IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i4p842-d1337081.html
   My bibliography  Save this article

Analysis of Variability in Electric Power Consumption: A Methodology for Setting Time-Differentiated Tariffs

Author

Listed:
  • Javier E. Duarte

    (EM&D Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

  • Javier Rosero-Garcia

    (EM&D Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

  • Oscar Duarte

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

Abstract

The increasing concern for environmental conservation has spurred government initiatives towards energy efficiency. One of the key research areas in this regard is demand response, particularly focusing on differential pricing initiatives such as Time-of-Use (ToU). Differential tariffs are typically designed based on mathematical or statistical models analyzing historical electricity price and consumption data. This study proposes a methodology for identifying time intervals suitable for implementing ToU energy tariffs, achieved by analyzing electric power demand variability to estimate demand flexibility potential. The methodology transforms consumption data into variation via the coefficient of variation and, then, employs k-means data analysis techniques and the a priori algorithm. Tested with real data from smart meters in the Colombian electrical system, the methodology successfully identified time intervals with potential for establishing ToU tariffs. Additionally, no direct relationship was found between external variables such as socioeconomic level, user type, climate, and consumption variability. Finally, it was observed that user behavior concerning consumption variability could be categorized into two types of days: weekdays and non-working days.

Suggested Citation

  • Javier E. Duarte & Javier Rosero-Garcia & Oscar Duarte, 2024. "Analysis of Variability in Electric Power Consumption: A Methodology for Setting Time-Differentiated Tariffs," Energies, MDPI, vol. 17(4), pages 1-24, February.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:842-:d:1337081
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/4/842/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/4/842/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    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. Li, Na & Okur, Özge, 2023. "Economic analysis of energy communities: Investment options and cost allocation," Applied Energy, Elsevier, vol. 336(C).
    2. 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.
    3. Xinan Zhang & Ruigang Wang & Jie Bao, 2018. "A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids," Mathematics, MDPI, vol. 6(4), pages 1-21, April.
    4. Chen Wang & Kaile Zhou & Lanlan Li & Shanlin Yang, 2018. "Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(3), pages 1309-1327, February.
    5. 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).
    6. Jaehyun Lee & Eunjung Lee & Jinho Kim, 2020. "Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme," Energies, MDPI, vol. 13(8), pages 1-18, April.
    7. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2021. "Cost allocation in integrated community energy systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Gärttner, Johannes & Flath, Christoph M. & Weinhardt, Christof, 2018. "Portfolio and contract design for demand response resources," European Journal of Operational Research, Elsevier, vol. 266(1), pages 340-353.
    9. 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.
    10. Abdelmotteleb, Ibtihal & Gómez, Tomás & Chaves Ávila, José Pablo & Reneses, Javier, 2018. "Designing efficient distribution network charges in the context of active customers," Applied Energy, Elsevier, vol. 210(C), pages 815-826.
    11. Grimm, Veronika & Orlinskaya, Galina & Schewe, Lars & Schmidt, Martin & Zöttl, Gregor, 2021. "Optimal design of retailer-prosumer electricity tariffs using bilevel optimization," Omega, Elsevier, vol. 102(C).
    12. Huang, He & Wang, Honglei & Hu, Yu-Jie & Li, Chengjiang & Wang, Xiaolin, 2022. "Optimal plan for energy conservation and CO2 emissions reduction of public buildings considering users' behavior: Case of China," Energy, Elsevier, vol. 261(PA).
    13. Dababneh, Fadwa & Li, Lin & Sun, Zeyi, 2016. "Peak power demand reduction for combined manufacturing and HVAC system considering heat transfer characteristics," International Journal of Production Economics, Elsevier, vol. 177(C), pages 44-52.
    14. Yang, Liu & Wang, Yonggui & Ma, Jun & Ng, Chi To & Cheng, T.C.E., 2014. "Technology investment under flexible capacity strategy with demand uncertainty," International Journal of Production Economics, Elsevier, vol. 154(C), pages 190-197.
    15. Choi, Dong Gu & Murali, Karthik, 2022. "The impact of heterogeneity in consumer characteristics on the design of optimal time-of-use tariffs," Energy, Elsevier, vol. 254(PB).
    16. Hari Agung Yuniarto & Nur Mayke Eka Normasari & Sella Friscilla Silalahi & Irene Clarisa Gunawan & Deendarlianto & Indra Ardhanayudha Aditya & Arionmaro Asi Simaremare & Fajar Nurrohman Haryadi, 2024. "Customer behaviour towards energy usage with time of use tariff: a systematic literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(1), pages 44-61, February.
    17. Park, S.C. & Jin, Y.G. & Song, H.Y. & Yoon, Y.T., 2015. "Designing a critical peak pricing scheme for the profit maximization objective considering price responsiveness of customers," Energy, Elsevier, vol. 83(C), pages 521-531.
    18. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2022. "Cost allocation in integrated community energy systems — Performance assessment," Applied Energy, Elsevier, vol. 307(C).
    19. Daeho Kim & Dong Gu Choi, 2023. "The aggregator’s contract design problem in the electricity demand response market," Operational Research, Springer, vol. 23(1), pages 1-47, March.
    20. 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.

    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:17:y:2024:i:4:p:842-:d:1337081. 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.