IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v192y2024ics1364032123011267.html
   My bibliography  Save this article

Assessment of hydrogen-based solutions associated to offshore wind farms: The case of the Iberian Peninsula

Author

Listed:
  • Calado, Goncalo
  • Castro, Rui
  • Pires, A.J.
  • Marques, Miguel J.

Abstract

Offshore wind energy has the potential to be associated with hydrogen production to overcome certain disadvantages, such as the high cost of electrical transmission systems. In this work, two hydrogen producing systems are modelled, one with the electrolyzer offshore, the other with the electrolyzer onshore, along with a conventional offshore wind farm. To do so, each component is individually modelled and combined to construct the systems. Furthermore, an hourly optimisation algorithm is used to control the operation of the systems and a neural network is implemented to forecast day ahead power production and electricity price, so that regulation costs could be modelled. This study extends the existing literature by modelling the regulation costs in the day ahead electricity market using neural networks to provide day ahead forecasts along with analysing the flexibility of using an electrolyzer coupled with an offshore wind farm. Furthermore, innovative floating offshore wind turbines were considered, enabling the assessment for offshore hydrogen production in deeper waters. Results show that, for the present case study, the onshore electrolyzer system is always more economically interesting than the offshore electrolyzer system, mainly due to its ability of purchasing electricity from the grid. The first has a levelized cost of hydrogen of 5.84 €/kg, 3.42 €/kg and 2.57 €/kg for 2020, 2030 and 2050, respectively, compared to 8.98 €/kg, 4.37 €/kg and 2.68 €/kg.

Suggested Citation

  • Calado, Goncalo & Castro, Rui & Pires, A.J. & Marques, Miguel J., 2024. "Assessment of hydrogen-based solutions associated to offshore wind farms: The case of the Iberian Peninsula," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:rensus:v:192:y:2024:i:c:s1364032123011267
    DOI: 10.1016/j.rser.2023.114268
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123011267
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.114268?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. Rodica Loisel & Laurent Baranger & Nezha Chemouri & Stefania Spinu & Sophie Pardo, 2015. "Economic evaluation of hybrid off-shore wind power and hydrogen storage system," Post-Print hal-03722685, HAL.
    2. Hong, Ying-Yi & Satriani, Thursy Rienda Aulia, 2020. "Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network," Energy, Elsevier, vol. 209(C).
    3. Reuß, M. & Grube, T. & Robinius, M. & Preuster, P. & Wasserscheid, P. & Stolten, D., 2017. "Seasonal storage and alternative carriers: A flexible hydrogen supply chain model," Applied Energy, Elsevier, vol. 200(C), pages 290-302.
    4. McDonagh, Shane & Ahmed, Shorif & Desmond, Cian & Murphy, Jerry D, 2020. "Hydrogen from offshore wind: Investor perspective on the profitability of a hybrid system including for curtailment," Applied Energy, Elsevier, vol. 265(C).
    5. Bhandari, Ramchandra & Shah, Ronak Rakesh, 2021. "Hydrogen as energy carrier: Techno-economic assessment of decentralized hydrogen production in Germany," Renewable Energy, Elsevier, vol. 177(C), pages 915-931.
    6. Franco, Brais Armiño & Baptista, Patrícia & Neto, Rui Costa & Ganilha, Sofia, 2021. "Assessment of offloading pathways for wind-powered offshore hydrogen production: Energy and economic analysis," Applied Energy, Elsevier, vol. 286(C).
    7. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 11(9), pages 1-18, August.
    8. Xiaoyu Shi & Xuewen Lei & Qiang Huang & Shengzhi Huang & Kun Ren & Yuanyuan Hu, 2018. "Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory," Energies, MDPI, vol. 11(11), pages 1-20, November.
    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. Ibrahim, Omar S. & Singlitico, Alessandro & Proskovics, Roberts & McDonagh, Shane & Desmond, Cian & Murphy, Jerry D., 2022. "Dedicated large-scale floating offshore wind to hydrogen: Assessing design variables in proposed typologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Abdulrahman Joubi & Yutaro Akimoto & Keiichi Okajima, 2022. "A Production and Delivery Model of Hydrogen from Solar Thermal Energy in the United Arab Emirates," Energies, MDPI, vol. 15(11), pages 1-14, May.
    3. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    4. Gea-Bermúdez, Juan & Bramstoft, Rasmus & Koivisto, Matti & Kitzing, Lena & Ramos, Andrés, 2023. "Going offshore or not: Where to generate hydrogen in future integrated energy systems?," Energy Policy, Elsevier, vol. 174(C).
    5. Zheng, Yi & You, Shi & Huang, Chunjun & Jin, Xin, 2023. "Model-based economic analysis of off-grid wind/hydrogen systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    6. Wiegner, J.F. & Andreasson, L.M. & Kusters, J.E.H. & Nienhuis, R.M., 2024. "Interdisciplinary perspectives on offshore energy system integration in the North Sea: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    7. Marek Jaszczur & Qusay Hassan & Aws Zuhair Sameen & Hayder M. Salman & Olushola Tomilayo Olapade & Szymon Wieteska, 2023. "Massive Green Hydrogen Production Using Solar and Wind Energy: Comparison between Europe and the Middle East," Energies, MDPI, vol. 16(14), pages 1-26, July.
    8. Carlson, Ewa Lazarczyk & Pickford, Kit & Nyga-Łukaszewska, Honorata, 2023. "Green hydrogen and an evolving concept of energy security: Challenges and comparisons," Renewable Energy, Elsevier, vol. 219(P1).
    9. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    10. Li, Yanfei & Taghizadeh-Hesary, Farhad, 2022. "The economic feasibility of green hydrogen and fuel cell electric vehicles for road transport in China," Energy Policy, Elsevier, vol. 160(C).
    11. Kolb, Sebastian & Plankenbühler, Thomas & Frank, Jonas & Dettelbacher, Johannes & Ludwig, Ralf & Karl, Jürgen & Dillig, Marius, 2021. "Scenarios for the integration of renewable gases into the German natural gas market – A simulation-based optimisation approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    12. Jiwon Yu & Young Jae Han & Hyewon Yang & Sugil Lee & Gildong Kim & Chulung Lee, 2022. "Promising Technology Analysis and Patent Roadmap Development in the Hydrogen Supply Chain," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    13. Acikgoz, Hakan & Budak, Umit & Korkmaz, Deniz & Yildiz, Ceyhun, 2021. "WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network," Energy, Elsevier, vol. 233(C).
    14. Wang, Jianxiao & An, Qi & Zhao, Yue & Pan, Guangsheng & Song, Jie & Hu, Qinran & Tan, Chin-Woo, 2023. "Role of electrolytic hydrogen in smart city decarbonization in China," Applied Energy, Elsevier, vol. 336(C).
    15. Lopez, Gabriel & Galimova, Tansu & Fasihi, Mahdi & Bogdanov, Dmitrii & Breyer, Christian, 2023. "Towards defossilised steel: Supply chain options for a green European steel industry," Energy, Elsevier, vol. 273(C).
    16. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    17. Dasireddy, Venkata D.B.C. & Likozar, Blaž, 2022. "Cu–Mn–O nano-particle/nano-sheet spinel-type materials as catalysts in methanol steam reforming (MSR) and preferential oxidation (PROX) reaction for purified hydrogen production," Renewable Energy, Elsevier, vol. 182(C), pages 713-724.
    18. Kaiyan Wang & Xueyan Wang & Rong Jia & Jian Dang & Yan Liang & Haodong Du, 2022. "Research on Coupled Cooperative Operation of Medium- and Long-Term and Spot Electricity Transaction for Multi-Energy System: A Case Study in China," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
    19. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    20. Jing Sun & Yonggang Peng & Di Lu & Xiaofeng Chen & Weifeng Xu & Liguo Weng & Jun Wu, 2022. "Optimized Configuration and Operating Plan for Hydrogen Refueling Station with On-Site Electrolytic Production," Energies, MDPI, vol. 15(7), pages 1-20, March.

    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:rensus:v:192:y:2024:i:c:s1364032123011267. 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/600126/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.