SDOM Documentation#

Welcome to the Storage Deployment Optimization Model (SDOM) documentation!

SDOM is an open-source, high-resolution grid capacity-expansion framework developed by the National Lab of the Rockies (NLR). It’s purpose-built to optimize the deployment and operation of energy storage technologies, leveraging hourly temporal resolution and granular spatial representation of Variable Renewable Energy (VRE) sources such as solar and wind.

Key Features#

  • ⚡ Accurate Storage Representation: Short, long, and seasonal storage technologies

  • 📆 Hourly Resolution: Full 8760-hour annual simulation

  • 🌍 Spatial Granularity: Fine-grained VRE resource representation

  • 🔌 Copper Plate Modeling: Computationally efficient system optimization

  • 💰 Cost Minimization: Optimizes total system cost (CAPEX + OPEX)

  • 🐍 Open Source: Fully Python-based using Pyomo

Installation#

System Setup and Prerequisites#

  • a. You’ll need to install python

  • b. Also, You’ll need an IDE (Integrated Development Environment), we recommend to install MS VS code

  • c. We also recommend to install extensions such as:

    • edit CSV: To edit and interact with input csv files for SDOM directly in vs code.

    • vscode-pdf: to read and see pdf files directly in vscode.

Installing SDOM python package#

# Install uv if you haven't already
pip install uv

# Create virtual environment
uv venv .venv

# Activate (Windows PowerShell)
.venv\Scripts\Activate.ps1

# Activate (Unix/MacOS)
source .venv/bin/activate

# Install SDOM
uv pip install sdom

# Or install from source
uv pip install -e .

Quick Start#

from sdom import (
    load_data, 
    initialize_model, 
    run_solver, 
    get_default_solver_config_dict,
    export_results
)

# Load input data
data = load_data("./Data/your_scenario/")

# Initialize model (8760 hours = full year)
model = initialize_model(data, n_hours=8760)

# Configure solver
solver_config = get_default_solver_config_dict(
    solver_name="cbc", 
    executable_path="./Solver/bin/cbc.exe"
)

# Solve optimization problem - returns OptimizationResults object
results = run_solver(model, solver_config)

# Access results
if results.is_optimal:
    print(f"Total Cost: ${results.total_cost:,.2f}")
    print(f"Wind Capacity: {results.total_cap_wind:.2f} MW")
    print(f"Solar Capacity: {results.total_cap_pv:.2f} MW")
    
    # Export results to CSV files
    export_results(results, case="scenario_1", output_dir="./results/")

Documentation Contents#

User Guide

API Reference

Development

Publications and Use Cases#

SDOM has been used in various research studies to analyze storage deployment needs under different renewable energy scenarios. See the publications page for details.

Contributing#

We welcome contributions! Please see our Contributing Guidelines for details on how to:

  • Lear how you can set-up your enviroment to contribute to SDOM source code

  • Report bugs

  • Suggest enhancements

  • Submit pull requests

  • Run tests locally

License#

SDOM is released under the MIT License.

Indices and tables#