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

Chunhyun Paik, Yongjoo Chung and Young Jin Kim

Renewable Energy, 2021, vol. 164, issue C, 833-841

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.

Keywords: Capacity credit; Solar power generation; Effective load carrying capability; Statistical confidence level; Prediction and tolerance intervals (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:164:y:2021:i:c:p:833-841

DOI: 10.1016/j.renene.2020.09.129

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