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How to implement real-time pricing in China? A solution based on power credit mechanism

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  • Sun, Mei
  • Ji, Jian
  • Ampimah, Benjamin Chris

Abstract

In view of real-time pricing implementation difficulties and certain deficiencies of the time-of-use price in China, a novel virtual real-time electricity pricing scheme is proposed based on power credit incentive program in this paper, which can help to achieve better results for peak load shifting than the conventional time-of-use price-based scheme. In addition, it can also work as virtual real-time pricing mechanism. The aim of this study from the perspective of load scheduling of residential appliance device, focuses on the optimization of cost and willingness to change of the user, and further examining the feasibility and effectiveness of electricity pricing mechanism in detail. Finally, the application prospect of the electricity pricing mechanism in demand response is evaluated and the performance of the model in our study established with illustration from our simulation results.

Suggested Citation

  • Sun, Mei & Ji, Jian & Ampimah, Benjamin Chris, 2018. "How to implement real-time pricing in China? A solution based on power credit mechanism," Applied Energy, Elsevier, vol. 231(C), pages 1007-1018.
    • Handle: RePEc:eee:appene:v:231:y:2018:i:c:p:1007-1018
    DOI: 10.1016/j.apenergy.2018.09.086
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Guo, Zhilong & Xu, Wei & Yan, Yue & Sun, Mei, 2023. "How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism," Applied Energy, Elsevier, vol. 343(C).
    2. Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.
    3. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    4. Wang, Ziyang & Sun, Mei & Gao, Cuixia & Wang, Xin & Ampimah, Benjamin Chris, 2021. "A new interactive real-time pricing mechanism of demand response based on an evaluation model," Applied Energy, Elsevier, vol. 295(C).
    5. Wang, Fei & Ge, Xinxin & Yang, Peng & Li, Kangping & Mi, Zengqiang & Siano, Pierluigi & Duić, Neven, 2020. "Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing," Energy, Elsevier, vol. 213(C).
    6. Wang, Li & Zhang, Xin-Hua & Zhang, Yue-Jun, 2023. "Designing the pricing mechanism of residents’ self-selection sales electricity based on household size," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 860-878.
    7. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    8. Bejan, Ioana & Jensen, Carsten Lynge & Andersen, Laura M. & Hansen, Lars Gårn, 2021. "Inducing flexibility of household electricity demand: The overlooked costs of reacting to dynamic incentives," Applied Energy, Elsevier, vol. 284(C).

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