Skip to main content

Advertisement

SpringerLink
Log in

Modelling of electricity savings in the Danish households sector: from the energy system to the end-user

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

In this paper, we examine the value of investing in energy-efficient household appliances from both an energy system and end-user perspectives. We consider a set of appliance categories constituting the majority of the electricity consumption in the private household sector, and focus on the stock of products which need to be replaced. First, we look at the energy system and investigate whether investing in improved energy efficiency can compete with the cost of electricity supply from existing or new power plants. To assess the analysis, Balmorel, a linear optimization model for the heat and power sectors, has been extended in order to endogenously determine the best possible investments in more efficient home appliances. Second, we propose a method to relate the optimal energy system solution to the end-user choices by incorporating consumer behaviour and electricity price addition due to taxes. The model is non-exclusively tested on the Danish energy system under different scenarios. Computational experiments show that several energy efficiency measures in the household sector should be regarded as valuable investments (e.g. an efficient lighting system) while others would require some form of support to become profitable. The analysis quantifies energy and economic savings from the consumer side and reveals the impacts on the Danish power system and surrounding countries. Compared to a business-as-usual energy scenario, the end-user attains net economic savings in the range of 30–40 EUR per year, and the system can benefit of an annual electricity demand reduction of 140–150 GWh. The paper enriches the existing literature about energy efficiency modelling in households, contributing with novel models, methods, and findings related to the Danish case.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. 1 At the time of the article writing (October 2016), this data is not available and is expected to be released in the upcoming months.

References

  • Allcott, H. (2011a). Rethinking real-time electricity pricing. Resource and Energy Economics, 33(4), 820–842. doi:10.1016/j.reseneeco.2011.06.003.

    Article  Google Scholar 

  • Allcott, H (2011b). Social norms and energy conservation. Journal of Public Economics, 95(9-10), 1082–1095. doi:10.1016/j.jpubeco.2011.03.003.

    Article  Google Scholar 

  • Baldini, M., & Klinge Jacobsen, H (2016). Optimal trade-offs between energy efficiency improvements and additional renewable energy supply: a review of international experiences, http://ieeexplore.ieee.org/document/7521245/.

  • Ball, M., Wietschel, M., & Rentz, O. (2007). Integration of a hydrogen economy into the German energy system: an optimising modelling approach. International Journal of Hydrogen Energy, 32(10–11), 1355–1368.

    Article  Google Scholar 

  • Balmorel (2015). Balmorel: energy system model, http://www.eabalmorel.dk/.

  • Bartiaux, F., & Gram-Hanssen, K. (2005). Socio-political factors influencing household electricity consumption: A comparison between Denmark and Belgium. ECEE 2005 Summer Study, 1313–1325.

  • Batih, H., & Sorapipatana, C (2016). Characteristics of urban households’ electrical energy consumption in Indonesia and its saving potentials. Renewable and Sustainable Energy Reviews, 57, 1160–1173. doi:10.1016/j.rser.2015.12.132.

    Article  Google Scholar 

  • Breum, H (2015). The danish energy model, Tech. rep., København. https://ens.dk/en/our-responsibilities/global-cooperation/danish-energy-model.

  • Broman Toft, M., Schuitema, G., & Thøgersen, J (2014). The importance of framing for consumer acceptance of the smart grid: a comparative study of Denmark, Norway and Switzerland. Energy Research and Social Science, 3(C), 113–123. doi:10.1016/j.erss.2014.07.010.

    Article  Google Scholar 

  • Buluş, A., & Topalli, N. (2011). Energy efficiency and rebound effect: does energy efficiency save energy? Energy and Power Engineering, 03(03), 355–360.

    Article  Google Scholar 

  • Bunch, D. S., Ramea, K., Yeh, S., & Yang, C (2015). Incorporating behavioral effects from vehicle choice models into bottom-up energy sector models (july).

  • Cabeza, L. F., Urge-Vorsatz, D., Mcneil, M. A., Barreneche, C., & Serrano, S (2014). Investigating greenhouse challenge from growing trends of electricity consumption through home appliances in buildings. Renewable and Sustainable Energy Reviews, 36, 188–193. doi:10.1016/j.rser.2014.04.053.

    Article  Google Scholar 

  • Carnall, M., Dale, L., & Lekov, A (2015). The economic effect of efficiency programs on energy consumers and producers. Energy efficiency, 647–662, doi:10.1007/s12053-015-9390-y.

    Article  Google Scholar 

  • Connolly, D., Lund, H., Mathiesen, B. V., Werner, S., Möller, B., Persson, U., Boermans, T., Trier, D., Østergaard, P. A., & Nielsen, S. (2014). Heat roadmap europe: combining district heating with heat savings to decarbonise the EU energy system. Energy Policy, 65, 475–489. doi:10.1016/j.enpol.2013.10.035.

    Article  Google Scholar 

  • COOPER, M (2011). Public attitudes toward energy efficiency and appliance efficiency standards: Consumer federation of america.

  • Danish Energy Agency (2014). Denmark’s national energy efficiency action plan (NEEAP). Tech. Rep. April.

  • Danish Energy Agency (2016a). Energy efficiency trends and policies in Denmark, Tech. Rep. January, Copenhagen. http://www.odyssee-mure.eu/publications/national-reports/energy-efficiency-denmark.pdf.

  • Danish Energy Agency (2016b). Forudsætninger for samfundsøkonomiske analyser på energiområdet (Guidelines for socio-economic analysis in the field of energy; in Danish), Tech. rep., Copenhagen.

  • Danish Ministry of Energy Utilities and Climate (2013). Smart meters in all the households (Smarte elmålere i alle hjem: in Danish). http://www.efkm.dk/nyheder/smarte-elmaalere-hjem.

  • Danish Ministry of Energy Utilities and Climate (2014). Bekendtgørelse om fjernaflæste elmålere og måling af elektricitet i. https://www.retsinformation.dk/pdfPrint.aspx?id=160434.

  • Danmark NationalBank (2016). Official Interest Rates. http://www.nationalbanken.dk/en/marketinfo/official_interestrates/Pages/default.aspx.

  • Davis, L. W., & Metcalf, E.G. (2014). Does better information lead to better choices? Evidence from energy-efficiency labels, http://www.nber.org/papers/w20720.

  • Energitilsynet (2016). ELPRIS.DK. http://elpris.dk/#/.

  • Enkvist, P.-A., Nauclér, T., & Rosander, J (2007). A cost curve for greenhouse gas reduction. McKinsey Quarterly, (1), 34–45. http://ezproxy.lib.utexas.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24215973&site=ehost-live.

  • European Commission (2010). Energy 2020. Tech. rep.

  • European Commission (2012). Green Paper—a 2030 framework for climate and energy policies.

  • Evora, J., Kremers, E., Morales, S., Hernandez, M., Hernandez, J. J., & Viejo, P (2011). Agent-based modelling of electrical load at household level. ECAL 2011: CoSMoS - Proceedings of the 2011 Workshop on Complex Systems Modelling and Simulation, 12.

  • Farinelli, U., Johansson, T. B., McCormick, K., Mundaca, L., Oikonomou, V., Örtenvik, M., Patel, M., & Santi, F (2005). White and green: comparison of market-based instruments to promote energy efficiency. Journal of Cleaner Production.

  • Faruqui, A., Harris, D., & Hledik, R (2010). Unlocking the 53 billion savings from smart meters in the EU: how increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy, 38(10), 6222–6231. doi:10.1016/j.enpol.2010.06.010.

    Article  Google Scholar 

  • Galarraga, I., Abadie, L. M., & Ansuategi, A (2013). Efficiency, effectiveness and implementation feasibility of energy efficiency rebates : the “Renove” plan in Spain.

  • Galvin, R. (2010). Thermal upgrades of existing homes in Germany: the building code, subsidies, and economic efficiency. Energy and Buildings.

  • Hansen, K., Connolly, D., Lund, H., Drysdale, D., & Thellufsen, J. Z (2016). Heat roadmap europe: identifying the balance between saving heat and supplying heat. Energy, 10.1016/j.energy.2016.06.033.

  • Hausman, J. A. (1979). Individual discount rates and the purchase and utilization of energy-using durables. The Bell Journal of Economics, 10:1, 33–54.

    Article  Google Scholar 

  • Houde, S (2014). How consumers respond to environmental certification and the value of energy information. National Bureau of Economic Research Working Paper Series No. 20019 (August). http://www.nber.org/papers/w20019.

  • IEA (2016). Nordic Energy Technology Perspectives 2016. Energy Technology Policy Division (April), 650. https://www.iea.org/etp/nordic/.

  • Ipcc (2007). Mitigation of climate change: contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change.

  • Jensen, S. G., & Meibom, P (2008). Investments in liberalised power markets. Gas turbine investment opportunities in the nordic power system. International Journal of Electrical Power and Energy Systems, 30(2), 113–124.

    Article  Google Scholar 

  • Karlsson, K., & Meibom, P (2008). Optimal investment paths for future renewable based energy systems-using the optimisation model Balmorel. International Journal of Hydrogen Energy, 33(7), 1777–1787.

    Article  Google Scholar 

  • Katz, J. (2014). Linking meters and markets: roles and incentives to support a flexible demand side. Utilities Policy, 31, 74–84. doi:10.1016/j.jup.2014.08.003.

    Article  Google Scholar 

  • Katz, J., Andersen, F. M., & Morthorst, P. E (2016). Load-shift incentives for household demand response: evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system. Energy, doi:10.1016/j.energy.2016.07.084.

    Article  Google Scholar 

  • Khazzoom, J. D. (1980). Economic implications of mandated efficiency in standards for household appliances. The Energy Journal, 1(Number 4), 21–40.

    Google Scholar 

  • Klinge Jacobsen, H., & Juul, N. (2015). Demand-side management: electricity savings in Danish households reduce load variation, capacity requirements, and associated emissions @ DTU International Energy Report 2015: Energy systems integration for the transition to non-fossil energy systems, Tech. rep., Technical University of Denmark DTU.

  • Krishnamurti, T., Schwartz, D., Davis, A., Fischhoff, B., de Bruin, W. B., Lave, L., & Wang, J (2012). Preparing for smart grid technologies: a behavioral decision research approach to understanding consumer expectations about smart meters. Energy Policy, 41, 790–797.

    Article  Google Scholar 

  • Lefebvre, S., & Desbiens, C. (2002). Residential load modeling for predicting distribution transformer load behavior, feeder load and cold load pickup. International Journal of Electrical Power and Energy Systems, 24(4), 285–293.

    Article  Google Scholar 

  • López-Peña, Á., Pérez-Arriaga, I, & Linares, P (2012). Renewables vs. energy efficiency: the cost of carbon emissions reduction in Spain. Energy policy.

  • Mills, B., & Schleich, J. (2010). What’s driving energy efficient appliance label awareness and purchase propensity? Energy Policy, 38(2), 814–825.

    Article  Google Scholar 

  • Mizobuchi, K., & Takeuchi, K (2016). Replacement or additional purchase: the impact of energy-efficient appliances on household electricity saving under public pressures. Energy Policy, 93, 137–148. doi:10.1016/j.enpol.2016.03.001.

    Article  Google Scholar 

  • Münster, M. (2009). Energy system analysis of waste-to-energy technologies. Energy (June).

  • Münster, M, & Meibom, P (2010). Long-term affected energy production of waste to energy technologies identified by use of energy system analysis. Waste Management, 30(12), 2510–2519.

    Article  Google Scholar 

  • Münster, M, Morthorst, P. E., Larsen, H. V., Bregnbæk, L, Werling, J, Lindboe, H. H., & Ravn, H. (2012). The role of district heating in the future danish energy system. Energy, 48(1), 47–55.

    Article  Google Scholar 

  • Murray, A. G., & Mills, B. F. (2011). Read the label! energy star appliance label awareness and uptake among u.s. consumers. Energy Economics, 33(6), 1103–1110. doi:10.1016/j.eneco.2011.04.013.

    Article  Google Scholar 

  • Nässén, J., & Holmberg, J. (2009). Quantifying the rebound effects of energy efficiency improvements and energy conserving behaviour in Sweden. Energy Efficiency, 2(3), 221–231.

    Article  Google Scholar 

  • Newell, R.G., & Siikamäki, J.V. (2013). Nudging energy efficiency behaviour: the role of information labels, http://www.nber.org/papers/w19224.pdf.

  • NordPoolSpot (2016). Nord Pool Spot. http://www.nordpoolspot.com/historical-market-data/.

  • Oilprice.com (2016). Crude oil prices today. http://oilprice.com/.

  • Parikh, K. S., & Parikh, J. K (2016). Realizing potential savings of energy and emissions from efficient household appliances in India. Energy Policy, 97, 102–111. doi:10.1016/j.enpol.2016.07.005.

    Article  Google Scholar 

  • Reuters, T. (2016). Point carbon energy research. http://financial.thomsonreuters.com/en/resources/articles/point-carbon.html.

  • Rodríguez Fernández, M., González Alonso, I., & Zalama Casanova, E. (2015). Online identification of appliances from power consumption data collected by smart meters. Pattern Analysis and Applications (September), 463–473. http://link.springer.com/10.1007/s10044-015-0487-x.

    Article  MathSciNet  Google Scholar 

  • Schaffrin, A., & Reibling, N (2015). Household energy and climate mitigation policies: investigating energy practices in the housing sector. Energy Policy, 77, 1–10. doi:10.1016/j.enpol.2014.12.002.

    Article  Google Scholar 

  • Shen, J., & Saijo, T (2009). Does an energy efficiency label alter consumers’ purchasing decisions? A latent class approach based on a stated choice experiment in shanghai. Journal of Environmental Management, 90(11), 3561–3573. doi:10.1016/j.jenvman.2009.06.010.

    Article  Google Scholar 

  • Shrestha, R. M., & Marpaung, C. O. P (2006). Integrated resource planning in the power sector and economy-wide changes in environmental emissions. Energy policy.

  • Statistics Denmark (2016). Statistics Denmark. https://www.dst.dk/en.

  • Swan, L. G., & Ugursal, V. I (2009). Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819–1835.

    Article  Google Scholar 

  • UserTEC (2016). UserTEC user practices, technologies and residential energy consumption. http://sbi.dk/usertec.

  • Wada, K., Akimoto, K., Sano, F., Oda, J., & Homma, T (2012). Energy efficiency opportunities in the residential sector and their feasibility. Energy, 48(1), 5–10. doi:10.1016/j.energy.2012.01.046.

    Article  Google Scholar 

  • Ward, D. O., Clark, C. D., Jensen, K. L., Yen, S. T., & Russell, C. S (2011). Factors influencing willingness-to-pay for the ENERGY STAR® label. Energy Policy, 39(3), 1450–1458. doi:10.1016/j.enpol.2010.12.017.

    Article  Google Scholar 

  • Xie, Q., Ouyang, H., & Gao, X. (2016). Estimation of electricity demand in the residential buildings of China based on household survey data. International Journal of Hydrogen Energy, 41(35), 15879–15886. doi:10.1016/j.ijhydene.2016.03.152.

    Article  Google Scholar 

  • Zvingilaite, E. (2013). Modelling energy savings in the danish building sector combined with internalisation of health related externalities in a heat and power system optimisation model. Energy Policy, 55, 57–72. doi:10.1016/j.enpol.2012.09.056.

    Article  Google Scholar 

  • Zvingilaite, E., & Balyk, O (2014). Heat savings in buildings in a 100% renewable heat and power system in Denmark with different shares of district heating. Energy and Buildings, 82, 173–186. doi:10.1016/j.enbuild.2014.06.046.

    Article  Google Scholar 

  • Zvingilaite, E., & Klinge Jacobsen, H (2015). Heat savings and heat generation technologies: modelling of residential investment behaviour with local health costs. Energy Policy, 77, 31–45. doi:10.1016/j.enpol.2014.11.032.

    Article  Google Scholar 

Download references

Acknowledgments

The research has been financed by the Innovation Fund Denmark under the research project SAVE-E, grant no. 4106-00009B.

Author information

Authors and Affiliations

  1. Department of Management Engineering, Technical University of Denmark, Produktionstorvet, Building 426, Room 110, 2800 Kgs., Lyngby, Denmark

    Mattia Baldini & Alessio Trivella

Authors
  1. Mattia Baldini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessio Trivella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessio Trivella.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baldini, M., Trivella, A. Modelling of electricity savings in the Danish households sector: from the energy system to the end-user. Energy Efficiency 11, 1563–1581 (2018). https://doi.org/10.1007/s12053-017-9516-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-017-9516-5

Keywords

Search

Navigation