PNNL

Abstract

Global CO2 emissions are increasing at about 1.5% rate per year. Fossil fuel-based plants are one of the main contributors to this rise. In the power generation industry, fossil fuel plants are dominant, and many plants are under development. In this study, a natural gas combined cycle (NGCC) power plant with post-combustion capture using a leading water-lean solvent is considered. For optimal design and operating schedule, large-scale dynamic optimization is undertaken for net present value (NPV) optimization. The first principles dynamic model of NGCC is developed including a model of the highly efficient H-class gas turbines. For computational tractability of the dynamic optimization problem, a reduced order model is developed by using the Hankel Singular Value (HSV) decomposition. A water-lean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (2-EEMPA), is used for carbon capture. A model of the capture system is developed in Aspen Plus, which is used to develop a reduced order model by using ALAMO, a machine learning software. In addition, a reduced model of the CO2 compression system with the dehydration unit is also considered. The integrated systems is used for NPV optimization by using the Python-based PYOMO platform. The PCC process is analyzed for three configurations-conventional packed bed, rotating packed bed (RPB), and combination of RPB and direct contact cooler (DCC). The NPV optimization is performed for 14 regional markets by considering year-long clustered and continuous locational marginal price (LMP) data with 1 hr interval. Optimization results show that the PCC can achieve 90% CO2 capture with a positive NPV for 6 regions. Sensitivity studies conducted by using the PCC configurations indicate that the process is economically feasible for 9 regions out of 14 regional electricity markets with NPV values in the range of 33- 540 $MM.