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Value and granularity of ICT and smart meter data in demand response systems

Author

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  • Feuerriegel, Stefan
  • Bodenbenner, Philipp
  • Neumann, Dirk

Abstract

The large-scale integration of intermittent resources of power generation leads to unprecedented fluctuations on the supply side. An electricity retailer can tackle these challenges by pursuing strategies of flexible load shifting — so-called demand response mechanisms. This work addresses the associated trade-off between ICT deployment and economic benefits. The ICT design of a demand response system serves as the basis of a cost-value model, which incorporates all relevant cost components and compares them to the expected savings of an electricity retailer. Our analysis is based on a typical German electricity retailer to determine the optimal read-out frequency of smart meters. For our set of parameters, a positive information value of smart meter read-outs is achieved within an interval of 21 to 57min regarding variable costs. Electricity retailers can achieve a profitable setting by restricting smart meter roll-out to large customers.

Suggested Citation

  • Feuerriegel, Stefan & Bodenbenner, Philipp & Neumann, Dirk, 2016. "Value and granularity of ICT and smart meter data in demand response systems," Energy Economics, Elsevier, vol. 54(C), pages 1-10.
  • Handle: RePEc:eee:eneeco:v:54:y:2016:i:c:p:1-10
    DOI: 10.1016/j.eneco.2015.11.016
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    References listed on IDEAS

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    1. Christoph Goebel & Hans-Arno Jacobsen & Victor Razo & Christoph Doblander & Jose Rivera & Jens Ilg & Christoph Flath & Hartmut Schmeck & Christof Weinhardt & Daniel Pathmaperuma & Hans-Jürgen Appelrat, 2014. "Energy Informatics," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(1), pages 25-31, February.
    2. Ullrich Jagstaidt & Janis Kossahl & Lutz Kolbe, 2011. "Smart Metering Information Management," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 3(5), pages 323-326, October.
    3. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    4. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    5. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    6. Brophy Haney, A. & Jamasb, T. & Pollitt, M.G., 2009. "Smart Metering and Electricity Demand: Technology, Economics and International Experience," Cambridge Working Papers in Economics 0905, Faculty of Economics, University of Cambridge.
    7. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    8. McKenna, Eoghan & Richardson, Ian & Thomson, Murray, 2012. "Smart meter data: Balancing consumer privacy concerns with legitimate applications," Energy Policy, Elsevier, vol. 41(C), pages 807-814.
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    Citations

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    Cited by:

    1. repec:eee:appene:v:198:y:2017:i:c:p:49-64 is not listed on IDEAS
    2. repec:eee:appene:v:210:y:2018:i:c:p:1290-1298 is not listed on IDEAS
    3. repec:gam:jeners:v:11:y:2018:i:5:p:1097-:d:143858 is not listed on IDEAS
    4. Bertsch, Valentin & Devine, Mel & Sweeney, Conor & Parnell, Andrew C., 2018. "Analysing long-term interactions between demand response and different electricity markets using a stochastic market equilibrium model," Papers WP585, Economic and Social Research Institute (ESRI).
    5. Feuerriegel, Stefan & Neumann, Dirk, 2016. "Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications," Energy Policy, Elsevier, vol. 96(C), pages 231-240.
    6. repec:eee:eneeco:v:69:y:2018:i:c:p:367-378 is not listed on IDEAS

    More about this item

    Keywords

    Demand response; Load shifting; Smart meters; Electricity markets; Information granularity; Business models;

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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