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Estimating the cost of future global energy supply

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  • Narbel, Patrick André
  • Hansen, Jan Petter

Abstract

This study produces an attempt to estimate the cost of future global energy supplies. The approach chosen to address this concern relies on a comparative static exercise of estimating the cost of three energy scenarios representing different energy futures. The first scenario, the business as usual scenario, predicts the future energy-mix based on the energy plans held by major countries. The second scenario is the renewable energy scenario, where as much of the primary energy supply as possible is replaced by renewable energy by 2050. The cost of the renewable energy generating technologies and their theoretical potential are taken into account in order to create a plausible scenario. The third scenario, the nuclear case, is based on the use of nuclear and renewable energy to replace fossil-fuels by 2050. Endogenous learning rates for each technology are modeled using an innovative approach where learning rates are diminishing overtime. It results from the analysis that going fully renewable would cost between −0.4 and 1.5% of the global cumulated GDP over the period 2009–2050 compared to a business as usual strategy. An extensive use of nuclear power can greatly reduce this gap in costs.

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  • Narbel, Patrick André & Hansen, Jan Petter, 2014. "Estimating the cost of future global energy supply," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 91-97.
  • Handle: RePEc:eee:rensus:v:34:y:2014:i:c:p:91-97
    DOI: 10.1016/j.rser.2014.03.011
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    5. Hansen, J.P. & Narbel, P.A. & Aksnes, D.L., 2017. "Limits to growth in the renewable energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 769-774.
    6. Wang, Qiang & Luo, Kun & Wu, Chunlei & Fan, Jianren, 2019. "Impact of substantial wind farms on the local and regional atmospheric boundary layer: Case study of Zhangbei wind power base in China," Energy, Elsevier, vol. 183(C), pages 1136-1149.
    7. van den Broek, Machteld & Berghout, Niels & Rubin, Edward S., 2015. "The potential of renewables versus natural gas with CO2 capture and storage for power generation under CO2 constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1296-1322.
    8. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
    9. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    10. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
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