{"title":"通过储能策略优化供应和生产管理:利用人工神经网络的太阳能冷生产方法","authors":"","doi":"10.1016/j.psep.2024.09.039","DOIUrl":null,"url":null,"abstract":"<div><p>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. 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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. 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引用次数: 0
摘要
清洁可再生能源的可靠性取决于强大的能源系统,其中储能系统发挥着至关重要的作用。本文研究了各种储能类型和配置对热性能的影响,重点是如何优化尺寸以降低经济和环境成本。为实现这一目标,我们模拟了一个太阳能冷却设施,该设施采用了不同的冷/热存储装置配置。这项研究采用了多层感知器的 ANN 方法,根据模拟过程中生成的数据预测每种配置的设备性能。为了追求最高效和高性能的网络,对神经元数量、激活函数和训练算法进行了全面研究。随后,通过遗传算法进行优化,确定代表最佳解决方案集的帕累托前沿。比较结果表明,采用双冷热储罐的系统设计显示出卓越的技术经济环境性能。在可能的最佳方案集中,选择了一个具有以下规格的点:流量比、最小流量比、制冷量比、冷藏比和热藏比分别为 1.2、0.4、0.91、3.4 和 3.8。该配置预计的平准冷却成本为 341 美元/兆瓦时,与基准相比降低了 13%。
Optimizing supply and production management through energy storage strategies: A solar cold production approach using artificial neural networks
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.
期刊介绍:
The Process Safety and Environmental Protection (PSEP) journal is a leading international publication that focuses on the publication of high-quality, original research papers in the field of engineering, specifically those related to the safety of industrial processes and environmental protection. The journal encourages submissions that present new developments in safety and environmental aspects, particularly those that show how research findings can be applied in process engineering design and practice.
PSEP is particularly interested in research that brings fresh perspectives to established engineering principles, identifies unsolved problems, or suggests directions for future research. The journal also values contributions that push the boundaries of traditional engineering and welcomes multidisciplinary papers.
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