Parameters of pvcompare: Definitions and Default Values

Within the pvcompare/pvcompare/data/ directory, two separate categories of inputs can be observed.

1. MVS parameters (found in the CSVs within the data/user_inputs/mvs_inputs/csv_elements/ directory)

2. pvcompare-specific parameters (found in the CSVs within the data/user_inputs/pvcompare_inputs/ directory)

3. Static input parameters of pvcompare (found in the data/static_inputs/ directory)

As pvcompare imports the Multi-vector Simulation (MVS) tool, the definitions of all the relevant parameters of MVS can be found in the documentation of MVS. The default values and it’s sources are described below.

The values used by default in pvcompare for the above parameters in each CSV, are detailed below.

1. MVS Parameters

Some parameters can be calculated automatically by pvcompare and do not need to be filled in by hand. These parameters are marked with * auto_calc.

project_data.csv

1. country: str, Spain (the country in which the project is located), * auto_calc

2. latitude: str, 45.641603 * auto_calc

3. longitude: str, 5.875387 * auto_calc

4. project_id: str, 1

5. project_name: str, net zero energy community

6. scenario_id,str,1

7. scenario_name,str, Scenario_A

8. scenario_description: str, Simulation of scenario scenario_name

economic_data.csv

1. curency: str, EUR (stands for euro; can be replaced by SEK, if the system is located in Sweden, for instance).

2. project_duration: year, 25 (number of years).

3. discount_factor: factor, 0.07 (see discussion paper.)

4. tax: factor, 0 (see documentation of MVS)

simulation_settings.csv

1. evaluated_period: days, 365 (number of days), * auto_calc

2. start_date: str, 2013-01-01 00:00:00, * auto_calc

3. timestep: minutes, 60 (hourly time-steps, 60 minutes)

4. output_lp_file: bool ,False

fixcost.csv

By default in pvcompare no fixcosts are considered. The lifetime of all assets is set to 1 only to prevent errors in MVS. This lifetime has no effect on the simulation unless costs are defined.

	Unit	distribution_grid	engineering	operation
age_installed	year	0	0	0
development_costs	currency	0	0	0
specific_cost	currency	0	0	0
lifetime	year	1	1	1
specific_cost_om	currency/year	0	0	0

energyConsumption.csv

1. unit: str, kW

2. inflow_direction: str, Electricity

3. file_name: str, electricity_load_2017_Spain_8.csv, * auto_calc

4. energyVector: str, Electricity

5. type_oemof: str, sink

6. type_asset: str, demand

7. dsm: str, False (dsm stands for Demand Side Management. This feature has not been implement in MVS as of now.)

energyConversion.csv

1. age_installed: year, 0 (for all components such as charge controllers, inverters, heat pumps, gas boilers)

2. development_costs: currency, 0 (for all components)

3. specific_costs: currency/kW

   1. storage_charge_controller_in and storage_charge_controller_out: 46 (According to this source, a 12 Volts, 80 Amperes Solar Charge Controller costs about 50 USD, which is about 46 €/kW.)

   2. solar_inverter_01: 230 (Per this, the cost of inverters are around 230 Euro/kW.)

   3. heat_pump_air_air_2015: 450 (According to Danish energy agency’s technology data of an air-to-air heat pump [dea_hp_aa] on page 87.)

   4. heat_pump_air_air_2020: 425 (According to dea_hp_aa)

   5. heat_pump_air_air_2030: 316.67 (According to dea_hp_aa)

   6. heat_pump_air_air_2050: 300 (According to dea_hp_aa)

   7. heat_pump_air_water_2015: 1000 (According to Danish energy agency’s technology data of an air-to-air heat pump [dea_hp_aw] on page 89.)

   8. heat_pump_air_water_2020: 940 (According to dea_hp_aw)

   9. heat_pump_air_water_2030: 850 (According to dea_hp_aw)

   10. heat_pump_air_water_2050: 760 (According to dea_hp_aw)

   11. heat_pump_brine_water_2015: 1600 (According to Danish energy agency’s technology data of an air-to-air heat pump [dea_hp_bw] on page 93.)

   12. heat_pump_brine_water_2020: 1500 (According to dea_hp_bw)

   13. heat_pump_brine_water_2030: 1400 (According to dea_hp_bw)

   14. heat_pump_brine_water_2050: 1200 (According to dea_hp_bw)

   15. natural_gas_boiler_2015: 320 (According to Danish energy agency’s technology data of a natural gas boiler [dea_ngb] on page 36.)

   16. natural_gas_boiler_2020: 310 (According to dea_ngb)

   17. natural_gas_boiler_2030: 300 (According to dea_ngb)

   18. natural_gas_boiler_2050: 270 (According to dea_ngb)

4. efficiency: factor

   1. storage_charge_controller_in and storage_charge_controller_out: 1

   2. solar_inverter_01: 0.95 (European efficiency is around 0.95, per several sources. Fronius, for example.)

   3. heat_pump: “{‘file_name’: ‘None’, ‘header’: ‘no_unit’, ‘unit’: ‘’}”

   4. natural_gas_boiler_2015: 0.97 (According to dea_ngb)

   5. natural_gas_boiler_2020: 0.97 (According to dea_ngb)

   6. natural_gas_boiler_2030: 0.98 (According to dea_ngb)

   7. natural_gas_boiler_2050: 0.99 (According to dea_ngb)

5. inflow_direction: str

   1. storage_charge_controller_in: Electricity

   2. storage_charge_controller_out: ESS Li-Ion

   3. solar_inverter_01: PV bus1 (if there are more inverters such as solar_inverter_02, then the buses from which the electricity flows into the inverter happens, will be named accordingly. E.g.: PV bus2.)

   4. heat_pump: Electricity bus

   5. natural_gas_boiler: Gas bus

6. installedCap: kW, 0 (for all components)

7. label: str

   1. storage_charge_controller_in and storage_charge_controller_out: Charge Contoller ESS Li-Ion (charge)

   2. solar_inverter_01: Solar inverter 1 (if there are more inverters, then will be named accordingly. E.g.: Solar inverter 2)

8. lifetime: year

   1. storage_charge_controller_in and storage_charge_controller_out: 15 (According to this website, the lifetime of charge controllers is around 15 years.)

   2. solar_inverter_01: 10 (Lifetime of solar (string) inverters is around 10 years.)

   3. heat_pump_air_air: 12 (According to dea_hp_aa)

   4. heat_pump_air_water: 18 (According to dea_hp_aw)

   5. heat_pump_brine_water: 20 (According to dea_hp_bw)

   6. natural_gas_boiler: 20 (According to dea_ngb)

9. specific_costs_om: currency/kW

   1. storage_charge_controller_in and storage_charge_controller_out: 0 (According to AM Solar, maintainence work on charge controllers is minimal. So we can consider the costs to be covered by specific_cost_om in fixcost.csv, which is just the system O&M cost.)

   2. solar_inverter_01: 6 (From page 11 in this 2015 Sandia document, assuming one maintainence activity per year, we can take 7 USD/kW or 6 €/kW.)

   3. heat_pump_air_air_2015: 42.5 (According to dea_hp_aa)

   4. heat_pump_air_air_2020: 40.5 (According to dea_hp_aa)

   5. heat_pump_air_air_2030: 24.33 (According to dea_hp_aa)

   6. heat_pump_air_air_2050: 22 (According to dea_hp_aa)

   7. heat_pump_air_water_2015: 29.1 (According to dea_hp_aw)

   8. heat_pump_air_water_2020: 27.8 (According to dea_hp_aw)

   9. heat_pump_air_water_2030: 25.5 (According to dea_hp_aw)

   10. heat_pump_air_water_2050: 23.9 (According to dea_hp_aw)

   11. heat_pump_brine_water_2015: 29.1 (According to dea_hp_bw)

   12. heat_pump_brine_water_2020: 27.8 (According to dea_hp_bw)

   13. heat_pump_brine_water_2030: 25.5 (According to dea_hp_bw)

   14. heat_pump_brine_water_2050: 23.9 (According to dea_hp_bw)

   15. natural_gas_boiler_2015: 20.9 (According to dea_ngb)

   16. natural_gas_boiler_2020: 20.5 (According to dea_ngb)

   17. natural_gas_boiler_2030: 19.9 (According to dea_ngb)

   18. natural_gas_boiler_2050: 18.1 (According to dea_ngb)

10. dispatch_price: currency/kWh, 0 (for all components)

11. optimizeCap: bool, True (for all components)

12. outflow_direction: str

    1. storage_charge_controller_in: ESS Li-Ion

    2. storage_charge_controller_out: Electricity

    3. solar_inverter_01: Electricity (if there are more solar inverters, this value applies for them as well)

    4. heat_pump: Heat bus

    5. natural_gas_boiler: Heat bus

13. energyVector: str

    1. storage_charge_controller_in: Electricity

    2. storage_charge_controller_out: Electricity

    3. solar_inverter_01: Electricity

    4. heat_pump: Heat

    5. natural_gas_boiler: eHeat (Because of convention to define energyVector based on output flow for an energy conversion asset. See mvs documentation on parameters)

14. type_oemof: str, transformer (same for all the components)

15. unit: str, kW (applies to all the components)

energyProduction.csv

1. age_installed: year, 0 (for all the components)

2. development_costs: currency, 0 (for all the components)

3. specific_costs: currency/unit, 934 (SI), 1019 (CPV) ,813 (PSI)

4. file_name: str, * auto_calc

5. installedCap: kWp, 0.0 (for all components)

6. maximumCap: kWp, * auto_calc

7. lifetime: year, 25 (for all the components)

8. specific_costs_om: currency/unit, 20 (SI) ,15 (CPV), 17 (PSI)

9. dispatch_price: currency/kWh, 0 (this is because there are no fuel costs associated with photovoltaics)

10. optimizeCap: bool, True (for all components)

11. outflow_direction: str, PV bus1 (for all of the components)

12. type_oemof: str, source (for all of the components)

13. unit: str, kWp (for all of the components)

14. energyVector: str, Electricity (for all of the components)

15. emission_factor: kgCO2eq/unit, * auto_calc

16. renewableAsset: bool, True (for all of the components)

energyProviders.csv

All default values of the energy price, feed-in tariff, renewable share and emission factor of European countries are stored in data/static_inputs/local_grid_parameters.xlsx

1. unit: str,kW

2. optimizeCap: bool, True (for all of the components)

3. energy_price: currency/kWh,

   1. Electricity grid: * auto_calc, EUROSTAT electricity,

   2. Gas plant: * auto_calc EUROSTAT Gas

4. feedin_tariff: currency/kWh,

   1. Electricity grid: * auto_calc feed-in tariff

   2. Gas plant: 0

5. peak_demand_pricing: currency/kW, 0 (for all of the components)

6. peak_demand_pricing_period: times per year (1,2,3,4,6,12), 1 (for all of the components)

7. renewable_share,factor, * auto_calc EUROSTAT renewable share

8. inflow_direction: str,

   1. Electricity grid: Electricity

   2. Gas plant: Gas bus

9. outflow_direction: str,

   1. Electricity grid: Electricity

   2. Gas plant: Heat bus

10. energyVector: str, a. Electricity grid: Electricity b. Gas plant: Heat

11. type_oemof: str, source (for all of the components)

12. emission factor: kgCO2eq/kWh a. Electricity grid: * auto_calc EEA EUROPA b. Gas plant: 0.2 (Obtained from Quaschning 06/2015.)

energyStorage.csv

1. inflow_direction: str, ESS Li-Ion

2. label: str, ESS Li-Ion

3. optimizeCap: bool, True

4. outflow_direction: str, ESS Li-Ion

5. type_oemof: str, storage

6. storage_filename: str, storage_01.csv

7. energyVector: str, Electricity

storage_01.csv

This storage example describes a battery storage

1. unit, str, kWh

2. installedCap: unit, 0 (for all components)

3. age_installed: year, 0 (for all components)

4. lifetime: year, 20 (for all components), (see Moosmoar S.3)

5. development_costs: currency, 0 (for all components)

6. specific_costs: currency/unit

   1. storage capacity: 250 - 550 (ZHB S.46 ff)

   2. input power and output power: 0

7. specific_costs_om: currency/unit/year

   1. storage capacity: 0.2 (energieheld)

   2. input power and output power: 0

8. dispatch_price: currency/kWh

   1. storage capacity: NA (does not apply)

   2. input power and output power: 0

9. c_rate: factor of total capacity (kWh)

   1. storage capacity: NA (does not apply)

   2. input power and output power: 1 (this just means that the whole capacity of the battery would be used during charging and discharging cycles)

10. efficiency: factor a. storage capacity: 1 b. input power and output power: 0.95 (Charging and discharging efficiency. The value has been sourced from MVS efficiency.)

11. soc_initial: None or factor a. storage capacity: None b. input power and output power: NA

12. soc_max: factor a. storage capacity: 0.98 (Solar charge controllers) b. input power and output power: NA

13. soc_min: factor a. storage capacity: 0.2 (Figure from this research article.) b. input power and output power: NA

storage_02.csv

This storage example describes a stratified thermal storage

1. age_installed: year, 0 (for all components of the stratified thermal storage)

2. development_costs: currency, 0 (for all components of the stratified thermal storage)

3. specific_costs: currency/unit

   1. storage capacity: 410, (see Danish energy agency’s technology data of small-scale hot water tanks [dea_swt] on p.66 - However investment costs of stratified TES could be higher.)

   2. input power and output power: 0

4. c_rate: factor of total capacity (kWh)

   1. storage capacity: NA (does not apply)

   2. input power and output power: 1 (this just means that the whole capacity of the stratified thermal storage would be used during charging and discharging cycles)

5. efficiency: factor

   1. storage capacity: 1, or “NA” if calculated

   2. input power and output power: 1

6. installedCap: unit 0, or “NA” if calculated

   1. storage capacity: 0, or “NA” if calculated

   2. input power and output power: 0

7. lifetime: year, 30 (applies for all the parameters of the stratified thermal energy storage)

8. specific_costs_om: currency/unit/year

   1. storage capacity: 16.67, ([dea_swt] p.66 - however fix om costs of stratified TES could differ)

   2. input power and output power: 0

9. dispatch_price: currency/kWh

   1. storage capacity: NA (does not apply)

   2. input power and output power: 0

10. soc_initial: None or factor a. storage capacity: None b. input power and output power: NA

11. soc_max: factor a. storage capacity: 0.925 (7.5% unused volume see European Commission study large-scale heating and cooling in EU [EUC_heat] p.168 - This applies for large scale TES but could be validated for a small scale storage too.) b. input power and output power: NA

12. soc_min: factor a. storage capacity: 0.075 (7.5% unused volume see [EUC_heat] p.168 - This applies for large scale TES but could be validated for a small scale storage too.) b. input power and output power: NA

13. unit: str a. storage capacity: kWh b. input power and output power: kW

14. fixed_thermal_losses_relative: factor a. storage capacity: “{‘file_name’: ‘None’, ‘header’: ‘no_unit’, ‘unit’: ‘’}”, is calculated in pvcompare b. input power and output power: NA (does not apply)

15. fixed_thermal_losses_absolute: kWh a. storage capacity: “{‘file_name’: ‘None’, ‘header’: ‘no_unit’, ‘unit’: ‘’}”, is calculated in pvcompare b. input power and output power: NA (does not apply)

2. pvcompare-specific parameters

In order to run pvcompare, a number of input parameters are needed; many of which are stored in csv files with default values in data/user_inputs/pvcompare_inputs/. The following list will give a brief introduction into the description of the csv files and the source of the given default parameters.

Some parameters can be calculated automatically by pvcompare and do not need to be filled it by hand. These parameters are marked with * auto_calc.

pv_setup.csv

The pv_setup.csv defines the number of facades that are covered with pv-modules.

1. surface_type: str, optional values are “flat_roof”, “gable_roof”, “south_facade”, “east_facade” and “west_facade”

2. surface_azimuth: integer, between -180 and 180, where 180 is facing south, 90 is facing east and -90 is facing west

3. surface_tilt: integer, between 0 and 90, where 90 represents a vertical module and 0 a horizontal.

4. technology: str, optional values are “si” for a silicone module, “cpv” for concentrator photovoltaics and “psi” for a perovskite silicone module

building_parameters.csv

Parameters that describe the characteristics of the building that should be considered in the simulation. The default values are taken from [1].

1. number of storeys,int, 5

2. number of houses: int, 20

3. population per storey: int, 32 (number of habitants per storey)

4. total storey area: int, 1232 (total area of one storey, equal to the flat roof area in m²)

5. length south facade: int, 56 (length of the south facade in m)

6. length eastwest facade:int, 22 (length of the east/west facade in m)

7. hight storey: int, 3 (hight of each storey in m)

8. room temperature: int, 20 (average room temperature inside the building, default: 20 °C)

9. heating limit temperature: int, 15 (temperature limit for space heating in °C, default: 15 °C)

10. include warm water: bool, False (condition about whether warm water is considered in the heat demand, default: False. If False, the warm water demand is neglected in the simulation.)

11. filename_total_SH: str, total_consumption_SH_residential.xlsx (name of the csv file that contains the total energy consumption for space heating of countries in the European Union [2])

12. filename_total_WH: str, total_consumption_WH_residential.xlsx (name of the csv file that contains the total energy consumption for water heating of countries in the European Union [2])

13. filename_elect_SH: str, electricity_consumption_SH_residential.xlsx (name of the csv file that contains the electrical energy consumption of space heating of countries in the European Union countries [2])

14. filename_elect_WH: str, electricity_consumption_WH_residential.xlsx (name of the csv file that contains the electrical energy consumption of warm water heating of countries in the European Union [2])

15. filename_residential_electricity_demand: str, electricity_consumption_residential.xlsx (name of the csv file that contains the total electricity energy consumption in residential sector of countries in the European Union [2])

16. filename_total_cooking_consumption: str, total_consumption_cooking_residential.xlsx (name of the csv file that contains the total energy consumption for cooking in residential sector of countries in the European Union [2])

17. filename_electricity_cooking_consumption: str,electricity_consumption_cooking_residential.xlsx (name of the csv file that contains the electrical residential cooking demand of countries in the European Union [2])

18. filename_country_population: str, EUROSTAT_population.csv (name of the csv file with total population of each country in the European Union [2])

heat_pumps_and_chillers.csv

Parameters that describe characteristics of the heat pumps and chillers in the simulated energy system. Values below assumed for each heat pump technology from research and comparison of three models, each of a different manufacturer. For each technology the quality grade has been calculated from the mean quality grade of the three models.

1. mode: str, options: ‘heat_pump’ or ‘chiller’

2. technology: str, options: ‘air-air’, ‘air-water’ or ‘brine-water’ (These three technologies can be processed so far. Default: If missing or different the plant will be modeled as air source)

3. quality_grade: float, scale-down factor to determine the COP of a real machine (Can be calculated from COP provided by manufacturer under nominal conditions and nominal temperatures. Required equations can be found in the oemof.thermal documentation of compression heat pump and chiller.)

   1. air-to-air heat pump: default: 0.1852, Average quality grade of the following heat pump models: (RAC-50WXE Hitachi, Ltd., MSZ-GL50 Mitsubishi Electric Corporation and KIT-E18-PKEA of Panasonic Corporation)

   2. air-to-water heat pump: default: 0.4030, Average quality grade of the following heat pump models: (WPLS6.2 of Bosch Thermotechnik GmbH – Buderus, WPL 17 ICS classic of STIEBEL ELTRON GmbH & Co. KG and 221.A10 of Viessmann Climate Solutions SE)

   3. brine-to-water heat pump: default: 0.53, Average quality grade of the following heat pump models: (WPS 6K-1 of Bosch Thermotechnik GmbH – Buderus, WPF 05 of STIEBEL ELTRON GmbH & Co. KG and 5008.5Ai of WATERKOTTE GmbH)

   4. air-to-air chiller: 0.3 (Obtained from monitored data of the GRECO project)

4. temp_high: float, temperature in °C of the sink (external outlet temperature at the condenser),

   1. air-to-air heat pump: 38, Internal condensor temperature assuming a room temperature of 20 °C, adding a dT of 2 K to heat exchange between air and external circuit, considering temperature spread of 6 K of the external medium [4] and assuming a 10 K temperature difference between external and internal condensor flow

   2. air-to-water heat pump: 50, Internal condensor temperature assuming a surface heating temperature of 40 °C (see for instance this advisor of Vaillant) and a 10 K temperature difference between external and internal condensor flow

   3. brine-to-water heat pump: 50, Internal condensor temperature assuming a surface heating temperature of 40 °C (see for instance this advisor of Vaillant) and a 10 K temperature difference between external and internal condensor flow

   4. air-to-air chiller: Passed empty or with NaN in order to model from ambient temperature

5. temp_low: float, temperature in °C of the source (external outlet temperature at the evaporator),

   1. air source heat pump: Passed empty or with NaN in order to model from ambient temperature

   2. air-to-water heat pump: Passed empty or with NaN in order to model from ambient temperature

   3. brine-to-water heat pump: Passed empty or with NaN in order to model from mean yearly ambient temperature as simplifying assumption of the ground temperature from depths of approximately 15 meters (see brandl_energy_2006)

   4. air-to-air chiller: 15 (The low temperature has been set for now to 15° C, a temperature lower the comfort temperature of 20–22 °C. The chiller has not been implemented in the model yet. However, should it been done so in the future, these temperatures must be researched and adjusted.)

6. factor_icing: float or None, COP reduction caused by icing, only for mode ‘heat_pump’, default: None

7. temp_threshold_icing: float or None, Temperature below which icing occurs, only for mode ‘heat_pump’, default: None

stratified_thermal_storage.csv

Parameters that describe characteristics of the stratified thermal storage in the simulated energy system. The parameters have been set on the example of the stratified thermal storage TH 1000 of Schindler+Hofmann GmbH & Co. KG

1. var_name: var_value, var_unit

2. height: Empty to model investment optimization or numeric to model with a fix storage size, m

3. diameter: 0.79 (cf. inner diameter in data sheet of [TH 1000] ), m

4. temp_h: 40 (Assuming a surface heating temperature of 40 °C), degC

5. temp_c: 34 (Considering temperature spread of 6 K of inlet and outlet temperature [4]), degC

6. s_iso: 100 (cf. [TH 1000]), mm

7. lamb_iso: 0.03 (Assumption taken from [5]), W/(m*K)

8. alpha_inside: 4.3 (Calculated with calculations in [6]), W/(m2*K)

9. alpha_outside 3.17 (Calculated with calculations in [6]), W/(m2*K)

3. Static input parameters

list_of_workalendar_countries.csv

list of countries for which a python.workalendar [3] exists with the column name “country”.

EUROSTAT_population.csv

“Population on 1 January by age, sex and broad group of citizenship for European countries” of the years 2008 to 2019 obtained from [7]

Energetic demands

Energetic demands were obtained from Odyssee Project of Enerdata

• electricity_consumption_residential.xlsx [8]

• electricity_consumption_SH_residential.xlsx [9]

• electricity_consumption_WH_residential.xlsx [10]

• total_consumption_cooking_residential.xlsx [11]

• electricity_consumption_cooking_residential.xlsx [12]

• total_consumption_SH_residential.xlsx [13]

• total_consumption_WH_residential.xlsx [14]

local_grid_parameters.xlsx

1. electricity_price: default: 0.18 else auto_calc, EUR/kWh, Obtained from [15]

2. gas_price: default: 0.05 else auto_calc, EUR/kWh, Gas prices of European countries obtained from [16]

3. feedin_tariff: default: 0.05 else auto_calc, EUR/kWh, Feed-in tariff obtained from [17]

4. emission_factor: default: 0.25, kgCO2eq/kWh, Emission factor of the electricity grid obtained from [18]

5. renewable_share: default: 0.15, factor, Share of renewables in the electricity grid obtained from [19]

[1] Hachem, 2014: Energy performance enhancement in multistory residential buildings. DOI: 10.1016/j.apenergy.2013.11.018

[2] EUROSTAT: https://ec.europa.eu/energy/en/eu-buildings-database#how-to-use

[3] Workalendar https://pypi.org/project/workalendar/

[4] Felix Ziegler, Dr. Ing, 1997: Sorptionswärmepumpen. Erding, Forschungsberichte des Deutschen Kälte- und Klimatechnischen Vereins Nr. 57, habilitation

[5] Beikircher, Thomas & Buttinger, Frank & Rottmann, Matthias & Herzog, Fabian & Konrad, Martin & Reuß, Manfred & Beikircher, Redaktion, 2013: Superisolierter Heißwasser-Langzeitwärmespeicher : Abschlussbericht zu BMU-Projekt Förderkennzeichen 0325964A, Projektlaufzeit: 01.05.2010 - 31.10.2012. 10.2314/GBV:749701188.

[6] In:Klan, H, 2002: Wärmeübergang durch freie Konvektion an umströmten Körpern. Berlin, Heidelberg: Springer Berlin Heidelberg, ISBN 978-3-662-10743-0, 567-591

[7] Eurostat, the Statistical Office of the European Union: Population on 1 January by age, sex and broad group of citizenship. online data code: MIGR_POP2CTZ. https://ec.europa.eu/eurostat/databrowser/view/migr_pop2ctz/default/table?lang=en

[8] Enerdata: Electricity consumption of residential sector. https://odyssee.enerdata.net/database/

[9] Enerdata: Electricity consumption of residential for space heating. https://odyssee.enerdata.net/database/

[10] Enerdata: Electricity consumption of households for water heating. https://odyssee.enerdata.net/database/

[11] Enerdata: Final consumption of residential for cooking. https://odyssee.enerdata.net/database/

[12] Enerdata: Electricity consumption of residential for cooking. https://odyssee.enerdata.net/database/

[13] Enerdata: Final consumption of residential for space heating. https://odyssee.enerdata.net/database/

[14] Enerdata: Final consumption of households for water heating. https://odyssee.enerdata.net/database/

[15] Eurostat, the Statistical Office of the European Union: Electricity prices by type of user. https://ec.europa.eu/eurostat/databrowser/view/ten00117/default/table?lang=en

[16] Eurostat, the Statistical Office of the European Union: Gas prices by type of user. https://ec.europa.eu/eurostat/databrowser/view/ten00118/default/table?lang=en

[17] magazine, pv: Feed-in tari s (FITs) in Europe. https://www.pv-magazine.com/features/archive/solar-incentives-and-fits/feed-in-tariffs-in-europe/

[18] Hans Bruyninckx: Greenhouse gas emission intensity of electricity generation in Europe | European Environment Agency. https://www.eea.europa.eu/data-and-maps/indicators/overview-of-the-electricity-production-3/assessment

[19] Eurostat, the Statistical Office of the European Union: Share of renewable energy in gross nal energy consumption. https://ec.europa.eu/eurostat/databrowser/view/t2020 31/default/table?lang=en