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ELCC-based capacity credit estimation accounting for uncertainties in capacity factors and its application to solar power in Korea

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  • Paik, Chunhyun
  • Chung, Yongjoo
  • Kim, Young Jin

Abstract

It is not uncommon that the power generation sector accounts for the most greenhouse gas (GHG) emissions in a country, and an increasing attention has been placed on the emissions reduction in the sector. In addition, many countries plan to phase out once-popular nuclear plants mainly due to the recent disastrous accident and expand the installation of renewable generations such as wind and solar power. The renewable generations are confronted with significant planning challenges stemming from their intermittent nature, though. Especially, the estimation of capacity credit has long been under heavy debate and its proper assessment is considered critical when introducing renewable energy. It has thus been discussed that the current estimation method may not efficiently account for temporal variability. An alternative approach based on the statistical interval estimates is outlined and demonstrated through the case study of the Republic of Korea. The result indicates that the proposed approach may render more conservative estimates depending upon the confidence level, and policy-makers may take the degree of uncertainty associated with temporal variability into consideration when implementing renewable generations.

Suggested Citation

  • Paik, Chunhyun & Chung, Yongjoo & Kim, Young Jin, 2021. "ELCC-based capacity credit estimation accounting for uncertainties in capacity factors and its application to solar power in Korea," Renewable Energy, Elsevier, vol. 164(C), pages 833-841.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:833-841
    DOI: 10.1016/j.renene.2020.09.129
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    References listed on IDEAS

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    2. Chen, Jiahao & Sun, Bing & Li, Yunfei & Jing, Ruipeng & Zeng, Yuan & Li, Minghao, 2022. "Credible capacity calculation method of distributed generation based on equal power supply reliability criterion," Renewable Energy, Elsevier, vol. 201(P1), pages 534-547.
    3. Song, Siming & Liu, Pei & Li, Zheng, 2022. "Low carbon transition of China's electric and heating sector considering reliability: A modelling and optimization approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    4. Yi Hao & Zhigang Huang & Shiqian Ma & Jiakai Huang & Jiahao Chen & Bing Sun, 2023. "Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation," Energies, MDPI, vol. 16(16), pages 1-17, August.
    5. Wang, Renshun & Wang, Shilong & Geng, Guangchao & Jiang, Quanyuan, 2024. "Multi-time-scale capacity credit assessment of renewable and energy storage considering complex operational time series," Applied Energy, Elsevier, vol. 355(C).
    6. Huang, Zhenyu & Liu, Youbo & Li, Kecun & Liu, Jichun & Gao, Hongjun & Qiu, Gao & Shen, Xiaodong & Liu, Junyong, 2023. "Evaluating long-term profile of demand response under different market designs: A comparison of scarcity pricing and capacity auction," Energy, Elsevier, vol. 282(C).

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