INVESTIGATION OF POTENTIAL REASONS TO ACCOUNT FOR THE UNDERPERFORMANCE OF AN OPERATIONAL WIND FARM

Detta är en Magister-uppsats från Uppsala universitet/Institutionen för geovetenskaper

Sammanfattning: Wind farms are costly projects and prior to the construction, comprehensive wind resource assessment processes are carried out in order to predict the future energy yield with a reliable accuracy. These estimations are made to constitute a basis for the financial assessment of the project. However, predicting the future always accommodates some uncertainties and sometimes these assessments might overestimate the production. Many different factors might account for a discrepancy between the pre-construction wind resource assessment and the operational production data. This thesis investigates an underperforming wind farm in order to ascertain the reasons of a discrepancy case. To investigate the case, the relevant data and information along with the actual production data of three years are shared with the author. Prior to the construction, a wind resource assessment was carried out by an independent wind consultancy company and the work overestimated the annual energy production (AEP) by 19.1% based on the average production value of available three years. An extensive literature review is performed to identify the possible contributing causes of the discrepancy. The data provided is investigated and a new wind resource assessment is carried out. The underestimation of the wind farm losses are studied extensively as a potential reason of the underperformance. For the AEP estimations, WAsP in WindPro interface and WindSim are employed. The use of WindSim led to about 2-2.5% less AEP estimations compared to the results of WAsP. In order to evaluate the influence of long term correlations on the AEP estimations, the climatology datasets are created using the two different reanalysis datasets (MERRA and CFSR-E) as long term references. WindSim results based on the climatology data obtained using the MERRA and CFSR-E datasets as long term references overestimated the results by 10.9% and 8.2% respectively.

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