IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v320y2025ics0360544225007492.html
   My bibliography  Save this article

Deep-learning-based scheduling optimization of wind-hydrogen-energy storage system on energy islands

Author

Listed:
  • Wu, Qingxia
  • Peng, Long
  • Han, Guoqing
  • Shu, Jin
  • Yuan, Meng
  • Wang, Bohong

Abstract

With the growing global demand for climate change mitigation, the development and utilization of renewable energy have become crucial for energy transition. This study introduces an innovative optimization framework for clean energy systems on energy islands, integrating offshore wind power, hydrogen production, and hydrogen storage. Advanced forecasting models based on Long Short-Term Memory (LSTM) and Attention-enhanced Convolutional Neural Networks combined with Bidirectional LSTM (Attention-CNN-BiLSTM) are proposed, achieving an impressive prediction accuracy of 98 % for both wind power and residential electricity load. A multi-objective optimization approach, combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is employed to perform 24-h rolling scheduling optimization of the energy system. The optimization model finds a compromise between maximizing profits and minimizing power fluctuations. Compared with the results of non-optimization, the power stability of the optimized system is improved by 45 %. When the wind power capacity is sufficient, the system operating profit reaches 4.41 million CNY, and the power fluctuation is 4.26 GW. This study provides a new theoretical basis and practical guidelines for the design and operation of energy islands, highlighting the potential applications of clean energy technologies in modern energy systems.

Suggested Citation

  • Wu, Qingxia & Peng, Long & Han, Guoqing & Shu, Jin & Yuan, Meng & Wang, Bohong, 2025. "Deep-learning-based scheduling optimization of wind-hydrogen-energy storage system on energy islands," Energy, Elsevier, vol. 320(C).
  • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225007492
    DOI: 10.1016/j.energy.2025.135107
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225007492
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.135107?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. Johansen, Katinka, 2021. "Blowing in the wind: A brief history of wind energy and wind power technologies in Denmark," Energy Policy, Elsevier, vol. 152(C).
    2. Elkadeem, M.R. & Younes, Ali & Sharshir, Swellam W. & Campana, Pietro Elia & Wang, Shaorong, 2021. "Sustainable siting and design optimization of hybrid renewable energy system: A geospatial multi-criteria analysis," Applied Energy, Elsevier, vol. 295(C).
    3. Shi, Jiatong & Guo, Yangying & Wang, Sen & Yu, Xinyi & Jiang, Qianyu & Xu, Weidong & Yan, Yamin & Chen, Yujie & Zhang, Hongyu & Wang, Bohong, 2024. "An optimisation method for planning and operating nearshore island power and natural gas energy systems," Energy, Elsevier, vol. 308(C).
    4. Lund, H. & Østergaard, P.A. & Yuan, M. & Sorknæs, P. & Thellufsen, J.Z., 2025. "Energy balancing and storage in climate-neutral smart energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
    5. Di Wang & Sha Li & Xiaojin Fu, 2024. "Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention," Energies, MDPI, vol. 17(16), pages 1-23, August.
    6. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    7. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    8. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    9. Liang, Tao & Chai, Lulu & Cao, Xin & Tan, Jianxin & Jing, Yanwei & Lv, Liangnian, 2024. "Real-time optimization of large-scale hydrogen production systems using off-grid renewable energy: Scheduling strategy based on deep reinforcement learning," Renewable Energy, Elsevier, vol. 224(C).
    10. Hannah Mareike Marczinkowski & Poul Alberg Østergaard & Søren Roth Djørup, 2019. "Transitioning Island Energy Systems—Local Conditions, Development Phases, and Renewable Energy Integration," Energies, MDPI, vol. 12(18), pages 1-20, September.
    11. Son, Yeong Geon & Kim, Sung Yul, 2025. "Distributionally robust planning for power-to- gas integrated large wind farm systems incorporating hydrogen production switch control model," Energy, Elsevier, vol. 314(C).
    12. Superchi, Francesco & Giovannini, Nathan & Moustakis, Antonis & Pechlivanoglou, George & Bianchini, Alessandro, 2024. "Optimization of the power output scheduling of a renewables-based hybrid power station using MILP approach: The case of Tilos island," Renewable Energy, Elsevier, vol. 220(C).
    13. Han, Li & Wang, Shiqi & Cheng, Yingjie & Chen, Shuo & Wang, Xiaojing, 2024. "Multi-timescale scheduling of an integrated electric-hydrogen energy system with multiple types of electrolysis cells operating in concert with fuel cells," Energy, Elsevier, vol. 307(C).
    14. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    15. Gkeka-Serpetsidaki, Pandora & Tsoutsos, Theocharis, 2022. "A methodological framework for optimal siting of offshore wind farms: A case study on the island of Crete," Energy, Elsevier, vol. 239(PD).
    16. Psarros, Georgios N. & Papathanassiou, Stavros A., 2023. "Generation scheduling in island systems with variable renewable energy sources: A literature review," Renewable Energy, Elsevier, vol. 205(C), pages 1105-1124.
    17. Hanifi, Shahram & Zare-Behtash, Hossein & Cammarano, Andrea & Lotfian, Saeid, 2023. "Offshore wind power forecasting based on WPD and optimised deep learning methods," Renewable Energy, Elsevier, vol. 218(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. Hu, Jinxue & Duan, Pengfei & Cao, Xiaodong & Xue, Qingwen & Zhao, Bingxu & Zhao, Xiaoyu & Yuan, Xiaoyang & Zhang, Chenyang, 2025. "A multi-energy load forecasting method based on the Mixture-of-Experts model and dynamic multilevel attention mechanism," Energy, Elsevier, vol. 324(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. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    2. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    3. Fang, Lei & He, Bin & Yu, Sheng, 2025. "A modular multi-step forecasting method for offshore wind power clusters," Applied Energy, Elsevier, vol. 380(C).
    4. Hauger, Salome & Lieb, Vanessa & Glaser, Rüdiger, 2025. "Spatial potential analysis and site selection for agrivoltaics in Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 213(C).
    5. Wimhurst, Joshua J. & Greene, J. Scott & Koch, Jennifer, 2023. "Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model," Applied Energy, Elsevier, vol. 352(C).
    6. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics.
    7. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    8. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    9. Athanasios Zisos & Dimitrios Chatzopoulos & Andreas Efstratiadis, 2024. "The Concept of Spatial Reliability Across Renewable Energy Systems—An Application to Decentralized Solar PV Energy," Energies, MDPI, vol. 17(23), pages 1-18, November.
    10. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    11. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    12. 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).
    13. Mohamed Abdel-Basset & Abduallah Gamal & Ibrahim M. Hezam & Karam M. Sallam, 2024. "Sustainability assessment of optimal location of electric vehicle charge stations: a conceptual framework for green energy into smart cities," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 11475-11513, May.
    14. Zheng Yuan & Baohua Wen & Cheng He & Jin Zhou & Zhonghua Zhou & Feng Xu, 2022. "Application of Multi-Criteria Decision-Making Analysis to Rural Spatial Sustainability Evaluation: A Systematic Review," IJERPH, MDPI, vol. 19(11), pages 1-31, May.
    15. Lan, Penghang & Chen, She & Li, Qihang & Li, Kelin & Wang, Feng & Zhao, Yaoxun, 2024. "Intelligent hydrogen-ammonia combined energy storage system with deep reinforcement learning," Renewable Energy, Elsevier, vol. 237(PB).
    16. Yanqian Li & Yanlai Zhou & Yuxuan Luo & Zhihao Ning & Chong-Yu Xu, 2024. "Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs," Energies, MDPI, vol. 17(21), pages 1-15, November.
    17. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    18. Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
    19. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    20. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(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:320:y:2025:i:c:s0360544225007492. 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.