Multi-objective thermoeconomic optimisation for combined-cycle power plant using particle swarm optimisation and compared with two approaches: an application

This paper shows a new possible way with particle swarm optimisation (PSO) to achieve an exergoeconomic optimisation of combined-cycle power plants. The optimisation has been done using a classic exergoeconomic and genetic algorithm, and the effects of using three methods are investigated and compared. The design data of an existing plant is used for the present analysis. Two different objective functions are proposed: one minimises the total cost of production per unit of output, and maximises the total exergetic efficiency. The analysis shows that the total cost of production per unit of output is 2%, 3% and 5% lower and exergy efficiency is 4%, 8% and 6% higher with respect to the base case for the classic, PSO and GA procedures, respectively. Finally, a sensitivity analysis to assess the effects of change in the decision variables of the plant on the objective functions performed, and the results are reported.