AWESPA

Airborne Wind Energy System Performance Assessment Toolchain

A modular Python toolchain for assessing Airborne Wind Energy (AWE) system performance using wind profile clustering, physics-based power estimation models, and Annual Energy Production (AEP) calculation.

Getting Started

Overview

AWESPA provides a complete, three-step pipeline:

  1. Wind module — Process wind data to extract representative wind profiles via clustering. See Wind Module.

  2. Power module — Compute power curves for each wind profile cluster using a physics-based model. See Power Module.

  3. Pipeline — Scripts which are not referring to an external library, this is a helper module that already contains for example the AEP calculation.

All inter-module data is exchanged through the awesIO-format YAML files as much as possible, so the output of one step is directly readable by the next. Each module follows an Abstract Base Class interface, making it straightforward to swap in other models of the same module. The setting files for each module are also YAML-based, ensuring that the entire analysis is reproducible from a single configuration file. But these configuration files are not in awesIO format.

Project Structure

AWESPA/
├── config/                    # YAML configuration files
│   └── example/               # Ready-to-run example configurations
├── data/                      # Input wind data (ERA5 NetCDF files)
├── results/                   # AEP results, power curves, and plots
├── scripts/                   # Runnable analysis scripts
│   ├── run_wind_clustering.py
│   ├── run_luchsinger.py
│   └── run_inertiafree_qsm.py
├── src/awespa/                # Package source code
│   ├── wind/                  # Wind module
│   ├── power/                 # Power module
│   └── pipeline/              # Pipline scripts and utilities
├── tests/                     # Test suite
└── docs/                      # This documentation

Installation

Prerequisites

  • Python 3.8 or higher

  • pip

  • Git (required for pip to fetch the GitHub-hosted dependencies)

Installation Instructions

  1. Clone the repository:

    git clone https://github.com/awegroup/AWESPA.git
cd AWESPA

  2. Create a virtual environment:

    Linux / macOS:

    python3 -m venv venv
source venv/bin/activate

    Windows (PowerShell):

    python -m venv venv
.\venv\Scripts\Activate

  3. Install the package:

    For users:

    pip install .

    For developers (editable install with dev tools):

    pip install -e .[dev]

  4. To deactivate the virtual environment:

    deactivate

Note

The three dependencies (inertiafree-qsm, power-luchsinger, wind-profile-clustering) are fetched automatically from GitHub during pip install. Git must be available on your PATH.

Usage

Running the example scripts

Each script uses the configuration files in config/example/ and writes output to results/example/.

Step 1 — Wind profile clustering:

python scripts/run_wind_clustering.py

Step 2 — Power curve generation (Luchsinger model):

python scripts/run_luchsinger.py

Step 2 (alternative) — Power curve generation (Inertia-Free QSM):

python scripts/run_inertiafree_qsm.py

Complete pipeline example

from pathlib import Path
from awespa.wind.clustering import WindProfileClusteringModel
from awespa.power.luchsinger_power import LuchsingerPowerModel
from awespa.pipeline.aep import calculate_aep

CONFIG = Path("config/example")
RESULTS = Path("results/example")
RESULTS.mkdir(parents=True, exist_ok=True)

# --- Step 1: Wind profile clustering ---
wind_model = WindProfileClusteringModel()
wind_model.load_configuration(CONFIG / "wind_clustering_settings.yml")
wind_model.cluster(
   dataPath=Path("data/wind_data/era5"),
   outputPath=RESULTS / "wind_resource.yml",
   verbose=True,
   showplot=False,
   saveplot=True,
)

# --- Step 2: Power curve generation ---
power_model = LuchsingerPowerModel()
power_model.load_configuration(
   system_path=CONFIG / "tudelft V3_25.yml",
   simulation_settings_path=CONFIG / "luchsinger_settings.yml",
   wind_resource_path=RESULTS / "wind_resource.yml",
)
power_model.compute_power_curves(
   output_path=RESULTS / "power_curves.yml",
   verbose=True,
   showplot=False,
   saveplot=True,
)

# --- Step 3: AEP calculation ---
aep_results = calculate_aep(
   power_curve_path=RESULTS / "power_curves.yml",
   wind_resource_path=RESULTS / "wind_resource.yml",
   output_path=RESULTS / "aep_results.yml",
   plot=True,
   plot_output_dir=RESULTS / "plots",
)
print(f"AEP: {aep_results['annual_energy_production']['total']['aep_mwh']:.1f} MWh/year")

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

See the Developer Guide for detailed development guidelines.

Resources

License

MIT License — Copyright (c) 2024 Airborne Wind Energy Research Group, TU Delft

API Reference

Indices and Tables