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From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning

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  • Handayani, Kamia
  • Krozer, Yoram
  • Filatova, Tatiana

Abstract

This study analyses a diffusion of renewable energy in an electricity system accounting for technological learning. We explore long-term scenarios for capacity expansion of the Java-Bali electricity system in Indonesia, considering the country’s renewable energy targets. We apply the Long-range Energy Alternative Planning (LEAP) model with an integration of technological learning. Our results reveal that, at the medium and high pace of technological learning, the total costs of electricity production to achieve the long-term renewable energy target are 4–10% lower than the scenario without considering technological learning. With respect to technology, solar PV and wind become competitive with other types of renewables and nuclear. Moreover, the fulfilment of the renewable energy targets decreases CO2 emissions by 25% compared to the reference scenario. Implications of our results indicate that energy policies should focus on the early deployment of renewables, upgrading the grid capacity to accommodate variable renewable energy, and enabling faster local learning.

Suggested Citation

  • Handayani, Kamia & Krozer, Yoram & Filatova, Tatiana, 2019. "From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning," Energy Policy, Elsevier, vol. 127(C), pages 134-146.
  • Handle: RePEc:eee:enepol:v:127:y:2019:i:c:p:134-146
    DOI: 10.1016/j.enpol.2018.11.045
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    5. Kim, Hansung & Lee, Hwarang & Koo, Yoonmo & Choi, Dong Gu, 2020. "Comparative analysis of iterative approaches for incorporating learning-by-doing into the energy system models," Energy, Elsevier, vol. 197(C).
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    9. Handayani, Kamia & Filatova, Tatiana & Krozer, Yoram & Anugrah, Pinto, 2020. "Seeking for a climate change mitigation and adaptation nexus: Analysis of a long-term power system expansion," Applied Energy, Elsevier, vol. 262(C).

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