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Price-Based Demand Response: A Three-Stage Monthly Time-of-Use Tariff Optimization Model

Author

Listed:
  • Peipei You

    (State Grid Energy Research Institute Co., Ltd., Beijing 102209, China)

  • Sitao Li

    (State Grid Energy Research Institute Co., Ltd., Beijing 102209, China)

  • Chengren Li

    (State Grid Energy Research Institute Co., Ltd., Beijing 102209, China)

  • Chao Zhang

    (State Grid Energy Research Institute Co., Ltd., Beijing 102209, China)

  • Hailang Zhou

    (Marketing Service Center of State Grid Chongqing Electric Power Company, Chongqing 400023, China)

  • Huicai Wang

    (Marketing Service Center of State Grid Chongqing Electric Power Company, Chongqing 400023, China)

  • Huiru Zhao

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yihang Zhao

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on load, power generation, and electricity pricing data from a typical northwest region in China in 2022. The findings indicate the following: (1) In producing the typical net load curves, the employed K-means++ technique outperformed the standard models in terms of the clustering effect by 4.27–26.70%. (2) Following optimization, there was a decrease of 1900 MW in the maximum monthly abandonment of renewable energy, a decrease of 0.31–53.94% in the peak–valley difference, and a decrease of 2.03–17.27% in the monthly net load cost. (3) By taking extra critical peak and deep valley time periods into account, the average net load cost decreased by 10.36% compared with conventional peak–flat–valley time period division criteria.

Suggested Citation

  • Peipei You & Sitao Li & Chengren Li & Chao Zhang & Hailang Zhou & Huicai Wang & Huiru Zhao & Yihang Zhao, 2023. "Price-Based Demand Response: A Three-Stage Monthly Time-of-Use Tariff Optimization Model," Energies, MDPI, vol. 16(23), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7858-:d:1291672
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    References listed on IDEAS

    as
    1. Min Li & Dachuan Xu & Dongmei Zhang & Juan Zou, 2020. "The seeding algorithms for spherical k-means clustering," Journal of Global Optimization, Springer, vol. 76(4), pages 695-708, April.
    2. Wanlei Xue & Xin Zhao & Yan Li & Ying Mu & Haisheng Tan & Yixin Jia & Xuejie Wang & Huiru Zhao & Yihang Zhao, 2023. "Research on the Optimal Design of Seasonal Time-of-Use Tariff Based on the Price Elasticity of Electricity Demand," Energies, MDPI, vol. 16(4), pages 1-17, February.
    3. Grimm, Veronika & Orlinskaya, Galina & Schewe, Lars & Schmidt, Martin & Zöttl, Gregor, 2021. "Optimal design of retailer-prosumer electricity tariffs using bilevel optimization," Omega, Elsevier, vol. 102(C).
    4. Wang, Sen & Li, Fengting & Zhang, Gaohang & Yin, Chunya, 2023. "Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy," Energy, Elsevier, vol. 267(C).
    Full references (including those not matched with items on IDEAS)

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