A Multi Objective Optimization of Co2-Mixture Based Power Cycles for Csp Applications

This study presents a multi-objective optimization framework for CO2-mixture based power cycles in Gen 2 CSP applications, targeting 550°C and 50°C maximum and minimum cycle temperatures. A unified computational approach integrates 14 dopants with 5 cycle configurations (Simple Recuperated, Precompression, Recompression, Partial Cooling, and Recompression with Intercooling) using multi-objective controlled elitist genetic algorithms to simultaneously optimize thermal efficiency and primary heat exchanger temperature difference as surrogates for solar field and thermal energy storage costs. The methodology enables seamless evaluation of both supercritical and transcritical operations through PT envelope integration, revealing that transcritical CO2-mixtures consistently outperform supercritical configurations for both pure CO2 and CO2-mixtures. Multi-criteria decision-making analysis identifies Recompression with Intercooling layout with 66% CO2-34% SO2 as the optimal solution, achieving 43% efficiency and 211°C temperature difference while maintaining power block costs below the critical 950 €/kWe threshold. Only 6 of the 14 investigated dopants (TiCl4, SiCl4, C2H3N, SO2, COS and H2S) achieve Pareto-optimal performance. The findings reveal that cycle complexity does not guarantee efficiency gains with CO2-mixtures, as simple layouts can outperform complex configurations as TiCl4 in simple cycles reaches 45% efficiency while complex layouts primarily benefit from temperature difference improvements up to 80°C. Additional primary heat exchanger sizing optimization further reduces power block costs to 766-778 $/kWe for recompression-type cycles, demonstrating the potential for cost-effective Gen 2 CSP power blocks.