Posters

Abstract

Wind farm extension investments must be assessed wisely when a wind-rich site is available and infrastructure-ready. The energy yield estimation of extension wind turbines can be performed using data from a pre-construction wind measurement monitoring campaign and Sitewind (Meso-scale + Micro-scale modeling) based wind resource assessment. This well-established and recognized procedure can be enriched with operational data obtained from the existing wind turbines. However, adding new wind turbine units to the site means future wake generation and the need to evaluate future losses. Energy yield assessment (EYA) and operational energy yield assessment (OEYA) are two studies that allow investors to obtain bankable reports. This study presents repowering/extension research and how to assess the future energy generation flow of existing wind farms via practical examples. A wind farm with 10 wind turbines is considered in two initial scenarios, which vary on the amount of operational data available (2 years vs 3 years) for the analysis. Associated findings result in different gross energy estimations, which lead to slightly different scaled wind resources for the extension wind farm. Then, for each initial scenario, two different layouts are studied for the extension wind farm. These two layouts vary in terms of distance to the existing wind turbines. Extension wind turbine positions are selected considering energy-based layout optimization using two polygons. The first polygon limits the placement distance of new turbines to 2 km from the existing wind turbines for wind farm owners who would like to benefit from existing land permits. The other polygon forces the search beyond a 2 km buffer from the existing wind turbines for wind farm owners willing to extend the wind farm site. The near-located extension wind turbine scenario presents a 1.2% wake loss on future energy generation of the operational wind farm, whereas the far-located extension scenario shows a wake loss of 0.4%. OEYA provides EYA with a wind resource grid scaling factor for extension wind turbine energy estimation, and EYA provides OEYA with future wake loss estimations for operational wind farm future energy estimations. Wake losses are an essential category for EYA analysis. However, they are not considered in OEYA since internal wakes have already left their fingerprint on the revenue meter. The same goes for the external wakes of existing neighbors. However, having new neighbors creates a need for considering future wake losses in OEYA. This study also presents the impact of energy uncertainty.  Key categories of EYA and OEYA in the wind resource uncertainty are long-term average speed and evaluation period wind resource. The impact of using longer operational data is initially assessed via the use of 2 years vs 3 years of operational data. Combined, all uncertainty sources for the case of 3 years of operational data are found to be 3.4%. With the same setup and only 2 years of operational data, uncertainty increased to 3.7%.