Impact of myopic decision-making and disruptive events in power systems planning


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Different model foresight options.
Fig. 2: Optimal capacity mix and system-wide carbon intensity from 2015 to 2050 under different scenarios.
Fig. 3: Annual average utilization factor for power generation technologies under perfect foresight and myopic planning from 2015 to 2050.
Fig. 4: Cumulative total system cost under different scenarios.


  1. 1.

    Pacala, S. & Socolow, R. Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science 305, 968–972 (2004).

    Article  Google Scholar 

  2. 2.

    Stern, N. H. Why Are We Waiting? The Logic, Urgency, and Promise of Tackling Climate Change (MIT Press, Cambridge, MA, 2015).

  3. 3.

    Statistical Review of World Energy (BP plc, 2017);

  4. 4.

    Davis, S., Cao, L., Caldeira, K. & Hoffert, M. I. Rethinking wedges. Environ. Res. Lett. 8, 011001 (2013).

    Article  Google Scholar 

  5. 5.

    Luderer, G. Economic mitigation challenges: How further delay closes the door for achieving climate targets. Environ. Res. Lett. 8, 034033 (2013).

    Article  Google Scholar 

  6. 6.

    Dessens, O., Anandarajah, G. & Gambhir, A. Review of Existing Emissions Pathways and Evaluation of Decarbonisation Rates (2014);

  7. 7.

    IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) (Cambridge Univ. Press, Cambridge, 2014).

  8. 8.

    Gambhir, A. Assessing the feasibility of global long-term mitigation scenarios. Energies 10, 89 (2017).

    Article  Google Scholar 

  9. 9.

    Reiner, D. M. Learning through a portfolio of carbon capture and storage demonstration projects. Nat. Energy 1, 15011 (2016).

    Article  Google Scholar 

  10. 10.

    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);

  11. 11.

    Energy Technology Perspectives 2017 (International Energy Agency, 2017);

  12. 12.

    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);

  13. 13.

    Kahouli-Brahmi, S. Technological learning in energy–environment–economy modelling: A survey. Energy Policy 36, 138–162 (2008).

    Article  Google Scholar 

  14. 14.

    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).

    Article  Google Scholar 

  15. 15.

    Hackett, L. Commercialisation of CCS: What Needs to Happen? (IChemE Energy Centre, 2016);

  16. 16.

    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).

    Article  Google Scholar 

  17. 17.

    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).

    Article  Google Scholar 

  18. 18.

    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).

    Article  Google Scholar 

  19. 19.

    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).

    Article  Google Scholar 

  20. 20.

    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).

    Article  Google Scholar 

  21. 21.

    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).

    Article  Google Scholar 

  22. 22.

    Li, F. G. N. Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions. Energy Strategy Rev. 15, 57–71 (2017).

    Article  Google Scholar 

  23. 23.

    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).

    Article  Google Scholar 

  24. 24.

    Technology Readiness Assessment Guide (US Department of Energy, 2011);

  25. 25.

    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).

    Article  Google Scholar 

  26. 26.

    The Power to Change: Solar and Wind Cost Reduction Potential to 2025 (International Renewable Energy Agency, 2016);

  27. 27.

    Iyer, G., Hultman, N., Fetter, S. & Kim, S. H. Implications of small modular reactors for climate change mitigation. Energy Econ. 45, 144–154 (2014).

    Article  Google Scholar 

  28. 28.

    UK Climate Action Following the Paris Agreement (Committee on Climate Change, 2016);

  29. 29.

    Power Sector Scenarios for the Fifth Carbon Budget (Committee on Climate Change, 2015);

  30. 30.

    Power Plant Siting Study: Project Summary Report (Atkins for Energy Technology Institute LLP, 2017);

  31. 31.

    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).

    Article  Google Scholar 

  32. 32.

    Roulstone, T. SMRs Will Transform the Nuclear Industry (Cambridge Nuclear Energy Centre, 2016);

  33. 33.

    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.

  34. 34.

    Poullikkas, A. Techno-economic investigation of a chemical looping combustion based power plant. Faraday Discuss. 192, 437–457 (2016).

    Article  Google Scholar 

  35. 35.

    Schmidt, O., Hawkes, A., Gambhir, A. & Staffell, I. The future cost of electrical energy storage based on experience rates. Nat. Energy 2, 17110 (2017).

    Article  Google Scholar 

  36. 36.

    Jacobsson, S. & Bergek, A. Transforming the energy sector: The evolution of technological systems in renewable energy technology. Ind. Corp. Change 13, 815–849 (2004).

    Article  Google Scholar 

  37. 37.

    Heuberger, C. F., Staffell, I., Shah, N. & Mac Dowell, N. Electricity Systems Optimisation Model Including Exogenous Technology Learning (ESO-XEL) (Zenedo, 2017);

  38. 38.

    Heuberger, C. F. & Mac Dowell, N. Electricity Systems Optimisation Model Including Exogenous Technology Learning (ESO-XEL): Myopic Foresight Option (Zenedo, 2018);

  39. 39.

    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).

  40. 40.

    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).

    Article  Google Scholar 

  41. 41.

    Heuberger, C. F., Staffell, I., Shah, N. & Mac Dowell, N. Valuing Flexibility in CCS Power Plants (IEAGHG Technical Report, 2017);

  42. 42.

    Merrick, J. H. On representation of temporal variability in electricity capacity planning models. Energy Econ. 59, 261–274 (2016).

    Article  Google Scholar 

  43. 43.

    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).

    Article  Google Scholar 

  44. 44.

    Staffell, I. & Pfenninger, S. Using bias-corrected reanalysis to simulate current and future wind power output. Energy 114, 1224–1239 (2016).

    Article  Google Scholar 

  45. 45.

    Pfenninger, S. Dealing with multiple decades of hourly wind and PV time series in energy models. Appl. Energy 197, 1–13 (2017).

    Article  Google Scholar 

  46. 46.

    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).

    Article  Google Scholar 

  47. 47.

    Napp, T. et al. Exploring the feasibility of low-carbon scenarios using historical energy transitions analysis. Energies 10, 116 (2017).

    Article  Google Scholar 

  48. 48.

    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).

    Article  Google Scholar 

  49. 49.

    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);

  50. 50.

    Teng, F., Trovato, V. & Strbac, G. Stochastic scheduling with inertia-dependent fast frequency response requirements. IEEE Trans. Power Syst. 31, 1557–1566 (2016).

    Article  Google Scholar 

Download references


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.

Author information




N.M.D. conceived and led this study. C.F.H. developed the model formulation and implementation, and carried out the analyses. C.F.H. wrote the paper, N.M.D., I.S. and N.S. contributed to the text and edited the paper.

Corresponding author

Correspondence to Niall Mac Dowell.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–3, Supplementary Tables 1–2, Supplementary Notes 1–7, Supplementary Discussion, Supplementary References

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Heuberger, C.F., Staffell, I., Shah, N. et al. Impact of myopic decision-making and disruptive events in power systems planning. Nat Energy 3, 634–640 (2018).

Download citation

Further reading


Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing