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Integration of CCS, emissions trading and volatilities of fuel prices into sustainable energy planning, and its robust optimization

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  • Koo, Jamin
  • Han, Kyusang
  • Yoon, En Sup

Abstract

In this paper, a new approach has been proposed that allows a robust optimization of sustainable energy planning over a period of years. It is based on the modified energy flow optimization model (EFOM) and minimizes total costs in planning capacities of power plants and CCS to be added, stripped or retrofitted. In the process, it reduces risks due to a high volatility in fuel prices; it also provides robustness against infeasibility with respect to meeting the required emission level by adopting a penalty constant that corresponds to the price level of emission allowances. In this manner, the proposed methodology enables decision makers to determine the optimal capacities of power plants and/or CCS, as well as volumes of emissions trading in the future that will meet the required emission level and satisfy energy demand from various user-sections with minimum costs and maximum robustness. They can also gain valuable insights on the effects that the price of emission allowances has on the competitiveness of RES and CCS technologies; it may be used in, for example, setting appropriate subsidies and tax policies for promoting greater use of these technologies. The proposed methodology is applied to a case based on directions and volumes of energy flows in South Korea during the year 2008.

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  • Koo, Jamin & Han, Kyusang & Yoon, En Sup, 2011. "Integration of CCS, emissions trading and volatilities of fuel prices into sustainable energy planning, and its robust optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 665-672, January.
    • Handle: RePEc:eee:rensus:v:15:y:2011:i:1:p:665-672
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    7. Kim, Seunghyok & Koo, Jamin & Lee, Chang Jun & Yoon, En Sup, 2012. "Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors," Energy, Elsevier, vol. 44(1), pages 126-134.
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    9. Dai, C. & Cai, Y.P. & Li, Y.P. & Sun, W. & Wang, X.W. & Guo, H.C., 2014. "Optimal strategies for carbon capture, utilization and storage based on an inexact mλ-measure fuzzy chance-constrained programming," Energy, Elsevier, vol. 78(C), pages 465-478.
    10. Hong-Hua Qiu & Jing Yang, 2018. "An Assessment of Technological Innovation Capabilities of Carbon Capture and Storage Technology Based on Patent Analysis: A Comparative Study between China and the United States," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    11. Mohsen Jalalimajidi & SM Seyedhosseini & Ahmad Makui & Masoud Babakhani, 2018. "Developing a comprehensive model for new energy replacement in the country’s development program using a robust optimization approach," Energy & Environment, , vol. 29(6), pages 868-890, September.
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    13. Ming, Zeng & Shaojie, Ouyang & Yingjie, Zhang & Hui, Shi, 2014. "CCS technology development in China: Status, problems and countermeasures—Based on SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 604-616.
    14. Guerra, Omar J. & Tejada, Diego A. & Reklaitis, Gintaras V., 2016. "An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems," Applied Energy, Elsevier, vol. 170(C), pages 1-21.
    15. Li, Jia & Liang, Xi & Cockerill, Tim, 2011. "Getting ready for carbon capture and storage through a ‘CCS (Carbon Capture and Storage) Ready Hub’: A case study of Shenzhen city in Guangdong province, China," Energy, Elsevier, vol. 36(10), pages 5916-5924.
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    17. Jin, S.W. & Li, Y.P. & Nie, S. & Sun, J., 2017. "The potential role of carbon capture and storage technology in sustainable electric-power systems under multiple uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 467-480.

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