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A multi-period optimisation model for planning carbon sequestration retrofits in the electricity sector

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  • Lee, Jui-Yuan

Abstract

Carbon capture and storage (CCS) is a low-carbon technology aiming to prevent carbon dioxide (CO2) generated in large industrial facilities (e.g. power plants) from entering the atmosphere, thus mitigating human-caused climate change. CCS is deemed to be one of the most promising approaches to reduce industrial CO2 emissions on a global scale, in addition to energy efficiency enhancement and increased use of renewables. This paper presents a mathematical programming model for multi-period planning of power plant retrofits with carbon capture (CC) technologies. The model allows for energy penalties due to CC retrofits and the need for compensatory power generation, as well as variations in technological parameters (such as electricity costs) over time. Furthermore, the model is formulated as a mixed integer linear programme (MILP), for which global optimality is guaranteed if a solution exists. Two case studies on carbon-constrained energy sector planning are presented to illustrate the proposed approach. Further analysis is carried out to examine the effect of the cost limit on the total increase in power generation cost.

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  • Lee, Jui-Yuan, 2017. "A multi-period optimisation model for planning carbon sequestration retrofits in the electricity sector," Applied Energy, Elsevier, vol. 198(C), pages 12-20.
    • Handle: RePEc:eee:appene:v:198:y:2017:i:c:p:12-20
    DOI: 10.1016/j.apenergy.2017.04.032
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    Cited by:

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    2. Rego, Erik Eduardo & Costa, Oswaldo L.V. & Ribeiro, Celma de Oliveira & Lima Filho, Roberto Ivo da R. & Takada, Hellinton & Stern, Julio, 2020. "The trade-off between demand growth and renewables: A multiperiod electricity planning model under CO2 emission constraints," Energy, Elsevier, vol. 213(C).
    3. Tan, Qinliang & Han, Jian & Liu, Yuan, 2023. "Examining the synergistic diffusion process of carbon capture and renewable energy generation technologies under market environment: A multi-agent simulation analysis," Energy, Elsevier, vol. 282(C).
    4. Tamaki, Tetsuya & Nozawa, Wataru & Managi, Shunsuke, 2017. "Evaluation of the ocean ecosystem: climate change modelling with backstop technology," MPRA Paper 80549, University Library of Munich, Germany.
    5. Li, Wei & Lu, Can & Ding, Yi & Zhang, Yan-Wu, 2017. "The impacts of policy mix for resolving overcapacity in heavy chemical industry and operating national carbon emission trading market in China," Applied Energy, Elsevier, vol. 204(C), pages 509-524.
    6. Tamaki, Tetsuya & Nozawa, Wataru & Managi, Shunsuke, 2017. "Evaluation of the ocean ecosystem: Climate change modelling with backstop technologies," Applied Energy, Elsevier, vol. 205(C), pages 428-439.
    7. Guangxiao Hu & Xiaoming Ma & Junping Ji, 2017. "A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China," Sustainability, MDPI, vol. 9(11), pages 1-12, October.
    8. Xiaorong Sun & Xueping Pan & Chenhao Jin & Yihan Li & Qijie Xu & Danxu Zhang & Hongyang Li, 2022. "Life Cycle Assessment-Based Carbon Footprint Accounting Model and Analysis for Integrated Energy Stations in China," IJERPH, MDPI, vol. 19(24), pages 1-20, December.
    9. Jui-Yuan Lee & Han-Fu Lin, 2019. "Multi-Footprint Constrained Energy Sector Planning," Energies, MDPI, vol. 12(12), pages 1-18, June.

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