IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v328y2022ics0306261922013630.html
   My bibliography  Save this article

Unified operation optimization model of integrated coal mine energy systems and its solutions based on autonomous intelligence

Author

Listed:
  • Wang, Yan
  • Hu, Hejuan
  • Sun, Xiaoyan
  • Zhang, Yong
  • Gong, Dunwei

Abstract

An integrated coal mine energy system involves the production, transmission, conversion, storage, and consumption of multiple types of energy with complicated coupling relationships. The operation optimization problem of this system is characterized by multi-scenario, multi-variable, multi-objective, and strong constraints, making it difficult to be solved. Based on the structure of this system, a unified system operation optimization model suitable for various scenarios is established to minimize the economic and carbon transaction costs. To effectively solve optimization problems in various scenarios, we propose an autonomous intelligent optimization strategy based on a support vector machine to make full use of their characteristics in various scenarios to generate the most target intelligent optimization algorithms. To improve the performance of a population in convergence under strong constraints, we develop three strategies for repairing infeasible solutions according to specific preferences. Taking a mine in Shanxi Province as the object under test, a series of experiments are conducted under four typical scenarios, and the experimental results show the effectiveness of the proposed algorithm.

Suggested Citation

  • Wang, Yan & Hu, Hejuan & Sun, Xiaoyan & Zhang, Yong & Gong, Dunwei, 2022. "Unified operation optimization model of integrated coal mine energy systems and its solutions based on autonomous intelligence," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922013630
    DOI: 10.1016/j.apenergy.2022.120106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922013630
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120106?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    2. Wang, Yongli & Wang, Yudong & Huang, Yujing & Li, Fang & Zeng, Ming & Li, Jiapu & Wang, Xiaohai & Zhang, Fuwei, 2019. "Planning and operation method of the regional integrated energy system considering economy and environment," Energy, Elsevier, vol. 171(C), pages 731-750.
    3. Wu, Chenyu & Gu, Wei & Xu, Yinliang & Jiang, Ping & Lu, Shuai & Zhao, Bo, 2018. "Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers," Applied Energy, Elsevier, vol. 232(C), pages 607-616.
    4. Vosloo, Jan & Liebenberg, Leon & Velleman, Douglas, 2012. "Case study: Energy savings for a deep-mine water reticulation system," Applied Energy, Elsevier, vol. 92(C), pages 328-335.
    5. Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.
    6. Qin, Chao & Yan, Qingyou & He, Gang, 2019. "Integrated energy systems planning with electricity, heat and gas using particle swarm optimization," Energy, Elsevier, vol. 188(C).
    7. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
    8. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2021. "Multi-objective optimization and evaluation of hybrid CCHP systems for different building types," Energy, Elsevier, vol. 215(PA).
    9. Fang, Xin & Cui, Hantao & Yuan, Haoyu & Tan, Jin & Jiang, Tao, 2019. "Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Huang, Hongxu & Liang, Rui & Lv, Chaoxian & Lu, Mengtian & Gong, Dunwei & Yin, Shulin, 2021. "Two-stage robust stochastic scheduling for energy recovery in coal mine integrated energy system," Applied Energy, Elsevier, vol. 290(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. Mouli-Castillo, Julien & van Hunen, Jeroen & MacKenzie, Michael & Sear, Thomas & Adams, Charlotte, 2024. "GEMSToolbox: A novel modelling tool for rapid screening of mines for geothermal heat extraction," Applied Energy, Elsevier, vol. 360(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. He, Shuaijia & Gao, Hongjun & Wang, Lingfeng & Xiang, Yingmeng & Liu, Junyong, 2020. "Distributionally robust planning for integrated energy systems incorporating electric-thermal demand response," Energy, Elsevier, vol. 213(C).
    2. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    3. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    4. Xiang, Yue & Cai, Hanhu & Gu, Chenghong & Shen, Xiaodong, 2020. "Cost-benefit analysis of integrated energy system planning considering demand response," Energy, Elsevier, vol. 192(C).
    5. Chen, Changming & Wu, Xueyan & Li, Yan & Zhu, Xiaojun & Li, Zesen & Ma, Jien & Qiu, Weiqiang & Liu, Chang & Lin, Zhenzhi & Yang, Li & Wang, Qin & Ding, Yi, 2021. "Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages," Applied Energy, Elsevier, vol. 302(C).
    6. Xi, Yufei & Fang, Jiakun & Chen, Zhe & Zeng, Qing & Lund, Henrik, 2021. "Optimal coordination of flexible resources in the gas-heat-electricity integrated energy system," Energy, Elsevier, vol. 223(C).
    7. Zhong, Junjie & Cao, Yijia & Li, Yong & Tan, Yi & Peng, Yanjian & Cao, Lihua & Zeng, Zilong, 2021. "Distributed modeling considering uncertainties for robust operation of integrated energy system," Energy, Elsevier, vol. 224(C).
    8. Wang, Yongli & Qi, Chengyuan & Dong, Huanran & Wang, Shuo & Wang, Xiaohai & Zeng, Ming & Zhu, Jinrong, 2020. "Optimal design of integrated energy system considering different battery operation strategy," Energy, Elsevier, vol. 212(C).
    9. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    10. 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).
    11. Lu, Zhiming & Gao, Yan & Xu, Chuanbo, 2021. "Evaluation of energy management system for regional integrated energy system under interval type-2 hesitant fuzzy environment," Energy, Elsevier, vol. 222(C).
    12. Ding, Jianyong & Gao, Ciwei & Song, Meng & Yan, Xingyu & Chen, Tao, 2022. "Bi-level optimal scheduling of virtual energy station based on equal exergy replacement mechanism," Applied Energy, Elsevier, vol. 327(C).
    13. Fan, Guozhu & Peng, Chunhua & Wang, Xuekui & Wu, Peng & Yang, Yifan & Sun, Huijuan, 2024. "Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC," Renewable Energy, Elsevier, vol. 226(C).
    14. Yao, Leyi & Liu, Zeyuan & Chang, Weiguang & Yang, Qiang, 2023. "Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty," Renewable Energy, Elsevier, vol. 212(C), pages 523-537.
    15. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    16. Asghari, Mohammad & Jaber, Mohamad Y. & Mirzapour Al-e-hashem, S.M.J., 2023. "Coordinating vessel recovery actions: Analysis of disruption management in a liner shipping service," European Journal of Operational Research, Elsevier, vol. 307(2), pages 627-644.
    17. Alex Gliesch & Marcus Ritt, 2022. "A new heuristic for finding verifiable k-vertex-critical subgraphs," Journal of Heuristics, Springer, vol. 28(1), pages 61-91, February.
    18. Zhou, Yuan & Wang, Jiangjiang & Dong, Fuxiang & Qin, Yanbo & Ma, Zherui & Ma, Yanpeng & Li, Jianqiang, 2021. "Novel flexibility evaluation of hybrid combined cooling, heating and power system with an improved operation strategy," Applied Energy, Elsevier, vol. 300(C).
    19. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    20. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.

    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:eee:appene:v:328:y:2022:i:c:s0306261922013630. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.