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<p>To mitigate greenhouse gas (GHG) emissions, the importance of carbon capture and utilization (CCU) is continuously increasing. Among alternative CCU options, electrochemical CO<sub>2</sub> reduction (CO2RR) is considered promising, as it can benefit from renewable electricity, which results in much less GHG emissions during the operation. Although CO2RR remains at a low technology readiness level, its long-term deployment planning is worth an investigation, as the grid mix is evolving to include more renewable energy resources. Conventionally, net present value (NPV) is used to evaluate the economic viability of commercialization projects, where the future cash flows are discounted to determine the present values. However, it cannot respond to dynamic uncertainties such as government policy and energy prices, because it makes all the decisions at the current timestep. To deal with dynamic uncertainties, in this work, real options (such as expansion and delay) are adopted to formulate a multi-period deployment planning problem for CO2RR. To solve such a problem efficiently, a reinforcement learning-based algorithm is used. The results obtained by solving this problem are compared with those obtained on the basis of NPV to highlight the necessity of multi-period deployment planning. Also, several scenario studies are conducted to evaluate the impacts of different uncertainties on the deployment of CO2RR with carbon monoxide production as an illustrative example.</p>

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Multi-period Deployment of Electrochemical CO2-to-CO Reduction Technology Considering Time Varying Uncertainties

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • Woopill Chun,
  • Suhyeon Lee,
  • Kosan Roh,
  • Seongmin Heo

摘要

To mitigate greenhouse gas (GHG) emissions, the importance of carbon capture and utilization (CCU) is continuously increasing. Among alternative CCU options, electrochemical CO2 reduction (CO2RR) is considered promising, as it can benefit from renewable electricity, which results in much less GHG emissions during the operation. Although CO2RR remains at a low technology readiness level, its long-term deployment planning is worth an investigation, as the grid mix is evolving to include more renewable energy resources. Conventionally, net present value (NPV) is used to evaluate the economic viability of commercialization projects, where the future cash flows are discounted to determine the present values. However, it cannot respond to dynamic uncertainties such as government policy and energy prices, because it makes all the decisions at the current timestep. To deal with dynamic uncertainties, in this work, real options (such as expansion and delay) are adopted to formulate a multi-period deployment planning problem for CO2RR. To solve such a problem efficiently, a reinforcement learning-based algorithm is used. The results obtained by solving this problem are compared with those obtained on the basis of NPV to highlight the necessity of multi-period deployment planning. Also, several scenario studies are conducted to evaluate the impacts of different uncertainties on the deployment of CO2RR with carbon monoxide production as an illustrative example.