README

gigpower solves power flow for radial and meshed networks. It supports solving power flow with both Newton-Raphson and Forward Backward Sweep algorithms, written completely in Python.

Features

• Parse *.dss files into Circuit objects, which you can interact with directly in Python.
• Solve power flow for radial or meshed networks using Newton-Raphson method.
• Solve power flow radial networks using Forward Backward Sweep.
• Compare solutions to OpenDSSDirect.py_.

How is gigpower different from OpenDSSDirect.py_?

OpenDSSDirect.py_ provides a Python interface to OpenDSS. In contrast, gigpower aims to be a stand-alone Python power flow tool, to allow users to use Python to view intermediate results and customize things more easily. gigpower provides:

• Implementations of Newton Raphson and Forward Backward Sweep algorithms in Python.
• Circuit objects, (similar to OpenDSSDirect.py_'s Circuit objects) that are native Python structures.
• CircuitElement objects (Bus, Line, Load, etc.) that are native Python structures.

However, gigpower is not yet a standalone package. It relies on OpenDSSDirect.py_'s API to parse *.dss files into gigpower objects. Construction of some initial matrices used by SolutionNR3 also rely on calls to OpenDSSDirect.py_. A future goal is to decouple gigpower from OpenDSSDirect.py_.

Installation

gigpower requires python3 >= 3.7. Install gigpower with pip, in a python3 environment::

> pip install gigpower

Solve Power Flow

Solve with Newton-Raphson::

from gigpower.SolutionNR3 import SolutionNR3

my_solution_nr3 = SolutionNR3('my_feeder.dss')
my_solution_nr3.solve()

Solve with Forward Backward Sweep::

from gigpower.SolutionFBS import SolutionFBS

my_solution_fbs = SolutionFBS('my_feeder.dss')
my_solution_fbs.solve()

Solve with _OpenDSSDirect::

from gigpower.SolutionDSS import SolutionDSS

my_solution_dss = SolutionDSS('my_feeder.dss')
my_solution_dss.solve()

Compare solutions::

# prints a useful comparison of the solutions
from gigpower.pretty_print import compare_solutions

compare_data_frames(my_solution_fbs, my_solution_dss)

See https://github.com/LBNL-ETA/gigpower/examples for Jupyter notebooks with examples.

CAVEAT!!!!

gigpower gives different results from OpenDSSDirect.py_.

A future goal of the project is to explain these disparities. One known source is that gigpower models line current differently from OpenDSS.

To view the disparities, see https://github.com/LBNL-ETA/gigpower/tests/v1_test_report.csv.

To reproduce them, you need pytest and pytest-assume. You can install these from our development requirements::

> cd ./gigpower/src
> pip install -r requirements-dev.txt

then run pytest as a module::

> python3 -m pytest ./gigpower/tests

Contribute

• Issue Tracker: https://github.com/LBNL-ETA/gigpower/issues
• Source Code: https://github.com/LBNL-ETA/gigpower

Development

Follow the steps below to develop this project.

1. Create a python virtual environment, and activate it.::

   python3 -m venv venv source bin/venv/activate

2. Navigate to ./gigpower/src. Install requirements.::

   cd ./gigpower/src pip install -r requirements.txt pip install -r requirements-dev.txt

3. Navigate to ./gigpower/src. Install modules locally in editable mode::

   pip install -e .

4. To run tests, run pytest as a module from ./gigpower::

   python3 -m pytest tests

Support

This project is maintained by the Lawrence Berkeley National Lab Grid Integration Group <https://gridintegration.lbl.gov/>_. For support, contact Daniel Arnold at: dbarnold@lbl.gov

Authors

The first releases of gigpower (<= 1.0.1) were written from August 2020 to June 2021 by researchers at the Lawrence Berkeley National Lab Grid Integration Group <https://gridintegration.lbl.gov/>, lead by Daniel Arnold <https://eta.lbl.gov/people/daniel-arnold>.

Development Team:

• Michael Sankur, @msankur <https://github.com/msankur>_
• Elaine Laguerta, @elaguerta <https://github.com/elaguerta>_
• Kathleen Chang, @kathleenchang <https://github.com/kathleenchang>_
• Sy-Toan Ngo, @toanngosy <https://github.com/toanngosy>_

License

Copyright © 2021 The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

.. _OpenDSSDirect.py: https://github.com/dss-extensions/OpenDSSDirect.py