• Energy Research

New network tariffs designed to recover grid operating costs can introduce up to a 500% increase in charges for some households. A transition from volumetric to peak-load-based tariffs will require targeted policy measures such as clear price signals, information about household electricity consumption and temporary compensation or mitigation mechanisms.

Abstract Photovoltaic (PV) units and electric vehicles (EVs) are two household goods that are the focus of much research, and many policy initiatives attempting to promote a more sustainable, low-carbon energy system. Despite both academic and practical interest in household adoption of PV units and EVs, potential linkages in these household decisions have only just begun to be explored. This paper presents q-complementarity between the goods as one possible form of a linkage between PV and EV purchases that is based on economic utility theory. We posit the goods could be q-complements due to a PV-owning household’s ability to offset and shift their electricity load from EV charging to increase the self-consumption of ‘home-made’ electricity, thereby increasing the positive feelings of environmental efficacy and monetary returns from the PV unit. We use data from 2,541 internet surveys of Austrian residential electricity customers collected in 2018 to explore these hypotheses. Probit models of household EV and PV adoption choice are estimated, including a recursive bivariate probit model of households who plan to purchase an EV in the future, with PV ownership endogenously determined. Controlling for household income, characteristics, environmental attitudes, and neighborhood characteristics, we find that EV and PV adoption are positively correlated, and that current PV unit owners are 21% more likely to plan an EV purchase in the next 5 years compared to non-PV owners. We interpret these results as evidence in support of our hypothesis of q-complementarity between PV units and EVs, and note the potential for added benefits from incentive policies promoting adoption of one good or the other that this linkage suggests.

Abstract The federal state of Upper Austria, at a crossing point for European energy grids, provides large-scale resources for storage of natural gas and is among the top infrastructures in this regard in Europe. Considering the ambitious plans for enhancements of energy infrastructures in this region, the issue of social acceptance of energy infrastructure is crucial. To foster an understanding of the challenges inherent in this issue we present an analysis concentrating on the social acceptance of energy infrastructure projects in Upper Austria. This paper addresses the issues with realizing energy infrastructure projects and analyzes the problems and benefits based on an empirical–qualitative study comprising expert interviews, discussions with stakeholders, and a round table workshop integrating the disparate viewpoints. The aim of the process was to integrate different attitudes, perspectives and positions of relevant stakeholders, members of citizens’ initiatives, environmental organizations and of the national government and local authorities. The results presented are based on both the analysis of the empirical–qualitative data and the existing studies and literature on social acceptance. The qualitative research compares experiences and current practices with social acceptance issues (like frameworks, participation, communication strategies) in a set of considered energy infrastructure projects.

Growing self-generation and storage are expected to cause significant changes in residential electricity utilization patterns. Commonly applied volumetric network tariffs may induce imbalance between different groups of households and their respective contribution to recovering the operating costs of the grid. Understanding consumer behaviour and appliance usage together with socio-economic factors can help regulatory authorities to adapt network tariffs to new circumstances in a fair way. Here, we assess the effects of 11 network tariff scenarios on household budgets using real load profiles from 765 households. Thus we explore the possibly disruptive impact of applying peak-load-based tariffs on the budgets of households when they have been mainly charged for consumed volumes before. Our analysis estimates the change in household network expenditure for different combinations of energy, peak and fixed charges, and can help to design tariffs that recover the costs needed for the sustainable operation of the grid. Home energy generation and storage are expected to alter residential energy usage. Careful tariff design is thus needed to ensure fair distribution of grid operation costs. Using smart-meter data and socio-economic profiles, this study explores the potential impact of different tariffs on household expenditure.

Abstract Grid tariffs are the main source of income for distribution system operators (DSOs). Reductions of tariffs increase the cost pressure on DSOs; assuming they work efficiently, tariff reductions potentially lead to a decrease of the electricity system's quality if no reliability of supply criteria are incorporated in the regulatory system. Our statistical analysis shows that the correlation between grid tariffs and electricity supply interruptions in a regulatory regime neglecting this incorporation is significant and that furthermore decreasing tariffs harm the reliability of supply even in the short run. Our econometric analysis of the influence of tariffs on reliability of supply shows a significant correlation between the grid tariffs and the duration of power outages in the Austrian electricity grid; an annual average interruption duration per installed capacity of a specific grid increases ceteris paribus by 1.36 min if the grid tariff of this specific grid is decreased in the previous year by 1€/MWh.

Predicted changes in temperature and other weather events may damage the electricity grid and cause power outages. Understanding the costs of power outages and how these costs change over time with global warming can inform outage-mitigation-investment decisions. Here we show that across 19 EU nations the value of uninterrupted electricity supply is strongly related to local temperatures, and will increase as the climate warms. Bayesian hierarchical modelling of data from a choice experiment and respondent-specific temperature measures reveals estimates of willingness to pay (WTP) to avoid an hour of power outage between €0.32 and €1.86 per household. WTP varies on the basis of season and is heterogeneous between European nations. Winter outages currently cause larger per household welfare losses than summer outages per hour of outage. However, this dynamic will begin to shift under plausible future climates, with summer outages becoming substantially more costly and winter outages becoming slightly less costly on a per-household, per-hour basis. Electricity grids are susceptible to damage from climate-related incidents, which can cause power outages. This study shows that the value of uninterrupted electricity supply across 19 EU nations is related to local temperature, with summer power outages becoming more costly with global warming.

The aim of this paper is to estimate the potential impacts of different COVID-19 scenarios on the Italian energy sector through 2030, with a specific focus on transport and industry. The analysis takes a multi-disciplinary approach to properly consider the complex interactions of sectors across Italy. This approach includes the assessment of economic conditions using macroeconomic and input-output models, modelling the evolution of the energy system using an energy and transport model, and forecasting the reaction of travel demand and modal choice using econometric models and expert interviews. Results show that the effect of COVID-19 pandemic may lead to mid-term effects on energy consumption. The medium scenario, which assumes a stop of the emergency by the end of 2021, shows that energy-related emissions remain 10% lower than the baseline in the industry sector and 6% lower in the transport sector by 2030, when compared with a pre-COVID trend. Policy recommendations to support a green recovery are discussed in light of the results.

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 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