feature: Option to Convert Quadratic Value Curves to PWL for Plexos Parser. Fix Reserve Units.

src/r2x/api.py

@@ -74,6 +74,7 @@ def _add_operation_cost_data(  # noqa: C901
         fields: list | None = None,
     ):
         operation_cost_fields = set()
+        x_y_coords = None
         for sub_dict in data:
             if "operation_cost" not in sub_dict.keys():
                 continue
@@ -104,6 +105,9 @@ def _add_operation_cost_data(  # noqa: C901
                         operation_cost_fields.add("heat_rate_a2")
                     if "x_coords" in function_data.model_fields:
                         x_y_coords = dict(zip(function_data.x_coords, function_data.y_coords))
+                    if "points" in function_data.model_fields:
+                        x_y_coords = dict((xyCoord.x, xyCoord.y) for xyCoord in function_data.points)
+                    if x_y_coords:
                         match type(variable_cost):
                             case infrasys.cost_curves.CostCurve:
                                 for i, (x_coord, y_coord) in enumerate(x_y_coords.items()):

src/r2x/exporter/sienna.py

@@ -217,6 +217,7 @@ def process_gen_data(self, fname="gen.csv"):
             "rating",
             "unit_type",
             "active_power",
+            "active_power_limits_max",
             "min_rated_capacity",
             "min_down_time",
             "min_up_time",
@@ -286,6 +287,7 @@ def process_reserves_data(self, fname="reserves.csv") -> None:
                 output_dict = reserve
                 output_dict["direction"] = reserve["direction"].name
                 output_dict["eligible_device_categories"] = "(Generator,Storage)"
+                output_dict["requirement"] = reserve["max_requirement"]
                 contributing_devices = reserve_map.get(reserve["name"])
                 output_dict["contributing_devices"] = str(tuple(contributing_devices)).replace(  # type: ignore
                     "'", ""
@@ -322,6 +324,7 @@ def process_storage_data(self, fname="storage.csv") -> None:
             "rating",
             "input_efficiency",
             "output_efficiency",
+            "initial_energy",
             "storage_capacity",
             "min_storage_capacity",
             "max_storage_capacity",

src/r2x/models/costs.py

@@ -17,29 +17,21 @@ class RenewableGenerationCost(OperationalCost):
 
 
 class HydroGenerationCost(OperationalCost):
-    fixed: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(0.0, "usd/MWh")
+    fixed: Annotated[float, Field(ge=0, description="Cost of using fuel in $.")] = 0.0
     variable: ProductionVariableCostCurve | None = None
 
 
 class ThermalGenerationCost(OperationalCost):
-    start_up: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(
-        0.0, "usd/MWh"
-    )
-    fixed: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(0.0, "usd/MWh")
-    shut_down: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(
-        0.0, "usd/MWh"
-    )
+    start_up: Annotated[float, Field(ge=0, description="Cost of using fuel in $.")] = 0.0
+    fixed: Annotated[float, Field(ge=0, description="Cost of using fuel in $.")] = 0.0
+    shut_down: Annotated[float, Field(ge=0, description="Cost of using fuel in $.")] = 0.0
     variable: ProductionVariableCostCurve | None = None
 
 
 class StorageCost(OperationalCost):
-    start_up: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(
-        0.0, "usd/MWh"
-    )
-    fixed: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(0.0, "usd/MWh")
-    shut_down: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(
-        0.0, "usd/MWh"
-    )
+    start_up: Annotated[float, Field(ge=0, description="Cost of using fuel in $.")] = 0.0
+    fixed: Annotated[float, Field(ge=0, description="Cost of using fuel in $.")] = 0.0
+    shut_down: Annotated[float, Field(ge=0, description="Cost of using fuel in $.")] = 0.0
     energy_surplus_cost: Annotated[FuelPrice, Field(description="Cost of using fuel in $/MWh.")] = FuelPrice(
         0.0, "usd/MWh"
     )

src/r2x/models/generators.py

@@ -116,6 +116,9 @@ class Generator(Device):
     shutdown_cost: (
         Annotated[NonNegativeFloat, Field(description="Cost in $ of shuting down a unit.")] | None
     ) = None
+    active_power_limits_max: Annotated[
+        ApparentPower | None, Field(ge=0, description="Maximum output power rating of the unit (MVA).")
+    ] = None
 
 
 class RenewableGen(Generator):

src/r2x/parser/parser_helpers.py

@@ -5,6 +5,10 @@
 import polars as pl
 import pandas as pd
 from datetime import datetime
+import numpy as np
+from typing import NamedTuple, List
+
+from infrasys.function_data import LinearFunctionData, QuadraticFunctionData, PiecewiseLinearData, XYCoords
 
 
 def pl_filter_year(df, year: int | None = None, year_columns=["t", "year"], **kwargs):
@@ -189,3 +193,33 @@ def resample_data_to_hourly(data_file: pl.DataFrame):
         )
         .select(["year", "month", "day", "hour", "value"])
     )
+
+
+def construct_pwl_from_quadtratic(fn, mapped_records, num_tranches=6):
+    """
+    Given function data of quadratic curve, construct piecewise linear curve with num_tranches tranches.
+    """
+    assert isinstance(fn, QuadraticFunctionData), "Input function data must be of type QuadraticFunctionData"
+
+    a = fn.quadratic_term
+    b = fn.proportional_term
+    c = fn.constant_term
+    x_min = mapped_records["min_rated_capacity"].magnitude
+    x_max = mapped_records["rating"].magnitude
+
+    # Use evenly spaced X values for the tranches
+    # Future iteration should accept custom X values for Bid Cost Markup
+    x_vals = np.linspace(x_min, x_max, num_tranches)
+    y_vals = a * x_vals**2 + b * x_vals + c
+
+    pwl_fn = PiecewiseLinearData(points=[XYCoords(x, y) for x, y in zip(x_vals, y_vals)])
+    return pwl_fn
+
+
+def bid_cost_mark_up(fn, mapped_records):
+    # TODO(ktehranchi): Implement bid-cost markup
+    # First we need to convert whichever type of function we have to a piecewise linear function
+    # This PWL function must have X values definted at the mark-up points
+    # We can easily modify the mark-up prices by changing the Y values of the PWL function
+    # Issue right now is we need to do this for time-varying data but market bid cost isnt implemented
+    pass