q100opt

setup_model module

Function for reading data and setting up an oemof-solph EnergySystem.

Please use this module with care. It is work in progress!

Contact: Johannes Röder <johannes.roeder@uni-bremen.de>

SPDX-License-Identifier: MIT

q100opt.setup_model.add_buses(table)

Instantiates the oemof-solph.Buses based on tabular data.

Retruns the Buses in a Dictionary and in a List. If excess and shortage is given, additional sinks and sources are created.

Parameters:table (pandas.DataFrame) – Dateframe with all Buses. Returns:tuple (a tuple containing:) –

• nodes ([list]): A list with all oemof-solph Buses of the

  Dataframe table.

• busd ([dict]): Dictionary with all oemof Bus object.

  Keys are equal to the label of the bus.

Examples

>>> import pandas as pd
>>> from q100opt.setup_model import add_buses
>>> data_bus = pd.DataFrame([['label_1', 0, 0, 0, 0],
... ['label_2', 0, 0, 0, 0]],
... columns=['label', 'excess', 'shortage', 'shortage_costs',
... 'excess_costs'])
>>> nodes, buses = add_buses(data_bus)

q100opt.setup_model.add_links(tab, busd)

…

q100opt.setup_model.add_sinks(tab, busd, timeseries=None)

Parameters:

• tab (pd.DataFrame) – Table with parameters of Sinks.
• busd (dict) – Dictionary with Buses.
• timeseries (pd.DataFrame) – (Optional) Table with all timeseries parameters.

Returns:

list (oemof Sinks (non fix sources) objects.)

Note

No investment possible.

q100opt.setup_model.add_sinks_fix(tab, busd, timeseries)

Add fix sinks, e.g. for energy demands.

Parameters:

• tab (pd.DataFrame) – Table with parameters of Sinks.
• busd (dict) – Dictionary with Buses.
• timeseries (pd.DataFrame) – (Required) Table with all timeseries parameters.

Returns:

list (oemof Sinks (non fix sources) objects.)

Note

No investment possible.

q100opt.setup_model.add_sources(tab, busd, timeseries=None)

Parameters:

• tab (pd.DataFrame) – Table with parameters of Sources.
• busd (dict) – Dictionary with Buses.
• timeseries (pd.DataFrame) – (Optional) Table with all timeseries parameters.

Returns:

list (Oemof Sources (non fix sources) objects.)

q100opt.setup_model.add_sources_fix(tab, busd, timeseries)

Parameters:

• tab (pd.DataFrame) – Table with parameters of Sources.
• busd (dict) – Dictionary with Buses.
• timeseries (pd.DataFrame) – Table with all timeseries parameters.

Returns:

list (List with oemof Source (only fix source) objects.)

Note

At the moment, there are no additional flow attributes allowed, and nominal_value must be given in the table.

q100opt.setup_model.add_storages(tab, busd)

Parameters:

• tab (pd.DataFrame) – Table with parameters of Storages.
• busd (dict) – Dictionary with Buses.

Returns:

list (oemof GenericStorage components.)

q100opt.setup_model.add_transformer(tab, busd, timeseries=None)

Parameters:

• tab (pandas.DataFrame) – Table with all Transformer parameter
• busd (dict) – Dictionary with all oemof-solph Bus objects.
• timeseries (pandas.DataFrame) – Table with all Timeseries for Transformer.

Returns:

list (oemof-solph Transformer objects.)

q100opt.setup_model.check_active(dct)

Checks for active components.

Delete not “active” rows, and the column ‘active’ of all components dataframes.

Parameters:dct (dict) – Holding the Dataframes of solph components Returns:dict

q100opt.setup_model.check_nonconvex_invest_type(dct)

Checks if flow attribute ‘invest.nonconvex’ is type bool, if the attribute is present.

Parameters:dct (dict) – Dictionary with all paramerters for the oemof-solph components. Returns:dict (Updated Dictionary is returned.)

q100opt.setup_model.get_flow_att(row, ts)

Parameters:

• row (pd.Series) – Series with all attributes given by the parameter table (equal 1 row)
• ts (pd.DataFrame) – DataFrame with all input time series for the oemof-solph model.

Returns:

dict (All Flow specific attribues.)

q100opt.setup_model.get_invest_obj(row)

Filters all attributes for the investment attributes with the prefix`invest.`, if attribute ‘investment’ occurs, and if attribute investment is set to 1.

If the invest attribute “offset” is given and if it is > 0, the invest attribute “nonconvex=True” is added.

Parameters:row (pd.Series) – Parameters for single oemof object. Returns:dict

q100opt.setup_model.load_csv_data(path)

Loading csv data.

Loading all csv files of the given path as pandas DataFrames into a dictionary. The keys of the dictionary are the names of the csv files (without .csv).

Parameters:path (str) Returns:dict

q100opt.setup_model.load_xlsx_data(filename)

Reads all sheets of xlsx file into dictionary.