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Targeting customers for an optimized energy procurement

A Cost Segmentation Based on Smart Meter Load Profiles

  • Special Issue Paper
  • Published:
Computer Science - Research and Development

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.

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Notes

  1. This objective is aimed at by Green IS, which tries to incorporate economics and ecology in one single goal, the efficient allocation of scarce resources [14].

  2. In [10] the authors estimate the total savings potential of smart meters in the EU to be € 67 billion, if the majority of customers are being incentivized by dynamic pricing to change their behavior at peak hours.

  3. In July of 2014, the granularity of contracts was extended to 15 min to match the demand for quick trading.

  4. Accessed via the Irish Social Science Data Archive-http://www.ucd.ie/issda. Those who carried out the original analysis and collection of the data bear no responsibility for the further analysis or interpretation of it.

  5. According to [31] customer engagement is one of the major issues to be addressed in order to implement dynamic pricing. This supplements our proposal to actively include the prime targets in newly developed programs.

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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.

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Authors and Affiliations

  1. Institute for Energy Economics (INEWI), Fresenius University of Applied Sciences, Bessie-Coleman-Str. 7, 60549, Frankfurt am Main, Germany

    Simon Albrecht & Jens Strüker

  2. University of Applied Sciences Furtwangen, Robert-Gerwig-Platz 1, 78120, Furtwangen im Schwarzwald, Germany

    Manuel Fritz & Holger Ziekow

Authors
  1. Simon Albrecht
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  2. Manuel Fritz
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  3. Jens Strüker
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  4. Holger Ziekow
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Correspondence to Simon Albrecht.

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Albrecht, S., Fritz, M., Strüker, J. et al. Targeting customers for an optimized energy procurement. Comput Sci Res Dev 32, 225–235 (2017). https://doi.org/10.1007/s00450-016-0303-x

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  • DOI: https://doi.org/10.1007/s00450-016-0303-x

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