Skip to content

Generator Clustering

PowerGenome groups real-world power plants into representative clusters to keep capacity expansion models tractable. Rather than modeling each unit individually (which could mean thousands of rows), generators with similar characteristics are merged into a smaller number of clusters per region and technology type.

Why clustering is needed

Detailed generator inventories from EIA data can contain thousands of individual units. Including each unit as a separate resource in GenX would make the optimization problem very large without meaningfully improving accuracy, because units of the same vintage and technology behave nearly identically.

Clustering reduces these to a manageable number of representative resources while preserving the key features the model needs: capacity, heat rate, cost, and retirement eligibility.

How existing generators are clustered

Step 1: Filter and group

Generators are first filtered to those that:

  • Operate in one of the model regions (after region aggregation)
  • Are not yet retired based on their operating year and retirement_ages setting

They are then grouped by (model region, technology) pairs. Each group is clustered independently.

Step 2: K-means clustering

Within each region–technology group, k-means clustering partitions units into k clusters where k is set by num_clusters (or alt_num_clusters for per-region overrides).

Clustering features (by default):

  • heat_rate_mmbtu_mwh — thermal efficiency
  • fom_per_mwyr — fixed O&M cost

Features are standardized (mean 0, unit variance) before clustering so that neither dominates the distance metric purely because of scale.

If a region–technology group has fewer plants than the requested number of clusters, the number of clusters is automatically reduced to match the number of plants.

Step 3: Cluster aggregation

For each cluster, representative values are computed:

Attribute Aggregation method
capacity_mw Sum of all plants in cluster
heat_rate_mmbtu_mwh Capacity-weighted average
fom_per_mwyr Capacity-weighted average
vom_per_mwh Capacity-weighted average
minimum_load_mw Capacity-weighted average

Step 4: Resource naming

Each cluster is assigned a resource label:

<region>_<technology_snake_case>_<cluster_number>

For example: CA_N_natural_gas_fired_combined_cycle_1

This name is used as the resource identifier throughout the output files.

Controlling the number of clusters

Default cluster count

num_clusters: 1  # Default: merge all plants of a type into one cluster

A value of 1 means all generators of the same type in a region become one representative unit. This is the most aggressive reduction and is appropriate when you need a compact model.

Per-region overrides

Use alt_num_clusters to set different cluster counts for specific combinations:

alt_num_clusters:
  CA_N:
    Natural Gas Fired Combined Cycle: 3   # Three CCGT clusters in CA_N
    Conventional Steam Coal: 1             # All coal in one cluster
  TX:
    Natural Gas Fired Combined Cycle: 5

Setting num_clusters to 0 or omitting a region/tech combination excludes those generators entirely. Technology names here are matched against the raw data values; they are not the ATB-style names.

Individual units (no clustering)

To preserve every generator as an individual resource, set num_clusters equal to the number of plants (or use a large number — PowerGenome reduces it to the number of plants automatically):

num_clusters: 999  # Each plant becomes its own resource

Technology grouping

tech_groups merges similar technologies into a single group before clustering, so they share a resource label:

tech_groups:
  Landfill Gas:
    - Landfill Gas
    - Municipal Solid Waste
    - Other Gases

This is useful for small fuel types where separating them would create many tiny resources.

Retirement age filtering

Generators are excluded from clustering if their age (current model year minus operating year) exceeds the value in retirement_ages:

retirement_ages:
  Conventional Steam Coal: 60
  Natural Gas Fired Combined Cycle: 55
  Nuclear: 60

Myopic multi-period models

In myopic models, retirement ages are evaluated independently for each planning period. If a generator retires between periods, its cluster membership changes, which can cause inconsistencies. Set retirement ages to 500 (or another high value) and handle retirements through exogenous capacity reduction files instead.

Viewing cluster assignments

When extra_outputs_path is configured (or by default in the extra_outputs sub-folder of each case), PowerGenome writes a CSV file showing which real plant belonged to which cluster. This is useful for debugging unexpected groupings.