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The round robin site assessment method: A new approach to wind energy site assessment

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  • Lackner, Matthew A.
  • Rogers, Anthony L.
  • Manwell, James F.

Abstract

Portability is one of the many potential advantages of utilizing ground-based measurement devices such as SODARs and LIDARs instead of meteorological towers for wind resource assessment. This paper investigates the use of a monitoring strategy that leverages the portability of ground-based devices, dubbed the “round robin site assessment method.” The premise is to measure the wind resource at multiple sites in a single year using a single portable device, but to discontinuously distribute the measurement time at each site over the whole year, so that the total measurement period comprises smaller segments of measured data. This measured data set is then utilized in the measure-correlate-predict (MCP) process to predict the long-term wind resource at the site. This method aims to increase the number of sites assessed in a single year, without the sacrifice in accuracy and precision that usually accompanies shorter measurement periods. The performance of the round robin site assessment method was compared to the standard method, in which the measured data are continuous. The results demonstrate that the round robin site assessment method is an effective monitoring strategy that improves the accuracy and reduces the uncertainty of MCP predictions for measurement periods less than 1 year. In fact, the round robin site assessment method compares favorably to the accuracy and uncertainty of a full year of resource assessment. While there are some tradeoffs to be made by using the round robin site assessment method, it is potentially a very useful strategy for wind resource assessment.

Suggested Citation

  • Lackner, Matthew A. & Rogers, Anthony L. & Manwell, James F., 2008. "The round robin site assessment method: A new approach to wind energy site assessment," Renewable Energy, Elsevier, vol. 33(9), pages 2019-2026.
  • Handle: RePEc:eee:renene:v:33:y:2008:i:9:p:2019-2026
    DOI: 10.1016/j.renene.2007.12.011
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    Cited by:

    1. Angelis-Dimakis, Athanasios & Biberacher, Markus & Dominguez, Javier & Fiorese, Giulia & Gadocha, Sabine & Gnansounou, Edgard & Guariso, Giorgio & Kartalidis, Avraam & Panichelli, Luis & Pinedo, Irene, 2011. "Methods and tools to evaluate the availability of renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1182-1200, February.
    2. Weekes, S.M. & Tomlin, A.S., 2014. "Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP," Renewable Energy, Elsevier, vol. 68(C), pages 529-539.
    3. Troncoso, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Riquelme, J.C. & Prieto, L., 2015. "Local models-based regression trees for very short-term wind speed prediction," Renewable Energy, Elsevier, vol. 81(C), pages 589-598.
    4. Weekes, S.M. & Tomlin, A.S., 2014. "Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy," Renewable Energy, Elsevier, vol. 63(C), pages 162-171.
    5. Woochul Nam & Ki-Yong Oh, 2020. "Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment," Mathematics, MDPI, vol. 8(10), pages 1-20, October.

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