The delayed deployment of low-carbon energy technologies is impeding energy system decarbonization. The continuing debate about the cost-competitiveness of low-carbon technologies has led to a strategy of waiting for a ‘unicorn technology’ to appear. Here, we show that myopic strategies that rely on the eventual manifestation of a unicorn technology result in either an oversized and underutilized power system when decarbonization objectives are achieved, or one that is far from being decarbonized, even if the unicorn technology becomes available. Under perfect foresight, disruptive technology innovation can reduce total system cost by 13%. However, a strategy of waiting for a unicorn technology that never appears could result in 61% higher cumulative total system cost by mid-century compared to deploying currently available low-carbon technologies early on.
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Pacala, S. & Socolow, R. Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science 305, 968–972 (2004).
Stern, N. H. Why Are We Waiting? The Logic, Urgency, and Promise of Tackling Climate Change (MIT Press, Cambridge, MA, 2015).
Statistical Review of World Energy (BP plc, 2017); http://tinyurl.com/yaok2dzo
Davis, S., Cao, L., Caldeira, K. & Hoffert, M. I. Rethinking wedges. Environ. Res. Lett. 8, 011001 (2013).
Luderer, G. Economic mitigation challenges: How further delay closes the door for achieving climate targets. Environ. Res. Lett. 8, 034033 (2013).
Dessens, O., Anandarajah, G. & Gambhir, A. Review of Existing Emissions Pathways and Evaluation of Decarbonisation Rates (2014); http://tinyurl.com/ybgyemql
IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) (Cambridge Univ. Press, Cambridge, 2014).
Gambhir, A. Assessing the feasibility of global long-term mitigation scenarios. Energies 10, 89 (2017).
Reiner, D. M. Learning through a portfolio of carbon capture and storage demonstration projects. Nat. Energy 1, 15011 (2016).
The Future of Carbon Capture and Storage in the UK: Government Response to the Committee’s Second Report of Session 2015-16, Appendix: Government Response (House of Commons, United Kingdom, 2017); https://tinyurl.com/ydan38yt
Energy Technology Perspectives 2017 (International Energy Agency, 2017); http://www.iea.org/etp/
Bassi, S. et al. Bridging the Gap: Improving the Economic and Policy Framework for Carbon Capture and Storage in the European Union (CCCEP, Grantham Research Institute on Climate Change and the Environment, 2015); http://tinyurl.com/q2dks75
Kahouli-Brahmi, S. Technological learning in energy–environment–economy modelling: A survey. Energy Policy 36, 138–162 (2008).
Rubin, E. S., Azevedo, I. M. L., Jaramillo, P. & Yeh, S. A review of learning rates for electricity supply technologies. Energy Policy 86, 198–218 (2015).
Hackett, L. Commercialisation of CCS: What Needs to Happen? (IChemE Energy Centre, 2016); https://tinyurl.com/ybkmjkm5
Heuberger, C. F., Staffell, I., Shah, N. & Mac Dowell, N. Quantifying the value of CCS for the future electricity system. Energy Environ. Sci. 9, 2497–2510 (2016).
Tavoni, M., de Cian, E., Luderer, G., Steckel, J. C. & Waisman, H. The value of technology and of its evolution towards a low carbon economy. Clim. Change 14, 39–57 (2012).
Bertram, C. et al. The value of technology and of its evolution towards a low carbon economy. Technol. Forecast. Soc. Change 90, 62–72 (2015).
Pye, S., Li, F. G. N., Price, J. & Fais, B. Achieving net-zero emissions through the reframing of UK national targets in the post-Paris Agreement era. Nat. Energy 2, 17024 (2017).
Lempert, R. J., Groves, D. G., Popper, S. W. & Bankes, S. C. A general, analytic method for generating robust strategies and narrative scenarios. Manag. Sci. 53, 514–528 (2006).
Bertsimas, D., Litvinov, E., Sun, X. A., Zhao, J. & Zheng, T. Adaptive robust optimization for the security constrained unit commitment problem. IEEE Trans. Power Syst. 28, 52–63 (2013).
Li, F. G. N. Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions. Energy Strategy Rev. 15, 57–71 (2017).
Fuso Nerini, F., Keppo, I. & Strachan, N. Myopic decision making in energy system decarbonisation pathways. A UK case study. Energy Strategy Rev. 17, 19–26 (2017).
Technology Readiness Assessment Guide (US Department of Energy, 2011); https://tinyurl.com/y7dlz2su
Heuberger, C. F., Rubin, E. S., Staffell, I., Shah, N. & Mac Dowell, N. Power capacity expansion planning considering endogenous technology cost learning. Appl. Energy 204, 831–845 (2017).
The Power to Change: Solar and Wind Cost Reduction Potential to 2025 (International Renewable Energy Agency, 2016); https://tinyurl.com/zakcdcz
Iyer, G., Hultman, N., Fetter, S. & Kim, S. H. Implications of small modular reactors for climate change mitigation. Energy Econ. 45, 144–154 (2014).
UK Climate Action Following the Paris Agreement (Committee on Climate Change, 2016); https://tinyurl.com/ot5sc9g
Power Sector Scenarios for the Fifth Carbon Budget (Committee on Climate Change, 2015); http://tinyurl.com/ycb58x74
Power Plant Siting Study: Project Summary Report (Atkins for Energy Technology Institute LLP, 2017); https://tinyurl.com/ybwyyj7g
Abdulla, A., Azevedo, I. L. & Morgan, M. G. Expert assessments of the cost of light water small modular reactors. Proc. Natl Acad. Sci. USA 110, 9686–9691 (2013).
Roulstone, T. SMRs Will Transform the Nuclear Industry (Cambridge Nuclear Energy Centre, 2016); https://tinyurl.com/y8m5cc8k
Doughty, D. H, Butler, P. C, Akhil, A. A, Clark, N. H. & Boyes, J. D. in Batteries for large-scale stationary electrical energy storage Electrochem. Soc. Interface. 19, 49–53 2010.
Poullikkas, A. Techno-economic investigation of a chemical looping combustion based power plant. Faraday Discuss. 192, 437–457 (2016).
Schmidt, O., Hawkes, A., Gambhir, A. & Staffell, I. The future cost of electrical energy storage based on experience rates. Nat. Energy 2, 17110 (2017).
Jacobsson, S. & Bergek, A. Transforming the energy sector: The evolution of technological systems in renewable energy technology. Ind. Corp. Change 13, 815–849 (2004).
Heuberger, C. F., Staffell, I., Shah, N. & Mac Dowell, N. Electricity Systems Optimisation Model Including Exogenous Technology Learning (ESO-XEL) (Zenedo, 2017); https://doi.org/10.5281/zenodo.1048943
Heuberger, C. F. & Mac Dowell, N. Electricity Systems Optimisation Model Including Exogenous Technology Learning (ESO-XEL): Myopic Foresight Option (Zenedo, 2018); https://doi.org/10.5281/zenodo.1212298
Heuberger, C. F., Staffell, I., Shah, N., Mac Dowell, N. Levelised value of electricity - a systemic approach to technology valuation. Comp. Aid. Chem. Eng. 38, 721–726 (2016).
Heuberger, C. F., Staffell, I., Shah, N. & Mac Dowell, N. A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks. Comput. Chem. Eng. 107, 247–256 (2017).
Heuberger, C. F., Staffell, I., Shah, N. & Mac Dowell, N. Valuing Flexibility in CCS Power Plants (IEAGHG Technical Report, 2017); https://tinyurl.com/yatnzrxc
Merrick, J. H. On representation of temporal variability in electricity capacity planning models. Energy Econ. 59, 261–274 (2016).
Pfenninger, S. & Staffell, I. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy 114, 1251–1265 (2016).
Staffell, I. & Pfenninger, S. Using bias-corrected reanalysis to simulate current and future wind power output. Energy 114, 1224–1239 (2016).
Pfenninger, S. Dealing with multiple decades of hourly wind and PV time series in energy models. Appl. Energy 197, 1–13 (2017).
Clack, C. T. M. et al. Evaluation of a proposal for reliable low-cost grid power with 100% wind, water, and solar. Proc. Natl. Acad. Sci. USA 114, 6722–6727 (2017).
Napp, T. et al. Exploring the feasibility of low-carbon scenarios using historical energy transitions analysis. Energies 10, 116 (2017).
Loftus, P. J., Cohen, A. M., Long, J. C. S. & Jenkins, J. D. A critical review of global decarbonization scenarios: what do they tell us about feasibility? WIREs Clim. Change 6, 93–112 (2015).
Gonzalez-Longatt, F., Chikuni, E., Stemmet, W. & Folly, K. Effects of the synthetic inertia from wind power on the total system inertia after a frequency disturbance. In Power Engineering Society Conference and Exposition in Africa (IEEE, 2012); https://doi.org/10.1109/PowerAfrica.2012.6498636
Teng, F., Trovato, V. & Strbac, G. Stochastic scheduling with inertia-dependent fast frequency response requirements. IEEE Trans. Power Syst. 31, 1557–1566 (2016).
We thank the IEA Greenhouse Gas R&D Programme (IEAGHG) and MESMERISE-CCS by the Engineering and Physical Sciences Research Council (EPSRC) under grant EP/M001369/1 for funding this work.
Supplementary Figures 1–3, Supplementary Tables 1–2, Supplementary Notes 1–7, Supplementary Discussion, Supplementary References