Techno-economic analysis of a solar thermophotovoltaic system for a residential building

被引:1
|
作者
Mosalpuri, Manish [1 ]
Toor, Fatima [2 ,3 ]
Mba-Wright, Mark [1 ,4 ]
机构
[1] Iowa State Univ, Dept Mech Engn, Ames, IA 50011 USA
[2] Univ Iowa, Coll Engn, Dept Elect & Comp Engn, Iowa City, IA USA
[3] Univ Iowa, Coll Liberal Arts & Sci, Dept Phys & Astron, Iowa City, IA USA
[4] Iowa State Univ, Bioecon Inst, Ames, IA 50011 USA
来源
JOURNAL OF PHOTONICS FOR ENERGY | 2024年 / 14卷 / 04期
关键词
thermophotovoltaics; decarbonization; techno-economic analysis; Monte Carlo uncertainty assessment; CLIMATE-CHANGE IMPACTS; STORAGE APPLICATIONS; HIGH-EFFICIENCY; ENERGY-STORAGE; UNCERTAINTY; FUTURE; OPTIMIZATION; FEASIBILITY; PERFORMANCE; SIMULATION;
D O I
10.1117/1.JPE.14.042404
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Thermophotovoltaics (TPV) is a technology that converts heat to electricity using a thermal emitter and a matched photovoltaic (PV) cell. TPV is becoming increasingly popular due to its advantages of silent power generation, higher power density (>2.5W/cm(2)), reduced cost, no moving parts (thus, low maintenance costs), reaching full power in less time as compared to turbines, operating at high temperatures, and suitability for long-duration energy storage applications. This study conducts a techno-economic analysis (TEA) of a solar energy conversion (using TPV) and storage system (using phase-change materials). We optimize the levelized cost of consumed energy (LCOE) and electricity (LCOEel) using the Nelder-Mead algorithm for four scenarios (as identified in the reference study). These scenarios differ in nominal-weighted average cost of capital (WACCnomWACCnom), fuel and electricity inflation rate, and capital cost factor (CAPEX) of high-temperature energy storage (HTES), power generation unit (PGU), and PV systems. We perform a sensitivity analysis that predicts a modest decrease in LCOE and LCOEel from the mean values of $0.038/kWh and $0.128/kWh, respectively. We perform a Monte Carlo uncertainty assessment and fit a probability distribution based on input variables' historical data from the literature. The fitted probability distribution for outputs (mean, the standard deviation in brackets) is LCOE ($/kWh)-general extreme value (0.035, 0.009), and LCOEel ($/kWh)-t (0.132, 0.016). The reduced mean values for the optimized system indicate a massive potential for TPV to be economically feasible; however, the LCOEel is higher than the current average electricity price of $0.124/kWh. The box plot shows that lifetime, PV CAPEX, inflation rate, natural gas price, and WACC(nom) significantly impact LCOE, and future research focused on them would lead to a better adoption of TPV technology.
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页数:23
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