add objective discounting across investment periods

Author: nick-gorman

ispypsa_runs/development/ispypsa_inputs/ispypsa_config.yaml

@@ -12,6 +12,8 @@ scenario: Step Change
 # Weighted average cost of capital for annuitisation of generation and transmission
 # costs, as a fraction, i.e. 0.07 is 7%.
 wacc: 0.07
+# Discount rate applied to model objective function, as a fraction, i.e. 0.07 is 7%.
+discount_rate: 0.05
 network:
   # Does the model consider the expansion of sub-region to sub-region transmission
   # capacity
@@ -44,11 +46,11 @@ temporal:
   path_to_parsed_traces: ENV
   year_type: fy
   start_year: 2025
-  end_year: 2025
+  end_year: 2028
   reference_year_cycle: [2018]
   # List of investment period start years. An investment period runs until the next the
   # periods begins.
-  investment_periods: [2025]
+  investment_periods: [2025, 2026]
   aggregation:
     # Representative weeks to use instead of full yearly temporal representation.
     # Options:

src/ispypsa/config/validators.py

@@ -90,9 +90,9 @@ def validate_end_year(cls, end_year: float, info):
     @field_validator("investment_periods")
     @classmethod
     def validate_investment_periods(cls, investment_periods: float, info):
-        if min(investment_periods) > info.data.get("start_year"):
+        if min(investment_periods) != info.data.get("start_year"):
             raise ValueError(
-                "config first investment period must be less than or equal to start_year"
+                "config first investment period must be equal to start_year"
             )
         if len(investment_periods) != len(set(investment_periods)):
             raise ValueError("config all years in investment_periods must be unique")
@@ -107,6 +107,7 @@ class ModelConfig(BaseModel):
     ispypsa_run_name: str
     scenario: Literal[tuple(_ISP_SCENARIOS)]
     wacc: float
+    discount_rate: float
     network: NetworkConfig
     temporal: TemporalConfig
     iasr_workbook_version: str

src/ispypsa/model/build.py

@@ -10,6 +10,7 @@
     _add_generators_to_network,
 )
 from ispypsa.model.initialise import _initialise_network
+from ispypsa.model.investment_period_weights import _add_investment_period_weights
 from ispypsa.model.lines import _add_lines_to_network
 
 
@@ -49,6 +50,10 @@ def build_pypsa_network(
     """
     network = _initialise_network(pypsa_friendly_tables["snapshots"])
 
+    _add_investment_period_weights(
+        network, pypsa_friendly_tables["investment_period_weights"]
+    )
+
     _add_carriers_to_network(network, pypsa_friendly_tables["generators"])
 
     _add_buses_to_network(

src/ispypsa/model/investment_period_weights.py

@@ -0,0 +1,21 @@
+import pandas as pd
+import pypsa
+
+
+def _add_investment_period_weights(
+    network: pypsa.Network, investment_period_weights: pd.DataFrame
+) -> None:
+    """Adds investment period weights defined in a pypsa-friendly `pd.DataFrame` to the `pypsa.Network`.
+
+    Args:
+        network: The `pypsa.Network` object
+        investment_period_weights: `pd.DataFrame` specifying the
+            investment period weights with columns 'period', "years" and 'objective'.
+            Where "period" is the start years of the investment periods, "years" is the
+            length of each investment period, and "objective" is the relative weight of
+            the objective function in each investment period.
+
+    Returns: None
+    """
+    investment_period_weights = investment_period_weights.set_index("period")
+    network.investment_period_weightings = investment_period_weights

src/ispypsa/translator/create_pypsa_friendly_inputs.py

@@ -26,14 +26,16 @@
 from ispypsa.translator.renewable_energy_zones import (
     _translate_renewable_energy_zone_build_limits_to_flow_paths,
 )
-from ispypsa.translator.snapshot import (
+from ispypsa.translator.snapshots import (
     _add_investment_periods,
     _create_complete_snapshots_index,
+    _create_investment_period_weightings,
 )
 from ispypsa.translator.temporal_filters import _filter_snapshots
 
 _BASE_TRANSLATOR_OUPUTS = [
     "snapshots",
+    "investment_period_weights",
     "buses",
     "lines",
     "generators",
@@ -92,6 +94,12 @@ def create_pypsa_friendly_inputs(
         snapshots, config.temporal.investment_periods, config.temporal.year_type
     )
 
+    pypsa_inputs["investment_period_weights"] = _create_investment_period_weightings(
+        config.temporal.investment_periods,
+        config.temporal.end_year,
+        config.discount_rate,
+    )
+
     pypsa_inputs["generators"] = _translate_ecaa_generators(
         ispypsa_tables["ecaa_generators"], config.network.nodes.regional_granularity
     )

src/ispypsa/translator/snapshot.py renamed to src/ispypsa/translator/snapshots.py

@@ -1,5 +1,6 @@
 from datetime import datetime
 
+import numpy as np
 import pandas as pd
 
 from ispypsa.translator.helpers import _get_iteration_start_and_end_time
@@ -106,3 +107,46 @@ def _add_investment_periods(
         )
 
     return result.loc[:, ["investment_periods", "snapshots"]]
+
+
+def _create_investment_period_weightings(
+    investment_periods: list[int], model_end_year: int, discount_rate: float
+) -> pd.DataFrame:
+    """Create a pd.DataFrame specifying the weighting of each investment period based on
+    the sum of discounted periods during the period.
+
+    Args:
+        investment_periods: list of years in which investment periods start.
+        model_end_year: int specifying the last year modelling
+        discount_rate: fraction (float) specifying the discount rate i.e. 5% is 0.05.
+
+    Returns: pd.DataFrame with columns 'period', "years" and 'objective'. Where
+        "period" is the start years of the investment periods, "years" is the length
+        of each investment period, and "objective" is the relative weight of the
+        objective function in each investment period.
+    """
+    # Add model_end_year to calculate final period length
+    all_years = investment_periods + [model_end_year]
+
+    # Calculate period lengths
+    investment_period_lengths = np.diff(all_years).astype("int64")
+
+    # Create DataFrame with periods and their lengths
+    investment_period_weightings = pd.DataFrame(
+        {"period": investment_periods, "years": investment_period_lengths}
+    )
+
+    model_start_year = investment_periods[0]
+
+    def calc_weighting(period_start_year, period_length):
+        T0 = period_start_year - model_start_year
+        T1 = T0 + period_length
+        r = discount_rate
+        discounted_weights = [(1 / (1 + r) ** t) for t in range(T0, T1)]
+        return sum(discounted_weights)
+
+    investment_period_weightings["objective"] = investment_period_weightings.apply(
+        lambda row: calc_weighting(row["period"], row["years"]), axis=1
+    )
+
+    return investment_period_weightings