Narasiah Hansley, Kitouni Ouail, Scorsoglio Andrea, Sturdza Bernd K, Hatcher Shawn, Katcher Kelsi, Khalesi Javad, Garcia Dolores, Kusner Matt J
University College London, London, UK.
Massachusetts Institute of Technology, Cambridge, USA.
Sci Rep. 2024 Aug 17;14(1):19086. doi: 10.1038/s41598-024-67346-6.
Concentrated solar power (CSP) is one of the few sustainable energy technologies that offers day-to-night energy storage. Recent development of the supercritical carbon dioxide (sCO2) Brayton cycle has made CSP a potentially cost-competitive energy source. However, as CSP plants are most efficient in desert regions, where there is high solar irradiance and low land cost, careful design of a dry cooling system is crucial to make CSP practical. In this work, we present a machine learning system to optimize the factory design and configuration of a dry cooling system for an sCO2 Brayton cycle CSP plant. For this, we develop a physics-based simulation of the cooling properties of an air-cooled heat exchanger. The simulator is able to construct a dry cooling system satisfying a wide variety of power cycle requirements (e.g., 10-100 MW) for any surface air temperature. Using this simulator, we leverage recent results in high-dimensional Bayesian optimization to optimize dry cooler designs that minimize lifetime cost for a given location, reducing this cost by 67% compared to recently proposed designs. Our simulation and optimization framework can increase the development pace of economically-viable sustainable energy generation systems.
聚光太阳能发电（CSP）是少数几种能够提供昼夜储能的可持续能源技术之一。超临界二氧化碳（sCO2）布雷顿循环的最新发展使CSP成为一种具有潜在成本竞争力的能源。然而，由于CSP发电厂在太阳能辐照度高且土地成本低的沙漠地区效率最高，因此精心设计干式冷却系统对于使CSP切实可行至关重要。在这项工作中，我们提出了一种机器学习系统，以优化用于sCO2布雷顿循环CSP发电厂的干式冷却系统的工厂设计和配置。为此，我们开发了一种基于物理的风冷式热交换器冷却特性模拟方法。该模拟器能够构建一个干式冷却系统，以满足任何地表空气温度下各种功率循环要求（例如10 - 100兆瓦）。利用这个模拟器，我们利用高维贝叶斯优化的最新成果来优化干式冷却器设计，从而使给定位置的使用寿命成本降至最低，与最近提出的设计相比，成本降低了67%。我们的模拟和优化框架可以加快经济上可行的可持续能源发电系统的开发步伐。
