Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

Mechanisms of hardware and soft technology evolution and the implications for solar energy cost trends

Nature Energy volume 8, pages 827–838 (2023)Cite this article

Subjects

Abstract

Technology hardware and deployment processes (‘soft technology’) seem fundamentally different, but little work examines the nature of this difference and its implications for technology improvement. Here we present a model to study the roles of hardware and soft technology in cost evolution and apply it to solar photovoltaic (PV) systems. Differing properties of hardware and soft technology help explain PV’s cost decline. Rapid improvements in hardware affected globally traded components that lowered both hardware and soft costs. Improvements in soft technology occurred more slowly, were not shared as readily across locations and only affected soft costs, ultimately contributing less than previously estimated. As a result, initial differences in soft technology across countries persisted and the share of soft costs rose. In general, we show the usefulness of modelling dependencies between technology costs and features to understand past drivers of cost change and inform future technology development.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Soft costs of several energy technologies.
Fig. 2: Conceptual framework for cost modelling.
Fig. 3: Estimated contributions to cost reduction in residential PV systems in the United States over the 1980–2017 period.
Fig. 4: Cost influence of individual variables and comparison to their cost change contribution.
Fig. 5: Estimated contributions to PV system cost reduction over different time periods in the United States, Germany and Japan.
Fig. 6: Evolution of PV system costs during the 1980–2018 period.

Similar content being viewed by others

Data availability

The data used to populate the cost and cost change equations (1)–(5) along with references are provided with this paper (Supplementary Tables 1–3 and 9). Data are available from the International Renewable Energy Agency (https://www.irena.org/publications) and the National Renewable Energy Laboratory (https://www.nrel.gov/research/publications.html ref. 18) for free and from the German Solar Association (https://www.solarwirtschaft.de/en/press/market-data/) at cost.

Code availability

All steps in this analysis are described in Methods equations (1)–(5). The code is available upon request.

References

  1. Nagy, B., Farmer, J. D., Bui, Q. M. & Trancik, J. E. Statistical basis for predicting technological progress. PLoS ONE 8, 52669 (2013).

    Article  Google Scholar 

  2. McNerney, J., Farmer, J. D. & Trancik, J. E. Historical costs of coal-fired electricity and implications for the future. Energy Policy 39, 3042–3054 (2011).

    Article  Google Scholar 

  3. McNerney, J., Farmer, J. D., Redner, S. & Trancik, J. E. Role of design complexity in technology improvement. Proc. Natl Acad. Sci. USA 108, 9008–9013 (2011).

    Article  Google Scholar 

  4. Moore, G.Cramming more components onto integrated circuits. Electronics 38, 114–117 (1965).

    Google Scholar 

  5. Nemet, G. F. Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Policy 34, 3218–3232 (2006).

    Article  Google Scholar 

  6. Grubler, A. The costs of the French nuclear scale-up: a case of negative learning by doing. Energy Policy 38, 5174–5188 (2010).

    Article  Google Scholar 

  7. Eash-Gates, P. et al. Sources of cost overrun in nuclear power plant construction call for a new approach to engineering design. Joule 4, 2348–2373 (2020).

    Article  Google Scholar 

  8. Feldman, D. et al. US Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2020 (National Renewable Energy Lab, 2021).

  9. Renewable Power Generation Costs in 2017 (IRENA, 2018).

  10. Kavlak, G., McNerney, J. & Trancik, J. E. Evaluating the causes of cost reduction in photovoltaic modules. Energy Policy 123, 700–710 (2018).

    Article  Google Scholar 

  11. Wright, T. P. Factors affecting the cost of airplanes. J. Aeronaut. Sci. 3, 122–128 (1936).

    Article  Google Scholar 

  12. 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 

  13. Elshurafa, A. M., Albardi, S. R., Bigerna, S. & Bollino, C. A. Estimating the learning curve of solar PV balance-of-system for over 20 countries: implications and policy recommendations. J. Cleaner Prod. 196, 122–134 (2018).

    Article  Google Scholar 

  14. McDonald, A. & Schrattenholzer, L. Learning rates for energy technologies. Energy Policy 29, 255–261 (2001).

    Article  Google Scholar 

  15. Meng, J., Way, R., Verdolini, E. & Anadon, L. D. Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition. Proc. Natl Acad. Sci. USA 118, e1917165118 (2021).

    Article  Google Scholar 

  16. Goodrich, A., James, T. & Woodhouse, M. Residential, Commercial, and Utility-Scale Photovoltaic (PV) System Prices in the United States: Current Drivers and Cost-Reduction Opportunities (NREL, 2012).

  17. Barbose, G. et al. Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States 2019 edn (Lawrence Berkeley National Laboratory, 2019).

  18. Fu, R., Feldman, D. J., Margolis, R. M., Woodhouse, M. A. & Ardani, K. B. US Solar Photovoltaic System Cost Benchmark: Q1 2017 (National Renewable Energy Laboratory, 2017).

  19. Solar Futures Study (US Department of Energy, 2021).

  20. Helveston, J. P., He, G. & Davidson, M. R. Quantifying the cost savings of global solar photovoltaic supply chains. Nature 612, 83–87 (2022).

    Article  Google Scholar 

  21. Junginger, M., van Sark, W. & Faaij, A. Technological Learning in the Energy Sector: Lessons for Policy, Industry and Science (Edward Elgar Publishing, 2010).

  22. Candelise, C., Winskel, M. & Gross, R. J. The dynamics of solar PV costs and prices as a challenge for technology forecasting. Renew. Sustain. Energy Rev. 26, 96–107 (2013).

    Article  Google Scholar 

  23. Gillingham, K. et al. Deconstructing solar photovoltaic pricing. Energy J. https://doi.org/10.5547/01956574.37.3.kgil (2016).

  24. Nemet, G. F. et al. Characteristics of low-priced solar PV systems in the US. Appl. Energy 187, 501–513 (2017).

    Article  Google Scholar 

  25. Ramasamy, V., Feldman, D., Desai, J. & Margolis, R. US Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2021 (National Renewable Energy Lab, 2021).

  26. Barbose, G. et al. Tracking US photovoltaic system prices 1998–2012: a rapidly changing market. Prog. Photovoltaics Res. Appl. 23, 692–704 (2015).

    Article  Google Scholar 

  27. Imamura, M., Hulstrom, R., Cookson, C., Waldman, B. & Lane, R. Definition Study for Photovoltaic Residential Prototype System (NASA, 1976).

  28. Kirpich, A. Conceptual Design and Systems Analysis of Photovoltaic Systems. Volume II. Study Results. Final Report (General Electric Co., Space Division, 1977).

  29. Hayter, S. J. & Martin, R. L. Photovoltaics for Buildings Cutting-edge PV (National Renewable Energy Lab, 1998).

  30. Briggs, R. et al. Automated Installation Methods for Photovoltaic Arrays (NASA, 1982).

  31. Mayer, J. N. Current and Future Costs of Photovoltaics: Long-term Scenarios for Market Development, System Prices and LCOE of Utility-scale Systems (Agora Energiewende, 2015).

  32. Bower, W. Inverters—critical photovoltaic balance-of-system components: status, issues, and new-millennium opportunities. Prog. Photovoltaics Res. Appl. 8, 113–126 (2000).

    Article  Google Scholar 

  33. Kenning, T. ABB in India doubles solar inverter manufacturing capacity with a new state-of-the-art factory. PV Tech https://www.pv-tech.org/abb-india-doubles-inverter-manufacturing-capacity-with-new-facility/#:~:text=Inverter%20manufacturer%20ABB%20India%2C%20a,firm’s%20capacity%20in%20the%20country (2016).

  34. World’s largest and CO2-neutral inverter factory inaugurated by SMA Technology AG. SMA Solar Technology AG Press Office https://www.sma.de/en/newsroom/current-news/news-details/news/2189-worlds-largest-and-co2-neutral-inverter-factory-inaugurated-by-sma-solar-technology-ag.html (2009).

  35. Trancik, J. E. et al. Technology Improvement and Emissions Reductions as Mutually Reinforcing Efforts: Observations from the Global Development of Solar and Wind Energy (Institute for Data, Systems and Society, MIT, 2015).

  36. Kimura, O. & Suzuki, T. 30 years of solar energy development in Japan: co-evolution process of technology, policies, and the market. In Berlin Conference on the Human Dimensions of Global Environmental Change: Resource Policies: Effectiveness, Efficiency, and Equity 17–18 (2006).

  37. Wiese, A., Kaltschmitt, M., Fahl, U. & Voß, A. Vergleichende Kostenanalyse Einer Windtechnischen und Photovoltaischen Stromerzeugung. Elektrizitätswirtschaft 91, 291–299 (1992).

    Google Scholar 

  38. Gabler, H. & Mohring, H.-D. Photovoltaische großanlagen–technologie und realisierung. Forschungsverbund Sonnenenergie Themen, 56–60 (2002).

  39. Fink, T., Reeves, M., Palma, R. & Farr, R. Serendipity and strategy in rapid innovation. Nat. Commun. 8, 2002 (2017).

    Article  Google Scholar 

  40. Renewable Power Generation Costs in 2018 (IRENA, 2018).

  41. Fu, R., Remo, T. W. & Margolis, R. M. 2018 US Utility-Scale Photovoltaics-Plus-Energy Storage System Costs Benchmark (National Renewable Energy Lab, 2018).

  42. Cox III, C. Residential Photovoltaic Systems Costs (Massachusetts Institute of Technology, Lincoln Lab, 1980).

  43. Ardani, K. et al. Non-hardware (‘Soft’) Cost-reduction Roadmap for Residential and Small Commercial Solar Photovoltaics, 2013–2020 (National Renewable Energy Laboratory, 2013).

  44. Green, M. A. Silicon photovoltaic modules: a brief history of the first 50 years. Prog. Photovoltaics Res. Appl. 13, 447–455 (2005).

    Article  Google Scholar 

  45. Watts, R., Smith, S. & Dirks, J. Photovoltaic Industry Progress Through 1984 (Pacific Northwest National Laboratory, 1985).

  46. Macomber, H., Ruzek, J. B. & Costello, F. A. Photovoltaic Stand Alone Systems (Monegon, 1981).

  47. Palmer, R., Penasa, D. & Thomas, M. Detailed Cost Estimate of Reference Residential Photovoltaic Designs Sandia Report SAND83 (Sandia National Laboratories, 1983).

  48. Photovoltaik-preismonitor Deutschland Q1 2018 (Bundesverband Solarwirtschaft, 2018).

  49. Friedman, B., Margolis, R. & Seel, J. Comparing Photovoltaic (PV) Costs and Deployment Drivers in the Japanese and US Residential and Commercial Markets (National Renewable Energy Laboratory, 2016).

  50. Burgess, E., Post, H. & Key, T. Subsystem engineering and development of grid-connected photovoltaic systems. In 4th EC Photovoltaic Solar Energy Conference (Bloss, W. H. ed) 238–247 (Springer, 1982).

  51. Costello, D. & Rappaport, P. The technological and economic development of photovoltaics. Ann. Rev. Energy 5, 335–356 (1980).

    Article  Google Scholar 

  52. Smith, J. L. Photovoltaics as a Terrestrial Energy Source. Vol. 3: An Overview (US Department of Energy Photovoltaics Program, 1980).

  53. Feldman, D. et al. Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-term Projections (Lawrence Berkeley National Lab, 2012).

  54. Christmas, S. et al. Solar Cost Databank (Solar Energy Research Institute, 1979).

  55. United Engineers & Constructors: Capital Cost: Pressurized Water Reactor Plant (Nuclear Regulatory Commission & Energy Research and Development Administration, 1977).

  56. United Engineers & Constructors: Energy Economic Data Base (EEDB) Program: Phase VIII (US Department of Energy, 1986).

  57. Popat, L. V. The economics of geothermal power. Eng. Costs Prod. Econ. 7, 159–170 (1983).

    Article  Google Scholar 

  58. Geothermal Power: Technology Brief (IRENA, 2017).

  59. Ahmed, K. Renewable Energy Technologies: A Review of the Status and Costs of Selected Technologies (The World Bank, 1994).

  60. Moore, L., Post, H. & Mysak, T. Photovoltaic power plant experience at Tucson Electric Power. In ASME 2005 International Mechanical Engineering Congress and Exposition (Grassi, G. ed) 387–394 (American Society of Mechanical Engineers, 2005).

  61. Fuhs, W. Photovoltaik–stand und Perspektiven (FVS DGS Themen, 2000).

  62. Bericht über den Stand der Markteinführung und der Kostenentwicklung von Anlagen zur Erzeugung von Strom aus Erneuerbaren Energien (Erfahrungsbericht zum Eeg) (Bundesministerium für Wirtschaft und Energie, 2002).

  63. Watt, M. & Passey, R. National Survey Report of PV Power Applications in Australia (Australian PV Association, 2010).

  64. Calhoun, K., Crofton, K., Goodman, G. & McIntosh, R. Lessons from Australia: Reducing Solar PV Costs Through Installation Labor Efficiency (Rocky Mountain Institute, 2014).

  65. Johnston, W. & Egan, R. National Survey Report of PV Power Applications in Australia 2016 (Australian PV Institute, 2017).

  66. Lin-Lin, Y. Economic Analysis of Solar Photovoltaic Based on Life Cycle Costing (State Electricity Regulatory Commission, 2010).

Download references

Acknowledgements

We thank R. Fu, P. Ralon and M. Taylor for help with national and international cost data. We thank K. Horowitz, R. Margolis, G. Nemet, F. O’Sullivan, E. Pecora and D. Rench-McCauley for helpful input and S. Reese and O. Stalter for sharing insights on the evolution of inverters. We also thank M. Ziegler for helpful input on code standardization. This work is funded in whole by the US Department of Energy Solar Energy Technologies Office under award number DE-EE0007662.

Author information

Authors and Affiliations

  1. Division of Public Policy, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Magdalena M. Klemun

  2. Energy Institute, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Magdalena M. Klemun

  3. Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA, USA

    Magdalena M. Klemun, Goksin Kavlak, James McNerney & Jessika E. Trancik

  4. Center for International Development and Harvard Kennedy School, Cambridge, MA, USA

    James McNerney

  5. Complexity Science Hub, Vienna, Austria

    James McNerney

  6. Santa Fe Institute, Santa Fe, NM, USA

    Jessika E. Trancik

Authors
  1. Magdalena M. Klemun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Goksin Kavlak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. James McNerney
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jessika E. Trancik
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.M.K., J.E.T., G.K. and J.M. conceptualized the study and developed the methodology. M.M.K. built the model. M.M.K., J.E.T, J.M. and G.K. analysed the results and wrote the paper. J.E.T. led the research team.

Corresponding authors

Correspondence to Magdalena M. Klemun or Jessika E. Trancik.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Energy thanks David Feldman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Estimated contributions from module price variables to cost reduction in residential PV systems in the U.S. Results are shown for the 1980-2017 period and a 5kW system.

Note that contrary to Fig. 3, module price variable contributions sum up to less than 100% (see legend box) because they represent the contributions of one technology component to total system cost change. In all panels, percentages give the fraction of the net cost change over the 1980-2017 period that was caused by each variable. Cost change contributions in absolute terms (in 2017 $/Wac) are given in Supplementary Table 8.

Extended Data Fig. 2 Estimated contributions from high-level mechanisms to PV system cost reduction.

Percentages give the estimated fraction of the net cost change over the 1980-2017 period (see Table 1) that was caused by each high-level mechanism for the chosen assignment between low- and high-level mechanisms (see Supplementary Table 10). Contributions are negative when they act in the opposite direction to the net cost change over a period. In the period shown here (a, Results for BOS cost change. b, Results for PV system cost change), the net change cost was negative, therefore positive contributions correspond to cost-reducing effects and negative contributions to cost-raising effects. Note that assignments of low-level mechanisms to high-level are based on a combination of quantitative modelling results and qualitative accounts in the literature. Due to data limitations, the decomposition for some cost components (that is, the decomposition of module mechanisms in Extended Data Fig. 1) is more fine-grained than for others; applying the same level of decomposition across all cost components may alter the results.

Supplementary information

Supplementary Information

Supplementary Figs. 1–41, Notes 1–13 and Tables 1–10.

Supplementary Data

Results of the sensitivity analysis shown in Supplementary Figs. 4–34.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Klemun, M.M., Kavlak, G., McNerney, J. et al. Mechanisms of hardware and soft technology evolution and the implications for solar energy cost trends. Nat Energy 8, 827–838 (2023). https://doi.org/10.1038/s41560-023-01286-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41560-023-01286-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing