docs: def _pypsa_bus_timeseries(network, buses, timesteps):
      """
      Time series in PyPSA compatible format for bus instances
  
      Set all buses except for the slack bus to voltage of 1 pu (it is assumed
      this setting is entirely ignored during solving the power flow problem).
      This slack bus is set to an operational voltage which is typically greater
      than nominal voltage plus a control deviation.
      The control deviation is always added positively to the operational voltage.
      For example, the operational voltage (offset) is set to 1.025 pu plus the
      control deviation of 0.015 pu. This adds up to a set voltage of the slack
      bus of 1.04 pu.
  
      .. warning::
  
          Voltage settings for the slack bus defined by this function assume the
          feedin case (reverse power flow case) as the worst-case for the power
          system. Thus, the set point for the slack is always greater 1.
  
  
      Parameters
      ----------
      network : Network
          The eDisGo grid topology model overall container
      timesteps : array_like
          Timesteps is an array-like object with entries of type
          :pandas:`pandas.Timestamp<timestamp>` specifying which time steps
          to export to pypsa representation and use in power flow analysis.
      buses : list
          Buses names
  
      Returns
      -------
      :pandas:`pandas.DataFrame<dataframe>`
          Time series table in PyPSA format
      """
  
      # get slack bus label
      slack_bus = '_'.join(
          ['Bus', network.mv_grid.station.__repr__(side='mv')])
  
      # set all buses (except slack bus) to nominal voltage
      v_set_dict = {bus: 1 for bus in buses if bus != slack_bus}
  
      # Set slack bus to operational voltage (includes offset and control
      # deviation
      control_deviation = network.config[
          'grid_expansion_allowed_voltage_deviations'][
          'hv_mv_trafo_control_deviation']
      if control_deviation != 0:
          control_deviation_ts = \
              network.timeseries.timesteps_load_feedin_case.case.apply(
                  lambda _: control_deviation if _ == 'feedin_case'
                                              else -control_deviation)
      else:
          control_deviation_ts = 0
  
      slack_voltage_pu = control_deviation_ts + 1 + \
                         network.config[
                             'grid_expansion_allowed_voltage_deviations'][
                             'hv_mv_trafo_offset']
  
      v_set_dict.update({slack_bus: slack_voltage_pu})
  
      # Convert to PyPSA compatible dataframe
      v_set_df = pd.DataFrame(v_set_dict, index=timesteps)
  
      return v_set_df