PyEPLAN

Project external link: https://pyeplan.sps-lab.org/

PyEPlan stands for “Python-based Energy Planning tool”. It is a free software toolbox for Planning and Operation of Sustainable Micro-grids.

PyEPlan provides a comprehensive framework for microgrid planning and operation optimization, featuring:

• Data Processing (datsys): Historical weather data extraction and representative day clustering using PVGIS API
• Network Routing (rousys): Optimal feeder routing using minimum spanning tree algorithms
• Investment & Operation (inosys): Long-term capacity expansion and short-term dispatch optimization using MILP

The tool supports both on-grid and off-grid microgrid configurations, handles uncertainty through scenario-based optimization, and integrates renewable energy sources with conventional generation and energy storage systems.

Installation

PyEPlan is available on PyPI and can be installed using pip:

pip install pyeplan

For development installation from source:

git clone https://github.com/sps-lab/pyeplan.git
cd pyeplan
pip install -e .

System Requirements

• Python 3.7 or higher
• MILP solver (e.g., Gurobi, CPLEX, or CBC)
• Internet connection for PVGIS API access
• Sufficient RAM for large-scale optimization problems

Applications

PyEPlan is designed for various energy planning and microgrid applications:

• Rural Electrification: Planning off-grid microgrids for remote communities
• Campus/Industrial Microgrids: Optimization of on-grid microgrid systems
• Renewable Energy Integration: Planning and operation with solar, wind, and storage
• Grid Expansion Studies: Optimal feeder routing and capacity planning
• Energy Storage Optimization: Battery sizing and operation strategies
• Scenario Analysis: Uncertainty handling through multiple weather and load scenarios

Key Features

• Multi-objective Optimization: Cost minimization, reliability maximization, and environmental impact reduction
• Weather Data Integration: Automatic historical data extraction from PVGIS
• Representative Day Clustering: Efficient handling of seasonal variations
• Network Topology Optimization: Optimal feeder routing using graph algorithms
• Mixed-Integer Linear Programming: Robust mathematical optimization framework
• Open Source: Apache License 2.0 for academic and commercial use

Documentation

Comprehensive documentation is available at https://pyeplan.sps-lab.org/ including:

• Getting Started guide with examples
• User guide for input data, planning, and output
• API reference for developers
• Release notes and updates

Citing PyEPlan

If you use PyEPlan in your research, please cite it using the DOI: 10.5281/zenodo.3894705

PyEPlan is available on PyPI and the source code is hosted on GitHub.