IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5292-d1191373.html
   My bibliography  Save this article

Robust Optimal Scheduling of Integrated Energy Systems Considering the Uncertainty of Power Supply and Load in the Power Market

Author

Listed:
  • Lang Zhao

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
    State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

  • Yuan Zeng

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Zhidong Wang

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

  • Yizheng Li

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

  • Dong Peng

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

  • Yao Wang

    (Economic and Technical Research Institute, State Grid Shanxi Electric Power Company, Taiyuan 030002, China)

  • Xueying Wang

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

Abstract

The integrated energy system is a complex energy system that involves multi-stakeholder and multi-energy coordinated operations. The key to improving its scale and sustainable development is to construct a better-integrated energy system dispatching method which is suitable for the power market. However, the randomness of the supply side and load side of the integrated energy system brings further challenges to system planning and scheduling. Therefore, the optimal scheduling method of an integrated energy system considering the uncertainty of supply and demand in the market environment is studied in this paper. Firstly, the uncertainty models of the supply side and load side of the integrated energy system are established. Then, the optimal scheduling model based on robust chance constraint is established. The reserve capacity constraint is set as a chance constraint with a certain confidence level to maximize the system profit in the power market. Finally, simulations show that the proposed method not only guarantees the robustness of the system but also improves the economy of the system. The method provides ideas for exploring the development mechanism and strategy of integrated energy systems in the electricity market environment.

Suggested Citation

  • Lang Zhao & Yuan Zeng & Zhidong Wang & Yizheng Li & Dong Peng & Yao Wang & Xueying Wang, 2023. "Robust Optimal Scheduling of Integrated Energy Systems Considering the Uncertainty of Power Supply and Load in the Power Market," Energies, MDPI, vol. 16(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5292-:d:1191373
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5292/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/14/5292/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fatma Zehra Doğru & Olcay Arslan, 2021. "Finite mixtures of skew Laplace normal distributions with random skewness," Computational Statistics, Springer, vol. 36(1), pages 423-447, March.
    2. Iver Bakken Sperstad & Magnus Korpås, 2019. "Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties," Energies, MDPI, vol. 12(7), pages 1-24, March.
    3. Min Li & Chao Zhang, 2020. "Two-Stage Stochastic Variational Inequality Arising from Stochastic Programming," Journal of Optimization Theory and Applications, Springer, vol. 186(1), pages 324-343, July.
    4. Fulya Gokalp Yavuz & Olcay Arslan, 2018. "Linear mixed model with Laplace distribution (LLMM)," Statistical Papers, Springer, vol. 59(1), pages 271-289, March.
    5. Sinha, Shyamalendu & Hart, Jeffrey D., 2019. "Estimating the mean and variance of a high-dimensional normal distribution using a mixture prior," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 201-221.
    6. Tengfei Ma & Junyong Wu & Liangliang Hao & Huaguang Yan & Dezhi Li, 2018. "A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach," Energies, MDPI, vol. 11(10), pages 1-19, October.
    7. Jiang, Tao & Zhang, Rufeng & Li, Xue & Chen, Houhe & Li, Guoqing, 2021. "Integrated energy system security region: Concepts, methods, and implementations," Applied Energy, Elsevier, vol. 283(C).
    8. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fan, Yukun & Liu, Weifeng & Zhu, Feilin & Wang, Sen & Yue, Hao & Zeng, Yurou & Xu, Bin & Zhong, Ping-an, 2024. "Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties," Applied Energy, Elsevier, vol. 372(C).

    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. 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.
    2. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(C).
    3. Dominique Barth & Benjamin Cohen-Boulakia & Wilfried Ehounou, 2022. "Distributed Reinforcement Learning for the Management of a Smart Grid Interconnecting Independent Prosumers," Energies, MDPI, vol. 15(4), pages 1-19, February.
    4. Gao, Yang & Ai, Qian & He, Xing & Fan, Songli, 2023. "Coordination for regional integrated energy system through target cascade optimization," Energy, Elsevier, vol. 276(C).
    5. Peixiao Fan & Jia Hu & Song Ke & Yuxin Wen & Shaobo Yang & Jun Yang, 2022. "A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    6. Zhao, Huiru & Li, Bingkang & Lu, Hao & Wang, Xuejie & Li, Hongze & Guo, Sen & Xue, Wanlei & Wang, Yuwei, 2022. "Economy-environment-energy performance evaluation of CCHP microgrid system: A hybrid multi-criteria decision-making method," Energy, Elsevier, vol. 240(C).
    7. Bu, Yuntao & Yu, Hao & Ji, Haoran & Song, Guanyu & Xu, Jing & Li, Juan & Zhao, Jinli & Li, Peng, 2024. "Hybrid data-driven operation method for demand response of community integrated energy systems utilizing virtual and physical energy storage," Applied Energy, Elsevier, vol. 366(C).
    8. Georgia Fargetta & Antonino Maugeri & Laura Scrimali, 2022. "A Stochastic Nash Equilibrium Problem for Medical Supply Competition," Journal of Optimization Theory and Applications, Springer, vol. 193(1), pages 354-380, June.
    9. Zhenghao Wang & Yonghui Liu & Zihao Yang & Wanhao Yang, 2021. "Load Frequency Control of Multi-Region Interconnected Power Systems with Wind Power and Electric Vehicles Based on Sliding Mode Control," Energies, MDPI, vol. 14(8), pages 1-15, April.
    10. Zhang, Chenwei & Wang, Ying & Zheng, Tao & Zhang, Kaifeng, 2024. "Complex network theory-based optimization for enhancing resilience of large-scale multi-energy System11The short version of the paper was presented at CUE2023. This paper is a substantial extension of," Applied Energy, Elsevier, vol. 370(C).
    11. Jie Jiang & Shengjie Li, 2021. "Regularized Sample Average Approximation Approach for Two-Stage Stochastic Variational Inequalities," Journal of Optimization Theory and Applications, Springer, vol. 190(2), pages 650-671, August.
    12. Qinqin Cai & Yongqiang Zhu & Xiaohua Yang & Lin E, 2020. "Alterable Electricity Pricing Mechanism Considering the Deviation of Wind Power Prediction," Sustainability, MDPI, vol. 12(5), pages 1-12, March.
    13. Mahtab Kaffash & Glenn Ceusters & Geert Deconinck, 2021. "Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation," Energies, MDPI, vol. 14(10), pages 1-20, May.
    14. Zhao, Peiyao & Li, Zhengshuo & Bai, Xiang & Su, Jia & Chang, Xinyue, 2024. "Stochastic real-time dispatch considering AGC and electric-gas dynamic interaction: Fine-grained modeling and noniterative decentralized solutions," Applied Energy, Elsevier, vol. 375(C).
    15. Renato Ferrero & Mario Collotta & Maria Victoria Bueno-Delgado & Hsing-Chung Chen, 2020. "Smart Management Energy Systems in Industry 4.0," Energies, MDPI, vol. 13(2), pages 1-3, January.
    16. Ioan Sarbu & Matei Mirza & Daniel Muntean, 2022. "Integration of Renewable Energy Sources into Low-Temperature District Heating Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-28, September.
    17. Nak Heon Choi & Diego del Olmo & Diego Milian & Nadia El Kissi & Peter Fischer & Karsten Pinkwart & Jens Tübke, 2020. "Use of Carbon Additives towards Rechargeable Zinc Slurry Air Flow Batteries," Energies, MDPI, vol. 13(17), pages 1-12, August.
    18. Yang, Xiaohui & Zhang, Zhonglian & Mei, Linghao & Wang, Xiaopeng & Deng, Yeheng & Wei, Shi & Liu, Xiaoping, 2023. "Optimal configuration of improved integrated energy system based on stepped carbon penalty response and improved power to gas," Energy, Elsevier, vol. 263(PD).
    19. 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).
    20. 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).

    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:16:y:2023:i:14:p:5292-:d:1191373. 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.