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Evaluation of the long-term power generation mix: The case study of South Korea's energy policy

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  • Min, Daiki
  • Chung, Jaewoo

Abstract

This paper presents a practical portfolio model for the long-term power generation mix problem. The proposed model optimizes the power generation mix by striking a trade-off between the expected cost of power generation and its variability. We use Monte Carlo simulation techniques to consider the uncertainty associated with future electricity demand, fuel prices and their correlations, and the capital costs of power plants. Unlike in the case of conventional power generation mix models, we employ CVaR (Conditional Value-at-Risk) in designing variability to consider events that are rare but enormously expensive. A comprehensive analysis on South Korea's generation policy using the portfolio model shows that a large annual cost is additionally charged to substitute a portion of nuclear energy with other alternatives. Nonetheless, if Korea has to reduce its dependency on nuclear energy because of undermined social receptivity from the Fukushima disaster, it turns out that LNG or coal could be a secure candidate from an economic perspective.

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  • Min, Daiki & Chung, Jaewoo, 2013. "Evaluation of the long-term power generation mix: The case study of South Korea's energy policy," Energy Policy, Elsevier, vol. 62(C), pages 1544-1552.
    • Handle: RePEc:eee:enepol:v:62:y:2013:i:c:p:1544-1552
    DOI: 10.1016/j.enpol.2013.07.104
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    7. Stefan Niederhafner, 2014. "The Korean Energy and GHG Target Management System: An Alternative to Kyoto-Protocol Emissions Trading Systems?," TEMEP Discussion Papers 2014118, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Sep 2014.
    8. Sadeghi, Hadi & Rashidinejad, Masoud & Abdollahi, Amir, 2017. "A comprehensive sequential review study through the generation expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1369-1394.
    9. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    10. Lynch & John Curtis, 2016. "The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 133-151, January.
    11. Ioannou, Anastasia & Fuzuli, Gulistiani & Brennan, Feargal & Yudha, Satya Widya & Angus, Andrew, 2019. "Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling," Energy Economics, Elsevier, vol. 80(C), pages 760-776.
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