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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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    Citations

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    Cited by:

    1. Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    2. Monaco, Roberto & Bergaentzlé, Claire & Leiva Vilaplana, Jose Angel & Ackom, Emmanuel & Nielsen, Per Sieverts, 2024. "Digitalization of power distribution grids: Barrier analysis, ranking and policy recommendations," Energy Policy, Elsevier, vol. 188(C).
    3. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    4. Cédric Clastres & Haikel Khalfallah, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Post-Print hal-03193212, HAL.
    5. Jun Dong & Rong Li & Hui Huang, 2018. "Performance Evaluation of Residential Demand Response Based on a Modified Fuzzy VIKOR and Scalable Computing Method," Energies, MDPI, vol. 11(5), pages 1-27, April.
    6. Liu, Guangqiang & Xu, Weiju & Nguyen, Quang Minh, 2024. "Can the energy transition drive economic development? Empirical analysis of China's provincial panel data," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    7. Saia, Artjom, 2023. "Digitalization and CO2 emissions: Dynamics under R&D and technology innovation regimes," Technology in Society, Elsevier, vol. 74(C).
    8. Cédric Clastres & Olivier Rebenaque & Patrick Jochem, 2020. "Provision of Demand Response from the prosumers in multiple markets," Working Papers 2008, Chaire Economie du climat.
    9. Haini, Hazwan, 2021. "Examining the impact of ICT, human capital and carbon emissions: Evidence from the ASEAN economies," International Economics, Elsevier, vol. 166(C), pages 116-125.
    10. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Contract durations in the electricity market: Causal impact of 15min trading on the EPEX SPOT market," Energy Economics, Elsevier, vol. 69(C), pages 367-378.
    11. Wang, Lei & Chen, Yangyang & Ramsey, Thomas Stephen & Hewings, Geoffrey J.D., 2021. "Will researching digital technology really empower green development?," Technology in Society, Elsevier, vol. 66(C).
    12. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei & Su, Bin, 2019. "How information and communication technology drives carbon emissions: A sector-level analysis for China," Energy Economics, Elsevier, vol. 81(C), pages 380-392.
    13. 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).
    14. Fanbao Xie & Xin Guan & Junfan Zhu & Jun Ruan & Zeyu Wang & Hejian Liu, 2023. "Environmental Protection Goes Digital: A Policy Perspective on Promoting Digitalization for Sustainable Development in China," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    15. 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.
    16. Clastres, Cédric & Khalfallah, Haikel, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Energy Economics, Elsevier, vol. 98(C).
    17. Arlt, Marie-Louise & Chassin, David & Rivetta, Claudio & Sweeney, James, 2024. "Impact of real-time pricing and residential load automation on distribution systems," Energy Policy, Elsevier, vol. 184(C).
    18. Cédric Clastres & Haikel Khalfallah, 2020. "Retailers' strategies facing demand response and markets interactions," Working Papers hal-03167543, HAL.
    19. Reif, Valerie & Meeus, Leonardo, 2022. "Smart metering interoperability issues and solutions: Taking inspiration from other ecosystems and sectors," Utilities Policy, Elsevier, vol. 76(C).
    20. Ansarin, Mohammad & Ghiassi-Farrokhfal, Yashar & Ketter, Wolfgang & Collins, John, 2020. "Cross-subsidies among residential electricity prosumers from tariff design and metering infrastructure," Energy Policy, Elsevier, vol. 145(C).
    21. Freier, Julia & von Loessl, Victor, 2022. "Dynamic electricity tariffs: Designing reasonable pricing schemes for private households," Energy Economics, Elsevier, vol. 112(C).

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    More about this item

    Keywords

    Demand response; Load shifting; Smart meters; Electricity markets; Information granularity; Business models;
    All these keywords.

    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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