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A Robust Scheduling Optimization Model for an Integrated Energy System with P2G Based on Improved CVaR

Author

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  • Zhongfu Tan

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

  • Qingkun Tan

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

  • Shenbo Yang

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

  • Liwei Ju

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

  • Gejirifu De

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

Abstract

The uncertainty of wind power and photoelectric power output will cause fluctuations in system frequency and power quality. To ensure the stable operation of the power system, a comprehensive scheduling optimization model for the electricity-to-gas integrated energy system is proposed. Power-to-gas (P2G) technology enhances the flexibility of the integrated energy system and the power system in absorbing renewable energy. In this context, firstly, an electricity-to-gas optimization scheduling model is proposed, and the improved Conditional Value at Risk ( CVaR ) is proposed to deal with the uncertainty of wind power and photoelectric power output. Secondly, taking the integrated energy system with the P2G operating cost and the carbon emission cost as the objective function, an optimal scheduling model of the multi-energy system is solved by the A Mathematical Programming Language (AMPL) solver. Finally, the results of the example illustrate the optimal multi-energy system scheduling model and analyze the economic benefits of the P2G technology to improve the system to absorb wind power and photovoltaic power. The simulation calculation of the proposed model demonstrates the necessity of taking into account the operating cost of the electrical gas conversion in the integrated energy system, and the feasibility of considering the economic and wind power acceptance capabilities.

Suggested Citation

  • Zhongfu Tan & Qingkun Tan & Shenbo Yang & Liwei Ju & Gejirifu De, 2018. "A Robust Scheduling Optimization Model for an Integrated Energy System with P2G Based on Improved CVaR," Energies, MDPI, vol. 11(12), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3437-:d:188955
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    References listed on IDEAS

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    1. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    2. Chaudry, Modassar & Jenkins, Nick & Qadrdan, Meysam & Wu, Jianzhong, 2014. "Combined gas and electricity network expansion planning," Applied Energy, Elsevier, vol. 113(C), pages 1171-1187.
    3. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
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    Cited by:

    1. Xinyu Sun & Hao Wu & Siqi Guo & Lingwei Zheng, 2022. "Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming," Energies, MDPI, vol. 15(18), pages 1-17, September.
    2. Dongmei Zhao & Xuan Xia & Ran Tao, 2019. "Optimal Configuration of Electric-Gas-Thermal Multi-Energy Storage System for Regional Integrated Energy System," Energies, MDPI, vol. 12(13), pages 1-22, July.
    3. Xiaoyu Lyu & Zhiyu Xu & Ning Wang & Min Fu & Weisheng Xu, 2019. "A Two-Layer Interactive Mechanism for Peer-to-Peer Energy Trading Among Virtual Power Plants," Energies, MDPI, vol. 12(19), pages 1-28, September.
    4. Masoud Agabalaye-Rahvar & Amin Mansour-Saatloo & Mohammad Amin Mirzaei & Behnam Mohammadi-Ivatloo & Kazem Zare & Amjad Anvari-Moghaddam, 2020. "Robust Optimal Operation Strategy for a Hybrid Energy System Based on Gas-Fired Unit, Power-to-Gas Facility and Wind Power in Energy Markets," Energies, MDPI, vol. 13(22), pages 1-21, November.
    5. Houfei Lin & Jianxin Jin & Qidai Lin & Bo Li & Chengzhi Wei & Wenfa Kang & Minyou Chen, 2019. "Distributed Settlement of Frequency Regulation Based on a Battery Energy Storage System," Energies, MDPI, vol. 12(1), pages 1-17, January.

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