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Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy

Author

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  • Svetlana Lawrence

    (Idaho National Laboratory, 1955 Fremont Ave., Idaho Falls, ID 83415, USA
    Department of Systems Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Daniel R. Herber

    (Department of Systems Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Kamran Eftekhari Shahroudi

    (Department of Systems Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins, CO 80523, USA)

Abstract

The United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, solar, clean hydrogen, and advanced nuclear technologies. Modern systems engineering methods and tools can provide deeper insights into these dynamics and future behaviors. This research aims to develop a comprehensive model that captures the main elements and behaviors of new energy technologies within the existing energy system. We hypothesized that the market uptake of novel energy systems is influenced by multiple diverse factors, such as technological learning, availability of resources, and economic incentives; examined the history of electricity generation using land-based wind technologies; and developed a system dynamics model to investigate the relationships between capacity growth and influencing factors, both internal and external. The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. This research aims to support informed decision-making for investments in novel energy systems and aid in developing effective policies for technology deployment.

Suggested Citation

  • Svetlana Lawrence & Daniel R. Herber & Kamran Eftekhari Shahroudi, 2025. "Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy," Energies, MDPI, vol. 18(8), pages 1-33, April.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2048-:d:1636017
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    References listed on IDEAS

    as
    1. Rong Wang & Sandra Hasanefendic & Elizabeth Von Hauff & Bart Bossink, 2023. "A System Dynamics Approach to Technological Learning Impact for the Cost Estimation of Solar Photovoltaics," Energies, MDPI, vol. 16(24), pages 1-17, December.
    2. Esmaieli, M. & Ahmadian, M., 2018. "The effect of research and development incentive on wind power investment, a system dynamics approach," Renewable Energy, Elsevier, vol. 126(C), pages 765-773.
    3. Bosetti, Valentina & Carraro, Carlo & Duval, Romain & Tavoni, Massimo, 2011. "What should we expect from innovation? A model-based assessment of the environmental and mitigation cost implications of climate-related R&D," Energy Economics, Elsevier, vol. 33(6), pages 1313-1320.
    4. Deschenes, Olivier & Malloy, Christopher & McDonald, Gavin, 2023. "Causal effects of Renewable Portfolio Standards on renewable investments and generation: The role of heterogeneity and dynamics," Resource and Energy Economics, Elsevier, vol. 75(C).
    5. Luca Ciacci & Fabrizio Passarini, 2020. "Life Cycle Assessment (LCA) of Environmental and Energy Systems," Energies, MDPI, vol. 13(22), pages 1-8, November.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Sarah Hafner & Lawrence Gottschamer & Merla Kubli & Roberto Pasqualino & Silvia Ulli-Beer, 2024. "Building the Bridge: How System Dynamics Models Operationalise Energy Transitions and Contribute towards Creating an Energy Policy Toolbox," Sustainability, MDPI, vol. 16(19), pages 1-34, September.
    8. Pietzcker, Robert C. & Ueckerdt, Falko & Carrara, Samuel & de Boer, Harmen Sytze & Després, Jacques & Fujimori, Shinichiro & Johnson, Nils & Kitous, Alban & Scholz, Yvonne & Sullivan, Patrick & Ludere, 2017. "System integration of wind and solar power in integrated assessment models: A cross-model evaluation of new approaches," Energy Economics, Elsevier, vol. 64(C), pages 583-599.
    9. Huang, Zishuo & Yu, Hang & Peng, Zhenwei & Feng, Yifu, 2017. "Planning community energy system in the industry 4.0 era: Achievements, challenges and a potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 710-721.
    10. Patrik Söderholm & Ger Klaassen, 2007. "Wind Power in Europe: A Simultaneous Innovation–Diffusion Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 36(2), pages 163-190, February.
    11. Howells, Mark & Rogner, Holger & Strachan, Neil & Heaps, Charles & Huntington, Hillard & Kypreos, Socrates & Hughes, Alison & Silveira, Semida & DeCarolis, Joe & Bazillian, Morgan & Roehrl, Alexander, 2011. "OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development," Energy Policy, Elsevier, vol. 39(10), pages 5850-5870, October.
    12. Andrea Savio & Luigi De Giovanni & Mariangela Guidolin, 2022. "Modelling Energy Transition in Germany: An Analysis through Ordinary Differential Equations and System Dynamics," Forecasting, MDPI, vol. 4(2), pages 1-18, April.
    13. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    14. Esmaeili, Parisa & Rafei, Meysam & Salari, Mahmoud & Balsalobre-Lorente, Daniel, 2024. "From oil surges to renewable shifts: Unveiling the dynamic impact of supply and demand shocks in global crude oil market on U.S. clean energy trends," Energy Policy, Elsevier, vol. 192(C).
    15. Simeoni, Patrizia & Nardin, Gioacchino & Ciotti, Gellio, 2018. "Planning and design of sustainable smart multi energy systems. The case of a food industrial district in Italy," Energy, Elsevier, vol. 163(C), pages 443-456.
    16. Jung-Tae Lee & Hyun-Goo Kim & Yong-Heack Kang & Jin-Young Kim, 2019. "Determining the Optimized Hub Height of Wind Turbine Using the Wind Resource Map of South Korea," Energies, MDPI, vol. 12(15), pages 1-13, July.
    17. Shrimali, Gireesh & Lynes, Melissa & Indvik, Joe, 2015. "Wind energy deployment in the U.S.: An empirical analysis of the role of federal and state policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 796-806.
    18. Neofytou, H. & Nikas, A. & Doukas, H., 2020. "Sustainable energy transition readiness: A multicriteria assessment index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    19. Hunter, Kevin & Sreepathi, Sarat & DeCarolis, Joseph F., 2013. "Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)," Energy Economics, Elsevier, vol. 40(C), pages 339-349.
    20. Prina, Matteo Giacomo & Cozzini, Marco & Garegnani, Giulia & Manzolini, Giampaolo & Moser, David & Filippi Oberegger, Ulrich & Pernetti, Roberta & Vaccaro, Roberto & Sparber, Wolfram, 2018. "Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model," Energy, Elsevier, vol. 149(C), pages 213-221.
    21. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    22. Lopez, Anthony & Mai, Trieu & Lantz, Eric & Harrison-Atlas, Dylan & Williams, Travis & Maclaurin, Galen, 2021. "Land use and turbine technology influences on wind potential in the United States," Energy, Elsevier, vol. 223(C).
    23. Morrison, J. Bradley, 2008. "Putting the learning curve in context," Journal of Business Research, Elsevier, vol. 61(11), pages 1182-1190, November.
    24. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    25. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng & Vaz-Serra, Paulo, 2022. "Economic and environmental impacts of public investment in clean energy RD&D," Energy Policy, Elsevier, vol. 168(C).
    26. Ben Maalla, El Mehdi & Kunsch, Pierre L., 2008. "Simulation of micro-CHP diffusion by means of System Dynamics," Energy Policy, Elsevier, vol. 36(7), pages 2308-2319, July.
    27. Laimon, Mohamd & Mai, Thanh & Goh, Steven & Yusaf, Talal, 2022. "System dynamics modelling to assess the impact of renewable energy systems and energy efficiency on the performance of the energy sector," Renewable Energy, Elsevier, vol. 193(C), pages 1041-1048.
    28. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    29. Krey, Volker & Guo, Fei & Kolp, Peter & Zhou, Wenji & Schaeffer, Roberto & Awasthy, Aayushi & Bertram, Christoph & de Boer, Harmen-Sytze & Fragkos, Panagiotis & Fujimori, Shinichiro & He, Chenmin & Iy, 2019. "Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models," Energy, Elsevier, vol. 172(C), pages 1254-1267.
    30. Plazas-Niño, F.A. & Ortiz-Pimiento, N.R. & Montes-Páez, E.G., 2022. "National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
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