IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v232y2018i5p524-532.html
   My bibliography  Save this article

On modeling insights for emerging engineering problems: A case study on the impact of climate uncertainty on the operational performance of offshore wind farms

Author

Listed:
  • Iain Dinwoodie
  • David McMillan
  • Iraklis Lazakis
  • Yalcin Dalgic
  • Matthew Revie

Abstract

This article considers the technical and practical challenges involved in modeling emerging engineering problems. The inherent uncertainty and potential for change in operating environment and procedures add significant complexity to the model development process. This is demonstrated by considering the development of a model to quantify the uncertainty associated with the influence of the wind and wave climate on the energy output of offshore wind farms which may result in sub-optimal operating decisions and site selection due to the competing influence of wind speed on power production and wave conditions on availability. The financial profitability of current and future projects may be threatened if climate uncertainty is not included in the planning and operational decision-making process. A comprehensive climate and wind farm operational model was developed using Monte Carlo operation to model the performance of offshore wind farms, identifying non-linear relationships between climate, availability and energy output. This model was evaluated by engineers planning upcoming offshore wind farms to determine its usefulness for supporting operational decision making. From this, consideration was given to the challenges in applying the Monte Carlo simulation for this decision process and in practice.

Suggested Citation

  • Iain Dinwoodie & David McMillan & Iraklis Lazakis & Yalcin Dalgic & Matthew Revie, 2018. "On modeling insights for emerging engineering problems: A case study on the impact of climate uncertainty on the operational performance of offshore wind farms," Journal of Risk and Reliability, , vol. 232(5), pages 524-532, October.
    • Handle: RePEc:sae:risrel:v:232:y:2018:i:5:p:524-532
    DOI: 10.1177/1748006X17751493
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X17751493
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X17751493?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
    ---><---

    References listed on IDEAS

    as
    1. Martin, Rebecca & Lazakis, Iraklis & Barbouchi, Sami & Johanning, Lars, 2016. "Sensitivity analysis of offshore wind farm operation and maintenance cost and availability," Renewable Energy, Elsevier, vol. 85(C), pages 1226-1236.
    2. Zitrou, Athena & Bedford, Tim & Walls, Lesley, 2016. "A model for availability growth with application to new generation offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 83-94.
    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. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    2. Ahmed Al-Ajmi & Yingzhao Wang & Siniša Djurović, 2021. "Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study," Energies, MDPI, vol. 14(6), pages 1-15, March.
    3. Leimeister, Mareike & Kolios, Athanasios, 2018. "A review of reliability-based methods for risk analysis and their application in the offshore wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1065-1076.
    4. Satir, Mert & Murphy, Fionnuala & McDonnell, Kevin, 2018. "Feasibility study of an offshore wind farm in the Aegean Sea, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2552-2562.
    5. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
    6. Lin, Zi & Cevasco, Debora & Collu, Maurizio, 2020. "A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines," Applied Energy, Elsevier, vol. 259(C).
    7. Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
    8. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2018. "A lifecycle techno-economic model of offshore wind energy for different entry and exit instances," Applied Energy, Elsevier, vol. 221(C), pages 406-424.
    9. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    10. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    11. Helene Seyr & Michael Muskulus, 2019. "Use of Markov Decision Processes in the Evaluation of Corrective Maintenance Scheduling Policies for Offshore Wind Farms," Energies, MDPI, vol. 12(15), pages 1-19, August.
    12. Hailun Xie & Lars Johanning, 2023. "A Hierarchical Met-Ocean Data Selection Model for Fast O&M Simulation in Offshore Renewable Energy Systems," Energies, MDPI, vol. 16(3), pages 1-20, February.
    13. Jan Frederick Unnewehr & Hans-Peter Waldl & Thomas Pahlke & Iván Herráez & Anke Weidlich, 2020. "Reducing Operational Costs of Offshore HVDC Energy Export Systems Through Optimized Maintenance," Energies, MDPI, vol. 13(5), pages 1-20, March.
    14. Zhang, Chen & Gao, Wei & Yang, Tao & Guo, Sheng, 2019. "Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management," Renewable Energy, Elsevier, vol. 133(C), pages 703-711.
    15. Lu Gan & Dirong Xu & Lin Hu & Lei Wang, 2017. "Economic Feasibility Analysis for Renewable Energy Project Using an Integrated TFN–AHP–DEA Approach on the Basis of Consumer Utility," Energies, MDPI, vol. 10(12), pages 1-21, December.
    16. Monforti, Fabio & Gonzalez-Aparicio, Iratxe, 2017. "Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union," Applied Energy, Elsevier, vol. 206(C), pages 439-450.
    17. Pliego Marugán, Alberto & García Márquez, Fausto Pedro & Pinar Pérez, Jesús María, 2022. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    18. Bains, Henna & Madariaga, Ander & Troffaes, Matthias C.M. & Kazemtabrizi, Behzad, 2020. "An economic model for offshore transmission asset planning under severe uncertainty," Renewable Energy, Elsevier, vol. 160(C), pages 1174-1184.
    19. Peyman Mazidi & Yaser Tohidi & Miguel A. Sanz-Bobi, 2017. "Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System," Energies, MDPI, vol. 10(3), pages 1-20, March.
    20. Li, He & Teixeira, Angelo P. & Guedes Soares, C., 2020. "A two-stage Failure Mode and Effect Analysis of offshore wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1438-1461.

    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:sae:risrel:v:232:y:2018:i:5:p:524-532. 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: SAGE Publications (email available below). General contact details of provider: .

    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.