IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v329y2025ics0360544225021176.html

Closed-loop integrated prediction-and-dispatching framework for unit commitment in power system with renewables and hydrogen energy

Author

Listed:
  • Zhang, Yi
  • Zhang, Xiaoming
  • Jia, Kejin

Abstract

Wind and photovoltaic power coupling hydrogen is an effective way to improve the accommodation of renewable energy. As an important task of power system operation, unit commitment needs to consider not only randomness of renewables, but also the operation characteristics of electrolyzer and fuel cell. The traditional model for unit commitment problem is based on predict-then-dispatch implemented in a queue. The relevant dispatching information (i.e. cost, objective, constraint) is not fed back to the accuracy-oriented prediction model. Models with similar prediction accuracy can achieve different economic performance. Collaborative framework for integrating prediction and dispatching is proposed in this paper, which forms a closed loop structure to reduce the system cost. Economics evaluation model contains the income from hydrogen sale and operation cost of hydrogen subsystem. In addition to conventional constraints, dispatching optimization model includes not only network constraint of power flow security, but also the operational exclusivity of electrolyzer and fuel cell. MILP solver for optimal dispatching cannot efficiently deal with the computational burden, which is caused by the rapid increase of decision variables and constraints due to many historical scenarios. Therefore, data-driven machine learning method is introduced into the optimization problem. The cost-oriented prediction model is trained by the presented adaptive neuro-fuzzy interference system. How to select statistical and temporal characteristics of time series as model inputs has an impact on the performance of proposed integration framework. Furthermore, this paper discusses in detail the relationship between the model structure and system cost. Case study on the modified IEEE 24-bus power system demonstrates that the integrated prediction-and-dispatching framework can availably improve economics compared with traditional model.

Suggested Citation

  • Zhang, Yi & Zhang, Xiaoming & Jia, Kejin, 2025. "Closed-loop integrated prediction-and-dispatching framework for unit commitment in power system with renewables and hydrogen energy," Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:energy:v:329:y:2025:i:c:s0360544225021176
    DOI: 10.1016/j.energy.2025.136475
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225021176
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.136475?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Muyeen, S.M., 2023. "Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures," Energy, Elsevier, vol. 270(C).
    2. Li, Bei & Roche, Robin, 2020. "Optimal scheduling of multiple multi-energy supply microgrids considering future prediction impacts based on model predictive control," Energy, Elsevier, vol. 197(C).
    3. Wu, Xinyu & Wu, Yiyang & Cheng, Xilong & Cheng, Chuntian & Li, Zehong & Wu, Yongqi, 2023. "A mixed-integer linear programming model for hydro unit commitment considering operation constraint priorities," Renewable Energy, Elsevier, vol. 204(C), pages 507-520.
    4. Gao, Chong & Lin, Junjie & Zeng, Jianfeng & Han, Fengwu, 2022. "Wind-photovoltaic co-generation prediction and energy scheduling of low-carbon complex regional integrated energy system with hydrogen industry chain based on copula-MILP," Applied Energy, Elsevier, vol. 328(C).
    5. Adam N. Elmachtoub & Paul Grigas, 2022. "Smart “Predict, then Optimize”," Management Science, INFORMS, vol. 68(1), pages 9-26, January.
    6. Abdin, Zainul & Zafaranloo, Ali & Rafiee, Ahmad & Mérida, Walter & Lipiński, Wojciech & Khalilpour, Kaveh R., 2020. "Hydrogen as an energy vector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    7. Zhao, Beizhen & He, Xin & Ran, Shaolin & Zhang, Yong & Cheng, Cheng, 2024. "Spatial correlation learning based on graph neural network for medium-term wind power forecasting," Energy, Elsevier, vol. 296(C).
    8. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2021. "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," Energy, Elsevier, vol. 225(C).
    9. Wang, Wenxiao & Li, Chaoshun & Liao, Xiang & Qin, Hui, 2017. "Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm," Applied Energy, Elsevier, vol. 187(C), pages 612-626.
    10. Fan, Xiao-chao & Wang, Wei-qing & Shi, Rui-jing & Cheng, Zhi-jiang, 2017. "Hybrid pluripotent coupling system with wind and photovoltaic-hydrogen energy storage and the coal chemical industry in Hami, Xinjiang," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 950-960.
    11. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    12. Dong, Fuxiang & Wang, Jiangjiang & Xu, Hangwei & Zhang, Xutao, 2024. "A robust real-time energy scheduling strategy of integrated energy system based on multi-step interval prediction of uncertainties," Energy, Elsevier, vol. 300(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. Pierluigi Morano & Francesco Tajani & Felicia Di Liddo & Paola Amoruso, 2024. "A Feasibility Analysis of Energy Retrofit Initiatives Aimed at the Existing Property Assets Decarbonisation," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    2. Bojana Škrbić & Željko Đurišić, 2023. "Novel Planning Methodology for Spatially Optimized RES Development Which Minimizes Flexibility Requirements for Their Integration into the Power System," Energies, MDPI, vol. 16(7), pages 1-34, April.
    3. Han, Peng & Huang, Zhiqiu & Li, Weiwei & He, Wei & Cao, You, 2025. "Trustworthy interval prediction method with uncertainty estimation based on evidence neural networks," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    4. Xu, Li-jun & Fan, Xiao-chao & Wang, Wei-qing & Xu, Lei & Duan, You-lian & Shi, Rui-jing, 2017. "Renewable and sustainable energy of Xinjiang and development strategy of node areas in the “Silk Road Economic Belt”," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 274-285.
    5. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    6. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    7. Wang, Entai & Yuan, Yin & Mo, Pengli & D’Ariano, Andrea & Yang, Lixing & Gao, Ziyou, 2025. "Real-time train rescheduling optimization with combined cross-line strategies for urban rail network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 201(C).
    8. Fernando deLlano-Paz & Juan José Cartelle-Barros & Paulino Martínez-Fernández, 2024. "Application of modern portfolio theory to the European electricity mix: an assessment of environmentally optimal scenarios," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(6), pages 15001-15029, June.
    9. Li, Fei & Wang, Dong & Guo, Hengdao & Liu, Zhan & Zhang, Jianhua & Lin, Zhifang, 2025. "Two-stage Distributionally robust optimization for hydrogen-IES participation in energy-carbon trading-frequency regulation ancillary services market," Energy, Elsevier, vol. 328(C).
    10. Tao, Kejun & Zhao, Jinghao & Tao, Ye & Qi, Qingqing & Tian, Yajun, 2024. "Operational day-ahead photovoltaic power forecasting based on transformer variant," Applied Energy, Elsevier, vol. 373(C).
    11. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Zhao, Yunlong & Gao, Chong, 2023. "A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games," Applied Energy, Elsevier, vol. 350(C).
    12. Zequn Chen & Wesley J. Marrero, 2026. "A survey on optimization and machine learning-based fair decision making in healthcare," Health Care Management Science, Springer, vol. 29(1), pages 1-18, March.
    13. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    14. Shi, Mengshu & Wang, Weiye & Han, Yaxuan & Huang, Yuansheng, 2022. "Research on comprehensive benefit of hydrogen storage in microgrid system," Renewable Energy, Elsevier, vol. 194(C), pages 621-635.
    15. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    16. Toufani, Parinaz & Nadar, Emre & Kocaman, Ayse Selin, 2022. "Short-term assessment of pumped hydro energy storage configurations: Up, down, or closed?," Renewable Energy, Elsevier, vol. 201(P1), pages 1086-1095.
    17. Yu, Qun & Li, Zhiyi & Han, Xutao & Ju, Ping & Shahidehpour, Mohammad, 2024. "End-to-end learning for stochastic preventive dispatch of renewables-rich power systems in abnormal weather conditions," Renewable Energy, Elsevier, vol. 234(C).
    18. Bošković, Sara & Švadlenka, Libor & Jovčić, Stefan & Simic, Vladimir & Dobrodolac, Momčilo & Elomiya, Akram, 2024. "Sustainable propulsion technology selection in penultimate mile delivery using the FullEX-AROMAN method," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    19. Li, Wanyu & Gong, Yu & Liu, Pan, 2025. "Identifying the ecological operating space for explicit decisions of hydro–hydrogen–wind–photovoltaic hybrid power systems," Energy, Elsevier, vol. 332(C).
    20. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:energy:v:329:y:2025:i:c:s0360544225021176. 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.journals.elsevier.com/energy .

    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.