dispaset.preprocessing package

Submodules

dispaset.preprocessing.data_check module

This files gathers different functions used in the DispaSET to check the input data

__author__ = ‘Sylvain Quoilin (sylvain.quoilin@ec.europa.eu)’

dispaset.preprocessing.data_check.check_AvailabilityFactors(plants, AF)[source]

Function that checks the validity of the provided availability factors and warns if a default value of 100% is used.

dispaset.preprocessing.data_check.check_FlexibleDemand(flex)[source]

Function that checks the validity of the provided flexibility demand time series

dispaset.preprocessing.data_check.check_MinMaxFlows(df_min, df_max)[source]

Function that checks that there is no incompatibility between the minimum and maximum flows

dispaset.preprocessing.data_check.check_NonNaNKeys(plants, NonNaNKeys)[source]

Checking if keys are of type NonNaN

Parameters:
  • plants – plants dataframe

  • NonNaNKeys – list of NonNaN keys

dispaset.preprocessing.data_check.check_PtLDemand(parameters, config)[source]
dispaset.preprocessing.data_check.check_StrKeys(plants, StrKeys)[source]

Checking if keys are of type Str

Parameters:
  • plants – plants dataframe

  • StrKeys – list of Str keys

dispaset.preprocessing.data_check.check_chp(config, plants)[source]

Function that checks the CHP plant characteristics

dispaset.preprocessing.data_check.check_clustering(plants, plants_merged)[source]

Function that checks that the installed capacities are still equal after the clustering process

Parameters:
  • plants – Non-clustered list of units

  • plants_merged – clustered list of units

dispaset.preprocessing.data_check.check_df(df, StartDate=None, StopDate=None, name='')[source]

Function that check the time series provided as inputs

dispaset.preprocessing.data_check.check_h2(config, plants)[source]

Function that checks the H2 (p2h) unit characteristics

dispaset.preprocessing.data_check.check_heat(config, plants)[source]

Function that checks the heat only unit characteristics

dispaset.preprocessing.data_check.check_heat_demand(plants, data, zones_th)[source]

Function that checks the validity of the heat demand profiles :param plants: List of plants :param data: Dataframe with the heat demand time series :param zones_th: list with the heating zones

dispaset.preprocessing.data_check.check_keys(plants, keys, unit)[source]

Checking mandatory keys

Parameters:
  • plants – plants dataframe

  • keys – list of keys

  • unit – string denoting type of units being checked

dispaset.preprocessing.data_check.check_p2h(config, plants)[source]

Function that checks the p2h unit characteristics

dispaset.preprocessing.data_check.check_reserves(Reserve2D, Reserve2U, Load)[source]

Function that checks the validity of the reserve requirement time series :param Reserve2D: DataFrame of reserves 2D :param Reserve2U: DataFrame of reserves 2U :param Load: DataFrame of Loads

dispaset.preprocessing.data_check.check_simulation_environment(SimulationPath, store_type='pickle', firstline=7)[source]

Function to test the validity of disapset inputs :param SimulationPath: Path to the simulation folder :param store_type: choose between: “list”, “excel”, “pickle” :param firstline: Number of the first line in the data (only if type==’excel’)

dispaset.preprocessing.data_check.check_sto(config, plants, raw_data=True)[source]

Function that checks the storage plant characteristics

dispaset.preprocessing.data_check.check_temperatures(plants, Temperatures)[source]

Function that checks the presence and validity of the temperatures profiles for units with temperature-dependent characteristics

Parameters:
  • plants – List of all units

  • Temperatures – Dataframe of input temperatures

dispaset.preprocessing.data_check.check_units(config, plants)[source]

Function that checks the power plant characteristics

dispaset.preprocessing.data_check.isStorage(tech)[source]

Function that returns true the technology is a storage technology

dispaset.preprocessing.data_check.isVRE(tech)[source]

Function that returns true the technology is a variable renewable energy technology

dispaset.preprocessing.data_handler module

dispaset.preprocessing.data_handler.GenericTable(headers, varname, config, default=None)[source]

This function loads the tabular data stored in csv files and assigns the proper values to each pre-specified column. If not found, the default value is assigned. :param headers: List with the column headers to be read :param varname: Variable to be read :param config: Config variable :param default: Default value to be applied if no data is found

Returns:

Dataframe with the time series for each unit

dispaset.preprocessing.data_handler.NodeBasedTable(varname, config, default=None)[source]

This function loads the tabular data stored in csv files relative to each zone of the simulation.

Parameters:
  • varname – Variable name (as defined in config)

  • config – Dispa-SET config data

  • default – Default value to be applied if no data is found

Returns:

Dataframe with the time series for each unit

dispaset.preprocessing.data_handler.UnitBasedTable(plants, varname, config, fallbacks=['Unit'], default=None, RestrictWarning=None)[source]

This function loads the tabular data stored in csv files and assigns the proper values to each unit of the plants dataframe. If the unit-specific value is not found in the data, the script can fallback on more generic data (e.g. fuel-based, technology-based, zone-based) or to the default value. The order in which the data should be loaded is specified in the fallback list. For example, [‘Unit’,’Technology’] means that the script will first try to find a perfect match for the unit name in the data table. If not found, a column with the unit technology as header is search. If not found, the default value is assigned.

Parameters:
  • plants – Dataframe with the units for which data is required

  • varname – Variable name (as defined in config)

  • config – Dispa-SET config file

  • fallbacks – List with the order of data source.

  • default – Default value to be applied if no data is found

  • RestrictWarning – Only display the warnings if the unit belongs to the list of technologies provided in this parameter

Returns:

Dataframe with the time series for each unit

dispaset.preprocessing.data_handler.define_parameter(sets_in, sets, value=0)[source]

Function to define a DispaSET parameter and fill it with a constant value

Parameters:
  • sets_in – List with the labels of the sets corresponding to the parameter

  • sets – dictionary containing the definition of all the sets (must comprise those referenced in sets_in)

  • value – Default value to attribute to the parameter

dispaset.preprocessing.data_handler.export_yaml_config(ExcelFile, YAMLFile)[source]

Function that loads the DispaSET excel config file and dumps it as a yaml file.

Parameters:
  • ExcelFile – Path to the Excel config file

  • YAMLFile – Path to the YAML config file to be written

dispaset.preprocessing.data_handler.load_config(ConfigFile, AbsPath=True)[source]

Wrapper function around load_config_excel and load_config_yaml

dispaset.preprocessing.data_handler.load_config_excel(ConfigFile, AbsPath=True)[source]

Function that loads the DispaSET excel config file and returns a dictionary with the values

Parameters:
  • ConfigFile – String with (relative) path to the DispaSET excel configuration file

  • AbsPath – If true, relative paths are automatically changed into absolute paths (recommended)

dispaset.preprocessing.data_handler.load_config_yaml(filename, AbsPath=True)[source]

Loads YAML file to dictionary

dispaset.preprocessing.data_handler.load_geo_data(path, header=None)[source]

Load geo data for individual zones.

Parameters:
  • path – absolute path to the geo data file

  • header – load header

dispaset.preprocessing.data_handler.load_time_series(config, path, header='infer')[source]

Function that loads time series data, checks the compatibility of the indexes and guesses when no exact match between the required index and the data is present

Param:

config dispaset config

Param:

path path towards the desired timeseries

Param:

header list of header names

Returns:

reindexed timeseries

dispaset.preprocessing.data_handler.merge_series(plants, oldplants, data, method='WeightedAverage', tablename='')[source]

Function that merges the times series corresponding to the merged units (e.g. outages, inflows, etc.)

Parameters:
  • plants – Pandas dataframe with final units after clustering (must contain ‘FormerUnits’)

  • oldplants – Pandas dataframe with the original units

  • data – Pandas dataframe with the time series and the original unit names as column header

  • method – Select the merging method (‘WeightedAverage’/’Sum’)

  • tablename – Name of the table being processed (e.g. ‘Outages’), used in the warnings

Return merged:

Pandas dataframe with the merged time series when necessary

dispaset.preprocessing.data_handler.read_Participation(sheet, rowstart, colstart, rowstop, colapart=1)[source]

Creates dict for each technology and add 0 for false and 1 for true (first value for without CHP second with CHP) :param sheet: Excel sheet to load data from :param rowstart: Row to start reading the data :param colstart: Column to start reading the data :param rowstop: Row to stop reading the data :param colapart: Columns apart to read the data :return:

dispaset.preprocessing.data_handler.read_truefalse(sheet, rowstart, colstart, rowstop, colstop, colapart=1)[source]

Function that reads a two column format with a list of strings in the first columns and a list of true false in the second column The list of strings associated with a True value is returned

dispaset.preprocessing.preprocessing module

This is the main file of the DispaSET pre-processing tool. It comprises a single function that generates the DispaSET simulation environment.

@author: S. Quoilin

dispaset.preprocessing.preprocessing.build_simulation(config, mts_plot=None, MTSTimeStep=24)[source]

A function that builds different simulation environments based on the hydro scheduling option in the config file Hydro scheduling options:

Off - Hydro scheduling turned off, normal call of BuildSimulation function Zonal - Zonal variation of hydro scheduling, if zones are not individually specified in a list

(e.a. zones = [‘AT’,’DE’]) hydro scheduling is imposed on all active zones from the Config file

Regional - Regional variation of hydro scheduling, if zones from a specific region are not individually

specified in a list (e.a. zones = [‘AT’,’DE’]), hydro scheduling is imposed on all active zones from the Config file simultaneously

Parameters:
  • config – Read config file

  • mts_plot – If ms_plot = True indicative plot with temporary computed reservoir levels is displayed

  • MTSTimeStep – Run the mid-term scheduling with a different (to speed things up). If unspecified, the old MTS formulation is used

Return SimData:

Simulation data for unit-commitment module

dispaset.preprocessing.preprocessing.mid_term_scheduling(config, TimeStep=None, mts_plot=None)[source]

This function reads the DispaSET config file, searches for active zones, loads data for each zone individually and solves model using UCM_h_simple.gms

Parameters:
  • config – Read config file

  • TimeStep – Time step (1, 2, 3, 4, 6, 8, 12, 24) number of hours to be considered at once.

  • mts_plot – If ms_plot = True indicative plot with temporary computed reservoir levels is displayed

Return profiles:

Newly computed profile levels

dispaset.preprocessing.utils module

This file gathers different functions used in the DispaSET pre-processing tools

@author: Sylvain Quoilin

dispaset.preprocessing.utils.EfficiencyTimeSeries(config, plants, Temperatures)[source]

Function that calculates an efficiency time series for each unit In case of generation unit, the efficiency is constant in time (for now) In case of of p2h units, the efficicncy is defined as the COP, which can be temperature-dependent or not If it is temperature-dependent, the formula is:

COP = COP_nom + coef_a * (T-T_nom) + coef_b * (T-T_nom)^2

Parameters:
  • config – Dispa-SET config file

  • plants – Pandas dataframe with the original list of units

  • Temperatures – Dataframe with the temperature for all relevant units

Returns:

Dataframe with a time series of the efficiency for each unit

dispaset.preprocessing.utils.adjust_capacity(inputs, tech_fuel, scaling=1, value=None, singleunit=False, write_gdx=False, dest_path='')[source]

Function used to modify the installed capacities in the Dispa-SET generated input data The function update the Inputs.p file in the simulation directory at each call

Parameters:
  • inputs – Input data dictionary OR path to the simulation directory containing Inputs.p

  • tech_fuel – tuple with the technology and fuel type for which the capacity should be modified

  • scaling – Scaling factor to be applied to the installed capacity

  • value – Absolute value of the desired capacity (! Applied only if scaling != 1 !)

  • singleunit – Set to true if the technology should remain lumped in a single unit

  • write_gdx – boolean defining if Inputs.gdx should be also overwritten with the new data

  • dest_path – Simulation environment path to write the new input data. If unspecified, no data is written!

Returns:

New SimData dictionary

dispaset.preprocessing.utils.adjust_flexibility(inputs, flex_units, slow_units, flex_ratio, singleunit=False, write_gdx=False, dest_path='')[source]

Function used to modify the share of the flexible capacity in the Dispa-SET input data The function update the Inputs.p file in the simulation directory at each call

Parameters:
  • inputs – Input data dictionary OR path to the simulation directory containing Inputs.p

  • flex_units – Dispa-SET units table filtered with only the flexible ones

  • slow_units – Dispa-SET units table filtered with only the slow ones

  • flex_ratio – Target flexibility ratio (single number for all zones)

  • singleunit – Set to true if the technology should remain lumped in a single unit

  • write_gdx – boolean defining if Inputs.gdx should be also overwritten with the new data

  • dest_path – Simulation environment path to write the new input data. If unspecified, no data is written!

Returns:

New SimData dictionary

dispaset.preprocessing.utils.adjust_ntc(inputs, value=None, write_gdx=False, dest_path='')[source]

Function used to modify the net transfer capacities in the Dispa-SET generated input data The function update the Inputs.p file in the simulation directory at each call

Parameters:
  • inputs – Input data dictionary OR path to the simulation directory containing Inputs.p

  • value – Absolute value of the desired capacity (! Applied only if scaling != 1 !)

  • write_gdx – boolean defining if Inputs.gdx should be also overwritten with the new data

  • dest_path – Simulation environment path to write the new input data. If unspecified, no data is written!

Returns:

New SimData dictionary

dispaset.preprocessing.utils.adjust_unit_capacity(SimData, u_idx, scaling=1, value=None, singleunit=False)[source]

Function used to modify the installed capacities in the Dispa-SET generated input data The function update the Inputs.p file in the simulation directory at each call

Parameters:
  • SimData – Input data dictionary

  • u_idx – names of the units to be scaled

  • scaling – Scaling factor to be applied to the installed capacity

  • value – Absolute value of the desired capacity (! Applied only if scaling != 1 !)

  • singleunit – Set to true if the technology should remain lumped in a single unit

Returns:

New SimData dictionary

dispaset.preprocessing.utils.clustering(plants_in, method='Standard', Nslices=20, PartLoadMax=0.1, Pmax=30)[source]

Merge excessively disaggregated power Units.

Parameters:
  • plants_in – Pandas dataframe with each power plant and their characteristics (following the DispaSET format)

  • method – Select clustering method (‘Standard’/’LP’/None)

  • Nslices – Number of slices used to fingerprint each power plant characteristics. Slices in the power plant data to categorize them (fewer slices involves that the plants will be aggregated more easily)

  • PartLoadMax – Maximum part-load capability for the unit to be clustered

  • Pmax – Maximum power for the unit to be clustered

Returns:

A list with the merged plants and the mapping between the original and merged units

@author: Matthias Zech

dispaset.preprocessing.utils.create_agg_dict(df_, method='Standard')[source]

This function returns a dictionnary with the proper aggregation method for each columns of the units table, depending on the clustering method

Author: Matthias Zech

dispaset.preprocessing.utils.group_plants(plants, method, df_grouped=False, group_list=None)[source]

This function returns the final dataframe with the merged units and their characteristics

Parameters:
  • plants – Pandas dataframe with each power plant and their characteristics (following the DispaSET format)

  • method – Select clustering method (‘Standard’/’LP’/None)

  • df_grouped – Set to True if this plants dataframe has already been grouped and contains the column “FormerIndexes”

  • group_list – List of columns whose values must be identical in order to group two units

Returns:

A list with the merged plants and the mapping between the original and merged units

dispaset.preprocessing.utils.incidence_matrix(sets, set_used, parameters, param_used)[source]

This function generates the incidence matrix of the lines within the nodes A particular case is considered for the node “Rest Of the World”, which is no explicitely defined in DispaSET

dispaset.preprocessing.utils.interconnections(Simulation_list, NTC_inter, Historical_flows)[source]

Function that checks for the possible interconnections of the zones included in the simulation. If the interconnections occurs between two of the zones defined by the user to perform the simulation with, it extracts the NTC between those two zones. If the interconnection occurs between one of the zones selected by the user and one country outside the simulation, it extracts the physical flows; it does so for each pair (country inside-country outside) and sums them together creating the interconnection of this country with the RoW.

Parameters:
  • Simulation_list – List of simulated zones

  • NTC_inter – Day-ahead net transfer capacities (pd dataframe)

  • Historical_flows – Historical flows (pd dataframe)

dispaset.preprocessing.utils.pd_timestep(hours)[source]

Function that converts time steps in hours into pandas frequencies (e.g ‘1h’, ‘15min’, …)

dispaset.preprocessing.utils.select_units(units, config)[source]

Function returning a new list of units by removing the ones that have unknown technology, zero capacity, or unknown zone

Parameters:
  • units – Pandas dataframe with the original list of units

  • config – Dispa-SET config dictionnary

Returns:

New list of units

Module contents