IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41970-8.html
   My bibliography  Save this article

Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups

Author

Listed:
  • Zhaohua Wang

    (Beijing Institute of Technology
    Research Center for Sustainable Development and Intelligent Decision Making
    Beijing Institute of Technology)

  • Bin Lu

    (Beijing Institute of Technology
    Research Center for Sustainable Development and Intelligent Decision Making
    Beijing Institute of Technology)

  • Bo Wang

    (Beijing Institute of Technology
    Research Center for Sustainable Development and Intelligent Decision Making
    Beijing Institute of Technology)

  • Yueming (Lucy) Qiu

    (University of Maryland College Park)

  • Han Shi

    (Beijing Institute of Technology
    Research Center for Sustainable Development and Intelligent Decision Making
    Beijing Institute of Technology)

  • Bin Zhang

    (Beijing Institute of Technology
    Research Center for Sustainable Development and Intelligent Decision Making
    Beijing Institute of Technology)

  • Jingyun Li

    (Beijing Institute of Technology
    Research Center for Sustainable Development and Intelligent Decision Making
    Beijing Institute of Technology)

  • Hao Li

    (Beijing Institute of Technology
    Research Center for Sustainable Development and Intelligent Decision Making
    Beijing Institute of Technology)

  • Wenhui Zhao

    (Sichuan University)

Abstract

The incentive-based emergency demand response measure serves as an important regulatory tool during energy system operations. However, whether people will sacrifice comfort to respond to it during heatwave and what the effect on heat vulnerable populations will be are still unclear. A large-scale emergency demand response pilot involving 205,129 households was conducted in southwestern China during continuous extreme high temperatures in summer. We found that the incentive-based emergency demand response causes a statistically significant decline in electricity use with no additional financial burden on vulnerable groups. The electricity conservation potential of urban households was higher than that of rural households. Households with children did not respond to the emergency demand response, while the response of households with elderly individuals proved to be more positive. The repeated and frequent implementation of this policy did not result in an attenuation of the regulatory effect. This research can serve as a reference for countries with similar regulated power markets.

Suggested Citation

  • Zhaohua Wang & Bin Lu & Bo Wang & Yueming (Lucy) Qiu & Han Shi & Bin Zhang & Jingyun Li & Hao Li & Wenhui Zhao, 2023. "Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41970-8
    DOI: 10.1038/s41467-023-41970-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41970-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-41970-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yueming Qiu & Loren Kirkeide & Yi David Wang, 2018. "Effects of Voluntary Time-of-Use Pricing on Summer Electricity Usage of Business Customers," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 417-440, February.
    2. Rong Xia & Dong Tian & Shyam Kattel & Bjorn Hasa & Haeun Shin & Xinbin Ma & Jingguang G. Chen & Feng Jiao, 2021. "Electrochemical reduction of acetonitrile to ethylamine," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. Felix Creutzig & Joyashree Roy & William F. Lamb & Inês M. L. Azevedo & Wändi Bruine de Bruin & Holger Dalkmann & Oreane Y. Edelenbosch & Frank W. Geels & Arnulf Grubler & Cameron Hepburn & Edgar G. H, 2018. "Towards demand-side solutions for mitigating climate change," Nature Climate Change, Nature, vol. 8(4), pages 260-263, April.
    4. Lee V. White & Nicole D. Sintov, 2018. "Inaccurate consumer perceptions of monetary savings in a demand-side response programme predict programme acceptance," Nature Energy, Nature, vol. 3(12), pages 1101-1108, December.
    5. ., 2021. "Rise of the modern electric vehicle," Chapters, in: The Global Rise of the Modern Plug-In Electric Vehicle, chapter 1, pages 1-33, Edward Elgar Publishing.
    6. Long He & Guangrui Ma & Wei Qi & Xin Wang, 2021. "Charging an Electric Vehicle-Sharing Fleet," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 471-487, March.
    7. Larry Erickson & Stephanie Ma, 2021. "Solar-Powered Charging Networks for Electric Vehicles," Energies, MDPI, vol. 14(4), pages 1-10, February.
    8. Cornelis J. van Diepen & Tzu-Kan Hsiao & Uditendu Mukhopadhyay & Christian Reichl & Werner Wegscheider & Lieven M. K. Vandersypen, 2021. "Electron cascade for distant spin readout," Nature Communications, Nature, vol. 12(1), pages 1-6, December.
    9. Katrina Jessoe & David Rapson, 2015. "Commercial and Industrial Demand Response Under Mandatory Time-of-Use Electricity Pricing," Journal of Industrial Economics, Wiley Blackwell, vol. 63(3), pages 397-421, September.
    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. Szostok, Agnieszka & Stanek, Wojciech, 2022. "Thermo-ecological analysis - The comparison of collector and PV to PV/T system," Renewable Energy, Elsevier, vol. 200(C), pages 10-23.
    2. Yun, Lingxiang & Xiao, Minkun & Li, Lin, 2022. "Vehicle-to-manufacturing (V2M) system: A novel approach to improve energy demand flexibility for demand response towards sustainable manufacturing," Applied Energy, Elsevier, vol. 323(C).
    3. Keck, Felix & Jütte, Silke & Lenzen, Manfred & Li, Mengyu, 2022. "Assessment of two optimisation methods for renewable energy capacity expansion planning," Applied Energy, Elsevier, vol. 306(PA).
    4. Ahn, Hyeunguk & Miller, William & Sheaffer, Paul & Tutterow, Vestal & Rapp, Vi, 2021. "Opportunities for installed combined heat and power (CHP) to increase grid flexibility in the U.S," Energy Policy, Elsevier, vol. 157(C).
    5. Manzolli, Jônatas Augusto & Trovão, João Pedro F. & Henggeler Antunes, Carlos, 2022. "Electric bus coordinated charging strategy considering V2G and battery degradation," Energy, Elsevier, vol. 254(PA).
    6. Yuru Guan & Jin Yan & Yuli Shan & Yannan Zhou & Ye Hang & Ruoqi Li & Yu Liu & Binyuan Liu & Qingyun Nie & Benedikt Bruckner & Kuishuang Feng & Klaus Hubacek, 2023. "Burden of the global energy price crisis on households," Nature Energy, Nature, vol. 8(3), pages 304-316, March.
    7. Yan, Pengyu & Yu, Kaize & Chao, Xiuli & Chen, Zhibin, 2023. "An online reinforcement learning approach to charging and order-dispatching optimization for an e-hailing electric vehicle fleet," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1218-1233.
    8. Castro, Damaris & Bleys, Brent, 2023. "Do people think they have enough? A subjective income sufficiency assessment," Ecological Economics, Elsevier, vol. 205(C).
    9. Lei, Mingyu & Cai, Wenjia & Liu, Wenling & Wang, Can, 2022. "The heterogeneity in energy consumption patterns and home appliance purchasing preferences across urban households in China," Energy, Elsevier, vol. 253(C).
    10. Paul Wolfram & Stephanie Weber & Kenneth Gillingham & Edgar G. Hertwich, 2021. "Pricing indirect emissions accelerates low—carbon transition of US light vehicle sector," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    11. Dugan, Anna & Mayer, Jakob & Thaller, Annina & Bachner, Gabriel & Steininger, Karl W., 2022. "Developing policy packages for low-carbon passenger transport: A mixed methods analysis of trade-offs and synergies," Ecological Economics, Elsevier, vol. 193(C).
    12. Bauer, Jan M. & Aarestrup, Simon C. & Hansen, Pelle G. & Reisch, Lucia A., 2022. "Nudging more sustainable grocery purchases: Behavioural innovations in a supermarket setting," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    13. Sovacool, Benjamin K. & Lipson, Matthew M. & Chard, Rose, 2019. "Temporality, vulnerability, and energy justice in household low carbon innovations," Energy Policy, Elsevier, vol. 128(C), pages 495-504.
    14. Raman, Gururaghav & Zhao, Bo & Peng, Jimmy Chih-Hsien & Weidlich, Matthias, 2022. "Adaptive incentive-based demand response with distributed non-compliance assessment," Applied Energy, Elsevier, vol. 326(C).
    15. Zapata, Oscar, 2022. "Renewable Energy and Community Development," OSF Preprints tk59y, Center for Open Science.
    16. Vipin K Sharma, 2023. "Memory, Media, and Modernity in Tennessee Williams’ The Glass Menagerie: A Twenty-first Century Perspective," Studies in Media and Communication, Redfame publishing, vol. 11(6), pages 181-187, September.
    17. Papineau, Maya, 2017. "Setting the standard? A framework for evaluating the cost-effectiveness of building energy standards," Energy Economics, Elsevier, vol. 64(C), pages 63-76.
    18. Runsen Zhang & Tatsuya Hanaoka, 2022. "Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    19. Virág, Doris & Wiedenhofer, Dominik & Baumgart, André & Matej, Sarah & Krausmann, Fridolin & Min, Jihoon & Rao, Narasimha D. & Haberl, Helmut, 2022. "How much infrastructure is required to support decent mobility for all? An exploratory assessment," Ecological Economics, Elsevier, vol. 200(C).
    20. Mariusz Izdebski & Marianna Jacyna, 2021. "An Efficient Hybrid Algorithm for Energy Expenditure Estimation for Electric Vehicles in Urban Service Enterprises," Energies, MDPI, vol. 14(7), pages 1-23, April.

    More about this item

    Statistics

    Access and download statistics

    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:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41970-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.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.