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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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    Cited by:

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    13. Cui, Weiwei & Li, Lin, 2018. "A game-theoretic approach to optimize the Time-of-Use pricing considering customer behaviors," International Journal of Production Economics, Elsevier, vol. 201(C), pages 75-88.
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