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digiplan.map.calculations#

Module for calculations used for choropleths or charts.

Capacities #

Bases: Calculation

Oemof postprocessing calculation to read capacities.

calculate_result() #

Read attribute "capacity" from parameters.

Flows #

Bases: Calculation

Oemof postprocessing calculation to read flows.

__init__(calculator, from_nodes=None, to_nodes=None) #

Init flows.

calculate_result() #

Read attribute "capacity" from parameters.

StorageCapacities #

Bases: Calculation

Oemof postprocessing calculation to read storage capacities.

calculate_result() #

Read attribute "storage_capacity" from parameters.

areas_per_municipality_2045(parameters, *, aggregate_pv_ground=True) #

Calculate areas for each municipality depending on capacities in user settings.

batteries_per_municipality() #

Return battery count per municipality.

battery_capacities_per_municipality() #

Return battery capacity per municipality.

calculate_capita_for_value(df) #

Calculate values related to population. If only one region is given, whole region is assumed.

Parameters#

df: pd.DataFrame Index holds municipality IDs, columns hold random entries

Returns#

pd.DataFrame Each value is multiplied by related municipality population share

calculate_potential_shares(parameters) #

Calculate potential shares depending on user settings.

calculate_square_for_value(df) #

Calculate values related to municipality areas.

Parameters#

df: pd.DataFrame Index holds municipality IDs, columns hold random entries

Returns#

pd.DataFrame Each value is multiplied by related municipality share

calculate_wind_and_pv_distribution(df, parameters) #

Calculate wind and pv distribution based on parameters.

capacities(simulation_id) #

Calculate capacities from simulation_id.

capacities_per_municipality() #

Calculate capacity of renewables per municipality in MW.

Returns#

pd.DataFrame Capacity per municipality (index) and technology (column)

capacities_per_municipality_2045(parameters, *, aggregate_pv_ground=True) #

Calculate capacities from 2045 scenario per municipality in MW.

companies_per_municipality() #

Return companies per municipality.

electricity_demand_per_municipality(year=2022) #

Calculate electricity demand per sector per municipality in GWh.

Returns#

pd.DataFrame Electricity demand per municipality (index) and sector (column)

electricity_demand_per_municipality_2045(user_settings) #

Calculate electricity demand per sector per municipality in GWh in 2045.

Returns#

pd.DataFrame Electricity demand per municipality (index) and sector (column)

electricity_from_from_biomass(simulation_id) #

Calculate electricity from biomass.

Biomass share to electricity comes from following parts: - biomass is turned into biogas - biogas powers central and decentral BPCHP which outputs to electricity bus - wood powers central and decentral EXTCHP which outputs to electricity bus

  • biogas is further upgraded into methane
  • methane powers following components which all output to electricity bus:
  • BPCHP (central/decentral)
  • EXTCHP (central/decentral)
  • gas turbine

Regarding the power delivered by methane, we have to distinguish between methane from import and methane from upgraded biomass. This is done, by calculating the share of both and multiplying output respectively.

Returns#

pd.Series containing one entry for electric energy powered by biomass

electricity_heat_demand(simulation_id) #

Return electricity demand for heat demand supply.

Parameters#

simulation_id: int Simulation ID to get results from

Returns#

pd.Series containing electricity demand of heating sector

electricity_overview(year) #

Return static data for electricity overview chart for given year.

Parameters#

year: int Year, either 2022 or 2045

Returns#

pd.Series containing electricity productions and demands (including heat sector demand for electricity)

electricity_overview_from_user(simulation_id) #

Return user specific data for electricity overview chart.

Parameters#

simulation_id: int Simulation ID to get results from

Returns#

pd.Series containing electricity productions and demands (including heat sector demand for electricity)

employment_per_municipality() #

Return employees per municipality.

energies_per_municipality() #

Calculate energy of renewables per municipality in GWh.

Returns#

pd.DataFrame Energy per municipality (index) and technology (column)

energies_per_municipality_2045(parameters) #

Calculate energies from 2045 scenario per municipality in MWh.

energy_shares_2045_per_municipality(parameters) #

Calculate energy shares of renewables from electric demand per municipality in 2045.

Returns#

pd.DataFrame Energy share per municipality (index) and technology (column)

energy_shares_2045_region(parameters) #

Calculate energy shares of renewables from electric demand for region in 2045.

Like energy_shares_2045_per_municipality() but with weighted demand for correct totals.

Returns#

pd.DataFrame Energy share per municipality (index) and technology (column)

energy_shares_per_municipality() #

Calculate energy shares of renewables from electric demand per municipality.

Returns#

pd.DataFrame Energy share per municipality (index) and technology (column)

energy_shares_region() #

Calculate energy shares of renewables from electric demand for region.

Like energy_shares_per_municipality() but with weighted demand for correct totals.

Returns#

pd.DataFrame Energy share per municipality (index) and technology (column)

get_heat_production(distribution, year) #

Calculate hea production per technology for given distribution and year.

get_reduction(simulation_id) #

Return electricity reduction from renewables and imports.

get_regional_independency(simulation_id) #

Return electricity autarky for 2022 and user scenario.

heat_demand_per_municipality(year) #

Calculate heat demand per sector per municipality in GWh.

Returns#

pd.DataFrame Heat demand per municipality (index) and sector (column)

heat_demand_per_municipality_2045(user_settings) #

Calculate heat demand per sector per municipality in GWh in 2045.

Returns#

pd.DataFrame Heat demand per municipality (index) and sector (column)

heat_overview(simulation_id, distribution) #

Return data for heat overview chart.

Parameters#

simulation_id: int Simulation ID to get results from distribution: str central/decentral

Returns#

dict containing heat demand and production for all sectors (hh, cts, ind) and technologies

renewable_electricity_production(simulation_id) #

Return electricity production from renewables including biomass.

select_wind_year(technology_df, wind_year) #

Select corresponding wind year based on wind year. Drop non-corresponding wind years.

storage_capacities(simulation_id) #

Calculate storage capacities from simulation_id.

storage_charge_discharge(simulation_id) #

Calculate battery charge and discharge of small and large scale batteries for given simulation_id.

value_per_municipality(series) #

Shares values across areas (dummy function).

wind_turbines_per_municipality_2045(parameters) #

Calculate number of wind turbines from 2045 scenario per municipality.