(226b) Optimal Design of Liquid Oxygen Storage System for Flexible Operation of a Direct-Fired Supercritical CO2 Power Cycle | AIChE

(226b) Optimal Design of Liquid Oxygen Storage System for Flexible Operation of a Direct-Fired Supercritical CO2 Power Cycle


Tumbalam Gooty, R. - Presenter, Purdue University
Soares Chinen, A., West Virginia University
Pidaparti, S., National Energy Technology Laboratory
Ghouse, J., McMaster University
Liese, E. A., National Energy Technology Laboratory
Low-carbon generators, such as fossil generators equipped with carbon capture systems, are needed to reduce the cost of decarbonization. Such generators need to be flexible to respond to grid conditions, especially in a grid containing a high percentage of variable renewable energy. The direct natural gas-fired supercritical CO2 (sCO2) power cycle is a promising technology for cost-effectiveness, highly efficient power generation with inherent carbon capture that captures more than 97% of the CO2. Nevertheless, slow dynamics and a long startup time associated with the operation of the air separation unit (ASU) required to produce oxygen needed for combustion make the overall operation less flexible. One proposed solution to improve operational flexibility is to include an on-site liquefaction and liquid-oxygen (LOx) storage system, which helps in shifting the load depending on the grid requirements. Therefore, in this work, the economic benefits of installing a LOx storage system are assessed along with whether the additional capital expenditure (CAPEX) needed for the liquefaction and storage system is justified. Three systems are considered: (1) an sCO2 power cycle without a LOx storage system to serve as a reference, (2) an sCO2 power cycle with a partial LOx withdrawal from the ASU, and (3) an sCO2 power cycle with a dedicated liquefaction unit and a LOx storage system. Following the workflow in [1], a multiperiod price-taker problem is formulated that determines the optimal design of the ASU and liquefaction and storage systems as well as the optimal hourly operating decisions, which maximize the net present value for a given electricity market. Detailed models with off-design performance relations for the subprocesses are developed in process simulators. Using the data obtained from detailed models, simple linear surrogate models are constructed and used in the multiperiod optimization problem for computational tractability. The resulting mixed-integer linear program is implemented in the IDAES integrated platform [2] and solved to global optimality using Gurobi. With this formulation, the overall economics of the system across multiple electricity markets is investigated for different carbon pricing scenarios. Finally, a sensitivity analysis over a few important parameters is performed (i.e., CAPEX), and target values, which make the overall economics favorable, are identified.


We gratefully acknowledge support from the U.S. DOE’s Office of Fossil Energy and Carbon Management through the Turbines Program. We thank Gurobi Optimization for providing a free research license.


This report was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, 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. R. Tumbalam Gooty, J. Ghouse, Q. Le, B. Thitakamol, S. Rezaei, D. Obiang, R. Gupta, S. J. Zhou, D. Bhattacharyya, D. Miller. 2023. Incorporation of Market Signals for the Optimal Design of Post Combustion Carbon Capture Systems. Applied Energy, 337, pp.120880.

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