IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i11p2770-d1409295.html
   My bibliography  Save this article

Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage

Author

Listed:
  • Yunlong Zhang

    (Power China Hubei Electric Engineering, Wuhan 430040, China)

  • Panhong Zhang

    (School of Finance, Hubei University of Economics, Wuhan 430205, China)

  • Sheng Du

    (School of Automation, China University of Geosciences, Wuhan 430074, China)

  • Hanlin Dong

    (Power China Hubei Electric Engineering, Wuhan 430040, China)

Abstract

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2770-:d:1409295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/11/2770/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/11/2770/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    2. 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. Shi, Zhengkun & Yang, Yongbiao & Xu, Qingshan & Wu, Chenyu & Hua, Kui, 2023. "A low-carbon economic dispatch for integrated energy systems with CCUS considering multi-time-scale allocation of carbon allowance," Applied Energy, Elsevier, vol. 351(C).
    2. Ma, Yixiang & Yu, Lean & Zhang, Guoxing & Lu, Zhiming & Wu, Jiaqian, 2023. "Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling," Renewable Energy, Elsevier, vol. 219(P1).
    3. Athanasios Ioannis Arvanitidis & Vivek Agarwal & Miltiadis Alamaniotis, 2023. "Nuclear-Driven Integrated Energy Systems: A State-of-the-Art Review," Energies, MDPI, vol. 16(11), pages 1-23, May.
    4. Liu, Zhi-Feng & Zhao, Shi-Xiang & Luo, Xing-Fu & Huang, Ya-He & Gu, Rui-Zheng & Li, Ji-Xiang & Li, Ling-Ling, 2025. "Two-layer energy dispatching and collaborative optimization of regional integrated energy system considering stakeholders game and flexible load management," Applied Energy, Elsevier, vol. 379(C).
    5. Jun He & Zimu Mao & Wentao Huang & Bohan Zhang & Jianbo Xiao & Zuoming Zhang & Xinyu Liu, 2024. "Low-Carbon Economic Dispatch of Virtual Power Plants Considering the Combined Operation of Oxygen-Enriched Combustion and Power-to-Ammonia," Sustainability, MDPI, vol. 16(10), pages 1-21, May.
    6. Fabio Massaro & Maria Luisa Di Silvestre & Marco Ferraro & Francesco Montana & Eleonora Riva Sanseverino & Salvatore Ruffino, 2024. "Energy Hub Model for the Massive Adoption of Hydrogen in Power Systems," Energies, MDPI, vol. 17(17), pages 1-31, September.
    7. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    8. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    9. Alain Aoun & Mehdi Adda & Adrian Ilinca & Mazen Ghandour & Hussein Ibrahim, 2024. "Optimizing Virtual Power Plant Management: A Novel MILP Algorithm to Minimize Levelized Cost of Energy, Technical Losses, and Greenhouse Gas Emissions," Energies, MDPI, vol. 17(16), pages 1-23, August.
    10. 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).
    11. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    12. 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).
    13. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    14. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    15. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    16. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    17. Li, Ruilian & Zeng, Deliang & Li, Tingting & Ti, Baozhong & Hu, Yong, 2023. "Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer," Energy, Elsevier, vol. 269(C).
    18. 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).
    19. Fang, Fang & Yu, Songyuan & Liu, Mingxi, 2020. "An improved Shapley value-based profit allocation method for CHP-VPP," Energy, Elsevier, vol. 213(C).
    20. Zhang, Guangming & Zhang, Chao & Wang, Wei & Cao, Huan & Chen, Zhenyu & Niu, Yuguang, 2023. "Offline reinforcement learning control for electricity and heat coordination in a supercritical CHP unit," Energy, Elsevier, vol. 266(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:17:y:2024:i:11:p:2770-:d:1409295. 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.