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
Takahito Yasui, Masahiro Aoki, Takayuki Uchino and Chihiro Fushimi*,
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引用次数: 0
Abstract
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.
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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