Potential analysis of a system hybridizing dye-sensitized solar cell with thermally regenerative electrochemical devices

To realize broad residual-heat utilization of energy, a novel solar-driven system is proposed by hybridizing a dye-sensitized solar cell (DSSC) with a thermally regenerative electrochemical cycle (TREC) and a thermally regenerative electrochemical refrigerator (TRER). Systems for power and cooling cogeneration consisting of DSSC and two-stage thermally regenerative electrochemical devices are rarely explored. Part of heat dissipated by DSSC is absorbed by TREC to generate power, which drives TRER for refrigeration. Mathematical formulas about the power output and efficiency of the subsystems and coupled system are deduced considering thermo-dynamic and electrochemical irreversible losses. Moreover, numerical calculations indicate that the maximum power output density (MPOD) and maximum energy efficiency (MEE) of the coupled system are 95.15W m(-2) and 24.13%, respectively, which are promoted by 10.68% and 8.89% compared to a single DSSC. Extensive parametric studies are performed for grasping the impacts of several key parameters on system performance, thereby providing data support for system optimization. There are optimal values for performance indicators when TiO2 film thickness is located between 1.0 x 10(-2)mm and 4.0 x 10(-2)mm, and the performance improve-ment is extremely inconspicuous after the photoelectron absorption coefficient reaches 5.0 x 10(5)m(-1). The conclusions show that this established model is suitable, and it opens up another avenue to design such a functional system.

Automated summary

A novel solar-driven system is proposed to realize broad residual-heat utilization by hybridizing a dye-sensitized solar cell (DSSC), a thermally regenerative electrochemical cycle (TREC), and a thermally regenerative electrochemical refrigerator (TRER). The system achieves power and cooling cogeneration and is shown to have improved power output and efficiency compared to a single DSSC. Extensive parametric studies are performed to understand the impacts of key parameters on system performance, providing data support for system optimization.