(628b) Incorporation of Market Signals for the Optimal Design and Operation of a Flexible Post-Combustion Capture System | AIChE

(628b) Incorporation of Market Signals for the Optimal Design and Operation of a Flexible Post-Combustion Capture System


Tumbalam Gooty, R. - Presenter, Purdue University
Ghouse, J., McMaster University
Le, Q. M., West Virginia University
Thitakamol, B., Svante Inc.
Rezaei, S., University of Alberta
Obiang, D., Los Angeles Department of Water and Power
Gupta, R., RTI International
Zhou, S. J., Susteon Inc.
Bhattacharyya, D., West Virginia University
Miller, D., National Energy Technology Laboratory
Fossil fuel-based power generators will be required to be more responsive to grid conditions with increasing penetration of variable renewable energy (VRE) generators. Post-combustion carbon capture (PCC) technologies with fast response will need to be deployed to mitigate the carbon emissions from such fossil-based generators. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use the Los Angeles Department of Water and Power’s (LADWP) NGCC unit as a representative of existing NGCC units, and the Svante rapid-temperature swing adsorption (TSA) system for PCC unit. Because of its ability to startup/shutdown and ramp-up/ramp-down in a short time, the chosen capture technology is very attractive for load-following operations. For a given market signal, we formulate a multi-period optimization problem, under a price-taker assumption, to simultaneously optimize the design of the capture system and the operation of the entire plant. Rigorous models for the LADWP NGCC configuration, Svante’s TSA system, and the CO2 compression train are implemented in Aspen Plus Dynamics, Aspen HYSYS, and Aspen Custom Modeler, respectively, and validated with operating data. However, for computational tractability of this large-scale optimization problem, we use surrogate/reduced-order models in the NPV optimization problem. The surrogate model for the NGCC plant is constructed by linearization under nominal conditions, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using the simulation data obtained from the rigorous models. The multiperiod optimization problem is formulated as a mixed integer bilinear programming problem (MIBLP) using IDAES [1] and solved to global optimality using Gurobi 9.5. The solution of the MIBLP yields the optimal size of the capture system and the optimal operating schedule maximizing the net present value. Using this MIBLP formulation, we investigate the profitability of retrofitting an existing NGCC unit located in five different regions, viz. CAISO, ERCOT, MISO-W, NYISO, and PJM-W, under multiple carbon tax scenarios. The results show that the optimal decision strongly depends on the region and on the carbon tax, thereby demonstrating the importance of the inclusion of market signals in the design process. Finally, we determine CAPEX and OPEX reduction targets which would make the inclusion of the capture system more profitable in most of the scenarios. Although we focus on the TSA-based capture system, the optimization framework developed in this work can be used to evaluate any carbon capture technology by suitably modifying the representative models for the capture system.


This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.


  1. Lee, A., Ghouse, J.H., Eslick, J.C., Laird, C.D., Siirola, J.D., Zamarripa, M.A., Gunter, D., Shinn, J.H., Dowling, A.W., Bhattacharyya, D., Biegler, L.T., Burgard, A.T., and Miller D. 2021. The IDAES process modeling framework and model library—Flexibility for process simulation and optimization. Journal of Advanced Manufacturing and Processing, 3(3), p.e10095.