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A Novel Multi-Timescale Optimal Scheduling Model for a Power–Gas Mutual Transformation Virtual Power Plant with Power-to-Gas Conversion and Comprehensive Demand Response

Author

Listed:
  • Shuo Yin

    (State Grid Henan Electric Power Company Economic and Technological Research Institute, Zhengzhou 450052, China)

  • Yang He

    (Henan Power Exchange Center, Zhengzhou 450003, China)

  • Zhiheng Li

    (Henan Power Exchange Center, Zhengzhou 450003, China)

  • Senmao Li

    (Henan Power Exchange Center, Zhengzhou 450003, China)

  • Peng Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Changping, Beijing 102206, China)

  • Ziyi Chen

    (School of Electrical and Electronic Engineering, North China Electric Power University, Changping, Beijing 102206, China)

Abstract

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day’s internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison.

Suggested Citation

  • Shuo Yin & Yang He & Zhiheng Li & Senmao Li & Peng Wang & Ziyi Chen, 2024. "A Novel Multi-Timescale Optimal Scheduling Model for a Power–Gas Mutual Transformation Virtual Power Plant with Power-to-Gas Conversion and Comprehensive Demand Response," Energies, MDPI, vol. 17(15), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3805-:d:1448565
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    References listed on IDEAS

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    1. Li, Qiang & Zhou, Yongcheng & Wei, Fanchao & Li, Shuangxiu & Wang, Zhonghao & Li, Jiajia & Zhou, Guowen & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2024. "Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems," Applied Energy, Elsevier, vol. 362(C).
    2. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    3. Ju, Liwei & Lv, ShuoShuo & Zhang, Zheyu & Li, Gen & Gan, Wei & Fang, Jiangpeng, 2024. "Data-driven two-stage robust optimization dispatching model and benefit allocation strategy for a novel virtual power plant considering carbon-green certificate equivalence conversion mechanism," Applied Energy, Elsevier, vol. 362(C).
    4. Xiaoqing Shi & Xiaoqing Bai & Puming Wang & Qinghua Shang, 2023. "Multi-Time-Scale Rolling Optimal Scheduling of Virtual Power Plants in Energy and Flexible Ramping Product Markets," Energies, MDPI, vol. 16(19), pages 1-21, September.
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