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Maximizing energy yield in organic Rankine cycles powered by variable renewable sources using Low-GWP zeotropic mixtures: A dynamic off-design optimization approach

Author

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  • Donate Sánchez, Félix
  • Mata Montes, Carmen
  • Barba Salvador, Javier
  • Calderón Herrera, David

Abstract

The effective integration of Organic Rankine Cycles (ORC) with variable renewable heat sources is hindered by the continuous off-design operation caused by the inherent intermittency of the resource. Conventional working fluids, designed for a fixed nominal point, suffer severe efficiency penalties when the heat source temperature deviates from design conditions. This study investigates the potential of low GWP zeotropic mixtures (HFO/HC blends) to act as a Thermal buffer and enhance the integral energy yield under transient thermal profiles. Unlike pure fluids, zeotropic mixtures offer a non-isothermal phase change (temperature glide) that can be dynamically matched to varying source temperatures. Using a quasi-stationary simulation model representing a daily fluctuation cycle (100–180 °C), the mixture composition was optimized to maximize total accumulated energy rather than instantaneous power. Results demonstrate that the optimized Mixture ID 1 configuration (88/12 wt% R1234yf/Isobutane blend) increases the total daily energy harvest by 88.54 % compared to the R245fa baseline. Importantly, this improvement does not arise from higher instantaneous efficiency, but from an extended operational window. While pure R245fa suffers prolonged technical shutdowns when the source temperature drops below 145 °C, the zeotropic mixture's Thermal buffer effect allows continuous power generation down to 100 °C, maximizing resource utilization throughout the entire 24-h cycle.

Suggested Citation

  • Donate Sánchez, Félix & Mata Montes, Carmen & Barba Salvador, Javier & Calderón Herrera, David, 2026. "Maximizing energy yield in organic Rankine cycles powered by variable renewable sources using Low-GWP zeotropic mixtures: A dynamic off-design optimization approach," Energy, Elsevier, vol. 360(C).
  • Handle: RePEc:eee:energy:v:360:y:2026:i:c:s0360544226018591
    DOI: 10.1016/j.energy.2026.141752
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