TEMOA

Overview

TEMOA (Tools for Energy Model Optimization and Analysis) is a sophisticated energy systems optimization framework that supports various modeling approaches including perfect foresight, myopic planning, uncertainty analysis, and alternative generation.

Quick Start

Standard Installation

# Install from PyPI in a virtual environment
python -m venv .venv

# Activate virtual environment
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

# Install temoa
pip install temoa

Get Started in 30 Seconds

In a virtual env with temoa installed, run:

# Create tutorial files in the current directory
# Creates tutorial_config.toml and tutorial_database.sqlite
temoa tutorial

# Run the model
temoa run tutorial_config.toml

Package Structure

The Temoa package is organized into clear modules:

• temoa.core - Public API for end users (TemoaModel, TemoaConfig, TemoaMode)
• temoa.cli - Command-line interface and utilities
• temoa.components - Model components and constraints
• temoa.data_io - Data loading and validation
• temoa.extensions - Optional extensions for different modeling approaches
  • modeling_to_generate_alternatives - MGA analysis
  • method_of_morris - Sensitivity analysis
  • monte_carlo - Uncertainty quantification
  • myopic - Sequential decision making
• temoa.model_checking - Model validation and integrity checking
• temoa.data_processing - Output analysis and visualization
• temoa.utilities - Helper scripts and migration tools

Installation & Setup

Development Installation

For users who want to contribute to or modify Temoa should install in development mode using uv:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone repository
git clone https://github.com/TemoaProject/temoa.git
cd temoa

# Setup development environment with uv
uv sync --all-extras --dev

# Install pre-commit hooks
uv run pre-commit install

# Run tests
uv run pytest

# Run type checking
uv run mypy

Command Line Interface

Temoa provides a modern, user-friendly CLI built with Typer:

Basic Commands

Run a model:

temoa run tutorial_config.toml
temoa run tutorial_config.toml --output results/
temoa run tutorial_config.toml --build-only  # Build without solving

Validate configuration:

temoa validate tutorial_config.toml
temoa validate tutorial_config.toml --debug

Database migration:

temoa migrate old_database.sql --output new_database.sql
temoa migrate old_database.db --type db
temoa migrate old_database.sqlite --output migrated_v4.sqlite

Generate tutorial files:

temoa tutorial                    # Creates tutorial_config.toml and tutorial_database.sqlite
temoa tutorial my_model my_db     # Custom names

Global Options

temoa --version                   # Show version information
temoa --how-to-cite              # Show citation information
temoa --help                     # Full help

Using with uv

When working with the source code, use uv run to ensure you're using the correct dependencies:

uv run temoa run tutorial_config.toml      # Run with project dependencies
uv run temoa validate tutorial_config.toml # Validate configuration
uv run temoa tutorial             # Create tutorial files

Programmatic Usage

You can use Temoa as a Python library:

from logging import getLogger
from pathlib import Path
from temoa._internal.temoa_sequencer import TemoaSequencer
from temoa.core.config import TemoaConfig
from temoa.core.modes import TemoaMode
from temoa.cli import setup_logging
from pyomo.environ import value, check_optimal_termination

output_path = Path('output_files/my_run')
output_path.mkdir(parents=True, exist_ok=True)

setup_logging(output_path, debug=False, silent=False)

logger = getLogger(__name__)

#  ======================================================================
#  Choose ONE of the following two options for configuring the model run:
#  ======================================================================

# OPTION 1: build the config in code
config = TemoaConfig(
    scenario='zulu',
    scenario_mode=TemoaMode.PERFECT_FORESIGHT,
    time_sequencing='seasonal_timeslices',
    input_database='tutorial_database.sqlite',
    output_database='tutorial_database.sqlite',
    solver='appsi_highs',
    output_path=output_path,
    silent=False,
)
# OPTION 2: load the config from a file
config = TemoaConfig.build_config(
    config_file=Path('tutorial_config.toml'),
    output_path=output_path,
    silent=False,
)

ts = TemoaSequencer(config=config)
ts.start()  # TemoaSequencer runs the model and handles results

# Results are saved to your output_database but can also be accessed in code, here
solved_instance = ts.pf_solved_instance  # solved Temoa instance with solution values
solver_results = ts.pf_results  # information from the solver

# Example code usage of the solved instance and solver results
logger.info(f'Solver results: {solver_results["solver"]}')
if check_optimal_termination(solver_results):
    logger.info(f'Model solved! Objective value: {value(solved_instance.total_cost):.2f}')
    built_capacity = value(solved_instance.v_new_capacity['utopia', 'E51', 2010])
    logger.info(f'Built {built_capacity:.2f} GW of storage in 2010')

Database Setup

Quick Setup with Tutorial

The fastest way to get started:

temoa tutorial

This creates:

• tutorial_config.toml - Configuration file with example settings
• tutorial_database.sqlite - Sample database for learning

Migration from older versions:

# Migrate from v3.1 to v4
temoa migrate old_database_v3.1.sql --output new_database_v4.sql

# or for SQLite databases
temoa migrate old_database_v4.sqlite --output new_database_v4.sqlite

Configuration Files

A configuration file is required to run the model. The tutorial command creates a complete example:

scenario = "tutorial"
scenario_mode = "perfect_foresight"
input_database = "tutorial_database.sqlite"
output_database = "tutorial_database.sqlite"
solver = "appsi_highs"

Configuration Options

Field	Notes
Scenario Name	Name used in output tables (cannot contain '-' symbol)
Temoa Mode	Execution mode (PERFECT_FORESIGHT, MYOPIC, MGA, etc.)
Input/Output DB	Source and output database paths
Price Checking	Run pricing analysis on built model
Source Tracing	Verify commodity flow network integrity
Plot Network	Generate HTML network visualizations
Solver	Solver executable name (appsi_highs, cbc, gurobi, cplex, etc.)
Save Excel	Export core output to Excel files
Save LP	Save LP model files for external solving

Supported Modes

Perfect Foresight

Solves the entire model at once. Most common mode for optimization.

Myopic

Sequential solving through iterative builds. Required for stepwise decision analysis.

MGA (Modeling to Generate Alternatives)

Explores near cost-optimal solutions for robustness analysis.

SVMGA (Single Vector MGA)

Two-solve process focusing on specific variables in the objective.

Method of Morris

Limited sensitivity analysis of user-selected variables.

Build Only

Builds model without solving. Useful for validation and troubleshooting.

Typical Workflow

1. Setup: Create configuration and database files:

   temoa tutorial

2. Configure: Edit the configuration file to match your scenario

3. Validate: Check configuration before running:

   temoa validate tutorial_config.toml

4. Run: Execute the model:

   temoa run tutorial_config.toml

5. Review: Check results in output_files/YYYY-MM-DD_HHMMSS/

6. Iterate: Modify configuration and run again

Advanced Features

Extensions

Temoa includes optional extensions for advanced analysis:

• Monte Carlo: Uncertainty quantification
• Stochastic Programming: Scenario-based optimization
• Method of Morris: Sensitivity analysis

Data Processing

• Excel output generation
• Graphviz network visualization
• Interactive network diagrams

Model Validation

• Built-in validation checks
• Commodity flow verification
• Price consistency analysis

Solver Dependencies

TEMOA requires at least one optimization solver:

• Free: HiGHS

  • Included via the highspy Python package (automatically installed with Temoa)
  • Default solver for tutorial and testing

• Free: CBC

  • Requires separate installation (see CBC documentation)
  • Alternative free solver option

• Commercial: Gurobi, CPLEX, or Xpress

  • Requires separate license and installation
  • See individual solver documentation

Troubleshooting

Solver Issues

If you encounter solver errors:

# For commercial solvers (Gurobi, CPLEX)
pip install ".[solver]"  # Include specific solver packages

# For free solver
temoa run tutorial_config.toml --debug  # Get detailed error information

Documentation & Support

• Full Documentation: Built by following docs/README.md
• API Reference: See temoa.core module for public API
• GitHub Issues: Report bugs and request features
• Tutorials: Run temoa tutorial for guided examples

Code Style & Quality

For contributors:

• Ruff: Code formatting and linting
• mypy: Type checking
• pytest: Testing framework
• Pre-commit: Automated quality checks

See CONTRIBUTING.md for detailed development guidelines.

Citation

If you use Temoa in your research, please cite:

@article{hunter2013modeling,
  title={Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)},
  journal={Energy Economics},
  volume={40},
  pages={339--349},
  year={2013},
  doi={10.1016/j.eneco.2013.07.014}
}

Or use: temoa --how-to-cite