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Targeting customers for an optimized energy procurement

A Cost Segmentation Based on Smart Meter Load Profiles
  • Simon AlbrechtEmail author
  • Manuel Fritz
  • Jens Strüker
  • Holger Ziekow
Special Issue Paper
  • 280 Downloads

Abstract

This research paper investigates consumer-specific costs on power spot markets. We use real-world smart meter data and market prices to analyze an energy procurement strategy based on the newsvendor model. The outcome displays a segmentation into an ordinal array of different costs-per-customer, which allow for a sensitivity analysis to examine appropriate measures and policy implications. We find the most relevant customer class to be the costliest one percent. These prime targets’ share of total costs is 1.5 times as high as the respective share of total consumption. Reallocating the targets into incentive based contracts may allow for a significant reduction of utilities’ costs while remaining on a relatively steady service provision level.

Keywords

Costs-per-customer Smart metering Customer segmentation Load profile analysis Load forecasting 

Notes

Acknowledgments

We would like to thank the Commission for Energy Regulation and the Irish Social Science Data Archive for the collection and provision of the used data.

References

  1. 1.
    European Commission (2011) Energy roadmap 2050. Communication from the commission to The European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions, vol 885. p 15Google Scholar
  2. 2.
    BMWi (2012) Erster monitoring-Bericht-Energie der Zukunft, Berlin, pp 1–132Google Scholar
  3. 3.
    BMWi (2015) Ein Strommarkt für die Energiewende-Ergebnispapier des Bundesministeriums für Wirtschaft und Energie (Weißbuch), Berlin, p 102Google Scholar
  4. 4.
    BMWi (2015) Solar- und Windenergie werden in Deutschland erhebliches Wachstum verzeichnen-Stärkere Konzentration auf Wärme und Verkehr Schlüssel zu noch höheren Anteilen von erneuerbaren Energien. Pressemitteilung, BerlinGoogle Scholar
  5. 5.
    VDE-ETG (2012) Erneuerbare Energie Braucht Flexible Kraftwerke–Szenarien bis 2020. Frankfurt am Main, p 142Google Scholar
  6. 6.
    Pape C, Gerhardt N, Härtel P, Scholz A (2014) Roadmap Speicher-Speicherbedarf für Erneuerbare Energien-Speicheralternativen-Speicheranreiz-Überwindung Rechtlicher Hindernisse, Kassel, p 49Google Scholar
  7. 7.
    Albert A, Rajagopal R (2014) Cost-of-service segmentation of energy consumers. Power Syst IEEE Trans 29(6):2795–2803CrossRefGoogle Scholar
  8. 8.
    Röpke L (2013) The development of renewable energies and supply security: a trade-off analysis. Energy Policy 61:1011–1021CrossRefGoogle Scholar
  9. 9.
    BMWi (2015) Baustein für die Energiewende: 7 Eckpunkte für das, Verordnungspaket Intelligente NetzeGoogle Scholar
  10. 10.
    European Commission (2014) Report from the commission - benchmarking smart metering deployment in the EU-27 with a focus on electricity, BrusselsGoogle Scholar
  11. 11.
    EEX (2015) EEX Group Achieves Impressive Results across All Markets. (Online). https://www.eex.com/en/about/newsroom/news-detail/2015--eex-group-achieves-impressive-results-across-all-markets/99668. Accessed 23 Mar 2016
  12. 12.
    EPEX Spot (2015) EPEX spot reaches in 2015 the highest spot power exchange volume ever (Online). https://www.epexspot.com/en/press-media/press/details/press/EPEX_SPOT_reaches_in_2015_the_highest_spot_power_exchange_volume_ever
  13. 13.
    Beckel C, Sadamori L, Santini S, Staake T (2015) Automated customer segmentation based on smart meter data with temperature and daylight sensitivity. In: Proceedings of the 6th IEEE international conference on smart grid communications (SmartGridComm 2015), MiamiGoogle Scholar
  14. 14.
    Beckel C, Sadamori L, Staake T, Santini S (2014) Revealing household characteristics from smart meter data. Energy 78:397–410CrossRefGoogle Scholar
  15. 15.
    Wijaya TK, Eberle J, Aberer K (2013) Symbolic representation of smart meter data. In: Proceedings of the joint EDBT/ICDT 2013 workshops, pp 242–248Google Scholar
  16. 16.
    Kavousian A, Rajagopal R, Fischer M (2013) Determinants of residential electricity consumption: using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior. Energy 55:184–194CrossRefGoogle Scholar
  17. 17.
    Arora S, Taylor JW (2016) Forecasting electricity smart meter data using conditional kernel density estimation. Omega 59:47–59CrossRefGoogle Scholar
  18. 18.
    Kwac J, Tan CW, Sintov N, Flora J, Rajagopal R (2013) Utility customer segmentation based on smart meter data: empirical study. In: 2013 IEEE international conference on smart grid communications (SmartGridComm), pp 720–725Google Scholar
  19. 19.
    McLoughlin F, Duffy A, Conlon M (2015) A clustering approach to domestic electricity load profile characterisation using smart metering data. Appl. Energy 141:190–199CrossRefGoogle Scholar
  20. 20.
    Nair J, Adlakha S, Wierman A (2014) Energy procurement strategies in the presence of intermittent sources. ACM Int Conf Meas Model Comput Syst 2014:85–97Google Scholar
  21. 21.
    Secomandi N, Kekre S (2014) Optimal energy procurement in spot and forward markets. Manuf Serv Oper Manag 16(2):270–282Google Scholar
  22. 22.
    Porteus EL (2002) Foundations of stochastic inventory theory. Stanford University Press, StanfordGoogle Scholar
  23. 23.
    Lora AT, Santos JR, Santos JR, Expósito AG, Ramos JLM (2002) A comparison of two techniques for next-day electricity price forecasting. In: Intelligent data engineering and automated learning—IDEAL. Springer, Berlin, pp 384–390Google Scholar
  24. 24.
    Vahidinasab V, Jadid S, Kazemi A (2008) Day-ahead price forecasting in restructured power systems using artificial neural networks. Electr Power Syst Res 78(8):1332–1342CrossRefGoogle Scholar
  25. 25.
    Irish Social Science Data Archives (2016) CER smart metering project, 2009. (Online). http://www.ucd.ie/issda/data/commissionforenergyregulationcer/. Accessed 20 March 2016
  26. 26.
    EPEX Spot (2016) History of EPEX spot. (Online). https://www.epexspot.com/en/company-info/History_of_EPEX_SPOT_new. Accessed 29 March 2016
  27. 27.
    EPEX Spot (2016) Market data. (Online). https://www.epexspot.com/en/market-data/intradaycontinuous/intraday-table/2009-07-01/FR. Accessed 11 March 2016
  28. 28.
    Agora Energiewende and Energy Brainpool (2014) Negative strompreise: ursachen und wirkungen. In: Eine Analyse der aktuellen Entwicklungen-und ein Vorschlag für ein Flexibilitätsgesetz, BerlinGoogle Scholar
  29. 29.
    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):6222–6231CrossRefGoogle Scholar
  30. 30.
    Hu Z, Kim J, Wang J, Byrne J (2015) Review of dynamic pricing programs in the U.S. and Europe: status quo and policy recommendations. Renew Sustain Energy Rev 42:743–751CrossRefGoogle Scholar
  31. 31.
    Kim JJ (2013) Automated price and demand response demonstration for large customers in New York city using OpenADR. In: ICEBO, pp 1–9Google Scholar
  32. 32.
    Biegel B, Hansen LH, Stoustrup J, Andersen P, Harbo S (2014) Value of flexible consumption in the electricity markets. Energy 66:354–362CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Simon Albrecht
    • 1
    Email author
  • Manuel Fritz
    • 2
  • Jens Strüker
    • 1
  • Holger Ziekow
    • 2
  1. 1.Institute for Energy Economics (INEWI)Fresenius University of Applied SciencesFrankfurt am MainGermany
  2. 2.University of Applied Sciences FurtwangenFurtwangen im SchwarzwaldGermany

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