This work develops a computational Digital-Twin framework to track and optimize the flow of solar power through complex, multipurpose, solar farm facilities, such as Agrophotovoltaic (APV) systems. APV systems symbiotically cohabitate power-generation facilities and agricultural production systems. In this work, 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, which are propagated forward in time to ascertain multiple reflections and absorption for various source-system configurations, varying multi-panel inclination, panel refractive indices, sizes, shapes, heights, ground refractive properties, etc. The method allows for a solar installation to be tested from multiple source directions quickly and uses a genomic-based Machine-Learning Algorithm to optimize the system. This is particularly useful for planning of complex next-generation solar farm systems involving bifacial (double-sided) panelling, which are capable of capturing ground albedo reflection, exemplified by APV systems. Numerical examples are provided to illustrate the results, with the overall goal being to provide a computational framework to rapidly design and deploy complex APV systems.

这项工作开发了一个计算数字孪生框架，以跟踪和优化通过复杂、多用途的太阳能农场设施（例如农业光伏 (APV) 系统）的太阳能流。APV 系统与发电设施和农业生产系统共生共存。在这项工作中，基于将辐照度高频分解为多条射线，这些射线及时向前传播以确定各种源系统的多次反射和吸收，使用麦克斯韦方程组的降阶模型快速计算太阳能流配置、不同的多面板倾斜度、面板折射率、尺寸、形状、高度、地面折射特性等。该方法允许从多个源方向快速测试太阳能装置，并使用基于基因组的机器学习算法来优化系统。这对于规划涉及双面（双面）面板的复杂下一代太阳能发电场系统特别有用，这些面板能够捕获地面反照率反射，例如 APV 系统。提供了数值例子来说明结果，总体目标是提供一个计算框架来快速设计和部署复杂的 APV 系统。