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Shifting Boundary for price-based residential demand response and applications

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  • Xu, Fang Yuan
  • Zhang, Tao
  • Lai, Loi Lei
  • Zhou, Hao

Abstract

Demand Response (DR) is one of the typical methods for optimizing load characteristics in power systems. Utilities offer DR schemes to generate incentives toward consumers’ power consumption behavior for load optimization. In tariff planning, power consumption variation is an important issue which is difficult to be analyzed quantifiably. This paper develops a boundary model for analyzing consumers’ power consumption behaviors, with a particular focus on residential home appliances. Candidate tariffs are analyzed in this model for their load variation potentials. Using three case studies, this paper reflects the potential for practical applications of the model on pricing and smart meter deployment.

Suggested Citation

  • Xu, Fang Yuan & Zhang, Tao & Lai, Loi Lei & Zhou, Hao, 2015. "Shifting Boundary for price-based residential demand response and applications," Applied Energy, Elsevier, vol. 146(C), pages 353-370.
    • Handle: RePEc:eee:appene:v:146:y:2015:i:c:p:353-370
    DOI: 10.1016/j.apenergy.2015.02.001
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    References listed on IDEAS

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    Cited by:

    1. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    2. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    3. 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.
    4. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
    5. Yang, Changhui & Meng, Chen & Zhou, Kaile, 2018. "Residential electricity pricing in China: The context of price-based demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2870-2878.
    6. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
    7. Gong, Chengzhu & Tang, Kai & Zhu, Kejun & Hailu, Atakelty, 2016. "An optimal time-of-use pricing for urban gas: A study with a multi-agent evolutionary game-theoretic perspective," Applied Energy, Elsevier, vol. 163(C), pages 283-294.
    8. Zheng, Junjie & Lai, Chun Sing & Yuan, Haoliang & Dong, Zhao Yang & Meng, Ke & Lai, Loi Lei, 2020. "Electricity plan recommender system with electrical instruction-based recovery," Energy, Elsevier, vol. 203(C).
    9. Yamaguchi, Yohei & Chen, Chien-fei & Shimoda, Yoshiyuki & Yagita, Yoshie & Iwafune, Yumiko & Ishii, Hideo & Hayashi, Yasuhiro, 2020. "An integrated approach of estimating demand response flexibility of domestic laundry appliances based on household heterogeneity and activities," Energy Policy, Elsevier, vol. 142(C).

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