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Stochastic MILP model for optimal timing of investments in CO2 capture technologies under uncertainty in prices

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  • Cristóbal, Jorge
  • Guillén-Gosálbez, Gonzalo
  • Kraslawski, Andrzej
  • Irabien, Angel

Abstract

Reduction in greenhouse gas emissions of existing coal-fired power plants is a necessary action to attain the global reductions committed in the Kyoto Protocol. In the framework of a cap and trade system, we propose a two-stage stochastic mixed-integer linear programming (MILP) approach for the optimal investment timing and operation of a CO2 capture system under uncertainty in the CO2 allowance price. In the MILP, uncertainties are modeled via scenarios that are generated from a set of probability functions obtained using the Geometric Brownian Motion (GBM) approach in conjunction with Monte Carlo sampling. The model takes into account two economic objectives: the expected net profit and the financial risk. We demonstrate the capabilities of the tool presented through a case study based on a coal fired power plant. Our MILP approach can be applied to a wide range of processes and industries that deal with carbon sequestration issues.

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  • Cristóbal, Jorge & Guillén-Gosálbez, Gonzalo & Kraslawski, Andrzej & Irabien, Angel, 2013. "Stochastic MILP model for optimal timing of investments in CO2 capture technologies under uncertainty in prices," Energy, Elsevier, vol. 54(C), pages 343-351.
    • Handle: RePEc:eee:energy:v:54:y:2013:i:c:p:343-351
    DOI: 10.1016/j.energy.2013.01.068
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    4. Fitiwi, Desta Z. & de Cuadra, F. & Olmos, L. & Rivier, M., 2015. "A new approach of clustering operational states for power network expansion planning problems dealing with RES (renewable energy source) generation operational variability and uncertainty," Energy, Elsevier, vol. 90(P2), pages 1360-1376.
    5. Romano, Teresa & Fumagalli, Elena, 2018. "Greening the power generation sector: Understanding the role of uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 272-286.
    6. Amigo, Pía & Cea-Echenique, Sebastián & Feijoo, Felipe, 2021. "A two stage cap-and-trade model with allowance re-trading and capacity investment: The case of the Chilean NDC targets," Energy, Elsevier, vol. 224(C).
    7. Tan, Siah Hong & Barton, Paul I., 2016. "Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part II: Dealing with uncertainty," Energy, Elsevier, vol. 96(C), pages 461-467.
    8. Fan, Jing-Li & Xu, Mao & Yang, Lin & Zhang, Xian & Li, Fengyu, 2019. "How can carbon capture utilization and storage be incentivized in China? A perspective based on the 45Q tax credit provisions," Energy Policy, Elsevier, vol. 132(C), pages 1229-1240.
    9. Hür Bütün & Ivan Kantor & François Maréchal, 2019. "An Optimisation Approach for Long-Term Industrial Investment Planning," Energies, MDPI, vol. 12(21), pages 1-33, October.
    10. Zhou, Wenji & Zhu, Bing & Chen, Dingjiang & Zhao, Fangxian & Fei, Weiyang, 2014. "How policy choice affects investment in low-carbon technology: The case of CO2 capture in indirect coal liquefaction in China," Energy, Elsevier, vol. 73(C), pages 670-679.
    11. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    12. Tan, Siah Hong & Barton, Paul I., 2015. "Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part I: Bakken shale play case study," Energy, Elsevier, vol. 93(P2), pages 1581-1594.

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