openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts

Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month).

The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv. 

Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file.

The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv.

A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to [e-mail].

Description of data provided

Achievable_NUTS2_summary.csv

Description

Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050

Data input

Full_potential.V9.csv

participation_rates_country.csv

P_inc_SH.csv

P_inc_WH.csv

P_inc_HP.csv

P_inc_DW.csv

P_inc_WM.csv

P_inc_TD.csv

Script

NUTS2_acheivable.R

COP_.1deg_11-21_V1.csv

Description

NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature

Data

tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc

NUTS_RG_01M_2021_3857.shp

nhhV2.csv

Script

COP_from_E-OBS.R

Country dd projections.csv

Description

Assumptions for annual change in CDD and HDD

Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.

Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario

EV NUTS projectionsV5.csv

Description

NUTS2 level EV projections 2018-2050

Data input

EV projectionsV5_ave.csv

Country level EV projections

NUTS 2 regional share of national vehicle fleet

Eurostat - Vehicle Nuts.xlsx

Script

EVprojections_NUTS_V5.py

EV_NVF_EV_path.xlsx

Description

Country level – EV share of new passenger vehicle fleet

From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. Carmakers’ Race to Meet the, 21.

EV_parameters.xlsx

Description

Parameters used to calculate future loads from EVs

Wunit_EV – represents annual kWh per EV

evLIFE_150kkm

number of years

represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).

Average age/#years assuming 150k life – represents

Number of years

Average between evLIFE_150kkm and average age of vehicle with respect to the country

full_potentialV9.csv

Description

Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year.

This data has not been adjusted with participation_rates_country.csv

Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.

Script

Full_potentialV9.py

gils projection assumptions.xlsx

Description

Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.

A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below.

nflh – full load hours

nflh_ac.csv

nflh_cp.csv

wunit – annual energy consumption

Wunit_rf_fr.csv

Pcycle – power demand per cycle

Pcycle_wm.csv

Pcycle_dw.csv

Pcycle_td.csv

Punit – power damand for device

Punit_ac.csv

Punit_cp.csv

r – country level household ownership rates of residential device

rfr.csv

rrf.csv

rwm.csv

rtd.csv

rdw.csv

rac.csv

rwh.csv

rcp.csv

rsh.csv

Script

openENTRANCE projections.py

heat_pump_hourly_share.csv

Description

Hours share of daily energy demand

From ENTROS TYNDP – Charts and Figures

https://2020.entsos-tyndp-scenarios.eu/download-data/#download

hourlyEVshares.csv

Description

Hours share of daily energy demand

From My Electric Avenue Study

https://eatechnology.com/consultancy-insights/my-electric-avenue/

HP_transitionV2.csv

Description

Used to create Qhp_thermal_MWh_projectedV2.csv

Final_energy_15-19

Average final energy demand for the residential heating sector between 2015-2019

Final_energy_15-19_nonEE

Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)

Final_energy_15-19_nonEE_share

share of inefficient heating sources

HP_thermal_2018

Thermal energy provided by residential heat pumps in 2018

HP_thermal_2019

Thermal energy provided by residential heat pumps in 2019

See publication for data sources

Nflh_ac.csv, nflh_cp.csv

See gils projection assumptions.xlsx

nhhV2

Description

Expected number of households for NUTS2 regions for 2020-2050

See publication for data sources

Script

EUROSTAT_POP2NUTSV2.R

NUTS0_thermal_heat_annum.csv

Description

Country level residential annual thermal heat requirements in kWh

Used to determine maximum dispatch in openENTRANCE final V14.py

Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note (No. JRC108244). Joint Research Centre (Seville site).

p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv

Description

Maximum capacity – load for a device can never exceed maximum capacity

Data

gils projection assumptions.xlsx

Script

openENTRANCE final V14.py

P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv

Description

Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050

Data

d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv

Theoretical maximum reduction / load of the respective device

p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv

Maximum capacity

Script

P_increaseV2.py

Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv

Description

power demand per cycle kWh

See gils projection assumptions.xlsx

Punit_ac.csv, Punit_cp.csv

Description

Unit capacities kWh

See gils projection assumptions.xlsx

Qhp_thermal_MWh_projectedV2.csv

Description

NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050

Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050

Data

HP_transitionV2.csv

nhhV2.csv

Script

HP_projection_nuts.py

rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv

Description

Household ownership rates

See gils projection assumptions.xlsx

s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv

Description

s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)

yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively

long run (2011-2021) average NUTS 2 level hdd and cdd

s_wash nuts_V2.csv

Description

Hours share of daily energy demand for washing machine, tumble drier, and dishwasher

Data

stamminger_V2.xlsx

Script

S_wash_nuts_V2.py

Stamminger_2009.csv

Description

Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP

From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.

Time_index.csv

Used to create the appropriate timestamp for representative hours

Wunit_rf_fr.csv

Annual energy consumption for refrigeration and freezers

See gils projection assumptions.xlsx