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A novel time-of-use tariff design based on Gaussian Mixture Model

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  • Li, Ran
  • Wang, Zhimin
  • Gu, Chenghong
  • Li, Furong
  • Wu, Hao

Abstract

This paper proposes a novel method to design feasible Time-of-Use (ToU) tariffs for domestic customers from flat rate tariffs by clustering techniques. The method is dedicated to designing the fundamental window patterns of ToU tariffs rather than optimising exact prices for each settlement period. It makes use of Gaussian Mixture Model clustering technique to group half-hour interval flat rate tariffs within a day into clusters to determine ToU tariffs. Two groups of ToU are designed following the variations in energy prices and system loading demand respectively. With a number of price-oriented and load-oriented ToU tariffs, the investigation is further carried out to explore the effects of these ToU tariffs on domestic demand response (DR), especially in terms of energy cost reduction and peak shaving. The DR in this paper is assumed to be enabled by household storage battery and the objective of the DR in response to each ToU tariff is to minimise the electricity bills for end customers and/or mitigate network pressures. An example study in the UK case is also carried out to demonstrate the effectiveness of the proposed methods.

Suggested Citation

  • Li, Ran & Wang, Zhimin & Gu, Chenghong & Li, Furong & Wu, Hao, 2016. "A novel time-of-use tariff design based on Gaussian Mixture Model," Applied Energy, Elsevier, vol. 162(C), pages 1530-1536.
    • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:1530-1536
    DOI: 10.1016/j.apenergy.2015.02.063
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    1. Massimo, Filippini, 2011. "Short- and long-run time-of-use price elasticities in Swiss residential electricity demand," Energy Policy, Elsevier, vol. 39(10), pages 5811-5817, October.
    2. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    3. Henley, Andrew & Peirson, John, 1994. "Time-of-use electricity pricing : Evidence from a British experiment," Economics Letters, Elsevier, vol. 45(3), pages 421-426.
    4. Orans, Ren & Woo, C.K. & Horii, Brian & Chait, Michele & DeBenedictis, Andrew, 2010. "Electricity Pricing for Conservation and Load Shifting," The Electricity Journal, Elsevier, vol. 23(3), pages 7-14, April.
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    13. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Belusko, Martin & Boer, Dieter & Cabeza, Luisa F., 2018. "Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV," Applied Energy, Elsevier, vol. 211(C), pages 604-616.
    14. Wang, Zhimin & Gu, Chenghong & Li, Furong, 2018. "Flexible operation of shared energy storage at households to facilitate PV penetration," Renewable Energy, Elsevier, vol. 116(PA), pages 438-446.
    15. Zou, Bin & Peng, Jinqing & Li, Sihui & Li, Yi & Yan, Jinyue & Yang, Hongxing, 2022. "Comparative study of the dynamic programming-based and rule-based operation strategies for grid-connected PV-battery systems of office buildings," Applied Energy, Elsevier, vol. 305(C).
    16. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    17. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    18. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2017. "Optimal battery storage operation for PV systems with tariff incentives," Applied Energy, Elsevier, vol. 203(C), pages 422-441.
    19. Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
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    21. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
    22. Azar, Elie & Al Ansari, Hamad, 2017. "Framework to investigate energy conservation motivation and actions of building occupants: The case of a green campus in Abu Dhabi, UAE," Applied Energy, Elsevier, vol. 190(C), pages 563-573.

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