The mv grid clustering is already independent of eTraGo results. However, it is currently implemented as part of the eDisGo integration and therefor after eTraGo.
To run the mv_grid_clustering this function is executed:
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def cluster_workflow(config=None): |
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""" |
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Get cluster attributes per grid if needed and conduct MV grid clustering. |
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Parameters |
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---------- |
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config : dict |
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Config dict from config json. Can be obtained by calling |
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ego.tools.utilities.get_scenario_setting(jsonpath=config_path). |
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Returns |
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-------- |
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pandas.DataFrame |
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DataFrame with clustering results. Columns are "representative" containing |
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the grid ID of the representative grid, "n_grids_per_cluster" containing the |
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number of grids that are represented, "relative_representation" containing the |
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percentage of grids represented, "represented_grids" containing a list of |
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grid IDs of all represented grids and "representative_orig" containing |
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information on whether the representative is the actual cluster center (in which |
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case this value is True) or chosen because the grid in the cluster center is |
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not a working grid. |
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It would make sense to do the mv_grid_clustering earlier in the process and maybe also to re-arrange the scenario_settings
