Metadata-Version: 2.1
Name: relsad
Version: 0.0.2
Summary: A package that facilitates reliability investigations in power systems
Home-page: https://github.com/stinefm/relsad
License: MIT
Author: Stine Fleischer Myhre
Requires-Python: >=3.8,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: black (>=20.8b1,<21.0)
Requires-Dist: flake8 (>=3.9.0,<4.0.0)
Requires-Dist: matplotlib (>=3.5.1,<4.0.0)
Requires-Dist: pandas (>=1.4.1,<2.0.0)
Requires-Dist: pre-commit (>=2.12.0,<3.0.0)
Requires-Dist: scipy (>=1.8.0,<2.0.0)
Requires-Dist: sphinx-book-theme (>=0.3.2,<0.4.0)
Requires-Dist: sphinxcontrib-bibtex (>=2.4.2,<3.0.0)
Project-URL: Documentation, https://relsad.readthedocs.io/
Project-URL: Repository, https://github.com/stinefm/relsad
Description-Content-Type: text/x-rst

######
RELSAD
######

`RELSAD` -- RELiability tool for Smart and Active Distribution networks, is a Python-based
reliability assessment tool that aims to function as a foundation for reliability
calculation of modern distribution systems.
The tool uses Monte Carlo simulation and stochastic variation to simulate the
reliability of a distribution system. The package supports user-selected time
increment steps over a user-defined time period. In the tool, active components
such as microgrids, wind power, solar power, batteries, and electrical vehicles
are implemented. To evaluate smart power systems, ICT components such as
automated switches, sensors, and control system for the power grid are also implemented.
In addition to component implementation, in order to evaluate the reliability of such
complex systems, the complexity, dependencies within a system, and interdependencies
between systems and components are accounted for.

The tool can be used in modern distribution network development to evaluate
the influence of active components on the network reliability. Relevant use cases
include investigating how:

1. The introduction of microgrids with active production
   affects the customers in the distribution network and vice versa
2. Vehicle\-to\-grid strategies might mitigate load peaks and
   improve the distribution network reliability
3. The reliability of the ICT network impacts the
   distribution network reliability

Examples using well known test networks are included.

============
Installation
============

See https://relsad.readthedocs.io/en/latest/installation.html.

========
Features
========

- Monte Carlo simulation based reliability analysis of active distribution networks
- Sequential simulation of the network behavoir with user defined loading and failure evolution

============
Dependencies
============

- Numpy
- Scipy
- Matplotlib
- Pandas

=====
Usage
=====

See https://relsad.readthedocs.io/en/latest/usage.html.

=============
Documentation
=============

The official documentation is hosted on Read the Docs: https://relsad.readthedocs.io/en/latest/

============
Contributors
============

We welcome and recognize all contributions. You can see a list of current contributors in the [contributors tab](https://github.com/stinefm/relsad/graphs/contributors).

