Metadata-Version: 2.1
Name: midas-mosaik
Version: 0.5.6
Summary: MultI-DomAin test Scenario for smart grid co-simulation.
Home-page: https://gitlab.com/midas-mosaik/midas
Author: Stephan Balduin
Author-email: stephan.balduin@offis.de
License: LGPL
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v2 (LGPLv2)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: appdirs
Requires-Dist: click
Requires-Dist: matplotlib
Requires-Dist: mosaik
Requires-Dist: mosaik-hdf5
Requires-Dist: numpy
Requires-Dist: pandapower
Requires-Dist: pysimmods
Requires-Dist: ruamel.yaml
Requires-Dist: simbench
Requires-Dist: setproctitle
Requires-Dist: sortedcontainers
Requires-Dist: tables
Requires-Dist: wget
Provides-Extra: dev
Requires-Dist: numba ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: tox ; extra == 'dev'
Requires-Dist: coverage ; extra == 'dev'
Requires-Dist: black ; extra == 'dev'
Requires-Dist: setuptools ; extra == 'dev'
Requires-Dist: twine ; extra == 'dev'
Requires-Dist: wheel ; extra == 'dev'

# MIDAS

The MultI-DomAin test Scenario (MIDAS) is a collection of mosaik simulators
(https://gitlab.com/mosaik) for smart grid co-simulation and contains a 
semi-automatic scenario configuration tool.

Version: 0.5.6

License: LGPL

## Requirements

The main requirements for midas are the co-simulation framework mosaik
and pandapower. You may also need a working C compiler to get a flawless
installation. For Windows users this means that you have to install the
VisualC++ compiler that usually comes with VisualStudio. All other users simply
install the gcc or similar packages via your distribution's package repository.  


## Installation

MIDAS requires Python >= 3.8 and is available on https://pypi.org. It can be
installed, preferably into a virtualenv,  with

    >>> pip install midas-mosaik

Alternatively, you can clone this repository with

    >>> git clone https://gitlab.com/midas-mosaik/midas.git 

then switch to the midas folder and type

    >>> pip install -e .

## Usage

MIDAS comes with a command line tool called `midasctl` that let's you
conveniently start your scenario and/or add minor modifications to it (e.g.
change the number of simulations steps, write to a different database, etc.. 
`midasctl` also helps doing the initial setup of MIDAS. Just type

    >>> midasctl configure

and you will be asked to specify where the runtime configuration of MIDAS
should be stored and where you want the datasets to be located. You can of
course let MIDAS decide this for you, just append `-a` to the command:

    >>> midasctl configure -a

Afterwards, you need to download the datasets that MIDAS will use. Type

    >>> midasctl download

and wait a moment until MIDAS is done. Finally, you can test your installation
with

    >>> midasctl run demo

This will run a demonstration scenario and should not take very long.

Pro tip: If you just run the last command, configuration and download will be
performed implicitly. 


## Troubleshooting

(Not fully tested)
If you're a Windows user and encounter issues during the installation, then 
maybe you don't have a working C++ compiler installed. Either install 
VisualStudio (there should be a community edition) or you have to rely on
precombiled binaries, which can be found ,e.g., here:
https://www.lfd.uci.edu/~gohlke/pythonlibs/.


## Documentation

A more comprehensive documentation is growing in the docs folder. 
To build the docu, sphinx (*pip install sphinx*) is required. Simply navigate
into the docs folder and type 

    >>> make html

Afterwards, navigate inside the docs/_build/html folder and double-click on the 
index.html file.


## Datasets and License

The datasets are pulled from different locations.

The default load profiles are publicly available at 
https://www.bdew.de/energie/standardlastprofile-strom/

The commercial dataset is retrieved from https://data.openei.org/submissions/153
and is released under the Creative Commons License:
https://creativecommons.org/licenses/by/4.0/
The energy values are converted from Kilowatt to Megawatt and slightly rearranged to be usable with MIDAS.

The simbench datasets are directly extracted from the simbench pypi package.

The smart nord dataset comes from the research project Smart Nord 
(www.smartnord.de).

The Weather datasets are publicly available at https://opendata.dwd.de/ (see
the Copyright information: 
https://www.dwd.de/EN/service/copyright/copyright_node.html)
Since sometimes values are missing, those values are filled with previous or
similar values.


