(249d) Net Present Value Optimization of an Integrated CO2 Capture Process Using Water Lean Solvents Under Dynamic Electricity Price for Multiple Regions

Global CO₂ emissions are increasing at an average rate of about 1.5% rate per year, with 6% increase solely in the year 2021 [1]. Fossil-fueled plants are one of the main contributors to the greenhouse gas emissions [2]. Therefore, various post-combustion capture technologies are being developed and tested for CO₂ capture from the flue gas from fossil-fueled plants. Typical aqueous amine-based capture technologies suffer from high parasitic losses mainly due to high energy requirement for water evaporation in the stripper reboiler. This study evaluates a highly efficient water-lean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (2-EEMPA), that exhibits low cost of capture [3]. For evaluating economic feasibility of EEMPA under variable locational marginal price (LMP) of electricity and varying CO₂ tax, we undertake net present value (NPV) optimization of a natural gas combined cycle (NGCC) plant retrofitted with the EEMPA-based capture process.

A first-principles dynamic model of the NGCC plant is developed. The plant has a 2-on-1 configuration with highly efficient H-class gas turbines [4,5]. For computational tractability of the NGCC plant for dynamic optimization underlying the NPV optimization problem, a reduced order model of the high-fidelity model of the NGCC plant is developed by using Hankel Singular Value decomposition. The first-principles model of the EEMPA-based capture process is computationally expensive and highly nonlinear. Therefore, a reduced order model of the capture process is developed by using ALAMO, a machine learning software [6]. In addition, a model of the CO₂ compression system with dehydration system is also integrated with the capture process.

NPV optimization is undertaken in Python-based PYOMO for 14 market regions that differ greatly in terms of LMP of electricity and carbon tax rates. Hourly data of LMP for a duration of one year is considered for NPV optimization. Decision variables include design variables such as the capture plant capacity, as well as hourly operating profile of the power plant, capture plant, and the compression unit. The underlying NPV optimization problem is a large-scale, highly nonlinear dynamic optimization problem. For computational tractability, several strategies are developed including model order reduction, surrogate model development, model reformulation, etc.

Three types of absorber configurations - conventional packed bed, rotating packed bed (RPB), and combination of RPB for absorber and direct contact cooler (DCC) are investigated for possible cost reduction opportunities. The optimization result shows that the PCC model can maintain 90% CO₂ capture with a positive NPV for 6 regions when using conventional packed bed. Sensitivity study with different absorber configurations indicates that the model is feasible for 9 regions out of 14 regional electricity markets with NPV values in the range of 33-540 $MM.

References:

1. IEA, 2020. Global CO₂ Emissions Rebounded to Their Highest Level in History in 2021. IEA. Press release. https://www.iea.org/news/global-co2-emissions-rebounded-to-their-highest-level-in-history-in-2021 (Accessed on March 30, 2024).
2. Blumberg, T.; Assar, M.; Morosuk, T.; Tsatsaronis, G. Comparative exergoeconomic evaluation of the latest generation of combined-cycle power plants. Energy Convers. Manage., 2017, 153, 616-626.
3. Jiang, Y.; Mathias, P.M.; Freeman, C.J.; Swisher, J.A.; Zheng, R.F.; Whyatt, G.A.; Heldebrant, D.J. Techno-economic comparison of various process configurations for post-combustion carbon capture using a single-component water-lean solvent. J. Greenhouse Gas Control, 2021, 106, 103279.
4. Moon, S.W.; Kwon, H.M.; Kim, T.S.; Kang, D.W. A novel coolant cooling method for enhancing the performance of the gas turbine combined cycle. Energy, 2018, 160, 625-634.
5. Choi, B.S.; Kim, M.J.; Ahn, J.H.; Kim, T.S. Influence of a recuperator on the performance of the semi-closed oxy-fuel combustion combined cycle. Therm. Eng., 2017, 124, 1301-1311.
6. Cozad, A.; Sahinidis, N.V.; Miller, D.C. Learning surrogate models for simulation-based optimization. AIChE Journal, 2014, 60(6), 2211-2227.