IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6130-d643479.html
   My bibliography  Save this article

Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Author

Listed:
  • Minseok Jang

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Hyun-Cheol Jeong

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Taegon Kim

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Sung-Kwan Joo

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

Abstract

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

Suggested Citation

  • Minseok Jang & Hyun-Cheol Jeong & Taegon Kim & Sung-Kwan Joo, 2021. "Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs," Energies, MDPI, vol. 14(19), pages 1-12, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6130-:d:643479
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6130/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6130/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Md Mamun Ur Rashid & Majed A. Alotaibi & Abdul Hasib Chowdhury & Muaz Rahman & Md. Shafiul Alam & Md. Alamgir Hossain & Mohammad A. Abido, 2021. "Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.
    2. Correia-da-Silva, João & Soares, Isabel & Fernández, Raquel, 2020. "Impact of dynamic pricing on investment in renewables," Energy, Elsevier, vol. 202(C).
    3. Yumi Yoshida & Kenta Tanaka & Shunsuke Managi, 2017. "Which dynamic pricing rule is most preferred by consumers?—Application of choice experiment," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 6(1), pages 1-11, December.
    4. Swantje Sundt & Katrin Rehdanz & Jürgen Meyerhoff, 2020. "Consumers’ Willingness to Accept Time-of-Use Tariffs for Shifting Electricity Demand," Energies, MDPI, vol. 13(8), pages 1-17, April.
    5. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    6. Hu, Zheng & Kim, Jin-ho & Wang, Jianhui & Byrne, John, 2015. "Review of dynamic pricing programs in the U.S. and Europe: Status quo and policy recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 743-751.
    7. Dütschke, Elisabeth & Paetz, Alexandra-Gwyn, 2013. "Dynamic electricity pricing—Which programs do consumers prefer?," Energy Policy, Elsevier, vol. 59(C), pages 226-234.
    8. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vasileios M. Laitsos & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2021. "An Incentive-Based Implementation of Demand Side Management in Power Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    2. Ignacio Benítez & José-Luis Díez, 2022. "Automated Detection of Electric Energy Consumption Load Profile Patterns," Energies, MDPI, vol. 15(6), pages 1-26, March.
    3. Minseok Jang & Hyun Cheol Jeong & Taegon Kim & Dong Hee Suh & Sung-Kwan Joo, 2021. "Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape," Energies, MDPI, vol. 14(22), pages 1-15, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nakai, Miwa & von Loessl, Victor & Wetzel, Heike, 2024. "Preferences for dynamic electricity tariffs: A comparison of households in Germany and Japan," Ecological Economics, Elsevier, vol. 223(C).
    2. Bernadeta Gołębiowska, 2020. "Preferences for demand side management—a review of choice experiment studies," Working Papers 2020-05, Faculty of Economic Sciences, University of Warsaw.
    3. El Gohary, F. & Nordin, M. & Juslin, P. & Bartusch, C., 2022. "Evaluating user understanding and exposure effects of demand-based tariffs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    4. von Loessl, Victor, 2023. "Smart meter-related data privacy concerns and dynamic electricity tariffs: Evidence from a stated choice experiment," Energy Policy, Elsevier, vol. 180(C).
    5. Dong, Jun & Jiang, Yuzheng & Liu, Dongran & Dou, Xihao & Liu, Yao & Peng, Shicheng, 2022. "Promoting dynamic pricing implementation considering policy incentives and electricity retailers’ behaviors: An evolutionary game model based on prospect theory," Energy Policy, Elsevier, vol. 167(C).
    6. Hortay, Olivér & Kökény, László, 2020. "A villamosenergia-fogyasztás elhalasztásával kapcsolatos lakossági attitűd felmérése Magyarországon [A survey of popular attitudes to deferment of electricity consumption in Hungary]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 657-687.
    7. Martin Spann & Bernd Skiera, 2020. "Dynamische Preisgestaltung in der digitalisierten Welt [Dynamic Pricing in a Digitized World]," Schmalenbach Journal of Business Research, Springer, vol. 72(3), pages 321-342, September.
    8. Freier, Julia & von Loessl, Victor, 2022. "Dynamic electricity tariffs: Designing reasonable pricing schemes for private households," Energy Economics, Elsevier, vol. 112(C).
    9. Krishnendranath Mitra & Goutam Dutta, 2021. "A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 162-184, April.
    10. Lucas Roth & Jens Lowitzsch & Özgür Yildiz, 2021. "An Empirical Study of How Household Energy Consumption Is Affected by Co-Owning Different Technological Means to Produce Renewable Energy and the Production Purpose," Energies, MDPI, vol. 14(13), pages 1-38, July.
    11. Parag, Yael, 2021. "Which factors influence large households’ decision to join a time-of-use program? The interplay between demand flexibility, personal benefits and national benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    12. Correia-da-Silva, João & Soares, Isabel & Fernández, Raquel, 2020. "Impact of dynamic pricing on investment in renewables," Energy, Elsevier, vol. 202(C).
    13. Curtis, John & Brazil, William & Harold, Jason, 2019. "Understanding preference heterogeneity in electricity services: the case of domestic appliance curtailment contracts," Papers WP638, Economic and Social Research Institute (ESRI).
    14. Patrick Ludwig & Christian Winzer, 2022. "Tariff Menus to Avoid Rebound Peaks: Results from a Discrete Choice Experiment with Swiss Customers," Energies, MDPI, vol. 15(17), pages 1-21, August.
    15. Leandra Scharnhorst & Thorben Sandmeier & Armin Ardone & Wolf Fichtner, 2021. "The Impact of Economic and Non-Economic Incentives to Induce Residential Demand Response—Findings from a Living Lab Experiment," Energies, MDPI, vol. 14(8), pages 1-24, April.
    16. Cortés-Arcos, Tomás & Bernal-Agustín, José L. & Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Contreras, Javier, 2017. "Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology," Energy, Elsevier, vol. 138(C), pages 19-31.
    17. Pereira, Diogo Santos & Marques, António Cardoso, 2020. "How should price-responsive electricity tariffs evolve? An analysis of the German net demand case," Utilities Policy, Elsevier, vol. 66(C).
    18. Swantje Sundt, 2021. "Influence of Attitudes on Willingness to Choose Time-of-Use Electricity Tariffs in Germany. Evidence from Factor Analysis," Energies, MDPI, vol. 14(17), pages 1-20, August.
    19. Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    20. Otuoze, Abdulrahaman Okino & Mustafa, Mohd Wazir & Abdulrahman, Abdulhakeem Temitope & Mohammed, Olatunji Obalowu & Salisu, Sani, 2020. "Penalization of electricity thefts in smart utility networks by a cost estimation-based forced corrective measure," Energy Policy, Elsevier, vol. 143(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6130-:d:643479. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.