IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9524-d1004887.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9524/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9524/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. 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).
    4. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alabi, Tobi Michael & Lawrence, Nathan P. & Lu, Lin & Yang, Zaiyue & Bhushan Gopaluni, R., 2023. "Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system," Applied Energy, Elsevier, vol. 333(C).
    2. Jiang, Tao & Wu, Chenghao & Huang, Tao & Zhang, Rufeng & Li, Xue, 2024. "Optimal market participation of VPPs in TSO-DSO coordinated energy and flexibility markets," Applied Energy, Elsevier, vol. 360(C).
    3. Wu, Qunli & Li, Chunxiang, 2024. "A bi-level optimization framework for the power-side virtual power plant participating in day-ahead wholesale market as a price-maker considering uncertainty," Energy, Elsevier, vol. 304(C).
    4. Meng, Yuan & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints," Applied Energy, Elsevier, vol. 356(C).
    5. Zhihan Shi & Weisong Han & Guangming Zhang & Zhiqing Bai & Mingxiang Zhu & Xiaodong Lv, 2022. "Research on Low-Carbon Energy Sharing through the Alliance of Integrated Energy Systems with Multiple Uncertainties," Energies, MDPI, vol. 15(24), pages 1-20, December.
    6. Andrade, Carlos & Selosse, Sandrine & Maïzi, Nadia, 2022. "The role of power-to-gas in the integration of variable renewables," Applied Energy, Elsevier, vol. 313(C).
    7. Gao, Xianhui & Wang, Sheng & Sun, Ying & Zhai, Junyi & Chen, Nan & Zhang, Xiao-Ping, 2024. "Low-carbon energy scheduling for integrated energy systems considering offshore wind power hydrogen production and dynamic hydrogen doping strategy," Applied Energy, Elsevier, vol. 376(PA).
    8. Yunlong Zhang & Panhong Zhang & Sheng Du & Hanlin Dong, 2024. "Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage," Energies, MDPI, vol. 17(11), pages 1-26, June.
    9. Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
    10. Li, Yanbin & Hu, Weikun & Zhang, Feng & Li, Yun, 2025. "Multi-objective collaborative operation optimization of park-level integrated energy system clusters considering green power forecasting and trading," Energy, Elsevier, vol. 319(C).
    11. Gao, Xianhui & Wang, Sheng & Sun, Ying & Zhai, Junyi, 2024. "Low-carbon operation of integrated electricity–gas system with hydrogen injection considering hydrogen mixed gas turbine and laddered carbon trading," Applied Energy, Elsevier, vol. 374(C).
    12. Ren, Junzhi & Zeng, Yuan & Qin, Chao & Li, Bao & Wang, Ziqiang & Yuan, Quan & Zhai, Hefeng & Li, Peng, 2024. "Characterization and application of flexible operation region of virtual power plant," Applied Energy, Elsevier, vol. 371(C).
    13. Song, Yuguang & Xia, Mingchao & Yang, Liu & Chen, Qifang & Su, Su, 2023. "Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid," Renewable Energy, Elsevier, vol. 205(C), pages 747-762.
    14. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    15. Xu, Jiazhu & Yi, Yuqin, 2023. "Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach," Energy, Elsevier, vol. 263(PB).
    16. Rezaee Jordehi, Ahmad & Mansouri, Seyed Amir & Tostado-Véliz, Marcos & Sirjani, Reza & Safaraliev, Murodbek & Nasir, Mohammad, 2024. "A three-level model for integration of hydrogen refuelling stations in interconnected power-gas networks considering vehicle-to-infrastructure (V2I) technology," Energy, Elsevier, vol. 308(C).
    17. Zixuan Liu & Yao Gao & Tingyu Li & Ruijin Zhu & Dewen Kong & Hao Guo, 2024. "Considering the Tiered Low-Carbon Optimal Dispatching of Multi-Integrated Energy Microgrid with P2G-CCS," Energies, MDPI, vol. 17(14), pages 1-18, July.
    18. Liu, Fan & Duan, Jiandong & Wu, Chen & Tian, Qinxing, 2024. "Risk-averse distributed optimization for integrated electricity-gas systems considering uncertainties of Wind-PV and power-to-gas," Renewable Energy, Elsevier, vol. 227(C).
    19. Pang, Xinfu & Wang, Yibao & Yu, Yang & Liu, Wei, 2024. "Optimal scheduling of a cogeneration system via Q-learning-based memetic algorithm considering demand-side response," Energy, Elsevier, vol. 300(C).
    20. Li, Ling-Ling & Miao, Yan & Lim, Ming K. & Sethanan, Kanchana & Tseng, Ming-Lang, 2024. "Integrated energy system for low-carbon economic operation optimization: Pareto compromise programming and master-slave game," Renewable Energy, Elsevier, vol. 222(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9524-:d:1004887. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.