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Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands

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  • Klaassen, E.A.M.
  • Kobus, C.B.A.
  • Frunt, J.
  • Slootweg, J.G.

Abstract

To efficiently facilitate the energy transition it is essential to evaluate the potential of demand response in practice. Based on the results of a Dutch smart grid pilot, this paper assesses the potential of both manual and semi-automated demand response in residential areas. To stimulate demand response, a dynamic tariff and smart appliances were used. The participating households were informed about the tariff day-ahead through a home energy management system, connected to a display installed on the wall in their living room. The tariff was intuitively displayed: self-consumption of photovoltaic generation was stimulated by means of a low tariff, but also the generation itself played a central role on the display. Household flexibility is analyzed, focusing on: (i) the load shift of (smart) appliances, and (ii) the response of the (overall) peak load towards the dynamic tariff. To assess the latter, i.e. price responsiveness, the participants were split up in two comparable groups which were subject to a different moment of evening peak-pricing. Based on the results, it is concluded that mainly the flexibility of the white goods (i.e. the washing machine, tumble dryer and dishwasher) is used for demand response. The main part of the flexible load of these (smart) appliances is shifted from the evening to the midday, to match local generation. This load shift remained stable over a long period of time (>1year) and is not responsive to the exact moment of peak-pricing. Therefore, it is concluded that a simple and transparent design for dynamic tariffs is sufficient and most effective to stimulate (manual) residential demand response. Such a tariff should emphasize the ‘right’ moments to use electricity, intuitively linked to renewable generation.

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  • Klaassen, E.A.M. & Kobus, C.B.A. & Frunt, J. & Slootweg, J.G., 2016. "Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands," Applied Energy, Elsevier, vol. 183(C), pages 1065-1074.
  • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:1065-1074
    DOI: 10.1016/j.apenergy.2016.09.051
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    9. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    10. Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1-29, November.
    11. Ioanna-M. Chatzigeorgiou & Christos Diou & Kyriakos C. Chatzidimitriou & Georgios T. Andreou, 2021. "Demand Response Alert Service Based on Appliance Modeling," Energies, MDPI, vol. 14(10), pages 1-15, May.
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    14. Chen, Yongbao & Zhang, Lixin & Xu, Peng & Di Gangi, Alessandra, 2021. "Electricity demand response schemes in China: Pilot study and future outlook," Energy, Elsevier, vol. 224(C).
    15. Simona-Vasilica Oprea & Adela Bâra & Răzvan Cristian Marales & Margareta-Stela Florescu, 2021. "Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
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    18. Edens, Marga G. & Lavrijssen, Saskia A.C.M., 2019. "Balancing public values during the energy transition – How can German and Dutch DSOs safeguard sustainability?," Energy Policy, Elsevier, vol. 128(C), pages 57-65.
    19. Francesco Liberati & Alessandro Di Giorgio, 2017. "Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node," Energies, MDPI, vol. 11(1), pages 1-23, December.
    20. Choi, Dong Gu & Murali, Karthik, 2022. "The impact of heterogeneity in consumer characteristics on the design of optimal time-of-use tariffs," Energy, Elsevier, vol. 254(PB).
    21. Luis Alejandro Arias & Edwin Rivas & Francisco Santamaria & Victor Hernandez, 2018. "A Review and Analysis of Trends Related to Demand Response," Energies, MDPI, vol. 11(7), pages 1-24, June.
    22. Abdelmotteleb, Ibtihal & Gómez, Tomás & Chaves Ávila, José Pablo & Reneses, Javier, 2018. "Designing efficient distribution network charges in the context of active customers," Applied Energy, Elsevier, vol. 210(C), pages 815-826.
    23. Yamaguchi, Yohei & Chen, Chien-fei & Shimoda, Yoshiyuki & Yagita, Yoshie & Iwafune, Yumiko & Ishii, Hideo & Hayashi, Yasuhiro, 2020. "An integrated approach of estimating demand response flexibility of domestic laundry appliances based on household heterogeneity and activities," Energy Policy, Elsevier, vol. 142(C).
    24. Guo, Zhilong & Xu, Wei & Yan, Yue & Sun, Mei, 2023. "How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism," Applied Energy, Elsevier, vol. 343(C).
    25. Muhammad Babar & Jakub Grela & Andrzej Ożadowicz & Phuong H. Nguyen & Zbigniew Hanzelka & I. G. Kamphuis, 2018. "Energy Flexometer: Transactive Energy-Based Internet of Things Technology," Energies, MDPI, vol. 11(3), pages 1-20, March.

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