Households' hourly electricity consumption and peak demand in Denmark

Abstract The electrification of residential energy demand for heating and transportation is expected to increase peak load and require additional generation and transmission capacities. Electrification also provides an opportunity to increase demand response. With a focus on household electricity consumption, we analyse the contribution of appliances and new services, such as individual heat pumps and electric vehicles, to peak consumption and the need for demand response incentives to reduce the peak. Initially, the paper presents a new model that represents the hourly electricity consumption profile of households in Denmark. The model considers hourly consumption profiles for different household appliances and their contribution to annual household electricity consumption. When applying the model to an official scenario for annual electricity consumption, assuming non-flexible consumption due to a considerable introduction of electric vehicles and individual heat pumps, household consumption is expected to increase considerably, especially peak hour consumption is expected to increase. Next the paper presents results from a new experiment where household customers are given economic and/or environmental incentives to shift consumption to or away from specified hours. The experiment focuses on the present classic consumption and shows that household customers do react to incentives, but today the flexibility of the classic consumption is limited. Considering electric vehicles and individual heat pumps, for an individual household, the consumption of each of these technologies roughly doubles the household’s consumption and considerably increases their potential for flexibility. Thus, in order to introduce incentives for demand flexibility, while considering reducing peak consumption, policy makers should initially focus on households that have a heat pump and/or an electric vehicle.

[1]  Mattia Marinelli,et al.  Impact of thermostatically controlled loads' demand response activation on aggregated power: A field experiment , 2016 .

[2]  R. Moreno,et al.  Opportunities for Energy Storage: Assessing Whole-System Economic Benefits of Energy Storage in Future Electricity Systems , 2017, IEEE Power and Energy Magazine.

[3]  Mariz B. Arias,et al.  Prediction of electric vehicle charging-power demand in realistic urban traffic networks , 2017 .

[4]  Jing Liao,et al.  Understanding usage patterns of electric kettle and energy saving potential , 2016 .

[5]  Nikolaos G. Paterakis,et al.  A methodology to generate power profiles of electric vehicle parking lots under different operational strategies , 2016 .

[6]  Jukka Paatero,et al.  A model for generating household electricity load profiles , 2006 .

[7]  Lieve Helsen,et al.  Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits , 2016 .

[8]  J. Widén,et al.  Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation , 2009 .

[9]  Colin Fitzpatrick,et al.  Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction , 2013 .

[10]  Sebastian Herkel,et al.  Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options , 2017 .

[11]  F. M. Andersen,et al.  An econometric analysis of electricity demand response to price changes at the intra-day horizon: The case of manufacturing industry in West Denmark , 2015 .

[12]  Lieve Helsen,et al.  Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response , 2017 .

[13]  A. Riddell,et al.  Parametrisation of domestic load profiles , 1996 .

[14]  Nina Juul,et al.  Strategies for Charging Electric Vehicles in the Electricity Market , 2015 .

[15]  Poul Alberg Østergaard,et al.  Energy systems scenario modelling and long term forecasting of hourly electricity demand , 2015 .

[16]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[17]  Helge V. Larsen,et al.  Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers , 2013 .

[18]  Mikael Togeby,et al.  The Effect of Feedback by Text Message (SMS) and Email on Household Electricity Consumption: Experimental Evidence , 2010 .

[19]  Catherine L. Kling,et al.  Conservation and Welfare Effects of Information in a Time-Of-Day Pricing Experiment (The) , 1989 .

[20]  I. Mansouri,et al.  Energy consumption in UK households: Impact of domestic electrical appliances , 1996 .

[21]  J. Widén,et al.  A high-resolution stochastic model of domestic activity patterns and electricity demand , 2010 .

[22]  Henrik Madsen,et al.  Economic valuation of heat pumps and electric boilers in the Danish energy system , 2016 .

[23]  P. A. Østergaard,et al.  Assessment and evaluation of flexible demand in a Danish future energy scenario , 2014 .

[24]  I. Matsukawa,et al.  The Effects of Information on Residential Demand for Electricity , 2004 .

[25]  Christoph M. Flath,et al.  Quantifying load flexibility of electric vehicles for renewable energy integration , 2015 .

[26]  Mustafa Bagriyanik,et al.  Demand Side Management by controlling refrigerators and its effects on consumers , 2012 .

[27]  J. Widén,et al.  Forecasting household consumer electricity load profiles with a combined physical and behavioral approach , 2014 .

[28]  Ronnie Belmans,et al.  Demand flexibility versus physical network expansions in distribution grids , 2016 .

[29]  Alex Summerfield,et al.  The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial , 2017 .

[30]  Helge V. Larsen,et al.  Differentiated long term projections of the hourly electricity consumption in local areas. The case of Denmark West , 2014 .

[31]  Geert Deconinck,et al.  Demand response flexibility and flexibility potential of residential smart appliances: Experiences from large pilot test in Belgium , 2015 .

[32]  Sarah C. Darby,et al.  Social implications of residential demand response in cool temperate climates , 2012 .

[33]  Karsten Emil Capion,et al.  Optimal charging of electric drive vehicles in a market environment , 2011 .

[34]  Mark O'Malley,et al.  Capacity value estimation of a load-shifting resource using a coupled building and power system model , 2017 .

[35]  Carsten Lynge Jensen,et al.  Providing Free Autopoweroff Plugs: Measuring the Effect on Households' Electricity Consumption through a Field Experiment , 2012 .

[36]  R. Belmans,et al.  Impact of residential demand response on power system operation: A Belgian case study , 2014 .

[37]  Hemanshu R. Pota,et al.  Forecasting the EV charging load based on customer profile or station measurement , 2016 .

[38]  Morten Boje Blarke,et al.  Towards an intermittency-friendly energy system: Comparing electric boilers and heat pumps in distributed cogeneration , 2012 .

[39]  Thomas Demeester,et al.  Modeling and analysis of residential flexibility: Timing of white good usage , 2016 .