edisgo.io.dsm_import
====================

.. py:module:: edisgo.io.dsm_import


Functions
---------

.. autoapisummary::

   edisgo.io.dsm_import.oedb
   edisgo.io.dsm_import.get_profiles_per_industrial_load
   edisgo.io.dsm_import.get_profile_cts


Module Contents
---------------

.. py:function:: oedb(edisgo_obj, scenario, engine, timeindex=None)

   Gets industrial and CTS DSM profiles from the
   `OpenEnergy DataBase <https://openenergyplatform.org/database/>`_.

   Profiles comprise minimum and maximum load increase in MW as well as maximum energy
   pre- and postponing in MWh.

   :param edisgo_object:
   :type edisgo_object: :class:`~.EDisGo`
   :param scenario: Scenario for which to retrieve DSM data. Possible options
                    are 'eGon2035' and 'eGon100RE'.
   :type scenario: str
   :param engine: Database engine.
   :type engine: :sqlalchemy:`sqlalchemy.Engine<sqlalchemy.engine.Engine>`
   :param timeindex: Specifies time steps for which to return data. Leap years can currently
                     not be handled. In case the given timeindex contains a leap year, the data will
                     be indexed using the default year (2035 in case of the 'eGon2035' and to 2045
                     in case of the 'eGon100RE' scenario) and returned for the whole year.
                     If no timeindex is provided, the timeindex set in
                     :py:attr:`~.network.timeseries.TimeSeries.timeindex` is used.
                     If :py:attr:`~.network.timeseries.TimeSeries.timeindex` is not set, the data
                     is indexed using the default year and returned for the whole year.
   :type timeindex: :pandas:`pandas.DatetimeIndex<DatetimeIndex>` or None

   :returns: Dictionary with DSM data with keys `p_min`, `p_max`, `e_min` and `e_max` (see
             :class:`~.network.dsm.DSM` for more information). Values contain dataframes with
             DSM profiles per load for one year in an hourly resolution in MW. Index of the
             dataframes are time indices. Columns contain the load name the DSM profile is
             associated with as in index of :attr:`~.network.topology.Topology.loads_df`.
   :rtype: dict(str, :pandas:`pandas.DataFrame<DataFrame>`)


.. py:function:: get_profiles_per_industrial_load(load_ids, scenario, engine)

   Gets industrial DSM profiles per site and OSM area.

   :param load_ids: List of industrial site and OSM IDs to retrieve DSM profiles for.
   :type load_ids: list(int)
   :param scenario: Scenario for which to retrieve DSM data. Possible options
                    are 'eGon2035' and 'eGon100RE'.
   :type scenario: str
   :param engine: Database engine.
   :type engine: :sqlalchemy:`sqlalchemy.Engine<sqlalchemy.engine.Engine>`

   :returns: Dictionary with DSM data with keys `p_min`, `p_max`, `e_min` and `e_max`. Values
             contain dataframes with DSM profiles per site and OSM area for one year in an
             hourly resolution in MW. Index contains hour of the year (from 0 to 8759) and
             column names are site ID as integer.
   :rtype: dict(str, :pandas:`pandas.DataFrame<DataFrame>`)


.. py:function:: get_profile_cts(edisgo_obj, scenario, engine)

   Gets CTS DSM profiles for all CTS loads in the MV grid.

   :param edisgo_object:
   :type edisgo_object: :class:`~.EDisGo`
   :param scenario: Scenario for which to retrieve DSM data. Possible options
                    are 'eGon2035' and 'eGon100RE'.
   :type scenario: str
   :param engine: Database engine.
   :type engine: :sqlalchemy:`sqlalchemy.Engine<sqlalchemy.engine.Engine>`

   :returns: Dictionary with DSM data with keys `p_min`, `p_max`, `e_min` and `e_max`. Values
             contain dataframes with DSM profiles per CTS load for one year in an
             hourly resolution in MW. Index contains hour of the year (from 0 to 8759) and
             column names are site ID as integer.
   :rtype: dict(str, :pandas:`pandas.DataFrame<DataFrame>`)

   .. rubric:: Notes

   Be aware, that in this function the DSM time series are disaggregated to all CTS
   loads in the grid. In some cases, this can lead to an over- or underestimation of
   the DSM potential, as in egon_data buildings are mapped to a grid based on the
   zensus cell they are in whereas in ding0 buildings are mapped to a grid based on
   the geolocation. As it can happen that buildings lie outside an MV grid but within
   a zensus cell that is assigned to that MV grid, they are mapped differently in
   egon_data and ding0.