IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i23p7828-d1289732.html
   My bibliography  Save this article

A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

Author

Listed:
  • Pinrolinvic D. K. Manembu

    (Electrical Engineering Department, Sam Ratulangi University, Manado 95115, Indonesia)

  • Angreine Kewo

    (Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
    Informatics Engineering Department, De La Salle University, Manado 95253, Indonesia)

  • Rasmus Bramstoft

    (Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

  • Per Sieverts Nielsen

    (Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

Abstract

Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes.

Suggested Citation

  • Pinrolinvic D. K. Manembu & Angreine Kewo & Rasmus Bramstoft & Per Sieverts Nielsen, 2023. "A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level," Energies, MDPI, vol. 16(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7828-:d:1289732
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/23/7828/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/23/7828/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
    2. Ampimah, Benjamin Chris & Sun, Mei & Han, Dun & Wang, Xueyin, 2018. "Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach," Applied Energy, Elsevier, vol. 210(C), pages 1299-1309.
    3. Fehrenbach, Daniel & Merkel, Erik & McKenna, Russell & Karl, Ute & Fichtner, Wolf, 2014. "On the economic potential for electric load management in the German residential heating sector – An optimising energy system model approach," Energy, Elsevier, vol. 71(C), pages 263-276.
    4. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2023. "A Rigorous Standalone Literature Review of Residential Electricity Load Profiles," Energies, MDPI, vol. 16(10), pages 1-27, May.
    5. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    6. Shirazi, Elham & Jadid, Shahram, 2017. "Cost reduction and peak shaving through domestic load shifting and DERs," Energy, Elsevier, vol. 124(C), pages 146-159.
    7. Tomaz Bartol & Gordana Budimir & Doris Dekleva-Smrekar & Miro Pusnik & Primoz Juznic, 2014. "Assessment of research fields in Scopus and Web of Science in the view of national research evaluation in Slovenia," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1491-1504, February.
    8. Yilmaz, S. & Weber, S. & Patel, M.K., 2019. "Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes," Energy Policy, Elsevier, vol. 133(C).
    9. Baeten, Brecht & Rogiers, Frederik & Helsen, Lieve, 2017. "Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response," Applied Energy, Elsevier, vol. 195(C), pages 184-195.
    10. Torriti, Jacopo, 2020. "Temporal aggregation: Time use methodologies applied to residential electricity demand," Utilities Policy, Elsevier, vol. 64(C).
    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. Ruhnau, Oliver & Hirth, Lion & Praktiknjo, Aaron, 2020. "Heating with wind: Economics of heat pumps and variable renewables," Energy Economics, Elsevier, vol. 92(C).
    2. Fuentes, E. & Arce, L. & Salom, J., 2018. "A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1530-1547.
    3. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    4. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
    5. Bugaje, Bilal & Rutherford, Peter & Clifford, Mike, 2022. "Convenience in a residence with demand response: A system dynamics simulation model," Applied Energy, Elsevier, vol. 314(C).
    6. M. Hardmeier & A. Berthold & M. Siegrist, 2024. "Factors Influencing People’s Willingness to Shift Their Electricity Consumption," Journal of Consumer Policy, Springer, vol. 47(2), pages 199-221, June.
    7. Gallo Cassarino, Tiziano & Sharp, Ed & Barrett, Mark, 2018. "The impact of social and weather drivers on the historical electricity demand in Europe," Applied Energy, Elsevier, vol. 229(C), pages 176-185.
    8. Simona-Vasilica Oprea & Adela Bâra & Adina Ileana Uță & Alexandru Pîrjan & George Căruțașu, 2018. "Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context," Sustainability, MDPI, vol. 10(7), pages 1-25, July.
    9. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2023. "A Rigorous Standalone Literature Review of Residential Electricity Load Profiles," Energies, MDPI, vol. 16(10), pages 1-27, May.
    10. Andersen, Frits Møller & Baldini, Mattia & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2017. "Households’ hourly electricity consumption and peak demand in Denmark," Applied Energy, Elsevier, vol. 208(C), pages 607-619.
    11. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    12. Zhou, Xin & Tian, Shuai & An, Jingjing & Yan, Da & Zhang, Lun & Yang, Junyan, 2022. "Modeling occupant behavior’s influence on the energy efficiency of solar domestic hot water systems," Applied Energy, Elsevier, vol. 309(C).
    13. Morstyn, Thomas & Collett, Katherine A. & Vijay, Avinash & Deakin, Matthew & Wheeler, Scot & Bhagavathy, Sivapriya M. & Fele, Filiberto & McCulloch, Malcolm D., 2020. "OPEN: An open-source platform for developing smart local energy system applications," Applied Energy, Elsevier, vol. 275(C).
    14. Javier Borquez & Hector Chavez & Karina A. Barbosa & Marcela Jamett & Rodrigo Acuna, 2020. "A Simple Distribution Energy Tariff under the Penetration of DG," Energies, MDPI, vol. 13(8), pages 1-17, April.
    15. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
    16. Olga Pilipczuk, 2022. "Building the Cognitive Enterprise in the Energy Sector," Energies, MDPI, vol. 15(24), pages 1-13, December.
    17. Satish Kumar & Filomena Maggino & Raj V. Mahto & Riya Sureka & Leonardo Salvatore Alaimo & Weng Marc Lim, 2022. "Social Indicators Research: A Retrospective Using Bibliometric Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(1), pages 413-448, July.
    18. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    19. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    20. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(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:16:y:2023:i:23:p:7828-:d:1289732. 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.