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The WASP model on the symbiotic strategy of renewable and nuclear power for the future of ‘Renewable Energy 3020’ policy in South Korea

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  • Vincent, Immanuel
  • Lee, Eun-Chong
  • Cha, Kyung-Ho
  • Kim, Hyung-Man

Abstract

The Korean government has recently implemented the energy transition ‘Renewable Energy 3020’ policy to expand dramatically renewable energy without adding nuclear and coal-fired power plants. This study provides an opportunity to use scientific analysis to investigate the ripple effects of the ‘Renewable Energy 3020’ policy, which expects renewable energy generation to reach 20% by 2030, and the policy enforcement of efficient energy transition. More specifically, the Wien Automatic System Planning (WASP) model has been used to verify the implementation of the long-term power development plan within specified constraints. In accordance with the methodology in the WASP-ΙV model, assuming that the ‘Renewable Energy 3020’ policy succeeds, additional charges of approximately 144 billion KRW by 2035 has been estimated. Therefore, a combination of continuous baseload power and variable renewable power plants can help to implement the ‘Renewable Energy 3020’ policy successfully. If technological and economic uncertainties related to renewable and nuclear powers are neglected, the second-best energy transition would need to be examined to promote an alternative energy policy, considering the technological innovation and the social consensus.

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  • Vincent, Immanuel & Lee, Eun-Chong & Cha, Kyung-Ho & Kim, Hyung-Man, 2021. "The WASP model on the symbiotic strategy of renewable and nuclear power for the future of ‘Renewable Energy 3020’ policy in South Korea," Renewable Energy, Elsevier, vol. 172(C), pages 929-940.
  • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:929-940
    DOI: 10.1016/j.renene.2021.03.094
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    References listed on IDEAS

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    1. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
    2. Ko, Won Il & Kwon, Eun-ha, 2009. "Implications of the new National Energy Basic Plan for nuclear waste management in Korea," Energy Policy, Elsevier, vol. 37(9), pages 3484-3488, September.
    3. Woong Ko & Jong-Keun Park & Mun-Kyeom Kim & Jae-Haeng Heo, 2017. "A Multi-Energy System Expansion Planning Method Using a Linearized Load-Energy Curve: A Case Study in South Korea," Energies, MDPI, vol. 10(10), pages 1-24, October.
    4. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    5. Manzano-Agugliaro, F. & Alcayde, A. & Montoya, F.G. & Zapata-Sierra, A. & Gil, C., 2013. "Scientific production of renewable energies worldwide: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 134-143.
    6. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
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    1. Yoro, Kelvin O. & Daramola, Michael O. & Sekoai, Patrick T. & Wilson, Uwemedimo N. & Eterigho-Ikelegbe, Orevaoghene, 2021. "Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

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