A Method for Projecting Cloud Shadows Onto a Central Receiver Field to Predict Receiver Damage

Matthew Mullin, Michael J. Wagner
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Abstract

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.
将云影投射到中央接收器场以预测接收器损坏的方法
这项工作展示了将卫星、全天空成像仪(TSI)和类似数据源的高空间分辨率直接法线辐照度(DNI)数据映射到定日镜场的方法,用于描述云瞬态事件期间中央接收塔上入射通量的空间和时间变化。制图方法被整合到一个光学软件模块中,该模块与CoPylot-SolarPILOT的python应用程序接口相连接,为定日镜场和太阳能塔提供计算效率高的光学模拟。最终,该光学模型将被纳入优化模型中,电站运营商可据此了解云瞬态事件对总体发电量和因蠕变疲劳损伤而导致的接收器寿命的影响,从而就接收器关闭事件做出更明智的决策。通过更准确地模拟云事件对接收器通量图的影响,这项工作可以使用实际或现实的云形状,而不是平均 DNI 值(可能会低估总循环次数),来确定接收器管道和面板上热循环的大小和频率。这项工作还可以避免因过于谨慎的控制策略而造成不必要的电厂停机,并确定各种云类型对接收器寿命的相对影响。我们计划最终将此方法集成到系统顾问模型 (SAM) 中,以提高多云期间性能模型的准确性。在本文中,我们演示了利用公开的新月沙丘电站上空哨兵-2 卫星数据中的 10 米分辨率数据生成 DNI 地图,并将其映射到 CoPylot 中的太阳场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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