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Temporal multiscalar decision support framework for flexible operation of carbon capture plants targeting low-carbon management of power plant emissions

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  • Abdul Manaf, Norhuda
  • Qadir, Abdul
  • Abbas, Ali

Abstract

A real-time control-optimization framework previously developed across multiple time scales is used for the analysis of power plant net operating revenue when retrofitted with a carbon capture plant. This framework which features high sample frequency commensurate with electricity dispatch and control instrumentation levels is proposed as a decision support tool for flexible operation of the carbon capture plant while considering electricity and carbon price dynamics. This paper presents the results of implementing this framework for operational flexibility of solvent-based post-combustion CO2 capture (PCC) process in response to power plant dynamic loads (load following and unit turndown). An integrated plant (power plant with PCC) subject to forecast 2020 electricity and carbon prices is shown to generate yearly net operating revenue of approximately 12% of the gross revenue. While, the same integrated plant generates net operating revenue loss of roughly 13% under 2011 electricity and carbon prices. These results underpin the strategy that employs the proposed optimization-based control framework for flexible operation of a PCC plant in the year 2020, because such framework captures financial benefits hidden in the dynamics of electricity load, electricity and carbon price trends, and does so at high temporal resolution.

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  • Abdul Manaf, Norhuda & Qadir, Abdul & Abbas, Ali, 2016. "Temporal multiscalar decision support framework for flexible operation of carbon capture plants targeting low-carbon management of power plant emissions," Applied Energy, Elsevier, vol. 169(C), pages 912-926.
  • Handle: RePEc:eee:appene:v:169:y:2016:i:c:p:912-926
    DOI: 10.1016/j.apenergy.2016.02.052
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    7. Kang, Charles A. & Brandt, Adam R. & Durlofsky, Louis J. & Jayaweera, Indira, 2016. "Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique," Applied Energy, Elsevier, vol. 179(C), pages 1209-1219.
    8. Hanak, Dawid P. & Kolios, Athanasios J. & Manovic, Vasilije, 2016. "Comparison of probabilistic performance of calcium looping and chemical solvent scrubbing retrofits for CO2 capture from coal-fired power plant," Applied Energy, Elsevier, vol. 172(C), pages 323-336.
    9. Li, Xue & Zhang, Rufeng & Bai, Linquan & Li, Guoqing & Jiang, Tao & Chen, Houhe, 2018. "Stochastic low-carbon scheduling with carbon capture power plants and coupon-based demand response," Applied Energy, Elsevier, vol. 210(C), pages 1219-1228.
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