A Midas module for Simbench dataset.
Project description
MIDAS Simbench Data Simulator
The sbdata module, provided by the midas-sbdata package, provides a simulator for simbench data sets even outside of simbench networks.
Version: 2.1
Installation
This package will be installed automatically with midas-mosaik if you opt-in the full extra.
It is available on pypi, so you can install it manually with
pip install midas-sbdata
Usage
The complete documentation is available at https://midas-mosaik.gitlab.io/midas.
Inside of MIDAS
To use the simulator inside of midas, add sbdata to your modules:
my_scenario:
modules:
- sbdata
- ...
and configure it with:
my_scenario:
# ...
sndata_params:
my_grid_scope:
is_load: false
is_sgen: false
combined_mapping:
1: [[[load_000_p_mw, load_000_q_mvar], 1.0]]
3: [[sgen_000_p_mw, 1.0]]
Any mosaik scenario
If you don't use midas, you can add the sbdata manually to your mosaik scenario file.
First, the entry in the sim_config:
sim_config: {
"SimbenchData": {
"python": "midas_powerseries.simulator:PowerSeriesSimulator",
}
}
Next, you need to start the simulator (assuming a step_size of 900):
sbdata_sim = world.start(
"SimbenchData",
step_size=900,
is_load=False,
is_sgen=False,
start_date="2020-01-01 00:00:00+0100",
data_path="/path/to/folder/where/dataset/is/located",
filename="1-LV-rural3--0-sw.csv", # this is default,
)
Then the models can be started:
load1 = sbdata_sim.CombindTimeSeries(name=["load_000_p_mw", "load_000_q_mvar"], scaling=1.0)
sgen1 = sbdata_sim.CalculatedQTimeSeries(name="sgen_000_p_mw", scaling=1.0)
Finally, the models need to be connected to other entities:
world.connect(load1, other_entity, "p_mw", "q_mvar")
License
This software is released under the GNU Lesser General Public License (LGPL). See the license file for more information about the details.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file midas_sbdata-2.1.0.tar.gz.
File metadata
- Download URL: midas_sbdata-2.1.0.tar.gz
- Upload date:
- Size: 6.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e9160e10359da79ed38889d5c8be9ae69578ff67ffacf3efc708cf066503da53
|
|
| MD5 |
6f74e5e437feb68dfb3dcf978a9cb0c2
|
|
| BLAKE2b-256 |
ed61f2f7ec77a9e1959637437a08a24998dd89eefcca3f8305363b2d1dccafbb
|
File details
Details for the file midas_sbdata-2.1.0-py3-none-any.whl.
File metadata
- Download URL: midas_sbdata-2.1.0-py3-none-any.whl
- Upload date:
- Size: 6.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
38d8f7af2739332afee8926ace58a97aa137a291cf582a66161b0a4b2f89b280
|
|
| MD5 |
f04ad6616e692e8a24922e3f96044393
|
|
| BLAKE2b-256 |
615406ee1e843228f2db75a492346b78eb16d9d60aaea1005d8d38f84a218eb4
|