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Development of a scalable infrastructure model for planning electricity generation and CO2 mitigation strategies under mandated reduction of GHG emission

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  • Han, Jee-Hoon
  • Lee, In-Beum

Abstract

In a power-generation system, power plants as major CO2 sources may be widely separated, so they must be connected into a comprehensive network to manage both electricity and CO2 simultaneously and efficiently. In this study, a scalable infrastructure model is developed for planning electricity generation and CO2 mitigation (EGCM) strategies under the mandated reduction of GHG emission. The EGCM infrastructure model is applied to case studies of Korean energy and CO2 scenarios in 2020; these cases consider combinations of prices of carbon credit and total electricity demand fulfilled by combustion power plants. The results highlight the importance of systematic planning for a scalable infrastructure by examining the sensitivity of the EGCM infrastructure. The results will be useful both to help decision makers establish a power-generation plan, and to identify appropriate strategies to respond to climate change.

Suggested Citation

  • Han, Jee-Hoon & Lee, In-Beum, 2011. "Development of a scalable infrastructure model for planning electricity generation and CO2 mitigation strategies under mandated reduction of GHG emission," Applied Energy, Elsevier, vol. 88(12), pages 5056-5068.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:12:p:5056-5068
    DOI: 10.1016/j.apenergy.2011.07.010
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Han, Jee-Hoon & Ahn, Yu-Chan & Lee, In-Beum, 2012. "A multi-objective optimization model for sustainable electricity generation and CO2 mitigation (EGCM) infrastructure design considering economic profit and financial risk," Applied Energy, Elsevier, vol. 95(C), pages 186-195.
    2. Chen, Shiyi & Xiang, Wenguo & Wang, Dong & Xue, Zhipeng, 2012. "Incorporating IGCC and CaO sorption-enhanced process for power generation with CO2 capture," Applied Energy, Elsevier, vol. 95(C), pages 285-294.
    3. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S. & Kopanos, Georgios M. & Pistikopoulos, Efstratios N. & Georgiadis, Michael C., 2014. "A spatial multi-period long-term energy planning model: A case study of the Greek power system," Applied Energy, Elsevier, vol. 115(C), pages 456-482.
    4. 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.
    5. Häntsch, Marius & Huchzermeier, Arnd, 2016. "Transparency of risk for global and complex network decisions in the automotive industry," International Journal of Production Economics, Elsevier, vol. 175(C), pages 81-95.
    6. Ahn, Yu-Chan & Lee, In-Beum & Lee, Kun-Hong & Han, Jee-Hoon, 2015. "Strategic planning design of microalgae biomass-to-biodiesel supply chain network: Multi-period deterministic model," Applied Energy, Elsevier, vol. 154(C), pages 528-542.
    7. Han, Jee-Hoon & Lee, In-Beum, 2014. "A systematic process integration framework for the optimal design and techno-economic performance analysis of energy supply and CO2 mitigation strategies," Applied Energy, Elsevier, vol. 125(C), pages 136-146.
    8. Ooi, Raymond E.H. & Foo, Dominic C.Y. & Tan, Raymond R., 2014. "Targeting for carbon sequestration retrofit planning in the power generation sector for multi-period problems," Applied Energy, Elsevier, vol. 113(C), pages 477-487.

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    Keywords

    Electricity generation; CO2 mitigation; Infrastructure;
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