Exergo-economic optimization framework for biogas fuelled gas turbine at design point

Abstract

Energy system optimization is the first step to address global warming, even for renewable sources like biogas. Optimization is necessary for efficient yet economic resource utilization, which has been a wide study area. However, no comprehensive general framework is proposed for optimization, mainly resolving the optimal point selection issue. This study aimed to provide a framework for exerting economic optimization of biogas fed systems and applying it to specific gas turbines. The proposed model in this research includes all steps from problem setup to final optimal point selection. A genetic algorithm was applied to obtain the Pareto front, and objective functions were evaluated by thermodynamic modeling of the system. A set of dimensionless parameters were introduced that smoothly defined the correlation between all design variables (decision variables) and optimal objectives (total cost and exergy efficiency). Then correlations between design parameters and optimal design variables were evaluated using meta functions of fourth-order. In this study, the design variables were compressor pressure ratio, gas turbine and compressor isentropic efficiencies, turbine inlet temperature, and preheater outlet temperature. Design parameters were cost of fuel, net power, and fuel methane content. To achieve a general optimal solution, a fuel costing approach based on the fuel exergy was proposed. The new costing approach allows disintegration and elimination of the fuel processing while accounting for the effect of the processing on the cost of fuel which allowed a general solution for the optimal gas turbine. A design problem was solved using the developed framework. Results of the design problem showed that the minimum cost ratio (cr) of 3.0 with minimum specific emission of 0.4962 kg/kWh. If cr increases to 3.5, the minimum specific emission will reduce to 0.4534 kg/kWh. The results demonstrate that the proposed framework is able to provide an optimal solution for a variety of CO2 emission levels, cost, and financing considerations where this optimization was not possible to determine by the previous approach.