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Modify Generator Attributes with Custom Formulas

This guide shows how to apply custom formulas to existing generator attributes, such as increasing fixed O&M costs based on plant age.

Overview

The resource_attr_modifiers parameter allows you to define custom formulas that modify generator attributes based on other data fields. This is useful for:

  • Age-based cost adjustments: Increase O&M costs as plants get older
  • Regional cost scaling: Apply region-specific multipliers
  • Capacity-based modifications: Scale costs with unit size
  • Custom business logic: Apply any formula using available data

Basic Usage

Structure

resource_attr_modifiers:
  <technology_name>:
    - attribute: <column_to_modify>
      formula:
        op: <operation>  # "add", "mul", "sub", "truediv", or "replace"
        rate: <numeric_value>
        multiplier: <column_name>

Parameters:

  • <technology_name>: Technology to modify (case-insensitive substring match)
  • attribute: Column in generator dataframe to modify
  • formula.op: Operation to apply (see below)
  • formula.rate: Numeric rate/coefficient
  • formula.multiplier: Column name containing values to multiply by rate

Operations

Operation Behavior Formula
add Add to existing value new_value = old_value + (rate × multiplier)
mul Multiply existing value new_value = old_value × (rate × multiplier)
sub Subtract from existing value new_value = old_value - (rate × multiplier)
truediv Divide existing value new_value = old_value / (rate × multiplier)
replace Replace existing value new_value = rate × multiplier

Examples

Increase O&M with Plant Age

Increase fixed O&M costs by $126/kW-yr for each year of plant age:

resource_attr_modifiers:
  conventional steam coal:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 126  # $/kW-yr per year of age
        multiplier: age

How it works:

  1. Matches all generators with "conventional steam coal" in technology name
  2. For each matched generator: fom_per_mwyr = fom_per_mwyr + (126 × age)
  3. Example: 20-year-old plant adds $2,520/kW-yr to base O&M

Multiple Attributes for One Technology

Apply multiple formulas to the same technology:

resource_attr_modifiers:
  natural gas fired combined cycle:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 50  # $/kW-yr per year
        multiplier: age
    - attribute: heat_rate_mmbtu_mwh
      formula:
        op: mul
        rate: 1.005  # 0.5% degradation per year
        multiplier: age

This increases both fixed O&M and heat rate as plants age.

Replace Instead of Add

Set a value based entirely on a formula (ignoring existing value):

resource_attr_modifiers:
  biomass:
    - attribute: fom_per_mwyr
      formula:
        op: replace
        rate: 100  # $/kW-yr base
        multiplier: capacity_factor_multiplier

With replace, the original fom_per_mwyr value is ignored and completely replaced by 100 × capacity_factor_multiplier.

Multiple Technologies

Modify different technologies with different formulas:

resource_attr_modifiers:
  conventional steam coal:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 126
        multiplier: age
  natural gas:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 75
        multiplier: age
  nuclear:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 200
        multiplier: age

Each technology gets its own age-based adjustment rate.

Available Attributes

Common attributes you can modify:

Attribute Description Units
fom_per_mwyr Fixed O&M cost $/MW-yr
vom_per_mwh Variable O&M cost $/MWh
heat_rate_mmbtu_mwh Heat rate MMBtu/MWh
capex_mw Capital cost $/MW
minimum_load_mw Minimum load MW
ramp_rate_mw_per_min Ramp rate MW/min

Available multiplier columns depend on your data schema. Common ones:

  • age: Plant age in years
  • capacity_mw: Nameplate capacity
  • operating_year: Year plant started operation
  • Custom columns from your data tables

Column Requirements

Both the attribute and multiplier columns must exist in the generator dataframe. If either is missing, PowerGenome will raise a KeyError.

Technology Matching

Technology names are matched case-insensitively using substring matching:

resource_attr_modifiers:
  coal:  # Matches "Conventional Steam Coal", "Coal IGCC", etc.
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 126
        multiplier: age

Matching rules:

  • Case-insensitive: "Coal" matches "conventional steam coal"
  • Substring: "gas" matches "Natural Gas Fired Combined Cycle"
  • Underscores ignored: "natural_gas" matches "NaturalGas"

Be specific enough to avoid unintended matches. For example:

  • ✅ "conventional steam coal": Matches only coal steam plants
  • ⚠️ "coal": Matches all coal technologies (steam, IGCC, etc.)
  • ❌ "steam": Matches coal steam AND gas steam turbines

Execution Order

resource_attr_modifiers is applied after:

  1. Generator data loaded from tables
  2. Clustering performed
  3. Costs loaded from ATB/cost tables
  4. Other cost modifiers applied

This means you can reference attributes that were calculated earlier in the pipeline, such as age (calculated from operating_date) or cluster-averaged values.

Validation

PowerGenome validates formulas at runtime:

Missing attribute:

resource_attr_modifiers:
  coal:
    - attribute: nonexistent_column  # ❌ KeyError
      formula:
        op: add
        rate: 100
        multiplier: age

Missing multiplier:

resource_attr_modifiers:
  coal:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 100
        multiplier: nonexistent_column  # ❌ KeyError

Invalid operation:

resource_attr_modifiers:
  coal:
    - attribute: fom_per_mwyr
      formula:
        op: multiply  # ❌ Should be "mul"
        rate: 2
        multiplier: age

Missing keys:

resource_attr_modifiers:
  coal:
    - formula:  # ❌ Missing "attribute" key
        op: add
        rate: 100
        multiplier: age

All errors raise exceptions during pipeline execution.

Complete Example

Full configuration applying age-based degradation to multiple technologies:

# Existing generator settings
num_clusters:
  Conventional Steam Coal: 3
  Natural Gas Fired Combined Cycle: 5
  Nuclear: 1

tech_groups:
  Combined Cycle:
    - Natural Gas Fired Combined Cycle
    - Natural Gas Steam Turbine

# Age-based cost adjustments
resource_attr_modifiers:
  conventional steam coal:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 126  # $/kW-yr per year of age
        multiplier: age
    - attribute: heat_rate_mmbtu_mwh
      formula:
        op: mul
        rate: 1.003  # 0.3% efficiency loss per year
        multiplier: age

  natural gas fired combined cycle:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 75
        multiplier: age
    - attribute: heat_rate_mmbtu_mwh
      formula:
        op: mul
        rate: 1.002  # 0.2% efficiency loss per year
        multiplier: age

  nuclear:
    - attribute: fom_per_mwyr
      formula:
        op: add
        rate: 200  # Higher aging costs for nuclear
        multiplier: age

Result: Older plants have higher O&M costs and worse heat rates, reflecting maintenance needs and efficiency degradation over time.

Debugging

To verify formula application:

  1. Check generator output: Examine Generators_data.csv for modified values
  2. Compare with/without modifiers: Run with resource_attr_modifiers commented out
  3. Inspect logs: PowerGenome logs formula applications
  4. Validate formulas: Test with simple cases before complex scenarios

Example validation:

# In Python after loading generators
import pandas as pd

gens = pd.read_csv("results/Generators_data.csv")

# Check coal plant O&M vs. age
coal = gens[gens["technology"].str.contains("Coal", case=False)]
print(coal[["Resource", "age", "fom_per_mwyr"]].sort_values("age"))

Expected pattern: Higher fom_per_mwyr for older plants.

See Also