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Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050


  • Li, Francis G.N.
  • Trutnevyte, Evelina


Deep decarbonisation of the electricity sector is central to achieving the United Kingdom’s (UK) climate policy targets for 2050 and meeting its international commitments under the Paris Agreement. While the overall strategy for decarbonising the energy system has been well established in previous studies, there remain deep uncertainties around the total investment cost requirements for the power system. The future of the power system is of critical importance because low carbon electricity may create significant opportunities for emissions reduction in buildings and transport. A key policy application of quantitative analysis using models is to explore how much investment needs to be mobilised for the energy transition. However, past estimates of energy transition costs for the UK power sector have focused only on 2030 rather than 2050 and consider a relatively narrow range of uncertainties. This paper addresses this important research gap. The UK government's main whole system energy economy model is linked to a power system model that employs an advanced approach to uncertainty analysis, combining Monte Carlo simulation with Modelling-to-Generate Alternatives (MGA), producing 800 different scenario pathways. These pathways simultaneously consider uncertainties in policy, technology and costs. The results show that with No Climate Policy, installed generation capacities in 2050 are found in the range 60–75GW, while under an 80% Reduction in GHG Emissions, between 100GW and 130GW of plant are required. Meeting climate targets for 2050 is also found to increase the investment requirements for new electricity generation. The interquartile range for cumulative investments in new generation under the No Climate Policy scenario ranges from £60bn to £75bn, while under an 80% Reduction in GHG Emissions, investment requirements approximately double to £110bn - £140bn. The exercise demonstrates the importance of uncertainty analysis to policy evaluation, yielding insights for future research practice both in the UK and internationally.

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  • Li, Francis G.N. & Trutnevyte, Evelina, 2017. "Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050," Applied Energy, Elsevier, vol. 189(C), pages 89-109.
  • Handle: RePEc:eee:appene:v:189:y:2017:i:c:p:89-109
    DOI: 10.1016/j.apenergy.2016.12.047

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    Cited by:

    1. Cox, Emily, 2018. "Assessing long-term energy security: The case of electricity in the United Kingdom," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2287-2299.
    2. Li, Pei-Hao & Pye, Steve & Keppo, Ilkka, 2020. "Using clustering algorithms to characterise uncertain long-term decarbonisation pathways," Applied Energy, Elsevier, vol. 268(C).
    3. Koltsaklis, Nikolaos E. & Nazos, Konstantinos, 2017. "A stochastic MILP energy planning model incorporating power market dynamics," Applied Energy, Elsevier, vol. 205(C), pages 1364-1383.
    4. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    5. Haghi, Ehsan & Qadrdan, Meysam & Wu, Jianzhong & Jenkins, Nick & Fowler, Michael & Raahemifar, Kaamran, 2020. "An iterative approach for optimal decarbonization of electricity and heat supply systems in the Great Britain," Energy, Elsevier, vol. 201(C).
    6. Pilpola, Sannamari & Lund, Peter D., 2020. "Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways," Energy, Elsevier, vol. 201(C).
    7. Zhang, M.M. & Wang, Qunwei & Zhou, Dequn & Ding, H., 2019. "Evaluating uncertain investment decisions in low-carbon transition toward renewable energy," Applied Energy, Elsevier, vol. 240(C), pages 1049-1060.
    8. Ji, Ling & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao & Song, Yi-Hang, 2017. "Explicit cost-risk tradeoff for renewable portfolio standard constrained regional power system expansion: A case study of Guangdong Province, China," Energy, Elsevier, vol. 131(C), pages 125-136.
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    10. Lopion, Peter & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "A review of current challenges and trends in energy systems modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 156-166.
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    14. Wegner, Marie-Sophie & Hall, Stephen & Hardy, Jeffrey & Workman, Mark, 2017. "Valuing energy futures; a comparative analysis of value pools across UK energy system scenarios," Applied Energy, Elsevier, vol. 206(C), pages 815-828.
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