Midas Default Load Profiles Data Simulator

The dlpdata module, provided by the midas-dlpdata package, provides a simulator for the default load profiles provided by the BDEW set.

Version: 2.1

Installation

This package will usually installed automatically together with midas-mosaik, if you opt-in the bh extra. It is available on pypi, so you can install it manually with

    pip install midas-dlpdata

Usage

The intended use-case for the time simulator is to be used inside of midas. However, it can be used in any mosaik simulation scenario.

Inside of midas

To use the dlp data inside of midas, add dlpdata to your modules

    my_scenario:
      modules:
        - dlpdata
        # - ...

and provide a scope and a configuration:

    my_scenario:
      # ...
      dlp_data_params:
        my_scenario:
          meta_scaling: 1.5
          interpolate: True
          randomize_data: True
          randomize_cos_phi: True
          active_mapping:
          15: [[G4, 262.8]]
          17: [[H0, 1038.06]]

The numbers 15 and 17 stand for the bus number which depends on the used grid.

Any Mosaik Scenario

If you don't use midas, you can add the dlpdata manually to your mosaik scenario_ file. First, the entry in the sim_config:

.. _mosaik scenario: https://mosaik.readthedocs.io/en/latest/tutorials/demo1.html

    sim_config = {
        "DLPSimulator": {
          "python": "midas_powerseries.simulator:PowerSeriesSimulator"},
        # ...
    }

Next, you need to start the simulator (assuming a step_size of 900). Since it uses a custom data model, you have to specify it here:

    dlpdata_sim = world.start(
        "DLPSimulator",
        step_size=900,
        model_import_str="midas_dlp.model:DLPModel",
        use_custom_time_series=True,
        start_date="2020-01-01 00:00:00+0100",
        data_path="/path/to/folder/where/dataset/is/located/",
        filename="bdew_default_load_profiles.csv",  # this is default
    )

Then the models can be started:

    houshold = dlpdata_sim.CalculatedQTimeSeries(name="H0", scaling=1038.06)
    trade = dlpdata_sim.CalculatedQTimeSeries(name="G4", scaling=262.8)

Finally, the models need to be connected to other entities:

    world.connect(trade, other_entity, "p_mw", "q_mvar")

License

The data is taken from BDEW.