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Methods and attributes for customer-centric dynamic electricity tariff design: A review

Author

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  • Rahman, Tasmeea
  • Othman, Mohammad Lutfi
  • Mohd Noor, Samsul Bahari
  • Binti Wan Ahmad, Wan Fatinhamamah
  • Sulaima, Mohamad Fani

Abstract

Most of the developed and developing countries around the world are delving into the implementation of demand response (DR) strategies in demand side management (DSM) to meet the needs of their own power industry and customers. Some major segments of demand response strategies are, customer segmentation, demand/price forecasting to design customer-oriented dynamic tariff that influences the customer engagement in those strategies. One of the crucial factors that influence customer engagement in those strategies is the input variables or attributes selected to conduct precise customer segmentation, which leads to precise and more accurate demand/price forecasting to design customer-centric dynamic tariff. Most of the existing literature focused on either one of those segments but a collective review on all these segments, particularly focusing on the methods and market attributes, is yet to be conducted. This study reviews the recent existing literature on customer segmentation, demand/price forecasting, customer engagement strategies for dynamic tariff design in power industry to map out the appropriate methods for respective input attributes from the electricity market. For this purpose, the input attributes in the electricity market have been divided into six broad categories and for each attribute category, appropriate methods have been illustrated through a proposed framework based on existing literature.

Suggested Citation

  • Rahman, Tasmeea & Othman, Mohammad Lutfi & Mohd Noor, Samsul Bahari & Binti Wan Ahmad, Wan Fatinhamamah & Sulaima, Mohamad Fani, 2024. "Methods and attributes for customer-centric dynamic electricity tariff design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:rensus:v:192:y:2024:i:c:s1364032123010869
    DOI: 10.1016/j.rser.2023.114228
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    References listed on IDEAS

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    1. Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
    2. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    3. Soares, Ana & Gomes, Álvaro & Antunes, Carlos Henggeler, 2014. "Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 490-503.
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