Risk management optimization framework for the optimal deployment of carbon capture and storage system under uncertainty

Shuai Zhang, Yu Zhuang, Linlin Liu, Lei Zhang and Jian Du

Renewable and Sustainable Energy Reviews, 2019, vol. 113, issue C, -

Abstract: In order to meet the CO2 reduction target and realize cleaner production, large emission sources have two options: investing in carbon capture and storage (CCS) system and/or paying surcharge for carbon tax. CCS system requires industry to make huge investment in infrastructure and the cost of making improper decisions on CCS is substantial. Many factors can impact the successful deployment of commercial-scale CCS, including significant uncertainty regarding carbon policy, technology and engineering performance. In this study, a two-stage stochastic mixed-integer linear programming (MILP) model is formulated to explore the relationship between these two reduction options by minimizing expected total cost (ETC) for CO2 reduction, considering carbon tax uncertainty. Furthermore, to assess the risk imposed by the uncertainty, three financial risk metrics are introduced for risk management and each metric is formulated as secondary objective to ETC in a multi-objective optimization model with two objectives. The trade-offs between the economic and risk objectives are obtained via the ε-constraint method. At last, a case of power plants in Northeast China is studied. Results show that the risk-neutral solution may confront high risk or even be infeasible in some extreme scenarios, and the carbon tax price of no less than $ 50/t is recommended for policy-makers. In risk-management cases, to control the risk, variability index metric is reduced from $ 40 × 108 to $ 0, probability financial metric is reduced from 0.20 to 0.12 and downside risk metric is reduced from $ 27.09 × 108 to $ 13.38 × 108 at the expense of increasing ETC.

Keywords: Carbon capture and storage (CCS); Uncertainty; Mixed integer linear programming (MILP); Stochastic programming; Risk management (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.rser.2019.109280

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