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用于枕板式潜热储能优化设计与热经济性分析的创新方法:以酿酒行业余热回收为例
Innovative methodology for optimized design and thermo-economic analysis of pillow-plate latent heat thermal energy storage: a case study on heat recovery in the brewing industry
语言:
中文摘要
摘要 本文研究了一类基于相变材料(PCM)与枕板式换热器技术相结合的新型枕板式潜热储能系统(PP-LHTES)。尽管近期研究表明PP-LHTES系统在热性能和经济性方面具有潜力,但相关研究仍较为有限。特别是目前缺乏支持决策制定的全面热经济性分析,尤其是在设计阶段。为弥补这一空白,本文通过分析其设计空间,系统地探讨了PP-LHTES系统的热流体性能与经济性能,并开发出一种针对此类装置的优化设计创新流程。所提出的 methodology 包含两个模型:一个称为设计模型的一维解析离散稳态模型,以及一个名为动态模型的一维解析离散动态模型。前者用于确定设计参数和成本,后者则用于在动态工况下验证所设计系统的性能。这两个模型均通过文献中相关的实验研究进行了验证,结果显示其具有良好的准确性,设计模型的误差约为10%,动态模型的误差约为2%。本文共评估了27种配置方案,适用于潜在的工业应用,考虑的能量储存容量范围为5至25 MWh,传热速率范围为1至5 MW。代表性案例研究与运行图谱揭示了入口与出口温度以及相变材料特性对系统性能的影响。储能材料的层厚和通道长度取决于放电时间,而在固定传热速率下通道数量保持不变。然而,换热面积则随能量储存容量和传热速率的变化而变化。此外,本文还系统地分析了单位能量容量成本($/kWh)和单位功率容量成本($/kW)的成本图谱,突出了关键PP-LHTES设计参数与该技术整体成本竞争力之间的密切关系。设计用于较大温差(ΔT)的系统表现出更优越的热性能和经济性能,能够减少所需的换热面积,并降低能量与功率容量的成本。本文还基于文献中的参考案例构建了一个示范性案例研究,以展示所提出方法的有效性。该案例详细说明了输入参数的收集过程,并演示了如何处理两个模型的输出结果以获得最终设计方案。最终结果表明,PP-LHTES是一种在中大型工业应用中具有前景且可行的解决方案,其能量容量成本介于每千瓦时30至90美元之间。
English Abstract
Abstract This paper investigates the novel class of pillow-plate latent heat thermal energy storage (PP-LHTES) systems based on the combined use of phase change materials (PCM) of pillow plate heat exchanger technology. Despite recent studies highlighting the promising thermal and economic performance of PP-LHTES systems, their investigation remains limited. In particular, there is a lack of comprehensive thermo-economic analyses to support informed decision-making, especially during the design phase. To address this gap, this paper systematically explores the thermo-fluid and economic performance of PP-LHTES systems by analyzing their design space. An innovative procedure for the optimal design of these devices was developed. The proposed methodology consists of two models: a 1D analytical discretized stationary model, called the design model, and a 1D analytical discretized dynamic model, named the dynamic model. The former is used to determine the design parameters and costs, while the latter is used to validate the designed system under dynamic conditions. The two models are validated against relevant experimental studies taken from the literature and show good performances with errors in the order of 10 % for the design model and 2 % for the dynamic model. A total of 27 configurations were evaluated for potential industrial applications, considering energy storage capacities between 5 and 25 MWh and heat transfer rates ranging from 1 to 5 MW. Representative case studies and operating maps highlight the effects of inlet and outlet temperatures and PCM properties on performance. The layer thickness of the storage material and the channel length depend on discharge time, while the channel count remains constant at a fixed heat transfer rate. The heat exchange area, however, varies with energy storage capacity and heat transfer rate. Additionally, cost maps are systematically examined in terms of energy capacity cost ($/kWh) and power capacity cost ($/kW), highlighting the critical relationship between key PP-LHTES design parameters and the overall cost-competitiveness of the technology. Systems designed for higher temperature differentials (ΔT) demonstrated superior thermal and economic performance, reducing the required heat exchange area and lowering both energy and power capacity costs. An exemplar case study is developed, starting from a reference case in the literature, to illustrate the effectiveness of the proposed methodology. This case study outlines the process of gathering input parameters and demonstrates how the outputs of the two models should be processed to achieve the final design. Ultimately, PP-LHTES emerges as a promising and viable solution for industrial applications at the medium and large scales, with energy capacity costs ranging from 30 to $90 per kWh.
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SunView 深度解读
该枕板式相变储热技术对阳光电源ST系列储能系统具有重要参考价值。研究提出的热经济优化设计方法可应用于工商业储能热管理系统优化,特别是5-25MWh大容量PowerTitan系统的温控设计。相变材料储热技术可与PCS功率器件散热结合,提升SiC/IGBT模块热管理效率。30-90美元/kWh的储能成本指标为综合能源解决方案提供经济性基准。建议将该热流体动态建模方法引入iSolarCloud平台,实现储能系统热管理的预测性维护和优化控制。