以使用致動盤模型的RANS方法預測彰濱離岸風力發電場間跡流干涉現象

Abstract

本研究使用基於穩態不可壓縮流雷諾平均納維斯托克斯方程組與致動盤模型的WIFA3D程式研究臺灣彰濱離岸風力發電場間的干涉效應，其中WIFA3D程式藉由求解連續方程式及動量守恆方程式以及k-ε紊流模型，模擬風力發電場中風機周圍的流場。首先使用四湖風力發電場的年容量因子驗證WIFA3D程式的精確度，發現預測值與量測值的誤差約為6.5％。本研究的目標風力發電場聚落包含彰濱外海的10座離岸風力發電場，採用DTU 10MW風機作為各個風力發電場內預計安裝的目標風機。本研究藉由模擬目標風力發電場在WRF風況資料下，周圍風力發電場干涉對目標風力發電場的年容量因子以及年容量因子損失的影響。在主要風向下，若兩側皆具有相鄰風力發電場的條件下，目標風力發電場年容量因子損失的最大值小於3％；在主要風向下，若僅單邊存在相鄰風力發電場，目標風場的年容量因子損失約為1％；在主要風向下，目標風力發電場前方若有其他的風力發電場存在，將產生10％以上的年容量因子損失。若在風力發電場單獨存在的條件下，風力發電場1至10的年容量因子分別為0.487、0.522、0.523、0.523、0.524、0.526、0.524、0.550、0.534、0.538。各個風力發電場年容量因子損失的最大值發生在10個風力發電場同時運作的情況，此時風力發電場1至10年容量因子損失分別為5.62％、7.4％、5.83％、17.31％、15.95％、16.09％、18.8％、15.85％、14.34％、8.31％，其中年容量因子損失最少的為風力發電場1，最高的為風力發電場7。

This study investigates the wake interaction among wind farms in the Zhangbin offshore area with the help of RANSE-based WIFA3D that solves continuity equation, momentum equations and k-ε turbulence model coupled with an actuator disk model. The yearly capacity factor of the Sihu wind farm is first predicted by WIFA3D that gives an error of 6.5% in comparison with the field measurement. The target of this study is ten offshore wind farms located in the Zhangbin offshore area. The DTU 10 MW wind turbine is presumed to be installed in those wind farms. The yearly capacity factor and capacity factor loss of the wind farms are predicted under WRF wind conditions. When the main wind direction is considered, a wind farm laterally neighboring one side of the target wind farm can result in a yearly capacity factor loss up to 1% and two wind farms laterally neighboring two opposite sides of the target wind farm can lead to a drop of yearly capacity factor up to 3%. Neighboring wind farms present in the upstream of the dominant wind direction are able to result in the decrease of yearly capacity factor of 10%. When a wind farm operates without the presence of other wind farms, the yearly capacity factor is predicted to be 0.487, 0.522, 0.523, 0.523, 0.524, 0.526, 0.524, 0.550, 0.534, and 0.538 for wind farm 1 to 10, respectively. When ten wind farms simultaneously operate, the yearly capacity factor loss reaches its maximum value of 5.62%, 7.4%, 5.83%, 17.31%, 15.95%, 16.09%, 18.8%, 15.85%, 14.34%, and 8.31% for wind farm 1 to 10, respectively. Among the studied wind farms, wind farm 1 is performing the best and wind farm 7 the worst when the yearly capacity factor loss is of interest.