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A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response

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  • Fan, Wei
  • Tan, Zhongfu
  • Li, Fanqi
  • Zhang, Amin
  • Ju, Liwei
  • Wang, Yuwei
  • De, Gejirifu

Abstract

A day-ahead and real-time two-stage risk economic optimal model of integrated energy system (IES) is established. First, considering the electricity and heating coupling characteristics of combined heat and power, the feasible region is described by mathematical model, and the integrated demand response model is expanded from the traditional demand response model. Second, the objective functions and constraints of two stages are established respectively. The first stage optimal objective is to minimize the pre-scheduled operation cost of day-ahead, which arranges the output power of renewable energy and the startup-shutdown plan, output power and reserve capacity of adjustable equipment. The second stage optimal objective is to minimize the re-scheduled expected cost of real-time, which will call reserve capacity, curtail renewable energy output, implement integrated demand response, and use energy storage to cope with power deviations. In order to quantify the risk cost of multiple uncertainties of power, load, and price, the real-time stage objective function is further improved to a form of conditional value at risk. Finally, simulations implemented on a green park show that: the proposed model can achieve the optimization of energy supply at different time scales and improve scheduling enforceability after considering economics and risk. Shapely Value can fairly and reasonably determines the benefit distribution scheme of different subjects in IES.

Suggested Citation

  • Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s036054422202669x
    DOI: 10.1016/j.energy.2022.125783
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    Cited by:

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    2. Qingbo Tan & Zhuning Wang & Wei Fan & Xudong Li & Xiangguang Li & Fanqi Li & Zihao Zhao, 2022. "Development Path and Model Design of a New Energy Vehicle in China," Energies, MDPI, vol. 16(1), pages 1-15, December.
    3. Zhang, Jinliang & Liu, Ziyi, 2024. "Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and fine utilization of hydrogen," Energy, Elsevier, vol. 290(C).
    4. Wang, Yongzhen & Zhang, Lanlan & Song, Yi & Han, Kai & Zhang, Yan & Zhu, Yilin & Kang, Ligai, 2024. "State-of-the-art review on evaluation indicators of integrated intelligent energy from different perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    5. Fan, Wei & Ju, Liwei & Tan, Zhongfu & Li, Xiangguang & Zhang, Amin & Li, Xudong & Wang, Yueping, 2023. "Two-stage distributionally robust optimization model of integrated energy system group considering energy sharing and carbon transfer," Applied Energy, Elsevier, vol. 331(C).
    6. Lili Mo & Zeyu Deng & Haoyong Chen & Junkun Lan, 2023. "Multi-Objective Co-Operative Game-Based Optimization for Park-Level Integrated Energy System Based on Exergy-Economic Analysis," Energies, MDPI, vol. 16(24), pages 1-19, December.
    7. Wang, Zhuo & Hou, Hui & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Xie, Changjun, 2024. "Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach," Applied Energy, Elsevier, vol. 357(C).

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