In many industrialized regions of the world, large-scale photovoltaic systems now contribute a significant part to the energy portfolio during daylight operation. However, as energy demands peak shortly before sunset and persist for several hours afterwards, the integration of solar-thermal systems is extremely advantageous as a green “bridge” energy source. Accordingly, this work develops a digital-twin model to track and optimize the flow of incoming solar power through a complex solar-thermal storage system, consisting of a large array of adaptable mirrors, an optical-receiver and a power distribution system for customers to extract energy. Specifically, the solar power flow is rapidly computed with a reduced order model of Maxwell’s equations, based on a high-frequency decomposition of the irradiance into multiple rays that experience mirror reflections, losses and ultimately receiver absorption and customer delivery. The method allows for rapid testing (in microseconds) of the performance of large numbers of mirror-receiver layout configurations in design space, over extremely long time periods, such as weeks, months and years, using a genetic-based machine-learning digital-twin framework, which integrates submodels for:

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  optics and tracking of the Fresnel multi-mirror system,

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  thermal absorption of the optical energy by the receiver and

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  optimal operating temperatures balancing radiative losses with heat storage.

在世界上许多工业化地区，大型光伏系统现在在白天运行期间为能源组合做出了重要贡献。然而，由于能源需求在日落前不久达到峰值并在日落后持续数小时，因此太阳能热系统的集成作为绿色“桥梁”能源极具优势。因此，这项工作开发了一个数字孪生模型来跟踪和优化通过复杂的太阳能热存储系统的输入太阳能流，该系统由大量适应性反射镜、光接收器和配电系统组成，供客户使用提取能量。具体来说，使用麦克斯韦方程组的降阶模型快速计算太阳能功率流，基于将辐照度高频分解为多条射线，这些射线经历镜面反射、损耗并最终被接收器吸收和客户交付。该方法允许使用基于遗传的机器学习数字-在极长的时间段（例如数周、数月和数年）内快速测试（以微秒为单位）设计空间中大量反射镜接收器布局配置的性能。孪生框架，它集成了以下子模型：

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  菲涅耳多镜系统的光学和跟踪，

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  接收器对光能的热吸收和

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  最佳工作温度平衡辐射损失与热存储。