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 ryano18@vt.edu.

Description of data provided

1. Achievable_NUTS2_summary.csv
   1. Description
      1. Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050
   2. Data input
      1. Full_potential.V9.csv
      2. participation_rates_country.csv
      3. P_inc_SH.csv
      4. P_inc_WH.csv
      5. P_inc_HP.csv
      6. P_inc_DW.csv
      7. P_inc_WM.csv
      8. P_inc_TD.csv
   3. Script
      1. NUTS2_acheivable.R
2. COP_.1deg_11-21_V1.csv
   1. Description
      1. NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature
   2. Data
      1. tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc
      2. NUTS_RG_01M_2021_3857.shp
      3. nhhV2.csv
   3. Script
      1. COP_from_E-OBS.R
3. Country dd projections.csv
   1. Description
      1. Assumptions for annual change in CDD and HDD
      2. 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.
      3. Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario
4. EV NUTS projectionsV5.csv
   1. Description
      1. NUTS2 level EV projections 2018-2050
   2. Data input
      1. EV projectionsV5_ave.csv
         1. Country level EV projections
      2. NUTS 2 regional share of national vehicle fleet
         1. Eurostat - Vehicle Nuts.xlsx
   3. Script
      1. EVprojections_NUTS_V5.py
5. EV_NVF_EV_path.xlsx
   1. Description
      1. Country level – EV share of new passenger vehicle fleet
      2. From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. Carmakers’ Race to Meet the, 21.
6. EV_parameters.xlsx
   1. Description
      1. Parameters used to calculate future loads from EVs
      2. Wunit_EV – represents annual kWh per EV
      3. evLIFE_150kkm
         1. number of years
         2. 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).
      4. Average age/#years assuming 150k life – represents
         1. Number of years
         2. Average between evLIFE_150kkm and average age of vehicle with respect to the country
7. full_potentialV9.csv
   1. Description
      1. Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year.
         1. This data has not been adjusted with participation_rates_country.csv
         2. Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.
   2. Script
      1. Full_potentialV9.py
8. gils projection assumptions.xlsx
   1. Description
      1. Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.
      2. 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.
         1. nflh – full load hours
            1. nflh_ac.csv
            2. nflh_cp.csv
         2. wunit – annual energy consumption
            1. Wunit_rf_fr.csv
         3. Pcycle – power demand per cycle
            1. Pcycle_wm.csv
            2. Pcycle_dw.csv
            3. Pcycle_td.csv
         4. Punit – power damand for device
            1. Punit_ac.csv
            2. Punit_cp.csv
         5. r – country level household ownership rates of residential device
            1. rfr.csv
            2. rrf.csv
            3. rwm.csv
            4. rtd.csv
            5. rdw.csv
            6. rac.csv
            7. rwh.csv
            8. rcp.csv
            9. rsh.csv
         6. Script
            1. openENTRANCE projections.py
9. heat_pump_hourly_share.csv
   1. Description
      1. Hours share of daily energy demand
      2. From ENTROS TYNDP – Charts and Figures
         1. https://2020.entsos-tyndp-scenarios.eu/download-data/#download
10. hourlyEVshares.csv
    1. Description
       1. Hours share of daily energy demand
       2. From My Electric Avenue Study
          1. https://eatechnology.com/consultancy-insights/my-electric-avenue/
11. HP_transitionV2.csv
    1. Description
       1. Used to create Qhp_thermal_MWh_projectedV2.csv
       2. Final_energy_15-19
          1. Average final energy demand for the residential heating sector between 2015-2019
       3. Final_energy_15-19_nonEE
          1. Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)
       4. Final_energy_15-19_nonEE_share
          1. share of inefficient heating sources
       5. HP_thermal_2018
          1. Thermal energy provided by residential heat pumps in 2018
       6. HP_thermal_2019
          1. Thermal energy provided by residential heat pumps in 2019
       7. See publication for data sources
12. Nflh_ac.csv, nflh_cp.csv
    1. See gils projection assumptions.xlsx
13. nhhV2
    1. Description
       1. Expected number of households for NUTS2 regions for 2020-2050
       2. See publication for data sources
    2. Script
       1. EUROSTAT_POP2NUTSV2.R
14. NUTS0_thermal_heat_annum.csv
    1. Description
       1. Country level residential annual thermal heat requirements in kWh
       2. Used to determine maximum dispatch in openENTRANCE final V14.py
       3. 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).
15. 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
    1. Description
       1. Maximum capacity – load for a device can never exceed maximum capacity
    2. Data
       1. gils projection assumptions.xlsx
    3. Script
       1. openENTRANCE final V14.py
16. 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
    1. Description
       1. Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050
    2. Data
       1. 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
          1. Theoretical maximum reduction / load of the respective device
       2. 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
          1. Maximum capacity
    3. Script
       1. P_increaseV2.py
17. Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv
    1. Description
       1. power demand per cycle kWh
       2. See gils projection assumptions.xlsx
18. Punit_ac.csv, Punit_cp.csv
    1. Description
       1. Unit capacities kWh
       2. See gils projection assumptions.xlsx
19. Qhp_thermal_MWh_projectedV2.csv
    1. Description
       1. NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050
       2. Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050
    2. Data
       1. HP_transitionV2.csv
       2. nhhV2.csv
    3. Script
       1. HP_projection_nuts.py
20. rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv
    1. Description
       1. Household ownership rates
       2. See gils projection assumptions.xlsx
21. s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv
    1. Description
       1. s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)
       2. yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively
       3. long run (2011-2021) average NUTS 2 level hdd and cdd
22. s_wash nuts_V2.csv
    1. Description
       1. Hours share of daily energy demand for washing machine, tumble drier, and dishwasher
    2. Data
       1. stamminger_V2.xlsx
    3. Script
       1. S_wash_nuts_V2.py
23. Stamminger_2009.csv
    1. Description
       1. Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP
       2. From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.
24. Time_index.csv
    1. Used to create the appropriate timestamp for representative hours
25. Wunit_rf_fr.csv
    1. Annual energy consumption for refrigeration and freezers
    2. See gils projection assumptions.xlsx