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Consumer Preferences for Smart Energy Services Based on AMI Data in the Power Sector

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  • Hye-Jeong Lee

    (Department of Future Energy Convergence, Seoul National University of Science & Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul 01811, Republic of Korea)

  • Beom Jin Chung

    (Research Center of Electrical and Information Technology, Seoul National University of Science & Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul 01811, Republic of Korea)

  • Sung-Yoon Huh

    (Department of Future Energy Convergence, Seoul National University of Science & Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul 01811, Republic of Korea)

Abstract

Advanced metering infrastructure (AMI) is becoming increasingly popular as an efficient means of energy demand management. By collecting energy data through AMI, it is possible to provide users with information that can induce them to change their behavior. To ensure that AMI continues to expand and to encourage the use of energy data, it is important to increase consumer participation and analyze their preferred service attributes. This study utilized a choice experiment to analyze consumer preferences for and acceptance of smart energy services based on AMI data. The results of a mixed logit model estimation show that consumers prefer the electricity information service for individual households and the social safety-net service among convergence services. A scenario analysis confirms that monetary compensation to offset any additional charges is important to maintain the level of consumer acceptance. These empirical findings offer insights for policymakers and companies seeking to develop policies and similar services.

Suggested Citation

  • Hye-Jeong Lee & Beom Jin Chung & Sung-Yoon Huh, 2023. "Consumer Preferences for Smart Energy Services Based on AMI Data in the Power Sector," Energies, MDPI, vol. 16(9), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3961-:d:1141988
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    References listed on IDEAS

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    1. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. Huh, Sung-Yoon & Woo, JongRoul & Lim, Sesil & Lee, Yong-Gil & Kim, Chang Seob, 2015. "What do customers want from improved residential electricity services? Evidence from a choice experiment," Energy Policy, Elsevier, vol. 85(C), pages 410-420.
    3. Yunusov, Timur & Torriti, Jacopo, 2021. "Distributional effects of Time of Use tariffs based on electricity demand and time use," Energy Policy, Elsevier, vol. 156(C).
    4. Susanne Geissler & Alexandros G. Charalambides & Michael Hanratty, 2019. "Public Access to Building Related Energy Data for Better Decision Making in Implementing Energy Efficiency Strategies: Legal Barriers and Technical Challenges," Energies, MDPI, vol. 12(10), pages 1-22, May.
    5. Huh, Sung-Yoon & Jo, Manseok & Shin, Jungwoo & Yoo, Seung-Hoon, 2019. "Impact of rebate program for energy-efficient household appliances on consumer purchasing decisions: The case of electric rice cookers in South Korea," Energy Policy, Elsevier, vol. 129(C), pages 1394-1403.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    7. Richter, Laura-Lucia & Pollitt, Michael G., 2018. "Which smart electricity service contracts will consumers accept? The demand for compensation in a platform market," Energy Economics, Elsevier, vol. 72(C), pages 436-450.
    8. Fettermann, Diego Castro & Cavalcante, Caroline Gobbo Sá & Ayala, Néstor Fabián & Avalone, Marianne Costa, 2020. "Configuration of a smart meter for Brazilian customers," Energy Policy, Elsevier, vol. 139(C).
    9. Kaufmann, Simon & Künzel, Karoline & Loock, Moritz, 2013. "Customer value of smart metering: Explorative evidence from a choice-based conjoint study in Switzerland," Energy Policy, Elsevier, vol. 53(C), pages 229-239.
    10. Balcombe, Kelvin & Chalak, Ali & Fraser, Iain, 2009. "Model selection for the mixed logit with Bayesian estimation," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 226-237, March.
    11. Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
    12. Wohlfarth, Katharina & Worrell, Ernst & Eichhammer, Wolfgang, 2020. "Energy efficiency and demand response – two sides of the same coin?," Energy Policy, Elsevier, vol. 137(C).
    13. Derakhshan, Ghasem & Shayanfar, Heidar Ali & Kazemi, Ahad, 2016. "The optimization of demand response programs in smart grids," Energy Policy, Elsevier, vol. 94(C), pages 295-306.
    14. Yang, Liu & Dong, Ciwei & Wan, C.L. Johnny & Ng, Chi To, 2013. "Electricity time-of-use tariff with consumer behavior consideration," International Journal of Production Economics, Elsevier, vol. 146(2), pages 402-410.
    15. Nicolson, Moira L. & Fell, Michael J. & Huebner, Gesche M., 2018. "Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 276-289.
    16. Rahel Renata Tanujaya & Chul-Yong Lee & JongRoul Woo & Sung-Yoon Huh & Min-Kyu Lee, 2020. "Quantifying Public Preferences for Community-Based Renewable Energy Projects in South Korea," Energies, MDPI, vol. 13(9), pages 1-13, May.
    17. Pepermans, Guido, 2014. "Valuing smart meters," Energy Economics, Elsevier, vol. 45(C), pages 280-294.
    18. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
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