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Assesses efficiency of modeling heat management in parabolic solar dish–Stirling technologies, suggesting improved performance evaluation.
Parabolic solar dish–Stirling (PSDS) technologies are among the most efficient solar-to-electric conversion options, but their system-level modeling remains challenging because optical losses, receiver heat losses, package leakage, and Stirling engine non-idealities are strongly coupled under variable operating conditions. This study develops a modular, energy-consistent system-level framework that couples dish receiver optics and thermal behavior, hot-end package losses, and a non-ideal Stirling engine under variable charge (Qu-mode) control. The key novelty is a receiver engine heat-matching formulation in which receiver temperature, useful heat, working gas charge/mean pressure, and engine output emerge from a closed energy balance rather than from prescribed hot-side temperature, fixed heat input, or prescribed mean pressure. The framework was benchmarked in stages against the Mendoza receiver formulation, GPU-3/LeRC Stirling engine data, and EuroDish dispatch-level measurements. At the integrated EuroDish level, it reproduced heat input, cooler rejection, and net electrical output with mean absolute percentage errors of 2.90%, 4.07%, and 4.28%, respectively, while preserving explicit traceability of optical, receiver, package, engine, generator, and parasitic losses. A receiver formulation comparison showed that the final receiver treatment reduced the cooler rejection MAPE from 8.11% to 4.07% relative to the Mendoza-type receiver swap baseline. A limited-input transferability study for representative pressure-controlled dish–Stirling platforms retained peak power and efficiency within a ±10% envelope for the quantitatively assessed cases. Parametric studies further showed a broad engine speed optimum, a heat exchanger sizing trade-off governed by conductance and pumping/friction losses, stronger sensitivity to ambient temperature than wind over the tested EuroDish range, and cooling boundary effects that redirect fixed thermal input from electricity to rejected heat. The resulting framework provides a compact predictive basis for loss diagnosis, design studies, and control-oriented evaluation of PSDS units.
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Moshe et al. (2026) studied this question.
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