IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i5p1146-d327985.html
   My bibliography  Save this article

Reducing Operational Costs of Offshore HVDC Energy Export Systems Through Optimized Maintenance

Author

Listed:
  • Jan Frederick Unnewehr

    (Department of Sustainable Systems Engineering, University of Freiburg, Emmy-Noether-Strasse 2, 79110 Freiburg, Germany)

  • Hans-Peter Waldl

    (Overspeed GmbH & CO. KG, Im Technologiepark 4, 26129 Oldenburg, Germany)

  • Thomas Pahlke

    (Overspeed GmbH & CO. KG, Im Technologiepark 4, 26129 Oldenburg, Germany)

  • Iván Herráez

    (University of Applied Science Emden/Leer, Constantiaplatz 4, 26723 Emden, Germany)

  • Anke Weidlich

    (Department of Sustainable Systems Engineering, University of Freiburg, Emmy-Noether-Strasse 2, 79110 Freiburg, Germany)

Abstract

For the grid connection of offshore wind farms today, in many cases a high-voltage direct current (HVDC) connection to the shore is implemented. The scheduled maintenance of the offshore and onshore HVDC stations makes up a significant part of the operational costs of the connected wind farms. The main factor for the maintenance cost is the lost income from the missing energy yield (indirect maintenance costs). In this study, we show an in-depth analysis of the used components, maintenance cycles, maintenance work for the on- and offshore station, and the risks assigned in prolonging the maintenance cycle of the modular multilevel converter (MMC). In addition, we investigate the potential to shift the start date of the maintenance work, based on a forecast of the energy generation. Our findings indicate that an optimized maintenance design with respect to the maintenance behavior of an HVDC energy export system can decrease the maintenance-related energy losses (indirect maintenance costs) for an offshore wind farm to almost one half. It was also shown that direct maintenance costs for the MMC (staff costs) have small effect on the total maintenance costs. This can be explained by the fact that the additional costs for maintenance staff are two orders of magnitude lower than the revenue losses during maintenance.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1146-:d:327985
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/5/1146/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/5/1146/
    Download Restriction: no
    ---><---

    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. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    3. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
    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. Santos, F. & Gómez-Gesteira, M. & deCastro, M. & Añel, J.A. & Carvalho, D. & Costoya, Xurxo & Dias, J.M., 2018. "On the accuracy of CORDEX RCMs to project future winds over the Iberian Peninsula and surrounding ocean," Applied Energy, Elsevier, vol. 228(C), pages 289-300.
    2. Ritter, Matthias & Deckert, Lars, 2017. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," Applied Energy, Elsevier, vol. 185(P2), pages 1087-1099.
    3. Maria Taljegard & Lisa Göransson & Mikael Odenberger & Filip Johnsson, 2021. "To Represent Electric Vehicles in Electricity Systems Modelling—Aggregated Vehicle Representation vs. Individual Driving Profiles," Energies, MDPI, vol. 14(3), pages 1-25, January.
    4. Lisa Göransson & Caroline Granfeldt & Ann-Brith Strömberg, 2021. "Management of Wind Power Variations in Electricity System Investment Models," SN Operations Research Forum, Springer, vol. 2(2), pages 1-30, June.
    5. 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.
    6. Hayes, Liam & Stocks, Matthew & Blakers, Andrew, 2021. "Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis," Energy, Elsevier, vol. 229(C).
    7. Xsitaaz T. Chadee & Naresh R. Seegobin & Ricardo M. Clarke, 2017. "Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago," Energies, MDPI, vol. 10(7), pages 1-23, July.
    8. Mikovits, Christian & Wetterlund, Elisabeth & Wehrle, Sebastian & Baumgartner, Johann & Schmidt, Johannes, 2021. "Stronger together: Multi-annual variability of hydrogen production supported by wind power in Sweden," Applied Energy, Elsevier, vol. 282(PB).
    9. Cuevas-Figueroa, Gabriel & Stansby, Peter K. & Stallard, Timothy, 2022. "Accuracy of WRF for prediction of operational wind farm data and assessment of influence of upwind farms on power production," Energy, Elsevier, vol. 254(PB).
    10. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    11. 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.
    12. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    13. González-Aparicio, I. & Monforti, F. & Volker, P. & Zucker, A. & Careri, F. & Huld, T. & Badger, J., 2017. "Simulating European wind power generation applying statistical downscaling to reanalysis data," Applied Energy, Elsevier, vol. 199(C), pages 155-168.
    14. He, Yuhang & Han, Xingxing & Xu, Chang & Cheng, Zhe & Wang, Jincheng & Liu, Wei & Xu, Dong, 2023. "Sensitivity of simulated wind power under diverse spatial scales and multiple terrains using the weather research and forecasting model," Energy, Elsevier, vol. 285(C).
    15. Olauson, Jon & Bergström, Hans & Bergkvist, Mikael, 2016. "Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data," Renewable Energy, Elsevier, vol. 96(PA), pages 784-791.
    16. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    17. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    18. Jurasz, Jakub & Dąbek, Paweł B. & Kaźmierczak, Bartosz & Kies, Alexander & Wdowikowski, Marcin, 2018. "Large scale complementary solar and wind energy sources coupled with pumped-storage hydroelectricity for Lower Silesia (Poland)," Energy, Elsevier, vol. 161(C), pages 183-192.
    19. 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.
    20. Nuño, Edgar & Maule, Petr & Hahmann, Andrea & Cutululis, Nicolaos & Sørensen, Poul & Karagali, Ioanna, 2018. "Simulation of transcontinental wind and solar PV generation time series," Renewable Energy, Elsevier, vol. 118(C), pages 425-436.

    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:13:y:2020:i:5:p:1146-:d:327985. 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.