Thermoeconomic analysis and optimization of a hybrid solar-thermal power plant using a genetic algorithm

The hybridization of solar-thermal and combustion technologies for power generation is an emerging concept that brings the possibility to mitigate carbon dioxide emissions while maintaining a firm supply. This work presents a thermoeconomic model for a distributed-scale (<100 kWe) hybrid solar-thermal power plant, developed to study its performance under different operating conditions in a region with a yearly direct normal irradiance of around 1600 kWh/m(2) yr. The proposed system consists of a combined Rankine-Brayton cycle with a solar receiver and natural gas combustor working in series as heat sources to the topping cycle. A genetic algorithm was employed to perform a multi-objective optimization of such system, and the result was a set of Pareto-optimal designs, which were compared to a pre-defined reference design. Resulting optimized solutions yield levelized electricity costs as low as 0.179 USD/kWh, as opposed to the 0.237 USD/kWh associated with the reference design. Average 1st and 2nd law efficiencies of up to 27.97 % and 33.53 % were achieved, respectively, which represent increases of up to 7.71 % and 7.31 %. Finally, average solar shares of up to 65 % are possible for optimized designs versus the 58.4 % yielded by the base design.