利用流化床反应器与生物质发电厂蒸汽朗肯循环相结合的热化学储能系统的能效和技术经济分析

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2023-10-20 DOI:10.1021/acsengineeringau.3c00029
Takahito Yasui, Masahiro Aoki, Takayuki Uchino and Chihiro Fushimi*, 
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引用次数: 0

摘要

流化床反应器(FBR)中使用 Ca(OH)2/CaO 的热化学储热系统与蒸汽朗肯循环(SRC)生物质发电厂相结合,是卡诺电池系统之一,有望高度灵活地提供可再生能源电力。本研究利用所提出的非稳态运行下的流化床模型,通过改变流化床配置和发电能力来评估能源效率和成本。此外,还比较了 SRC 和有机郎肯循环(ORC)的性能,并考虑了通过节省生物质来降低燃料成本的问题。当充电电费为 0.100 美元/千瓦时和 0 美元/千瓦时时,基本情况(6.25 兆瓦时,床体积 = 100 立方米,床高/直径比 = 4,FBR 入口气体速度 = 0.087 米/秒)下 SRC 的平准化储能成本(LCOS)分别为 0.804 美元/千瓦时和 0.197 美元/千瓦时。充电电费对 LCOS 有主要影响。储能效率和往返效率分别为 58.2% 和 13.7%(不含生物质能),净发电量为 1247.3 兆瓦时/年。流化床体积、床高/直径比和 SRC 的发电量对能源效率和 LCOS 的影响较小。但是,FBR 中的气体速度对净发电量和 LCOS 有很大影响。在发电量为 3 MWe、充电电费为 0 美元/千瓦时的情况下,LCOS 分别为 0.204 美元/千瓦时(SRC)和 0.520 美元/千瓦时(ORC),表明 SRC 在 3 MWe 级电厂中具有成本优势。这是因为 SRC 的发电效率(24.3%)高于 ORC 的发电效率(11.4%),可利用储存的热量产生更多电力。节约生物质对 LCOS 的影响为 0.026-0.053 美元/千瓦时(SRC)和 0.096 美元/千瓦时(ORC)。提高发电效率和/或有效利用涡轮机排出的热量对于提高能源效率和降低 LCOS 非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Energy Efficiency and Techno-Economic Analysis of a Thermochemical Energy Storage System by Using a Fluidized Bed Reactor Integrated with a Steam Rankine Cycle of a Biomass Power Plant

A thermochemical heat storage system using Ca(OH)2/CaO in a fluidized bed reactor (FBR) is integrated with a biomass power plant of a steam Rankine cycle (SRC) as one of the Carnot battery systems that are expected to provide renewable electricity highly flexibly. This study utilizes the proposed fluidized bed model under the nonsteady state operation to evaluate the energy efficiency and cost by varying the fluidized bed configuration and the power generation capacities. In addition, the performances of the SRC and those of the organic Rankine cycle (ORC) were compared, and the fuel cost reduction by the biomass savings was considered. The levelized cost of storage (LCOS) of the SRC in the base case (6.25 MWe, bed volume = 100 m3, bed height/diameter ratio = 4, FBR inlet gas velocity = 0.087 m/s) was 0.804 and 0.197 USD/kWhe when the charging electricity cost was 0.100 and 0 USD/kWhe, respectively. The charging electricity cost has a dominant effect on the LCOS. The stored energy efficiency and the round-trip efficiency were 58.2 and 13.7% (without biomass saving), respectively, and the net power generation was 1247.3 MWhe/year. The effect of fluidized bed volume, bed height/diameter ratio, and power generation capacity of the SRC has a slight influence on the energy efficiency and LCOS. However, the gas velocity in the FBR has a substantial influence on the net energy generation and LCOS. In the case that power generation capacity is 3 MWe and the charging electricity cost is 0 USD/kWhe, the LCOS is 0.204 USD/kWhe (SRC) and 0.520 USD/kWhe (ORC), respectively, indicating that SRC has a cost advantage for a 3 MWe-class power plant. This is because SRC has higher power generation efficiencies (24.3%) than that of the ORC (11.4%), generating more electricity from the stored heat. The effect of biomass saving on LCOS was 0.026–0.053 USD/kWhe (SRC) and 0.096 USD/kWhe (ORC). Increase of power generation efficiency and/or effective utilization of exhaust heat from the turbine is important to increase energy efficiency and decrease LCOS.

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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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