Simulation and Optimization of an Combined Cycle Power Plant Including CO2 Sequestration

This paper describes a methodology to optimize complex power plants characterized by a large number of technical, economic and environmental parameters. A combined power plant with a solvent chemical separation CO2 unit (MEA absorption) has been considered. It’s known that the CO2 treatment increases the electricity cost because of the increase of investment and operating costs for CO2 separation, transportation and storage. Therefore, the technical-economic model of the plant has been coupled with a multi-objective optimization tool based on genetic algorithms. Since the reduction of CO2 can be competitive with other goals like the reduction of the cost of energy and the increasing of efficiency, a Pareto dominance approach is proposed and Multi Criteria Decision Techniques are considered to help the user choosing the “best” compromise between cost of energy and emission of CO2. The results of the optimization showed that the electricity average cost can be significantly higher than in the case without CO2 treatment. However a set of plant configurations, having electricity average cost comparable to the same configurations without CO2 treatment, has been identified through application of the optimization method. The application of the method allowed a reduction of the level of CO2 emission in atmosphere of 44% respect to the baseline configuration without CO2 treatment with a small increase in the cost of energy. Note that, thanks to the multiobjective nature of the optimization it is possible to choose to further reduce emissions with a higher cost of energy or to obtain a lower cost of energy with a slightly higher level of CO2 emissions without repeating the optimization. The methodology could be a strategic support to investors in the power production and for energy policy decisions in order to reduction of carbon dioxide emissions.