# PyEPlan: A Python-based Energy Planning tool
[](https://pypi.python.org/pypi/pyeplan) [](LICENSE.txt) [](https://doi.org/10.5281/zenodo.3894705)
PyEPlan is a [free software](http://www.gnu.org/philosophy/free-sw.en.html) toolbox for designing resilient mini-grids in developing countries. The initial development was funded under the UKRI GCRF project [CRESUM-HYRES](https://cera.leeds.ac.uk/cresum-hyres/) at the [University of Leeds](https://leeds.ac.uk/), but is now supported and co-developed by people in different universities (such as [CUT](https://sps.cut.ac.cy), [ICL](https://www.imperial.ac.uk/)).
## Overview
PyEPlan is a comprehensive Python library designed for the planning and operation of resilient microgrids. It provides tools for data processing, network routing, and investment/operation optimization of microgrid systems, with particular focus on applications in developing countries and rural electrification projects.
## Key Features
- **Data Processing & Resource Assessment**: Integration with PVGIS API for solar irradiance and wind speed data
- **Network Topology Optimization**: Minimum spanning tree algorithms for optimal network design
- **Investment & Operation Optimization**: Mixed-integer linear programming for microgrid planning
- **Multi-Objective Optimization**: Cost, reliability, and sustainability optimization
- **Geographic Information System**: GIS capabilities for location-based planning
- **Battery Energy Storage**: Comprehensive modeling of energy storage systems
- **Renewable Energy Integration**: Support for solar PV, wind turbines, and hybrid systems
- **Multiple Solver Support**: GLPK, CBC, IPOPT, Gurobi optimization solvers
## Installation
### Prerequisites
- Python 3.7 or higher
- pip package manager
### From PyPI (Recommended)
```bash
pip install pyeplan
```
### From Source
```bash
git clone https://github.com/SPS-L/pyeplan.git
cd pyeplan
pip install -e .
```
### Dependencies
PyEPlan requires the following Python packages:
- pandas
- numpy
- networkx
- matplotlib
- pyomo
- timezonefinder
- scikit-learn
- mplleaflet
These will be automatically installed when installing PyEPlan.
## Quick Start
### Basic Usage
```python
import pyeplan
# 1. Data Processing
data_sys = pyeplan.datsys("input_folder", lat=0.25, lon=32.40, year=2016)
data_sys.data_extract()
data_sys.kmeans_clust()
# 2. Network Routing
route_sys = pyeplan.rousys("input_folder", crs=35, typ=7, vbase=415)
route_sys.min_spn_tre()
# 3. Investment and Operation Optimization
inv_sys = pyeplan.inosys("input_folder", ref_bus=0)
inv_sys.solve(solver='glpk', invest=True, onlyopr=False)
# 4. View Results
inv_sys.resCost()
inv_sys.resSolar()
inv_sys.resWind()
inv_sys.resBat()
```
### Input Data Structure
PyEPlan requires specific CSV files in your input folder:
**Required Files:**
- `mgpc_dist.xlsx`: Load point and load level data
- `cgen_dist.csv`: Conventional generator candidate data
- `egen_dist.csv`: Existing conventional generator data
- `csol_dist.csv`: Solar PV candidate data
- `esol_dist.csv`: Existing solar PV data
- `cwin_dist.csv`: Wind turbine candidate data
- `ewin_dist.csv`: Existing wind turbine data
- `cbat_dist.csv`: Battery storage candidate data
- `elin_dist.csv`: Electrical line data
- `pdem_dist.csv`: Active power demand profiles
- `qdem_dist.csv`: Reactive power demand profiles
- `psol_dist.csv`: Solar power scenarios
- `pwin_dist.csv`: Wind power scenarios
- `dtim_dist.csv`: Time duration for each scenario
## Core Modules
### 1. Data Processing (`datsys`)
Handles renewable energy resource assessment and data preprocessing:
```python
from pyeplan.dataproc import datsys
# Initialize data processing system
data_sys = datsys(
inp_folder="input_folder",
lat=0.25, # Latitude in decimal degrees
lon=32.40, # Longitude in decimal degrees
year=2016, # Year for data collection
pvcalc=1, # PV calculation method (0=radiation, 1=power+radiation)
pp=50, # Nominal power of PV system in kW
n_clust=5, # Number of clusters for time series
sbase=1000 # Base apparent power in kW
)
# Extract and process time series data
data_sys.data_extract()
# Perform K-means clustering for scenario reduction
data_sys.kmeans_clust()
```
### 2. Network Routing (`rousys`)
Implements network topology optimization using minimum spanning tree algorithms:
```python
from pyeplan.routing import rousys
# Initialize routing system
route_sys = rousys(
inp_folder="input_folder",
crs=35, # Cross section of cables in mm²
typ=7, # Type of cables
vbase=415, # Line-to-line voltage in V
sbase=1 # Base apparent power in kW
)
# Generate minimum spanning tree network topology
route_sys.min_spn_tre()
```
### 3. Investment & Operation Optimization (`inosys`)
Formulates and solves mixed-integer linear programming problems:
```python
from pyeplan.investoper import inosys
# Initialize optimization system
inv_sys = inosys(
inp_folder="input_folder",
ref_bus=0, # Reference bus number
dshed_cost=1000000, # Demand shedding cost
rshed_cost=500, # Renewable shedding cost
vmin=0.85, # Minimum voltage limit
vmax=1.15, # Maximum voltage limit
sbase=1 # Base apparent power in kW
)
# Solve optimization problem
inv_sys.solve(
solver='glpk', # Optimization solver
invest=True, # Include investment decisions
onlyopr=False # Solve both investment and operation
)
# View results
inv_sys.resCost() # Total costs
inv_sys.resSolar() # Solar investment results
inv_sys.resWind() # Wind investment results
inv_sys.resBat() # Battery investment results
inv_sys.resConv() # Conventional generator results
inv_sys.resCurt() # Curtailment results
```
## Mathematical Formulation
The optimization problem minimizes total system cost:
```
min Z = C_inv + C_opr + C_shed
```
Subject to constraints:
- Power balance constraints (active and reactive)
- Generator capacity limits
- Battery storage constraints
- Network flow constraints
- Voltage limits
- Investment constraints
Where:
- `C_inv`: Investment costs (generators, storage, renewables)
- `C_opr`: Operational costs (fuel, maintenance, etc.)
- `C_shed`: Penalty costs for demand shedding and curtailment
## Examples
### Example 1: 5-Bus Microgrid Planning
```python
import pyeplan
# Complete microgrid planning workflow
data_sys = pyeplan.datsys("examples/5_bus/", lat=0.25, lon=32.40, year=2016)
data_sys.data_extract()
data_sys.kmeans_clust()
route_sys = pyeplan.rousys("examples/5_bus/", crs=35, typ=7, vbase=415)
route_sys.min_spn_tre()
inv_sys = pyeplan.inosys("examples/5_bus/", ref_bus=0)
inv_sys.solve(solver='glpk', invest=True, onlyopr=False)
inv_sys.resCost()
```
### Example 2: Standalone Hybrid System
```python
import pyeplan
# SHS (Standalone Hybrid System) planning
data_sys = pyeplan.datsys("examples/wat_inv/", lat=0.25, lon=32.40, year=2016)
data_sys.data_extract()
data_sys.kmeans_clust()
inv_sys = pyeplan.inosys("examples/wat_inv/", ref_bus=0)
inv_sys.solve(solver='glpk', invest=True, onlyopr=False)
inv_sys.resSolar()
inv_sys.resBat()
```
## Output Files
PyEPlan generates comprehensive output files in the `results/` directory:
**Investment Results:**
- `xg.csv`: Conventional generator investments
- `xs.csv`: Solar PV investments
- `xw.csv`: Wind turbine investments
- `xb.csv`: Battery storage investments
**Operational Results:**
- `pcg.csv`, `qcg.csv`: Conventional generator operation
- `pcs.csv`, `qcs.csv`: Solar PV operation
- `pcw.csv`, `qcw.csv`: Wind turbine operation
- `pbc.csv`, `pbd.csv`: Battery charging/discharging
- `vol.csv`: Bus voltages
- `pel.csv`, `qel.csv`: Line flows
**Cost Results:**
- `obj.csv`: Total cost breakdown
## Documentation
- **API Reference**: [https://pyeplan.sps-lab.org/](https://pyeplan.sps-lab.org/)
- **User Guide**: Available in the `docs/` directory
- **Examples**: Jupyter notebooks in the `examples/` directory
## Testing
PyEPlan includes comprehensive unit tests covering all major functionality. The test suite ensures code quality and reliability.
### Test Structure
The tests are organized in the `tests/` directory:
- **`test_dataproc.py`**: Data processing module tests (353 lines)
- PVGIS API integration
- Time series clustering
- Data preprocessing and file generation
- Power factor calculations
- Timezone handling
- **`test_routing.py`**: Network routing module tests (385 lines)
- Minimum spanning tree algorithm
- Geographic distance calculations
- Cable parameter calculations
- Network topology generation
- **`test_investoper.py`**: Investment/operation optimization tests (626 lines)
- Model initialization and data loading
- Optimization problem formulation
- Solver integration
- Result processing and output generation
- **`test_pyeplan_integration.py`**: Integration tests (539 lines)
- End-to-end workflows
- Module interactions
- Real-world example scenarios
- Error handling and edge cases
### Running Tests
#### Prerequisites
```bash
# Install test dependencies
pip install -r requirements.txt
```
#### Quick Test Run
```bash
# Run all tests
python tests/run_tests.py
# Run with verbose output
python tests/run_tests.py --verbose
# Run specific test module
python tests/run_tests.py --module test_dataproc
# List available test modules
python tests/run_tests.py --list
```
#### Using unittest directly
```bash
# Run all tests
python -m unittest discover tests
# Run specific test file
python -m unittest tests.test_dataproc
# Run specific test class
python -m unittest tests.test_dataproc.TestDatsys
# Run specific test method
python -m unittest tests.test_dataproc.TestDatsys.test_init_basic_parameters
```
#### Using pytest (recommended)
```bash
# Install pytest
pip install pytest
# Run all tests
pytest tests/
# Run with verbose output
pytest tests/ -v
# Run specific test file
pytest tests/test_dataproc.py
# Run tests matching pattern
pytest tests/ -k "test_init"
```
### Test Coverage
The test suite covers:
✅ **Data Processing Module**
- Initialization with various parameters
- PVGIS API integration (mocked)
- Time series data extraction and clustering
- File generation and output validation
- Error handling for invalid inputs
✅ **Network Routing Module**
- Cable parameter calculations
- Minimum spanning tree generation
- Geographic distance calculations
- Network topology validation
✅ **Investment/Operation Module**
- Model initialization and data loading
- Optimization problem formulation (mocked)
- Result processing and output generation
- Utility functions
✅ **Integration Tests**
- Complete workflow testing
- Module interaction testing
- Real example data processing
- Error handling and edge cases
### Mocking Strategy
Tests use mocking to ensure reliability:
- **PVGIS API**: Mocked to avoid network calls
- **Optimization Solvers**: Mocked to avoid solver dependencies
- **File System**: Uses temporary directories
- **Network Operations**: Mocked where appropriate
### Continuous Integration
Tests are designed for CI/CD environments:
- No external network dependencies
- No external solver dependencies
- Fast execution with minimal data processing
- Clear pass/fail criteria
For detailed testing information, see [tests/README.md](tests/README.md).
## Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
## Citation
If you use PyEPlan in your research, please cite:
```bibtex
@software{pyeplan2020,
title={PyEPlan: A Python-based Energy Planning tool},
author={Dehghan, Shahab and Nakiganda, Agnes and Aristidou, Petros},
year={2020},
url={https://github.com/SPS-L/pyeplan},
doi={10.5281/zenodo.3894705}
}
```
## References
- Dehghan, S., Nakiganda, A., & Aristidou, P. (2020). "Planning and Operation of Resilient Microgrids: A Comprehensive Review." IEEE Transactions on Smart Grid.
- Nakiganda, A., Dehghan, S., & Aristidou, P. (2021). "PyEPlan: An Open-Source Framework for Microgrid Planning and Operation." IEEE Power & Energy Society General Meeting.
## License
This project is licensed under the Apache Software License - see the [LICENSE.txt](LICENSE.txt) file for details.
## Support
- **Email**: s.dehghan@ieee.org
- **Website**: [https://pyeplan.sps-lab.org/](https://pyeplan.sps-lab.org/)
- **GitHub Issues**: [https://github.com/SPS-L/pyeplan/issues](https://github.com/SPS-L/pyeplan/issues)
## Acknowledgments
- UKRI GCRF project [CRESUM-HYRES](https://cera.leeds.ac.uk/cresum-hyres/)
- [University of Leeds](https://leeds.ac.uk/)
- [Cyprus University of Technology](https://sps.cut.ac.cy)
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"description": "# PyEPlan: A Python-based Energy Planning tool\n\n[](https://pypi.python.org/pypi/pyeplan) [](LICENSE.txt) [](https://doi.org/10.5281/zenodo.3894705)\n\nPyEPlan is a [free software](http://www.gnu.org/philosophy/free-sw.en.html) toolbox for designing resilient mini-grids in developing countries. The initial development was funded under the UKRI GCRF project [CRESUM-HYRES](https://cera.leeds.ac.uk/cresum-hyres/) at the [University of Leeds](https://leeds.ac.uk/), but is now supported and co-developed by people in different universities (such as [CUT](https://sps.cut.ac.cy), [ICL](https://www.imperial.ac.uk/)).\n\n## Overview\n\nPyEPlan is a comprehensive Python library designed for the planning and operation of resilient microgrids. It provides tools for data processing, network routing, and investment/operation optimization of microgrid systems, with particular focus on applications in developing countries and rural electrification projects.\n\n## Key Features\n\n- **Data Processing & Resource Assessment**: Integration with PVGIS API for solar irradiance and wind speed data\n- **Network Topology Optimization**: Minimum spanning tree algorithms for optimal network design\n- **Investment & Operation Optimization**: Mixed-integer linear programming for microgrid planning\n- **Multi-Objective Optimization**: Cost, reliability, and sustainability optimization\n- **Geographic Information System**: GIS capabilities for location-based planning\n- **Battery Energy Storage**: Comprehensive modeling of energy storage systems\n- **Renewable Energy Integration**: Support for solar PV, wind turbines, and hybrid systems\n- **Multiple Solver Support**: GLPK, CBC, IPOPT, Gurobi optimization solvers\n\n## Installation\n\n### Prerequisites\n\n- Python 3.7 or higher\n- pip package manager\n\n### From PyPI (Recommended)\n\n```bash\npip install pyeplan\n```\n\n### From Source\n\n```bash\ngit clone https://github.com/SPS-L/pyeplan.git\ncd pyeplan\npip install -e .\n```\n\n### Dependencies\n\nPyEPlan requires the following Python packages:\n- pandas\n- numpy\n- networkx\n- matplotlib\n- pyomo\n- timezonefinder\n- scikit-learn\n- mplleaflet\n\nThese will be automatically installed when installing PyEPlan.\n\n## Quick Start\n\n### Basic Usage\n\n```python\nimport pyeplan\n\n# 1. Data Processing\ndata_sys = pyeplan.datsys(\"input_folder\", lat=0.25, lon=32.40, year=2016)\ndata_sys.data_extract()\ndata_sys.kmeans_clust()\n\n# 2. Network Routing\nroute_sys = pyeplan.rousys(\"input_folder\", crs=35, typ=7, vbase=415)\nroute_sys.min_spn_tre()\n\n# 3. Investment and Operation Optimization\ninv_sys = pyeplan.inosys(\"input_folder\", ref_bus=0)\ninv_sys.solve(solver='glpk', invest=True, onlyopr=False)\n\n# 4. View Results\ninv_sys.resCost()\ninv_sys.resSolar()\ninv_sys.resWind()\ninv_sys.resBat()\n```\n\n### Input Data Structure\n\nPyEPlan requires specific CSV files in your input folder:\n\n**Required Files:**\n- `mgpc_dist.xlsx`: Load point and load level data\n- `cgen_dist.csv`: Conventional generator candidate data\n- `egen_dist.csv`: Existing conventional generator data\n- `csol_dist.csv`: Solar PV candidate data\n- `esol_dist.csv`: Existing solar PV data\n- `cwin_dist.csv`: Wind turbine candidate data\n- `ewin_dist.csv`: Existing wind turbine data\n- `cbat_dist.csv`: Battery storage candidate data\n- `elin_dist.csv`: Electrical line data\n- `pdem_dist.csv`: Active power demand profiles\n- `qdem_dist.csv`: Reactive power demand profiles\n- `psol_dist.csv`: Solar power scenarios\n- `pwin_dist.csv`: Wind power scenarios\n- `dtim_dist.csv`: Time duration for each scenario\n\n## Core Modules\n\n### 1. Data Processing (`datsys`)\n\nHandles renewable energy resource assessment and data preprocessing:\n\n```python\nfrom pyeplan.dataproc import datsys\n\n# Initialize data processing system\ndata_sys = datsys(\n inp_folder=\"input_folder\",\n lat=0.25, # Latitude in decimal degrees\n lon=32.40, # Longitude in decimal degrees\n year=2016, # Year for data collection\n pvcalc=1, # PV calculation method (0=radiation, 1=power+radiation)\n pp=50, # Nominal power of PV system in kW\n n_clust=5, # Number of clusters for time series\n sbase=1000 # Base apparent power in kW\n)\n\n# Extract and process time series data\ndata_sys.data_extract()\n\n# Perform K-means clustering for scenario reduction\ndata_sys.kmeans_clust()\n```\n\n### 2. Network Routing (`rousys`)\n\nImplements network topology optimization using minimum spanning tree algorithms:\n\n```python\nfrom pyeplan.routing import rousys\n\n# Initialize routing system\nroute_sys = rousys(\n inp_folder=\"input_folder\",\n crs=35, # Cross section of cables in mm\u00b2\n typ=7, # Type of cables\n vbase=415, # Line-to-line voltage in V\n sbase=1 # Base apparent power in kW\n)\n\n# Generate minimum spanning tree network topology\nroute_sys.min_spn_tre()\n```\n\n### 3. Investment & Operation Optimization (`inosys`)\n\nFormulates and solves mixed-integer linear programming problems:\n\n```python\nfrom pyeplan.investoper import inosys\n\n# Initialize optimization system\ninv_sys = inosys(\n inp_folder=\"input_folder\",\n ref_bus=0, # Reference bus number\n dshed_cost=1000000, # Demand shedding cost\n rshed_cost=500, # Renewable shedding cost\n vmin=0.85, # Minimum voltage limit\n vmax=1.15, # Maximum voltage limit\n sbase=1 # Base apparent power in kW\n)\n\n# Solve optimization problem\ninv_sys.solve(\n solver='glpk', # Optimization solver\n invest=True, # Include investment decisions\n onlyopr=False # Solve both investment and operation\n)\n\n# View results\ninv_sys.resCost() # Total costs\ninv_sys.resSolar() # Solar investment results\ninv_sys.resWind() # Wind investment results\ninv_sys.resBat() # Battery investment results\ninv_sys.resConv() # Conventional generator results\ninv_sys.resCurt() # Curtailment results\n```\n\n## Mathematical Formulation\n\nThe optimization problem minimizes total system cost:\n\n```\nmin Z = C_inv + C_opr + C_shed\n```\n\nSubject to constraints:\n- Power balance constraints (active and reactive)\n- Generator capacity limits\n- Battery storage constraints\n- Network flow constraints\n- Voltage limits\n- Investment constraints\n\nWhere:\n- `C_inv`: Investment costs (generators, storage, renewables)\n- `C_opr`: Operational costs (fuel, maintenance, etc.)\n- `C_shed`: Penalty costs for demand shedding and curtailment\n\n## Examples\n\n### Example 1: 5-Bus Microgrid Planning\n\n```python\nimport pyeplan\n\n# Complete microgrid planning workflow\ndata_sys = pyeplan.datsys(\"examples/5_bus/\", lat=0.25, lon=32.40, year=2016)\ndata_sys.data_extract()\ndata_sys.kmeans_clust()\n\nroute_sys = pyeplan.rousys(\"examples/5_bus/\", crs=35, typ=7, vbase=415)\nroute_sys.min_spn_tre()\n\ninv_sys = pyeplan.inosys(\"examples/5_bus/\", ref_bus=0)\ninv_sys.solve(solver='glpk', invest=True, onlyopr=False)\ninv_sys.resCost()\n```\n\n### Example 2: Standalone Hybrid System\n\n```python\nimport pyeplan\n\n# SHS (Standalone Hybrid System) planning\ndata_sys = pyeplan.datsys(\"examples/wat_inv/\", lat=0.25, lon=32.40, year=2016)\ndata_sys.data_extract()\ndata_sys.kmeans_clust()\n\ninv_sys = pyeplan.inosys(\"examples/wat_inv/\", ref_bus=0)\ninv_sys.solve(solver='glpk', invest=True, onlyopr=False)\ninv_sys.resSolar()\ninv_sys.resBat()\n```\n\n## Output Files\n\nPyEPlan generates comprehensive output files in the `results/` directory:\n\n**Investment Results:**\n- `xg.csv`: Conventional generator investments\n- `xs.csv`: Solar PV investments\n- `xw.csv`: Wind turbine investments\n- `xb.csv`: Battery storage investments\n\n**Operational Results:**\n- `pcg.csv`, `qcg.csv`: Conventional generator operation\n- `pcs.csv`, `qcs.csv`: Solar PV operation\n- `pcw.csv`, `qcw.csv`: Wind turbine operation\n- `pbc.csv`, `pbd.csv`: Battery charging/discharging\n- `vol.csv`: Bus voltages\n- `pel.csv`, `qel.csv`: Line flows\n\n**Cost Results:**\n- `obj.csv`: Total cost breakdown\n\n## Documentation\n\n- **API Reference**: [https://pyeplan.sps-lab.org/](https://pyeplan.sps-lab.org/)\n- **User Guide**: Available in the `docs/` directory\n- **Examples**: Jupyter notebooks in the `examples/` directory\n\n## Testing\n\nPyEPlan includes comprehensive unit tests covering all major functionality. The test suite ensures code quality and reliability.\n\n### Test Structure\n\nThe tests are organized in the `tests/` directory:\n\n- **`test_dataproc.py`**: Data processing module tests (353 lines)\n - PVGIS API integration\n - Time series clustering\n - Data preprocessing and file generation\n - Power factor calculations\n - Timezone handling\n\n- **`test_routing.py`**: Network routing module tests (385 lines)\n - Minimum spanning tree algorithm\n - Geographic distance calculations\n - Cable parameter calculations\n - Network topology generation\n\n- **`test_investoper.py`**: Investment/operation optimization tests (626 lines)\n - Model initialization and data loading\n - Optimization problem formulation\n - Solver integration\n - Result processing and output generation\n\n- **`test_pyeplan_integration.py`**: Integration tests (539 lines)\n - End-to-end workflows\n - Module interactions\n - Real-world example scenarios\n - Error handling and edge cases\n\n### Running Tests\n\n#### Prerequisites\n\n```bash\n# Install test dependencies\npip install -r requirements.txt\n```\n\n#### Quick Test Run\n\n```bash\n# Run all tests\npython tests/run_tests.py\n\n# Run with verbose output\npython tests/run_tests.py --verbose\n\n# Run specific test module\npython tests/run_tests.py --module test_dataproc\n\n# List available test modules\npython tests/run_tests.py --list\n```\n\n#### Using unittest directly\n\n```bash\n# Run all tests\npython -m unittest discover tests\n\n# Run specific test file\npython -m unittest tests.test_dataproc\n\n# Run specific test class\npython -m unittest tests.test_dataproc.TestDatsys\n\n# Run specific test method\npython -m unittest tests.test_dataproc.TestDatsys.test_init_basic_parameters\n```\n\n#### Using pytest (recommended)\n\n```bash\n# Install pytest\npip install pytest\n\n# Run all tests\npytest tests/\n\n# Run with verbose output\npytest tests/ -v\n\n# Run specific test file\npytest tests/test_dataproc.py\n\n# Run tests matching pattern\npytest tests/ -k \"test_init\"\n```\n\n### Test Coverage\n\nThe test suite covers:\n\n\u2705 **Data Processing Module**\n- Initialization with various parameters\n- PVGIS API integration (mocked)\n- Time series data extraction and clustering\n- File generation and output validation\n- Error handling for invalid inputs\n\n\u2705 **Network Routing Module**\n- Cable parameter calculations\n- Minimum spanning tree generation\n- Geographic distance calculations\n- Network topology validation\n\n\u2705 **Investment/Operation Module**\n- Model initialization and data loading\n- Optimization problem formulation (mocked)\n- Result processing and output generation\n- Utility functions\n\n\u2705 **Integration Tests**\n- Complete workflow testing\n- Module interaction testing\n- Real example data processing\n- Error handling and edge cases\n\n### Mocking Strategy\n\nTests use mocking to ensure reliability:\n- **PVGIS API**: Mocked to avoid network calls\n- **Optimization Solvers**: Mocked to avoid solver dependencies\n- **File System**: Uses temporary directories\n- **Network Operations**: Mocked where appropriate\n\n### Continuous Integration\n\nTests are designed for CI/CD environments:\n- No external network dependencies\n- No external solver dependencies\n- Fast execution with minimal data processing\n- Clear pass/fail criteria\n\nFor detailed testing information, see [tests/README.md](tests/README.md).\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.\n\n## Citation\n\nIf you use PyEPlan in your research, please cite:\n\n```bibtex\n@software{pyeplan2020,\n title={PyEPlan: A Python-based Energy Planning tool},\n author={Dehghan, Shahab and Nakiganda, Agnes and Aristidou, Petros},\n year={2020},\n url={https://github.com/SPS-L/pyeplan},\n doi={10.5281/zenodo.3894705}\n}\n```\n\n## References\n\n- Dehghan, S., Nakiganda, A., & Aristidou, P. (2020). \"Planning and Operation of Resilient Microgrids: A Comprehensive Review.\" IEEE Transactions on Smart Grid.\n- Nakiganda, A., Dehghan, S., & Aristidou, P. (2021). \"PyEPlan: An Open-Source Framework for Microgrid Planning and Operation.\" IEEE Power & Energy Society General Meeting.\n\n## License\n\nThis project is licensed under the Apache Software License - see the [LICENSE.txt](LICENSE.txt) file for details.\n\n## Support\n\n- **Email**: s.dehghan@ieee.org\n- **Website**: [https://pyeplan.sps-lab.org/](https://pyeplan.sps-lab.org/)\n- **GitHub Issues**: [https://github.com/SPS-L/pyeplan/issues](https://github.com/SPS-L/pyeplan/issues)\n\n## Acknowledgments\n\n- UKRI GCRF project [CRESUM-HYRES](https://cera.leeds.ac.uk/cresum-hyres/)\n- [University of Leeds](https://leeds.ac.uk/)\n- [Cyprus University of Technology](https://sps.cut.ac.cy)\n",
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