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How to effectively implement an incentive-based residential electricity demand response policy? Experience from large-scale trials and matching questionnaires

Author

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  • Wang, Zhaohua
  • Li, Hao
  • Deng, Nana
  • Cheng, Kaiwei
  • Lu, Bin
  • Zhang, Bin
  • Wang, Bo

Abstract

Incentive-based demand response (DR) policy plays an important role in guiding residents' electricity consumption behavior. How to effectively implement the DR policy has become a scientific issue that needs to be addressed urgently. To this end, based on the data from large-scale DR trials and matching questionnaires, the policy implementation path has been analyzed. The results show that households responded to the DR policy saved 0.09 kW h more electricity in the 1.5-h response period than households that did not respond to the DR policy. On the management implementation side, the subsidy price is crucial. Community publicity can also enhance the electricity saving effect. It also has been found that the electricity-saving potential of the low-level community is relatively limited. On the households' response side, households with higher household incomes, younger income earners, more air conditioners and small appliances, and higher gas consumption have higher policy participation. The novelty and originality of this article is that the data collected through large-scale controlled trials are unique and valuable. And we creatively combined trial data and surveys data together, which will enable us to further explore the implementation-side management factors and response-side household attributes. At the end, we put forward systematic policy recommendations for the implementation of DR policy, which has important references significance for countries with similar regulated electricity markets.

Suggested Citation

  • Wang, Zhaohua & Li, Hao & Deng, Nana & Cheng, Kaiwei & Lu, Bin & Zhang, Bin & Wang, Bo, 2020. "How to effectively implement an incentive-based residential electricity demand response policy? Experience from large-scale trials and matching questionnaires," Energy Policy, Elsevier, vol. 141(C).
  • Handle: RePEc:eee:enepol:v:141:y:2020:i:c:s0301421520302032
    DOI: 10.1016/j.enpol.2020.111450
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    8. Ioanna-M. Chatzigeorgiou & Christos Diou & Kyriakos C. Chatzidimitriou & Georgios T. Andreou, 2021. "Demand Response Alert Service Based on Appliance Modeling," Energies, MDPI, vol. 14(10), pages 1-15, May.
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