IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3475-d573602.html
   My bibliography  Save this article

Improvement of AEP Predictions with Time for Swedish Wind Farms

Author

Listed:
  • Erik Möllerström

    (School of Business, Innovation and Sustainability, Halmstad University, P.O. Box 823, SE-301 18 Halmstad, Sweden)

  • Sean Gregory

    (School of Business, Innovation and Sustainability, Halmstad University, P.O. Box 823, SE-301 18 Halmstad, Sweden)

  • Aromal Sugathan

    (School of Business, Innovation and Sustainability, Halmstad University, P.O. Box 823, SE-301 18 Halmstad, Sweden)

Abstract

Based on data from 2083 wind turbines installed in Sweden from 1988 onwards, the accuracy of the predictions of the annual energy production (AEP) from the project planning phases has been compared to the actual wind-index-corrected production. Both the electricity production and the predicted AEP come from Vindstat, a database that collects information directly from wind turbine owners. The mean error for all analyzed wind turbines was 13.0%, which means that, overall, the predicted AEP has been overestimated. There has been an improvement of accuracy with time with an overestimation of 8.2% for wind turbines installed in the 2010s, however, the continuous improvement seems to have stagnated around 2005 despite better data availability and continuous refinement of methods. Dividing the results by terrain, the error is larger for wind turbines in open and flat terrain than in forest areas, indicating that the reason behind the error is not the higher complexity of the forest terrain. Also, there is no apparent increase of error with wind farm size which could have been expected if wind farm blockage effect was a main reason for the overestimations. Besides inaccurate AEP predictions, a higher-than-expected performance decline due to inadequate maintenance of the wind turbines may be a reason behind the AEP overestimations. The main sources of error are insecurity regarding the source of AEP predictions and the omission of mid-life alterations of rated power.

Suggested Citation

  • Erik Möllerström & Sean Gregory & Aromal Sugathan, 2021. "Improvement of AEP Predictions with Time for Swedish Wind Farms," Energies, MDPI, vol. 14(12), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3475-:d:573602
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3475/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/12/3475/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    2. Sonja Germer & Axel Kleidon, 2019. "Have wind turbines in Germany generated electricity as would be expected from the prevailing wind conditions in 2000-2014?," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-16, February.
    3. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    4. Enevoldsen, Peter, 2016. "Onshore wind energy in Northern European forests: Reviewing the risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1251-1262.
    5. Osvaldo Rodríguez & Jesús A del Río & Oscar A Jaramillo & Manuel Martínez, 2015. "Wind Power Error Estimation in Resource Assessments," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    6. James Bleeg & Mark Purcell & Renzo Ruisi & Elizabeth Traiger, 2018. "Wind Farm Blockage and the Consequences of Neglecting Its Impact on Energy Production," Energies, MDPI, vol. 11(6), pages 1-20, June.
    7. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    8. Hyun-Goo Kim & Jin-Young Kim, 2021. "Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement," Energies, MDPI, vol. 14(8), pages 1-12, April.
    9. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    10. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    11. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
    12. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    13. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    14. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2021. "Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis," Energies, MDPI, vol. 14(4), pages 1-25, February.
    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. Erik Möllerström, 2022. "Energy—History and Time Trends: Special Issue Editorial," Energies, MDPI, vol. 15(15), pages 1-3, July.
    2. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.

    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. Davide Astolfi & Ravi Pandit, 2022. "Wind Turbine Performance Decline with Age," Energies, MDPI, vol. 15(14), pages 1-4, July.
    2. Davide Astolfi & Ravi Pandit & Ludovico Terzi & Andrea Lombardi, 2022. "Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis," Energies, MDPI, vol. 15(15), pages 1-17, July.
    3. 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.
    4. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2021. "Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis," Energies, MDPI, vol. 14(4), pages 1-25, February.
    5. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
    6. Hyun-Goo Kim & Jin-Young Kim, 2021. "Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement," Energies, MDPI, vol. 14(8), pages 1-12, April.
    7. 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).
    8. Aliashim Albani & Mohd Zamri Ibrahim & Kim Hwang Yong, 2018. "Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia," Energies, MDPI, vol. 11(11), pages 1-18, November.
    9. Luis M. Abadie & Nestor Goicoechea, 2021. "Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain," Energies, MDPI, vol. 14(12), pages 1-24, June.
    10. Benjamin Pakenham & Anna Ermakova & Ali Mehmanparast, 2021. "A Review of Life Extension Strategies for Offshore Wind Farms Using Techno-Economic Assessments," Energies, MDPI, vol. 14(7), pages 1-23, March.
    11. Davide Astolfi & Francesco Castellani, 2022. "Editorial on the Special Issue “Wind Turbine Monitoring through Operation Data Analysis”," Energies, MDPI, vol. 15(10), pages 1-4, May.
    12. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    13. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    14. Toft, Henrik Stensgaard & Svenningsen, Lasse & Sørensen, John Dalsgaard & Moser, Wolfgang & Thøgersen, Morten Lybech, 2016. "Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads," Renewable Energy, Elsevier, vol. 90(C), pages 352-361.
    15. Birgir Hrafnkelsson & Gudmundur V. Oddsson & Runar Unnthorsson, 2016. "A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation," Energies, MDPI, vol. 9(4), pages 1-14, April.
    16. Ahmed, Faraedoon & Foley, Aoife & Dowds, Carole & Johnston, Barry & Al Kez, Dlzar, 2024. "Assessing the engineering, environmental and economic aspects of repowering onshore wind energy," Energy, Elsevier, vol. 301(C).
    17. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    18. Dao, Phong B. & Barszcz, Tomasz & Staszewski, Wieslaw J., 2024. "Anomaly detection of wind turbines based on stationarity analysis of SCADA data," Renewable Energy, Elsevier, vol. 232(C).
    19. Yip, Chak Man Andrew & Gunturu, Udaya Bhaskar & Stenchikov, Georgiy L., 2016. "Wind resource characterization in the Arabian Peninsula," Applied Energy, Elsevier, vol. 164(C), pages 826-836.
    20. Benini, Giacomo & Cattani, Gilles, 2022. "Measuring the long run technical efficiency of offshore wind farms," Applied Energy, Elsevier, vol. 308(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:14:y:2021:i:12:p:3475-:d:573602. 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.