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Time-varying price elasticity of demand estimation for demand-side smart dynamic pricing

Author

Listed:
  • Ruan, Jiaqi
  • Liu, Guolong
  • Qiu, Jing
  • Liang, Gaoqi
  • Zhao, Junhua
  • He, Binghao
  • Wen, Fushuan

Abstract

The rapid development of the smart energy system promotes bidirectional communications between the supply-side and demand-side. End users can handily receive real-time prices and adjust their electric energy consumption behaviors. Acquiring the time-varying price elasticity of demand (PED) of electricity can help utility companies to understand the time-varying electricity consumption behavior affected by price, thereby facilitating demand-side management. However, estimating time-varying PED is rarely considered in existing studies. This paper bridges the gap, proposing a time-varying PED estimation algorithm. To analyze PEDs more precisely, the proposed algorithm is applied to each appliance based on the advanced non-intrusive load monitoring (NILM) technology. Moreover, a demand-side smart dynamic pricing mechanism is also proposed to provide decision support of individually optimal dynamic pricing for utility companies to encourage end users to participate in the demand response (DR) program. Comprehensive experiments have been conducted to validate the practical feasibility of the proposed mechanism. Numerical simulations show that the proposed mechanism can facilitate the DR program by reducing the peak-to-average ratio (PAR) in electricity consumption without suffering from the price risk.

Suggested Citation

  • Ruan, Jiaqi & Liu, Guolong & Qiu, Jing & Liang, Gaoqi & Zhao, Junhua & He, Binghao & Wen, Fushuan, 2022. "Time-varying price elasticity of demand estimation for demand-side smart dynamic pricing," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922008418
    DOI: 10.1016/j.apenergy.2022.119520
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    References listed on IDEAS

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    1. Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
    2. Lijesen, Mark G., 2007. "The real-time price elasticity of electricity," Energy Economics, Elsevier, vol. 29(2), pages 249-258, March.
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    Cited by:

    1. Luan, Wenpeng & Tian, Longfei & Zhao, Bochao, 2023. "Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design," Applied Energy, Elsevier, vol. 342(C).
    2. Meng, Fanlin & Ma, Qian & Liu, Zixu & Zeng, Xiao-Jun, 2023. "Multiple dynamic pricing for demand response with adaptive clustering-based customer segmentation in smart grids," Applied Energy, Elsevier, vol. 333(C).
    3. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    4. Wu, Jiaman & Lu, Chenbei & Wu, Chenye & Shi, Jian & Gonzalez, Marta C. & Wang, Dan & Han, Zhu, 2024. "A cluster-based appliance-level-of-use demand response program design," Applied Energy, Elsevier, vol. 362(C).
    5. Sławomir Bielecki & Tadeusz Skoczkowski & Lidia Sobczak & Marcin Wołowicz, 2022. "Electricity Usage Settlement System Based on a Cryptocurrency Instrument," Energies, MDPI, vol. 15(19), pages 1-35, September.

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