DOI: 10.5281/zenodo.7186521
title: openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts

description: 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. <strong>Description of data provided</strong> 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. <em>Carmakers’ Race to Meet the</em>, <em>21</em>. 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). <em>JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note</em> (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