Applying real options with reinforcement learning to assess commercial CCU deployment

被引:7
作者
Lee, Jeehwan S. [1 ]
Chun, Woopill [1 ]
Roh, Kosan [2 ]
Heo, Seongmin [1 ]
Lee, Jay H. [3 ]
机构
[1] Korea Adv Inst Sci & Technol KAIST, Dept Chem & Biomol Engn, 291 Daehak Ro, Daejeon 34141, South Korea
[2] Chungnam Natl Univ, Dept Chem Engn & Appl Chem, Daejeon 34141, South Korea
[3] Univ Southern Calif, Dept Chem Engn & Mat Sci, Los Angeles, CA 90089 USA
基金
新加坡国家研究基金会;
关键词
Carbon capture and utilization; Multi-period deployment; Real options theory; Reinforcement learning; Dynamic uncertainty; CARBON CAPTURE; FORMIC-ACID; ENVIRONMENTAL EVALUATION; CO2; INVESTMENT; METHANOL; STORAGE; POWER; COPOLYMERIZATION; HYDROGENATION;
D O I
10.1016/j.jcou.2023.102613
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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 al-gorithm. 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.
引用
收藏
页数:13
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