template_technology_capacity_targets(iasr_tables)
+
+    return {
+        "renewable_share_targets": renewable_share_targets,
+        "powering_australia_plan": power_aus_plan,
+        "renewable_generation_targets": renewable_generation_targets,
+        "technology_capacity_targets": technology_capacity_targets,
+    }
+
+
+def _template_renewable_share_targets(
+    iasr_tables: dict[str : pd.DataFrame],
+) -> pd.DataFrame:
+    """Creates ISPyPSA templates for renewable share targets from trajectory CSVs.
+    Uses TEMPLATE_RENEWABLE_ENERGY_TARGET_MAP to identify files and their
+        corresponding regions.
+
+    Args:
+        iasr_tables: Dict of tables from the IASR workbook that have been parsed using
+            `isp-workbook-parser`.
+
+    Returns:
+        `pd.DataFrame`: Template containing renewable share targets with columns for
+            financial year, region_id, policy_id, and percentage values in decimal form
+    """
+    logging.info("Creating template for renewable share targets")
+    state_renewable_share_targets = []
+
+    # Get mapping for this function
+    target_files = _TEMPLATE_RENEWABLE_ENERGY_TARGET_MAP[
+        "template_renewable_share_targets"
+    ]
+
+    for target in target_files:
+        df = iasr_tables[target["csv"]]
+
+        df = df.melt(id_vars=df.columns[0], var_name="FY", value_name="pct")
+        df = df[df[df.columns[0]].str.contains("share", case=False)]
+        df["region_id"] = target["region_id"]
+        df["policy_id"] = target["policy_id"]
+        df["pct"] = df["pct"].astype(float)
+
+        state_renewable_share_targets.append(
+            df[["FY", "region_id", "policy_id", "pct"]]
+        )
+
+    merged_state_renewable_share_targets = pd.concat(
+        state_renewable_share_targets, ignore_index=True
+    )
+    merged_state_renewable_share_targets["FY"] = merged_state_renewable_share_targets[
+        "FY"
+    ].str.replace("-", "_")
+
+    return merged_state_renewable_share_targets
+
+
+def _template_powering_australia_plan(
+    power_aus_plan: Path | str, scenario: str
+) -> pd.DataFrame:
+    """Creates ISPyPSA template for the Powering Australia Plan renewable share
+    trajectories for selected scenarios.
+
+    Args:
+        powering_aus: pd.DataFrame table from IASR workbook specifying Powering Australia Plan renewable share targets.
+        scenario: Scenario obtained from the model configuration
+
+    Returns:
+        `pd.DataFrame`: Template containing Powering Australia Plan targets
+            with columns for financial year, policy_id and percentage values in
+            decimal form for the selected scenario
+    """
+    logging.info("Creating template for Powering Australia Plan")
+
+    # Remove rows containing "Notes" in the first column
+    power_aus_plan = power_aus_plan[
+        ~power_aus_plan.iloc[:, 0].str.contains("Notes", case=False, na=False)
+    ]
+
+    # Filter for rows where the first column matches the specified scenario
+    power_aus_plan = power_aus_plan[power_aus_plan.iloc[:, 0].eq(scenario)]
+
+    # Drop the first column (scenario name) to keep only year values
+    power_aus_plan = power_aus_plan.iloc[:, 1:].reset_index(drop=True)
+
+    # Melt the dataframe, excluding the first column from id_vars
+    power_aus_plan = power_aus_plan.melt(var_name="FY", value_name="pct").dropna(
+        subset=["pct"]
+    )
+
+    # Convert percentage to decimal if needed
+    power_aus_plan["pct"] = power_aus_plan["pct"].astype(float)
+
+    power_aus_plan["FY"] = power_aus_plan["FY"].str.replace("-", "_")
+
+    # append new column which is the policy_id
+    power_aus_plan["policy_id"] = "power_aus"
+    return power_aus_plan
+
+
+def _template_technology_capacity_targets(
+    iasr_tables: dict[str : pd.DataFrame],
+) -> pd.DataFrame:
+    """Creates ISPyPSA templates for technology capacity targets including
+    CIS renewable target and storage and offshore wind trajectories.
+    Uses TEMPLATE_RENEWABLE_ENERGY_TARGET_MAP to identify
+    files and their corresponding regions.
+
+    Args:
+        iasr_tables: Dict of tables from the IASR workbook that have been parsed using
+            `isp-workbook-parser`.
+    Returns:
+        `pd.DataFrame`: Template containing technology capacity trajectories
+            with columns for financial year, region_id and capacity in MW
+    """
+    logging.info("Creating template for technology capacity targets")
+
+    technology_capacity_targets = []
+    target_files = _TEMPLATE_RENEWABLE_ENERGY_TARGET_MAP[
+        "template_technology_capacity_targets"
+    ]
+
+    for target in target_files:
+        df = iasr_tables[target["csv"]]
+        # Extract technology type from the row containing "target (MW)"
+        target_row_mask = df.iloc[:, 0].str.contains("target", case=False) & df.iloc[
+            :, 0
+        ].str.contains("MW", case=False)
+
+        target_row_idx = df.index[target_row_mask][0]
+        # Create a new dataframe with just FY and capacity
+        values_df = pd.DataFrame(
+            {"FY": df.columns[1:], "capacity_mw": df.iloc[target_row_idx, 1:]}
+        )
+
+        values_df["capacity_mw"] = values_df["capacity_mw"].astype(float)
+        values_df["region_id"] = target["region_id"]
+        values_df["policy_id"] = target["policy_id"]
+
+        technology_capacity_targets.append(values_df)
+
+    merged_technology_capacity_targets = pd.concat(
+        technology_capacity_targets, ignore_index=True
+    )
+    merged_technology_capacity_targets["FY"] = merged_technology_capacity_targets[
+        "FY"
+    ].str.replace("-", "_")
+
+    merged_technology_capacity_targets = merged_technology_capacity_targets.sort_values(
+        ["region_id", "policy_id", "FY"]
+    ).reset_index(drop=True)
+
+    return merged_technology_capacity_targets
+
+
+def _template_renewable_generation_targets(
+    iasr_tables: dict[str : pd.DataFrame],
+) -> pd.DataFrame:
+    """Creates ISPyPSA templates for renewable generation targets.
+    Uses TEMPLATE_RENEWABLE_ENERGY_TARGET_MAP to identify files and their corresponding regions.
+
+    Args:
+        iasr_tables: Dict of tables from the IASR workbook that have been parsed using
+            `isp-workbook-parser`.
+
+    Returns:
+        `pd.DataFrame`: Template containing renewable capacity trajectories with columns for
+            financial year, region_id and capacity in MW (converted from GWh)
+
+    """
+    logging.info("Creating template for renewable generation trajectories")
+
+    renewable_generation_targets = []
+    target_files = _TEMPLATE_RENEWABLE_ENERGY_TARGET_MAP[
+        "template_renewable_generation_targets"
+    ]
+
+    for target in target_files:
+        df = iasr_tables[target["csv"]]
+        # Check for GWh in row indices
+        if not df.iloc[:, 0].str.contains("GWh", case=False).any():
+            raise ValueError(f"No GWh values found in {target['csv']}.csv")
+
+        # if exists, remove the "Notes" row
+        df = df[~df.iloc[:, 0].str.contains("Notes", case=False)]
+
+        renewable_gen_target = df.melt(
+            id_vars=df.columns[0], var_name="FY", value_name="capacity_gwh"
+        )
+
+        # Convert GWh to MWh
+        renewable_gen_target["capacity_mwh"] = (
+            renewable_gen_target["capacity_gwh"].astype(float) * 1000
+        )
+        renewable_gen_target["region_id"] = target["region_id"]
+        renewable_gen_target["policy_id"] = target["policy_id"]
+        renewable_generation_targets.append(
+            renewable_gen_target[["FY", "region_id", "policy_id", "capacity_mwh"]]
+        )
+
+    # Combine all dataframes
+    merged_renewable_generation_targets = pd.concat(
+        renewable_generation_targets, ignore_index=True
+    )
+    merged_renewable_generation_targets["FY"] = merged_renewable_generation_targets[
+        "FY"
+    ].str.replace("-", "_")
+
+    return merged_renewable_generation_targets

src/ispypsa/templater/manually_extracted_template_tables/6.0/policy_generator_types.csv

@@ -0,0 +1,55 @@
+policy_id,generator
+cis_generator,Large scale Solar PV
+cis_generator,Wind
+cis_generator,Wind - offshore (fixed)
+cis_generator,Wind - offshore (floating)
+cis_storage,Solar Thermal (15hrs storage)
+cis_storage,Pumped Hydro
+cis_storage,Hydrogen reciprocating engines
+cis_storage,Battery Storage (1hr storage)
+cis_storage,Battery Storage (2hrs storage)
+cis_storage,Battery Storage (4hrs storage)
+cis_storage,Battery Storage (8hrs storage)
+nsw_generator,Large scale Solar PV
+nsw_generator,Wind
+nsw_generator,Wind - offshore (fixed)
+nsw_generator,Wind - offshore (floating)
+nsw_generator,Solar Thermal (15hrs storage)
+nsw_generator,Biomass
+nsw_storage,Battery Storage (8hrs storage)
+nsw_storage,Pumped Hydro (8hrs storage)
+nsw_storage,Pumped Hydro (24hrs storage)
+nsw_storage,Pumped Hydro (48hrs storage)
+vic_storage,Battery Storage (1hr storage)
+vic_storage,Battery Storage (2hrs storage)
+vic_storage,Battery Storage (4hrs storage)
+vic_storage,Battery Storage (8hrs storage)
+vic_storage,Pumped Hydro (8hrs storage)
+vic_storage,Pumped Hydro (24hrs storage)
+vic_storage,Pumped Hydro (48hrs storage)
+vic_offshore_wind,Wind - offshore (fixed)
+vic_offshore_wind,Wind - offshore (floating)
+qret,Hydro
+qret,Large scale Solar PV
+qret,Wind
+qret,Biomass
+qret,Solar Thermal (15hrs storage)
+vret,Hydro
+vret,Large scale Solar PV
+vret,Wind
+vret,Wind - offshore (fixed)
+vret,Wind - offshore (floating)
+vret,Biomass
+vret,Solar Thermal (15hrs storage)
+tret,Hydro
+tret,Large scale Solar PV
+tret,Solar Thermal (15hrs storage)
+tret,Wind
+tret,Wind - offshore (fixed)
+tret,Wind - offshore (floating)
+power_aus,Hydro
+power_aus,Large scale Solar PV
+power_aus,Solar Thermal (15hrs storage)
+power_aus,Wind
+power_aus,Wind - offshore (fixed)
+power_aus,Wind - offshore (floating)