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Applying real options with reinforcement learning to assess commercial CCU deployment

Authors :
Jeehwan S. Lee
Woopill Chun
Kosan Roh
Seongmin Heo
Jay H. Lee
Source :
Journal of CO2 Utilization, Vol 77, Iss , Pp 102613- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Carbon capture and utilization (CCU), which emerged as a means to reduce anthropogenic carbon emissions, has been highlighted to close the carbon cycle and combat climate change. CCU involves utilizing or converting captured CO2 to create value-added products that can replace or supplement fossil fuel-derived products. In order to meet climate goals, commercial-scale CCU facilities need to be built and their capacities increased, but barriers to large-scale CCU deployment still exist, primarily caused by large uncertainties in product/energy prices, markets, technologies, and policies. Conventional techno-economic analysis (TEA) methods cannot appropriately assess viability of CCU deployment projects which include dynamic uncertainties and deployment strategies. More flexible methods that can account for both time-varying uncertainties and dynamic capacity building are needed. We propose a systematic framework for the evaluation of commercial CCU deployment using the theory of real options and reinforcement learning (RL). RL is needed as considering options of adding capacities or delaying/abandoning the project at multiple time points under dynamic uncertainties lead to a large-scale stochastic optimal control problem which cannot be solved using conventional optimization methods like stochastic programming. The framework consists of three steps: surrogate modeling, uncertainty modeling, and assessment modeling. The framework is dependent on the type of CCU technology, uncertainties, real options and RL algorithm. We demonstrate the application of the framework through a case study of CO2 hydrogenation-to-methanol process in Europe, which is a late-stage, i.e., high technology readiness level (TRL), technology.

Details

Language :
English
ISSN :
22129839
Volume :
77
Issue :
102613-
Database :
Directory of Open Access Journals
Journal :
Journal of CO2 Utilization
Publication Type :
Academic Journal
Accession number :
edsdoj.2a1120f778174ee7b9d54de9c947eec0
Document Type :
article
Full Text :
https://doi.org/10.1016/j.jcou.2023.102613