The reliability of clean renewable energy hinges on robust energy systems, with storage serving a critical function. This paper investigates the influence of various storage types and configurations on thermal performance, with a focus on optimal sizing for economic and environmental cost reduction. To achieve this objective, we simulate a solar cooling facility with varied configurations of hot/cold storage installations. This study employs an ANN methodology with a multi-layer perceptron approach to forecast unit performance for each configuration based on data generated during the simulation process. In the pursuit of the most efficient and high-performance network, a comprehensive investigation is conducted on the number of neurons, activation functions, and training algorithms. Subsequently, the optimization process, conducted through a genetic algorithm, determines the Pareto fronts representing the best solution sets. The comparison shows that a system design with double hot and cold storage tanks shows superior techno-economic-environmental performance. Among possible optimum solution sets, a point with this specification is selected; flow rate ratio, minimum flow ratio, cooling capacity ratio, cold storage ratio, and hot storage ratio of 1.2, 0.4, 0.91, 3.4, and 3.8, respectively. This configuration anticipates a levelized cost of cooling at 341 USD/MWhr, representing a 13 % reduction compared to the benchmark.

中文翻译：

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通过储能策略优化供应和生产管理：使用人工神经网络的太阳能冷生产方法


清洁可再生能源的可靠性取决于强大的能源系统，而储能发挥着关键功能。本文研究了各种存储类型和配置对热性能的影响，重点是最佳尺寸以降低经济和环境成本。为了实现这一目标，我们模拟了具有不同热/冷存储装置配置的太阳能冷却设施。本研究采用 ANN 方法和多层感知器方法，根据仿真过程中生成的数据预测每种配置的单元性能。为了追求最高效和高性能的网络，对神经元的数量、激活函数和训练算法进行了全面的调查。随后，通过遗传算法执行的优化过程确定代表最佳解集的 Pareto 前沿。比较表明，具有双热储水箱和储水箱的系统设计显示出卓越的技术经济环境性能。在可能的最优解集中，选择一个具有此规格的点;流速比、最小流速比、制冷量比、冷库比和热库比分别为 1.2、0.4、0.91、3.4 和 3.8。此配置预计平准化冷却成本为 341 美元/MWhr，与基准相比降低了 13%。