A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf and Hermann Knaus
Additional contact information
Sarah Barber: Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Alain Schubiger: Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Sara Koller: Meteotest AG, Fabrikstrasse 14, 3012 Bern, Switzerland
Dominik Eggli: Meteotest AG, Fabrikstrasse 14, 3012 Bern, Switzerland
Alexander Radi: Enercon GmbH, 14 E Rue des Clairières, 44840 Les Soriniéres, France
Andreas Rumpf: Hochschule Esslingen, Kanalstr. 33, 73728 Esslingen am Neckar, Germany
Hermann Knaus: Hochschule Esslingen, Kanalstr. 33, 73728 Esslingen am Neckar, Germany

Energies, 2022, vol. 15, issue 3, 1-24

Abstract: In wind energy, the accuracy of the estimation of the wind resource has an enormous effect on the expected rate of return of a project. For a given project, the wind resource assessor is faced with a difficult choice of a wide range of simulation tools and workflows with varying accuracies (or “skill”) and costs. There is currently no guideline or process available in the industry for helping with the decision of the most “optimal” choice—and this is particularly challenging in mountainous (or “complex”) terrain. In this work, a new decision process for selecting the Wind Resource Assessment (WRA) workflow that would expect to deliver the best compromise between skill and costs for a given wind energy project is developed, with a focus on complex terrain. This involves estimating the expected skill and cost scores using a set of pre-defined weighted parameters. The new process is designed and tested by applying seven different WRA workflows to five different complex terrain sites. The quality of the decision process is then assessed for all the sites by comparing the decision made (i.e., choice of optimal workflow) using the expected skill and cost scores with the decision made using the actual skill and cost scores (obtained by comparing measurements and simulations at a validation location). The results show that the decision process works well, but the accuracy decreases as the site complexity increases. It is therefore concluded that some of the parameter weightings should be dependent on site complexity. On-going work involves collecting more data from a large range of sites, implementing measures to reduce the subjectivity of the process and developing a reliable and robust automated decision tool for the industry.

Keywords: wind energy; decision process; Wind Resource Assessment; risks; costs; skill (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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