CERF Data Centers

A comprehensive tool for identifying optimal locations for data centers based on a combination of geospatial, economic, and infrastructure constraints and costs.

What Does This Program Do?

CERF Data Centers is designed to help project feasible locations to build new data centers. It does this by:

• Integrating geospatial data: Uses raster files representing land cost, electricity rates, property taxes, and other constraints across a region.
• Applying user-defined constraints: Allows you to specify which factors are most important (e.g., land cost, energy cost, proximity to infrastructure).
• Calculating locational costs: For each potential site, the tool computes a total cost that includes land, building and equipemnt capex, energy, taxes, and interconnection.
• Siting algorithm: Finds clusters of suitable grid cells that meet the minimum size and constraint requirements for a data center campus.
• Market gravity scoring: Optionally incorporates market size and distance to market to prioritize sites with better access to demand.
• Flexible configuration: All inputs and parameters are specified in a YAML configuration file, making it easy to adapt to different regions or scenarios.
• Output: Produces a GeoDataFrame (and optionally a GeoJSON file) with the selected sites, their costs, and relevant attributes for further analysis or mapping.

Typical Workflow

1. Prepare input rasters: Gather or generate raster files for all relevant constraints (land cost, electricity, taxes, etc.) and a region raster.
2. Write a configuration file: Specify the paths to your input files, the constraints to consider, and the data center requirements for each region.
3. Run the tool: Use the CLI or Python API to process the data and identify optimal sites.
4. Review results: Analyze the output GeoDataFrame or GeoJSON to see the recommended sites, their costs, and other attributes.

---

Installation

You can install the package using pip:

# Install from the repository
pip install -e .

# Install with development dependencies
pip install -e ".[dev]"

Command Line Interface

The tool provides a command-line interface through the cerf-siting command. The CLI is built using Click and provides a modern, user-friendly interface.

Basic Usage

cerf-siting site config.yaml

Available Commands

• site: Run the data center siting process
• --help: Show help message and exit

Command Line Options

For the site command:

• config: Path to the configuration file in YAML format (required)
• -o, --output: Path to save the output GeoJSON file (overrides config file setting)
• -v, --verbose: Enable verbose logging output
• --log-file: Path to save the log file

Examples

# Basic usage with config file
cerf-siting site config.yaml

# Run with verbose logging
cerf-siting site config.yaml --verbose

# Run with custom output file
cerf-siting site config.yaml --output results.geojson

# Run with log file
cerf-siting site config.yaml --log-file siting.log

# Get help
cerf-siting --help
cerf-siting site --help

Configuration File

The configuration file should be in YAML format and include the following sections:

• settings: General settings including paths to input files
• constraints: Cost and constraint layers
• expansion_plan: Data center specifications for each region

Example configuration structure:

settings:
  region_raster_path: "path/to/region.tif"
  siting_raster_path: "path/to/suitability.tif"
  output_file: "output.geojson"  # optional

constraints:
  land_cost_per_sqft: "path/to/land_cost.tif"
  electricity_rate_per_kwh: "path/to/electricity.tif"
  # ... other constraint layers

expansion_plan:
  region_name:
    region_id: 1
    campus_size_square_ft: 1000000
    equipment_capital_expenditure: 1000000
    building_capital_expenditure: 500000
    data_center_power_mw: 10
    pue: 1.5
    interconnection_cost_km: 1000
    n_sites: 1

Python API

You can also use the tool programmatically:

from cerf_data_centers.run_siting import run

# Run the siting process
output_gdf = run("config.yaml")

Development

To set up the development environment:

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .

How to Cite

If you use this package in your research, please cite:

Mongird, K., & Vernon, C. R. (2025). CERF-DC: A Python package to evaluate the feasibility and costs of data center siting options. Zenodo. https://doi.org/10.5281/zenodo.17859233