This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

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 ryano18@vt.edu.

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