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A new interactive real-time pricing mechanism of demand response based on an evaluation model

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  • Wang, Ziyang
  • Sun, Mei
  • Gao, Cuixia
  • Wang, Xin
  • Ampimah, Benjamin Chris

Abstract

Electricity price mechanism is an important means to implement demand response, especially in the home energy management system. A reasonable pricing mechanism therefore can stimulate the enthusiasm of residential users and as well balance power supply and demand effectively. From the perspective of residential users, this paper establishes a residential user evaluation system based on an evaluation model by selecting indicators related to user characteristics and electricity consumption data, and as well proposes a new interactive real-time pricing mechanism. A constrained multi-objective optimization model is then constructed, and the optimal operation scheme of each appliance is optimized. Numerical simulation and case studies show that, the optimization model can accurately schedule operations of household appliances. Besides, under the action of interactive real-time pricing, the electricity load fluctuation rate and electricity cost are significantly reduced compared with other comparative cases. The results confirm that, interactive real-time pricing aside reducing the cost of energy for users, can also play an active role in reducing peak loads and increase off-peak load, thereby stabilizing the load fluctuation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:295:y:2021:i:c:s0306261921005109
    DOI: 10.1016/j.apenergy.2021.117052
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    References listed on IDEAS

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    Cited by:

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    2. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2024. "Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources," Applied Energy, Elsevier, vol. 364(C).
    3. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    4. Fahim Muntasir & Anusheel Chapagain & Kishan Maharjan & Mirza Jabbar Aziz Baig & Mohsin Jamil & Ashraf Ali Khan, 2023. "Developing an Appropriate Energy Trading Algorithm and Techno-Economic Analysis between Peer-to-Peer within a Partly Independent Microgrid," Energies, MDPI, vol. 16(3), pages 1-21, February.
    5. Li, Ningning & Gao, Yan, 2023. "Real-time pricing based on convex hull method for smart grid with multiple generating units," Energy, Elsevier, vol. 285(C).
    6. 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).
    7. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    8. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    9. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
    10. Fang, Guochang & Chen, Gang & Yang, Kun & Yin, Weijun & Tian, Lixin, 2024. "How does green fiscal expenditure promote green total factor energy efficiency? — Evidence from Chinese 254 cities," Applied Energy, Elsevier, vol. 353(PA).

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