pypsa-to-gems-converter 0.0.1

Convert PyPSA networks into GEMS study folders for modelling and simulation.

PyPSA to GEMS Converter

About

The PyPSA-to-GEMS Converter is an open-source & standalone python package that enables the conversion of studies conducted in PyPSA into the GEMS format: it exports a PyPSA Network as a GEMS folder. This converter is based on the representation of the PyPSA models of components as a GEMS library of models: pypsa_models.yml.

Key Features

• Conversion of linear optimal power flow & economical dispatch studies
• Conversion of two-stage stochastic optimization studies

Table of Contents

• How the Converter Works
• Input and Output of the Converter
• Current Limitations of the Converter
• Step-by-Step Guide: Manually Executing a Simulation in GEMS Modeler
• Comparing Results Between GEMS Modeler and PyPSA

How the Converter Works

The PyPSA to GEMS Converter transforms PyPSA Network into a GEMS study folder, through the following steps.

1. Input Validation and Preprocessing

The converter first validates that the PyPSA network meets the requirements for conversion.
It performs necessary preprocessing steps such as normalizing component names, handling missing attributes, and ensuring data consistency.
This stage ensures the input PyPSA model is compatible with the conversion process.

2. Component Registration and Data Extraction

The converter identifies and extracts all relevant components from the PyPSA network, including both static (constant) and dynamic (time-dependent) parameters.
It maps PyPSA-specific parameter names to their GEMS equivalents and organizes the data for conversion.

3. Time Series Processing

For parameters that vary over time, the converter extracts time series data and writes them to separate data files (CSV or TSV format).
The converter handles both deterministic studies (single time series) and stochastic studies (multiple scenarios), maintaining the temporal structure of the original PyPSA model.

4. GEMS Component Generation

Each PyPSA component is transformed into its corresponding GEMS representation.
The converter creates GEMS components with appropriate parameters, distinguishing between constant values and time-dependent references.
Connections between components (such as generators and loads connected to buses) are established through GEMS port connections.

5. Global Constraints Handling

If the PyPSA model includes global constraints (such as CO₂ emission limits), the converter identifies these and creates corresponding GEMS constraint components, linking them to the relevant system components.

6. Study Structure Generation

Finally, the converter generates the complete GEMS study structure.

Input and Output of the Converter

Input: The converter requires the following inputs:

• PyPSA network object
  The fully defined PyPSA network that will be converted into a GEMS-compatible study.
  
• Output path
  The directory where the generated GEMS study will be created.
  
• Time series file format
  Format used for exported time-dependent data (e.g. csv, tsv).

Output

The converter generates a structured GEMS study directory at the provided output path.
The directory layout follows the conventions expected by the GEMS modeler:

    study_directory/
    └── systems/
        └── system_name/
            └── input/
                ├── optim-config.yml--------> Benders decomposition parameters, used by the modeler to generate MPS files
                ├── system.yml -------------> Main system description
                ├── parameters.yml----------> Solver and simulation parameters
                ├── model-libraries/ 
                │   └── pypsa_models.yml---> Model library definitions 
                └── data-series/ ----------> Time and/or scenarion dependent parameters
                    └── ...

Current Limitations of the Converter

For the full list of unsupported components, component restrictions, and network-level constraints, see COMPATIBILITY.md.

Step-by-Step Guide: Manually Executing a Simulation in GEMS Modeler

• Build or load a PyPSA network

# Setup
study_dir = Path("tmp/my_study")  # Absolute path to the GEMS study directory

# Option A: build the network in code
network = Network()
network.add("Bus", "bus1", v_nom=1)
network.add("Load", "load1", bus="bus1", p_set=[10, 20, 30])
network.add("Generator", "gen1", bus="bus1", p_nom=100, marginal_cost=50)

# Option B: load the network from a file
network = Network("simple_network.nc")  # Absolute path to the PyPSA file

• Convert the PyPSA network to a GEMS study

# Convert PyPSA network to GEMS
PyPSAStudyConverter(
    pypsa_network=network,
    study_dir=study_dir,
    series_file_format=".tsv",  # Supported formats: .tsv, .csv
).to_gems_study()

• Run the GEMS(Antares) optimization

# Path to the Antares modeler binary
modeler_bin = Path("antares-9.3.5-Ubuntu-22.04/bin/antares-modeler")

# Run the optimization
subprocess.run([
    str(modeler_bin),
    str(study_dir / "systems")
])

Comparing Results Between GEMS Modeler and PyPSA

If you want to see detailed statistics and a comparison between Antares Modeler and PyPSA study optimization, you can check the full analysis here:

👉 View benchmark analysis