IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6329-d902002.html
   My bibliography  Save this article

Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin

Author

Listed:
  • Changhyun Kim

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

  • Minh-Chau Dinh

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Hae-Jin Sung

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Kyong-Hwan Kim

    (Korea Research Institute of Ships & Ocean Engineering, Daejeon 34103, Korea)

  • Jeong-Ho Choi

    (Korea Electric Power Corp, Naju 58322, Korea
    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 20332, USA)

  • Lukas Graber

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 20332, USA)

  • In-Keun Yu

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Minwon Park

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

Abstract

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.

Suggested Citation

  • Changhyun Kim & Minh-Chau Dinh & Hae-Jin Sung & Kyong-Hwan Kim & Jeong-Ho Choi & Lukas Graber & In-Keun Yu & Minwon Park, 2022. "Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin," Energies, MDPI, vol. 15(17), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6329-:d:902002
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6329/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/17/6329/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Ju-Hee & Kim, Hee-Hoon & Yoo, Seung-Hoon, 2022. "Social acceptance toward constructing a combined heat and power plant near people's dwellings in South Korea," Energy, Elsevier, vol. 244(PB).
    2. Zhou, Ella & Cole, Wesley & Frew, Bethany, 2018. "Valuing variable renewable energy for peak demand requirements," Energy, Elsevier, vol. 165(PA), pages 499-511.
    3. Denholm, Paul & Hand, Maureen, 2011. "Grid flexibility and storage required to achieve very high penetration of variable renewable electricity," Energy Policy, Elsevier, vol. 39(3), pages 1817-1830, March.
    4. Kang, Jichuan & Sun, Liping & Guedes Soares, C., 2019. "Fault Tree Analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 133(C), pages 1455-1467.
    5. Jinje Park & Changhyun Kim & Minh-Chau Dinh & Minwon Park, 2022. "Design of a Condition Monitoring System for Wind Turbines," Energies, MDPI, vol. 15(2), pages 1-16, January.
    6. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    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. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    2. Lv, Zhihan & Cheng, Chen & Lv, Haibin, 2023. "Digital twins for secure thermal energy storage in building," Applied Energy, Elsevier, vol. 338(C).
    3. Konstantinos Prantikos & Lefteri H. Tsoukalas & Alexander Heifetz, 2022. "Physics-Informed Neural Network Solution of Point Kinetics Equations for a Nuclear Reactor Digital Twin," Energies, MDPI, vol. 15(20), pages 1-22, October.
    4. Sinawo Nomandela & Mkhululi E. S. Mnguni & Atanda K. Raji, 2023. "Modeling and Simulation of a Large-Scale Wind Power Plant Considering Grid Code Requirements," Energies, MDPI, vol. 16(6), pages 1-24, March.
    5. Xiaotong Dong & Jing Huang & Ningzhao Luo & Wenshan Hu & Zhongcheng Lei, 2023. "Design and Implementation of Digital Twin Diesel Generator Systems," Energies, MDPI, vol. 16(18), pages 1-16, September.

    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. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Ruan, Yingjun, 2020. "Capacity credit and market value analysis of photovoltaic integration considering grid flexibility requirements," Renewable Energy, Elsevier, vol. 159(C), pages 908-919.
    2. Jenkins, J.D. & Zhou, Z. & Ponciroli, R. & Vilim, R.B. & Ganda, F. & de Sisternes, F. & Botterud, A., 2018. "The benefits of nuclear flexibility in power system operations with renewable energy," Applied Energy, Elsevier, vol. 222(C), pages 872-884.
    3. Maarten Wolsink, 2020. "Framing in Renewable Energy Policies: A Glossary," Energies, MDPI, vol. 13(11), pages 1-31, June.
    4. Andrychowicz, Mateusz & Olek, Blazej & Przybylski, Jakub, 2017. "Review of the methods for evaluation of renewable energy sources penetration and ramping used in the Scenario Outlook and Adequacy Forecast 2015. Case study for Poland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 703-714.
    5. Ilaria Vigna & Jessica Balest & Wilmer Pasut & Roberta Pernetti, 2020. "Office Occupants’ Perspective Dealing with Energy Flexibility: A Large-Scale Survey in the Province of Bolzano," Energies, MDPI, vol. 13(17), pages 1-20, August.
    6. Gallo, A.B. & Simões-Moreira, J.R. & Costa, H.K.M. & Santos, M.M. & Moutinho dos Santos, E., 2016. "Energy storage in the energy transition context: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 800-822.
    7. Schellenberg, C. & Lohan, J. & Dimache, L., 2020. "Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    8. Sinn, Hans-Werner, 2017. "Buffering volatility: A study on the limits of Germany's energy revolution," European Economic Review, Elsevier, vol. 99(C), pages 130-150.
    9. Lu, Bin & Blakers, Andrew & Stocks, Matthew, 2017. "90–100% renewable electricity for the South West Interconnected System of Western Australia," Energy, Elsevier, vol. 122(C), pages 663-674.
    10. Gyanwali, Khem & Komiyama, Ryoichi & Fujii, Yasumasa, 2020. "Representing hydropower in the dynamic power sector model and assessing clean energy deployment in the power generation mix of Nepal," Energy, Elsevier, vol. 202(C).
    11. Stinner, Sebastian & Huchtemann, Kristian & Müller, Dirk, 2016. "Quantifying the operational flexibility of building energy systems with thermal energy storages," Applied Energy, Elsevier, vol. 181(C), pages 140-154.
    12. Coninx, Kristof & Deconinck, Geert & Holvoet, Tom, 2018. "Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics," Applied Energy, Elsevier, vol. 218(C), pages 104-113.
    13. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S. & Panapakidis, Ioannis P., 2017. "Impact of the penetration of renewables on flexibility needs," Energy Policy, Elsevier, vol. 109(C), pages 360-369.
    14. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
    15. O'Shaughnessy, Eric & Heeter, Jenny & Shah, Chandra & Koebrich, Sam, 2021. "Corporate acceleration of the renewable energy transition and implications for electric grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    16. Cárdenas, Bruno & Ibanez, Roderaid & Rouse, James & Swinfen-Styles, Lawrie & Garvey, Seamus, 2023. "The effect of a nuclear baseload in a zero-carbon electricity system: An analysis for the UK," Renewable Energy, Elsevier, vol. 205(C), pages 256-272.
    17. Teirilä, Juha, 2020. "The value of the nuclear power plant fleet in the German power market under the expansion of fluctuating renewables," Energy Policy, Elsevier, vol. 136(C).
    18. Hua Zhou & Huahua Wu & Chengjin Ye & Shijie Xiao & Jun Zhang & Xu He & Bo Wang, 2019. "Integration Capability Evaluation of Wind and Photovoltaic Generation in Power Systems Based on Temporal and Spatial Correlations," Energies, MDPI, vol. 12(1), pages 1-12, January.
    19. Changgi Min, 2020. "Impact Analysis of Transmission Congestion on Power System Flexibility in Korea," Energies, MDPI, vol. 13(9), pages 1-11, May.
    20. Arabzadeh, Vahid & Miettinen, Panu & Kotilainen, Titta & Herranen, Pasi & Karakoc, Alp & Kummu, Matti & Rautkari, Lauri, 2023. "Urban vertical farming with a large wind power share and optimised electricity costs," Applied Energy, Elsevier, vol. 331(C).

    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:15:y:2022:i:17:p:6329-:d:902002. 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.