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Low-Carbon Scheduling of Integrated Electricity and Gas Distribution System Considering V2G

Author

Listed:
  • Yicheng Li

    (College of Electrical Engineering, Sichuan University, Chengdu 610017, China)

  • Lixiong Xu

    (College of Electrical Engineering, Sichuan University, Chengdu 610017, China)

  • Xiangmei Lv

    (College of Electrical Engineering, Sichuan University, Chengdu 610017, China)

  • Yiran Xiao

    (College of Electrical Engineering, Sichuan University, Chengdu 610017, China)

Abstract

With the development of EVs (Electric Vehicles) and the rapidly developing policies on low carbon and environmental protection, electric power systems and natural gas systems become increasingly larger. Under these circumstances, the V2G (Vehicle-to-grid) and the coordinated operation of an integrated electricity–gas distribution system (IEGDS), considering CO 2 emissions, can play a part together in the process of energy conservation. Firstly, the V2G model is discussed; this paper presents the cost differences between out-of-order and order for the car. Secondly, the IEGDS model presents coupling constraints of gas turbines and power-to-gas. Lastly, carbon emission is considered in this paper; a carbon capture plant (CCP) captures the CO 2 burning by fossil fuel in the power generation process and stores it in a carbon storage tank. This paper also considers trading with the carbon market via a carbon storage warehouse. With the cooperation of various components, a comprehensive model considers the use of V2G to store power in the IEGDS system, with consideration of the carbon trade. Numerical experiments validate the effectiveness of the combination between V2G and IEGDS, considering carbon emissions and carbon trading.

Suggested Citation

  • Yicheng Li & Lixiong Xu & Xiangmei Lv & Yiran Xiao, 2022. "Low-Carbon Scheduling of Integrated Electricity and Gas Distribution System Considering V2G," Energies, MDPI, vol. 15(24), pages 1-18, December.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9524-:d:1004887
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    References listed on IDEAS

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    1. Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
    2. Gu, Chenjia & Zhang, Yao & Wang, Jianxue & Li, Qingtao, 2021. "Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle," Applied Energy, Elsevier, vol. 301(C).
    3. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2022. "Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy," Applied Energy, Elsevier, vol. 314(C).
    4. Fambri, Gabriele & Diaz-Londono, Cesar & Mazza, Andrea & Badami, Marco & Sihvonen, Teemu & Weiss, Robert, 2022. "Techno-economic analysis of Power-to-Gas plants in a gas and electricity distribution network system with high renewable energy penetration," Applied Energy, Elsevier, vol. 312(C).
    5. Zhang, Guangming & Wang, Wei & Chen, Zhenyu & Li, Ruilian & Niu, Yuguang, 2022. "Modeling and optimal dispatch of a carbon-cycle integrated energy system for low-carbon and economic operation," Energy, Elsevier, vol. 240(C).
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