TITLE:
On the Improvement of Wind Power Predictions Based on Terrain Characteristics and Measurements of the Annual Energy Production
AUTHORS:
Jonathan Dorval, Christian Masson, Yves Gagnon
KEYWORDS:
Wind Energy, Wind Farm Assessment, WAsP, MS-Micro, Roughness Classes, Roughness Complexity
JOURNAL NAME:
Journal of Flow Control, Measurement & Visualization,
Vol.5 No.1,
February
9,
2017
ABSTRACT: The assessment of the production capacity of wind farms is a crucial step in wind farm design processes, where a poor assessment can cause significant economic losses. Data from Canadian wind farms benefiting from national production incentive programs show that wind farms are typically characterized by an overestimation of the production capacity. In this context, a study has been done to provide insight on the origin of the discrepancies between the energy production estimates and the measured energy generation, and to develop a method to reduce these discrepancies. To this end, the WAsP and MS-Micro models have been studied. Besides the wind speed measurements, topography indices have been developed to identify the influence of the various characteristics of the site on the error in the annual energy production (AEP). In addition, roughness classes have been created, including a reference roughness and a roughness complexity. The indices have also allowed establishing correlations and developing equations to evaluate the error based on the site characteristics and the positions of wind turbines on the measured annual energy production. An average reduction of up to 83% on the AEP errors was obtained when the methodology was applied to five wind farms in Canada.