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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def run_powerflow_onthefly(components, components_data, grid, export_pypsa_dir=None, debug=False):
 """ Run powerflow to test grid stability Two cases are defined to be tested here: i) load case ii) feed-in case Parameters components: dict of pandas.DataFrame components_data: dict of pandas.DataFrame export_pypsa_dir: str Sub-directory in output/debug/grid/ where csv Files of PyPSA network are exported to. Export is omitted if argument is empty. """


    scenario = cfg_ding0.get("powerflow", "test_grid_stability_scenario")
    start_hour = cfg_ding0.get("powerflow", "start_hour")
    end_hour = cfg_ding0.get("powerflow", "end_hour")

    # choose temp_id
    temp_id_set = 1
    timesteps = 2
    start_time = datetime(1970, 1, 1, 00, 00, 0)
    resolution = 'H'

    # inspect grid data for integrity
    if debug:
        data_integrity(components, components_data)

    # define investigated time range
    timerange = DatetimeIndex(freq=resolution,
                              periods=timesteps,
                              start=start_time)

    # TODO: Instead of hard coding PF config, values from class PFConfigDing0 can be used here.

    # create PyPSA powerflow problem
    network, snapshots = create_powerflow_problem(timerange, components)

    # import pq-sets
    for key in ['Load', 'Generator']:
        for attr in ['p_set', 'q_set']:
            # catch MV grid districts without generators
            if not components_data[key].empty:
                series = transform_timeseries4pypsa(components_data[key][
                                                        attr].to_frame(),
                                                    timerange,
                                                    column=attr)
                import_series_from_dataframe(network,
                                             series,
                                             key,
                                             attr)
    series = transform_timeseries4pypsa(components_data['Bus']
                                        ['v_mag_pu_set'].to_frame(),
                                        timerange,
                                        column='v_mag_pu_set')

    import_series_from_dataframe(network,
                                 series,
                                 'Bus',
                                 'v_mag_pu_set')

    # add coordinates to network nodes and make ready for map plotting
    # network = add_coordinates(network)

    # start powerflow calculations
    network.pf(snapshots)

    # # make a line loading plot
    # # TODO: make this optional
    # plot_line_loading(network, timestep=0,
    #                   filename='Line_loading_load_case.png')
    # plot_line_loading(network, timestep=1,
    #                   filename='Line_loading_feed-in_case.png')

    # process results
    bus_data, line_data = process_pf_results(network)

    # assign results data to graph
    assign_bus_results(grid, bus_data)
    assign_line_results(grid, line_data)

    # export network if directory is specified
    if export_pypsa_dir:
        export_to_dir(network, export_dir=export_pypsa_dir)