ego.tools package¶

ego.tools.economics¶

This module collects useful functions for economic calculation of eGo which can mainly distinguished in operational and investment costs.

ego.tools.economics.annuity_per_period(capex, n, wacc, t, p)[source]¶

Calculate per given period

Parameters:	capex (float) – Capital expenditure (NPV of investment) n (int) – Number of years that the investment is used (economic lifetime) wacc (float) – Weighted average cost of capital t (int) – Timesteps in hours p (float) – interest rate

ego.tools.economics.edisgo_convert_capital_costs(overnight_cost, t, p, json_file)[source]¶

Get scenario and calculation specific annuity cost by given capital costs and lifetime.

Parameters:	json_file (:obj:dict) – Dictionary of the `scenario_setting.json` file _start_snapshot (int) – Start point of calculation from `scenario_setting.json` file _end_snapshot (int) – End point of calculation from `scenario_setting.json` file _p (numeric) – interest rate of investment _t (int) – lifetime of investment
Returns:	annuity_cost – Scenario and calculation specific annuity cost by given capital costs and lifetime
Return type:	numeric

Examples

$PVA = (1 / p) - (1 / (p*(1 + p)^t))$

ego.tools.economics.etrago_convert_overnight_cost(annuity_cost, json_file, t=40, p=0.05)[source]¶

Get annuity cost of simulation and calculation total overnight_costs by given capital costs and lifetime.

Parameters:	json_file (:obj:dict) – Dictionary of the `scenario_setting.json` file _start_snapshot (int) – Start point of calculation from `scenario_setting.json` file _end_snapshot (int) – End point of calculation from `scenario_setting.json` file _p (numeric) – interest rate of investment _T (int) – lifetime of investment
Returns:	overnight_cost – Scenario and calculation total `overnight_costs` by given annuity capital costs and lifetime.
Return type:	numeric

Examples

$PVA = (1 / p) - (1 / (p*(1 + p)^t)) K_{ON} = K_a*PVA*((t/(period+1))$

ego.tools.economics.etrago_operating_costs(network)[source]¶

Function to get all operating costs of eTraGo.

Parameters:	network_etrago (`etrago.tools.io.NetworkScenario`) – eTraGo network object compiled by `etrago.appl.etrago()`
Returns:	operating_costs – DataFrame with aggregate operational costs per component and voltage level in [EUR] per calculated time steps.
Return type:	pandas.Dataframe

Example

>>> from ego.tools.io import eGo
>>> ego = eGo(jsonpath='scenario_setting.json')
>>> ego.etrago.operating_costs

component	operation_costs	voltage_level
biomass	27.0	ehv
line losses	0.0	ehv
wind_onshore	0.0	ehv

ego.tools.economics.etrago_grid_investment(network, json_file, session)[source]¶

Function to get grid expantion costs from eTraGo

Parameters:	network_etrago (`etrago.tools.io.NetworkScenario`) – eTraGo network object compiled by `etrago.appl.etrago()` json_file (:obj:dict) – Dictionary of the `scenario_setting.json` file
Returns:	grid_investment_costs – Dataframe with `voltage_level`, `number_of_expansion` and `capital_cost` per calculated time steps
Return type:	pandas.Dataframe

Example

>>> from ego.tools.io import eGo
>>> ego = eGo(jsonpath='scenario_setting.json')
>>> ego.etrago.grid_investment_costs

differentiation	voltage_level	number_of_expansion	capital_cost
cross-border	ehv	27.0	31514.1305
domestic	hv	0.0	0.0

ego.tools.economics.edisgo_grid_investment(edisgo, json_file)[source]¶

Function aggregates all costs, based on all calculated eDisGo grids and their weightings :param edisgo: Contains multiple eDisGo networks :type edisgo: ego.tools.edisgo_integration.EDisGoNetworks

Returns:	Dataframe containing annuity costs per voltage level
Return type:	None or pandas.DataFrame

ego.tools.economics.get_generator_investment(network, scn_name)[source]¶: Get investment costs per carrier/ generator.

ego.tools.edisgo_integration¶

This file is part of the eGo toolbox. It contains the class definition for multiple eDisGo networks.

class ego.tools.edisgo_integration.EDisGoNetworks(json_file, etrago_network)[source]¶

Bases: object

Performs multiple eDisGo runs and stores the resulting edisgo_grids

Parameters:	json_file (:obj:dict) – Dictionary of the `scenario_setting.json` file etrago_network (`etrago.tools.io.NetworkScenario`) – eTraGo network object compiled by `etrago.appl.etrago()`

network¶

Container for EDisGo objects, including all results

Returns:	Dictionary of EDisGo objects, keyed by MV grid ID
Return type:	dict[int, `edisgo.EDisGo`]

grid_choice¶

Container for the choice of MV grids, including their weighting

Returns:	Dataframe containing the chosen grids and their weightings ‘no_of_points_per_cluster’, ‘the_selected_network_id’, ‘represented_grids’
Return type:	pandas.DataFrame

successful_grids¶

Relative number of successfully calculated MV grids (Includes clustering weighting)

Returns:	Relative number of grids
Return type:	int

grid_investment_costs¶

Grid investment costs

Returns:	Dataframe containing annuity costs per voltage level
Return type:	None or pandas.DataFrame

plot_storage_integration(mv_grid_id, **kwargs)[source]¶: Plots storage position in MV grid of integrated storages. For more information see edisgo.tools.plots.mv_grid_topology().

plot_grid_expansion_costs(mv_grid_id, **kwargs)[source]¶: Plots costs per MV line. For more information see edisgo.tools.plots.mv_grid_topology().

plot_line_loading(mv_grid_id, **kwargs)[source]¶: Plots relative line loading (current from power flow analysis to allowed current) of MV lines. For more information see edisgo.tools.plots.mv_grid_topology().

plot_mv_grid_topology(mv_grid_id, **kwargs)[source]¶: Plots plain MV grid topology. For more information see edisgo.tools.plots.mv_grid_topology().

run_edisgo(mv_grid_id)[source]¶

Performs a single eDisGo run

Parameters:	mv_grid_id (int) – MV grid ID of the ding0 grid
Returns:	Returns the complete eDisGo container, also including results
Return type:	`edisgo.EDisGo`

ego.tools.edisgo_integration.parallelizer(ding0_id_list, func, func_arguments, max_calc_time, workers=2, worker_lifetime=1)[source]¶

Use python multiprocessing toolbox for parallelization

Several grids are analyzed in parallel based on your custom function that defines the specific application of eDisGo.

Parameters:	ding0_id_list (list of int) – List of ding0 grid data IDs (also known as HV/MV substation IDs) func (any function) – Your custom function that shall be parallelized func_arguments (tuple) – Arguments to custom function `func` workers (int) – Number of parallel process worker_lifetime (int) – Bunch of grids sequentially analyzed by a worker

Notes

Please note, the following requirements for the custom function which is to be executed in parallel

It must return an instance of the type EDisGo.
The first positional argument is the MV grid district id (as int). It is prepended to the tuple of arguments func_arguments

Returns:	containers – Dict of EDisGo instances keyed by its ID
Return type:	dict of `EDisGo`

ego.tools.io¶

This file contains the eGo main class as well as input & output functions of eGo in order to build the eGo application container.

class ego.tools.io.egoBasic(*args, **kwargs)[source]¶

Bases: object

The eGo basic class select and creates based on your scenario_setting.json file your definded eTraGo and eDisGo results container. And contains the session for the database connection.

Parameters:	jsonpath (`json`) – Path to `scenario_setting.json` file.
Returns:	json_file (:obj:dict) – Dictionary of the `scenario_setting.json` file session (sqlalchemy.orm.session.Session) – SQLAlchemy session to the OEDB

class ego.tools.io.eTraGoResults(*args, **kwargs)[source]¶

Bases: ego.tools.io.egoBasic

The eTraGoResults class creates and contains all results of eTraGo and it’s network container for eGo.

Returns:	network_etrago (`etrago.tools.io.NetworkScenario`) – eTraGo network object compiled by `etrago.appl.etrago()` etrago (pandas.Dataframe) – DataFrame which collects several eTraGo results

class ego.tools.io.eDisGoResults(*args, **kwargs)[source]¶

Bases: ego.tools.io.eTraGoResults

The eDisGoResults class create and contains all results of eDisGo and its network containers.

edisgo¶

Contains basic informations about eDisGo

Returns:
Return type:	pandas.DataFrame

class ego.tools.io.eGo(jsonpath, *args, **kwargs)[source]¶

Bases: ego.tools.io.eDisGoResults

Main eGo module which includs all results and main functionalities.

Returns:	network_etrago (`etrago.tools.io.NetworkScenario`) – eTraGo network object compiled by `etrago.appl.etrago()` edisgo.network (`ego.tools.edisgo_integration.EDisGoNetworks`) – Contains multiple eDisGo networks edisgo (pandas.Dataframe) – aggregated results of eDisGo etrago (pandas.Dataframe) – aggregated results of eTraGo

total_investment_costs¶

Contains all investment informations about eGo

Returns:
Return type:	pandas.DataFrame

total_operation_costs¶

Contains all operation costs information about eGo

Returns:
Return type:	pandas.DataFrame

plot_total_investment_costs(filename=None, display=False, **kwargs)[source]¶: Plot total investment costs

plot_power_price(filename=None, display=False)[source]¶: Plot power prices per carrier of calculation

plot_storage_usage(filename=None, display=False)[source]¶: Plot storage usage by charge and discharge

plot_edisgo_cluster(filename=None, display=False, **kwargs)[source]¶: Plot the Clustering of selected Dingo networks

plot_line_expansion(**kwargs)[source]¶: Plot line expantion per line

plot_storage_expansion(**kwargs)[source]¶: Plot storage expantion per bus

iplot¶: Get iplot of results as html

ego.tools.io.results_to_excel(ego)[source]¶: Wirte results of ego.total_investment_costs to an excel file

ego.tools.io.etrago_from_oedb(session, json_file)[source]¶

Function which import eTraGo results for the Database by the result_id number.

Parameters:	session (sqlalchemy.orm.session.Session) – SQLAlchemy session to the OEDB json_file (`dict`) – Dictionary of the `scenario_setting.json` file
Returns:	network_etrago – eTraGo network object compiled by `etrago.appl.etrago()`
Return type:	`etrago.tools.io.NetworkScenario`

ego.tools.io.recover_resultsettings(session, json_file, orm_meta, result_id)[source]¶: Recover scenario_setting from database

ego.tools.mv_cluster¶

ego.tools.plots¶

Module which collects useful functions for plotting eTraGo, eDisGo and eGo results.

ego.tools.plots.carriers_colore()[source]¶

Return matplotlib colore set per carrier (technologies of generators) of eTraGo.

Returns:	colors – List of carriers and matplotlib colores
Return type:	`dict`

ego.tools.plots.ego_colore()[source]¶

Get the four eGo colores

Returns:	colors – List of eGo matplotlib hex colores
Return type:	`dict`

ego.tools.plots.plot_storage_expansion(ego, filename=None, dpi=300, column='overnight_costs', scaling=1)[source]¶

Plot line expantion

Parameters:	ego (`ego.tools.io.eGo`) – eGo `eGo` inclueds eTraGo and eDisGo results filename (str) – Filename and/or path of location to store graphic dpi (int) – dpi value of graphic column (str) – column name of eTraGo’s line costs. Default: `overnight_costs` in EURO. Also available `s_nom_expansion` in MVA or annualized `investment_costs` in EURO scaling (numeric) – Factor to scale storage size of bus_sizes
Returns:	https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show
Return type:	plot `matplotlib.pyplot.show`

ego.tools.plots.plot_line_expansion(ego, filename=None, dpi=300, column='overnight_costs')[source]¶

Plot line expantion

Parameters:	ego (`ego.tools.io.eGo`) – eGo `eGo` inclueds eTraGo and eDisGo results filename (str) – Filename and or path of location to store graphic dpi (int) – dpi value of graphic column (str) – column name of eTraGo’s line costs. Default: `overnight_costs` in EUR. Also available `s_nom_expansion` in MVA or annualized `investment_costs` in EUR
Returns:	https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show
Return type:	plot `matplotlib.pyplot.show`

ego.tools.plots.plot_grid_storage_investment(costs_df, filename, display, var=None)[source]¶

Plot total grid and storage investment.

Parameters:	costs_df (pandas.DataFrame) – Dataframe containing total_investment_costs of ego filename (str) – Filename and or path of location to store graphic display (bool) – Display plot var (str) – Cost variable of `overnight_cost` by default displays annualized costs of timesteps
Returns:	https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show
Return type:	plot `matplotlib.pyplot.show`

ego.tools.plots.power_price_plot(ego, filename, display)[source]¶

Plot power price of calculated scenario of timesteps and carrier

Parameters:	ego (`ego.tools.io.eGo`) – eGo `eGo` inclueds eTraGo and eDisGo results filename (str) – Filename and or path of location to store graphic display (bool) – Display plot
Returns:	https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show
Return type:	plot `matplotlib.pyplot.show`

ego.tools.plots.plot_storage_use(ego, filename, display)[source]¶

Plot storage use by charge and discharge values

Parameters:	ego (`ego.tools.io.eGo`) – eGo `eGo` inclueds eTraGo and eDisGo results filename (str) – Filename and or path of location to store graphic display (bool) – Display plot
Returns:	https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show
Return type:	plot `matplotlib.pyplot.show`

ego.tools.plots.get_country(session, region=None)[source]¶

Get Geometries of scenario Countries.

Parameters:	session (sqlalchemy.orm.session.Session) – SQLAlchemy session to the OEDB region (list) – List of background countries e.g. [‘DE’, ‘DK’]
Returns:	country – GeoDataFrame inclueds MultiPolygon of selected regions or countries
Return type:	`geopandas.GeoDataFrame`

ego.tools.plots.prepareGD(session, subst_id=None, version=None)[source]¶

Get MV grid districts for plotting form oedb.

Parameters:	session (sqlalchemy.orm.session.Session) – SQLAlchemy session to the OEDB subst_id (list) – List of integer ids of substation of the pf ehv/hv grid model_draft version (str) – Name of data version saved in the OEDB
Returns:	region – GeoDataFrame inclueds MultiPolygon of selected MV grids
Return type:	`geopandas.GeoDataFrame`

ego.tools.plots.plot_edisgo_cluster(ego, filename, region=['DE'], display=False, dpi=150, add_ehv_storage=False, grid_choice=None, title='', cmap='jet', labelsize=10, fontsize=10)[source]¶

Plot the Clustering of selected Dingo networks

Parameters:	ego (`ego.tools.io.eGo`) – eGo `eGo` inclueds on eTraGo and eDisGo results filename (str) – file name for plot e.g. `cluster_plot.pdf` region (list) – List of background countries e.g. [‘DE’, ‘DK’] display (bool) – True show plot false print plot as `filename` add_ehv_storage (bool) – Display eTraGo ehv/hv storage distribution grid_choice (str) – path to seperate mv/lv grid choice csv file title (str) – Title of Plot cmap (str) – Name of colormap from https://matplotlib.org/gallery/color/colormap_reference.html
Returns:	https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show
Return type:	plot `matplotlib.pyplot.show`

ego.tools.plots.igeoplot(ego, tiles=None, geoloc=None, save_image=False)[source]¶

Plot function in order to display eGo results on leaflet OSM map. This function will open the results in your main web browser.

Parameters:	ego (`ego.tools.io.eGo`) – eGo `eGo` inclueds eTraGo and eDisGo results tiles (str) – Folium background map style None as OSM or Nasa geoloc (list) – List which define center of map as (lon, lat) save_image (bool) – save iplot map as image
Returns:	plot – HTML file with .js plot
Return type:	html

ego.tools.plots.colormapper_lines(colormap, lines, line, column='s_nom')[source]¶: Make Colore Map for lines.

ego.tools.plots.iplot_griddistrict_legend(mp, repre_grids, start=False)[source]¶: Add legend to iplot function of mv grids.

ego.tools.plots.iplot_totalresults_legend(mp, ego, start=False)[source]¶: Add total results as legend to iplot function.

ego.tools.results¶

This module include the results functions for analyze and creating results based on eTraGo or eDisGo for eGo.

ego.tools.results.create_etrago_results(network, scn_name)[source]¶

Create eTraGo results

Parameters:	network (`NetworkScenario`) – eTraGo `NetworkScenario` based on PyPSA Network. See also pypsa.network scn_name (str) – Name of used scenario
Returns:	generator – Result of generator as DataFrame in `ego.etrago.generator`
Return type:	pandas.DataFrame

ego.tools.specs¶

ego.tools.storages¶

This module contains functions for storage units.

ego.tools.storages.etrago_storages(network)[source]¶

Sum up the pysical storage values of the total scenario based on eTraGo results.

Parameters:	network (`etrago.tools.io.NetworkScenario`) – eTraGo `NetworkScenario` based on PyPSA Network. See also pypsa.network
Returns:	results – Summarize and returns a `DataFrame` of the storage optimaziation.
Return type:	pandas.DataFrame

Notes

The results dataframe incluedes following parameters:

charge : numeric: Quantity of charged energy in MWh over scenario time steps
discharge : numeric: Quantity of discharged energy in MWh over scenario time steps
count : int: Number of storage units
p_nom_o_sum: numeric: Sum of optimal installed power capacity

ego.tools.storages.etrago_storages_investment(network, json_file, session)[source]¶

Calculate storage investment costs of eTraGo

Parameters:	network (`etrago.tools.io.NetworkScenario`) – eTraGo `NetworkScenario` based on PyPSA Network. See also pypsa.network
Returns:	storage_costs – Storage costs of selected snapshots in [EUR]
Return type:	numeric

ego.tools.utilities¶

This module contains utility functions for the eGo application.

ego.tools.utilities.define_logging(name)[source]¶

Helps to log your modeling process with eGo and defines all settings.

Parameters:	log_name (str) – Name of log file. Default: `ego.log`.
Returns:	logger – Set up `logger` object of package `logging`
Return type:	`logging.basicConfig`.

ego.tools.utilities.get_scenario_setting(jsonpath=None)[source]¶

Get and open json file with scenaio settings of eGo. The settings incluede eGo, eTraGo and eDisGo specific settings of arguments and parameters for a reproducible calculation.

Parameters:	json_file (str) – Default: `scenario_setting.json` Name of scenario setting json file
Returns:	json_file – Dictionary of json file
Return type:	dict

ego.tools.utilities.fix_leading_separator(csv_file, **kwargs)[source]¶: Takes the path to a csv-file. If the first line of this file has a leading separator in its header, this field is deleted. If this is done the second field of every row is removed, too.

ego.tools.utilities.get_time_steps(json_file)[source]¶

Get time step of calculation by scenario settings.

Parameters:	json_file (`dict`) – Dictionary of the `scenario_setting.json` file
Returns:	time_step – Number of timesteps of the calculation.
Return type:	int

ego.tools.utilities.open_oedb_session(ego)[source]¶