Otimização multiobjetivo de uma usina termelétrica de ciclo combinado nas condições de projeto e fora de projeto

Abstract

There is a constant search for greater availability of energy resources since energy is a vital item for the development of a country. Projections for electricity demand until 2030 point to an increase, in which 20% of the total electricity generation will come from combined cycle power plants with natural gas, considered one of the most developed technologies for electricity production. Given the relevance of natural gas fuel and combined cycle power plants in the electrical and energy matrix, it is important to use available resources in the most efficient way. In this Thesis, three optimization techniques were analyzed (genetic algorithm, particle swarm and simulated annealing) applied to a combined cycle power plant in steady state, under design and off-design conditions, to perform a multi-objective optimization. The proposed method was previously applied in the CGAM cogeneration system for validation and later applied in the combined cycle power plant. Thermodynamic, traditional and advanced exergetic, and economic analyzes were employed. The GateCycleTM software together with the CycleLink tool were used to simulate the energy system model in design and in the partial loads of 90%, 80%, 70%, 60%, 50% and 40%. The minimized objective functions were the electricity cost and the total unavoidable exergy destruction rate. The decision variables were air compressor pressure ratio, isentropic air compressor efficiency, isentropic gas turbine efficiency, exhaust gas temperature at the inlet of the gas turbine, mass flow of fuel to the duct burner, isentropic efficiency of the steam turbine and isentropic pump efficiency. The modeFRONTIERTM software was used to apply the three optimization techniques in order to evaluate the objective functions. The two energy systems were optimized with each of the optimization techniques in all analyzed conditions. There was no unanimous technique in performance under all conditions. In the design condition, the combined cycle power plant presented an electricity cost 11.11% lower and an inevitable destroyed exergy 23.48% lower, when applying the particle swarm technique in the search for the best values of the decision variables, being the algorithm of better performance in this specific condition. In all other conditions an optimal solution was also obtained.