Thermodynamic analysis of a novel SOFC-based CCHP system integrated supercritical carbon dioxide and organic Rankine cycles for waste heat cascade utilization

To address the challenges of inefficient flue gas waste heat utilization and inflexible seasonal energy adjustments in conventional combined cooling, heating, and power systems, a novel solid oxide fuel cell-based polygeneration system is proposed by implementing a deep cascade of waste heat utilization. High-temperature flue gas from the solid oxide fuel cell-gas turbine unit drives a supercritical CO2 cycle to maximize energy extraction, while a thermoelectric generator replaces the traditional condenser to harvest low-grade waste heat energy. Middle-temperature waste heat is then split between an organic Rankine cycle and an absorption chiller/heat pump arranged in parallel, enabling seamless transitions among summer cooling, transitional hybrid, and winter heating modes to meet seasonal load variations. Finally, the low-temperature waste heat is utilized for domestic hot water production. Additionally, three operation schemes are designed to adaptively meet seasonal demands and optimize energy output year-round. Energy efficiencies of 80.74 %, 78.18 %, and 87.70 % and exergy efficiencies of 59.94 %, 60.58 %, and 60.87 % are achieved in summer, transition, and winter modes, respectively. The supercritical CO2 cycle and solid oxide fuel cell reach efficiencies of 28.58 % and 46.88 %, with the fuel cell contributing 24.59 % of total exergy losses. Parametric studies show that increasing solid oxide fuel cell pressure, temperature, and fuel utilization improves output, while a higher steam-to-carbon ratio reduces performance. The proposed system's cascaded waste heat recovery and parallel subsystem coordination significantly enhance flexibility and efficiency over traditional combined cooling, heating, and power systems. Future work will explore comprehensive techno-economic and environmental evaluation, renewable energy integration, and artificial intelligence-driven operational optimization to support real-world deployment and scalability.