Novel design optimization of concentrated solar power plant with S-CO2 Brayton cycle based on annual off-design performance

This study proposes a novel design optimization method for the solar power tower (SPT) system with a supercritical CO2 (S-CO2) Brayton cycle. The design conditions are determined based on annual off-design performance. The fluctuating solar irradiation and ambient temperature in different locations, and various power demand scenarios, including stable, light industry, and hybrid PV + CSP system types, are considered. The parameters of solar multiple (SM), thermal energy storage (TES) capacity, and designed compressor inlet temperature are simultaneously optimized by the multi-objective optimization algorithm. Results show that compared with the conventional design method, the novel design method can improve the maximum load cover factor by 6.38% under stable power demand and reduce the levelized cost of energy by 5.62% with a load cover factor of 0.9 under light industry power demand in Daggett. The optimal design conditions and actual operating performance are significantly affected by the weather conditions, power demand scenarios, and optimization objectives. For most cases to increase load cover factor in the most economical way, increasing SM and TES capacity coordinately is preferred to be implemented first, followed by increasing designed compressor inlet temperature. Whereas, for cases with a high frequency of simultaneous high ambient temperature and high power demand, increasing designed compressor inlet temperature will be advanced to the same priority as increasing SM and TES capacity.

Automated summary

This study introduces a novel design optimization method for solar power tower systems with a supercritical CO2 Brayton cycle, considering fluctuating solar irradiation and ambient temperature in different locations and various power demand scenarios. Through multi-objective optimization algorithm, parameters such as solar multiple, thermal energy storage capacity, and compressor inlet temperature are optimized simultaneously, resulting in improved load cover factor and reduced levelized cost of energy. The optimal design conditions are significantly influenced by weather conditions, power demand scenarios, and optimization objectives.