IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p8005-d1297813.html
   My bibliography  Save this article

A System Dynamics Approach to Technological Learning Impact for the Cost Estimation of Solar Photovoltaics

Author

Listed:
  • Rong Wang

    (Breakthrough Technology Innovation Group, Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands)

  • Sandra Hasanefendic

    (Breakthrough Technology Innovation Group, Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands)

  • Elizabeth Von Hauff

    (Fraunhofer Institute for Organic Electronics, Electron Beam and Plasma Technology FEP, Winterbergstraße 28, D-01277 Dresden, Germany
    Institute of Solid State Electronics (IFE), Technische Universität Dresden, Mommsenstraße 15, D-01069 Dresden, Germany)

  • Bart Bossink

    (Breakthrough Technology Innovation Group, Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands)

Abstract

Technological learning curve models have been continuously used to estimate the cost development of solar photovoltaics (PV) for climate mitigation targets over time. They can integrate several technical sources that influence the learning process. Yet, the accurate and realistic learning curve that reflects the cost estimations of PV development is still challenging to determine. To address this question, we develop four hypothetical-alternative learning curve models by proposing different combinations of technological learning sources, including both local and global technological experience and knowledge stock. We specifically adopt the system dynamics approach to focus on the non-linear relationship and dynamic interaction between the cost development and technological learning source. By applying this approach to Chinese PV systems, the results reveal that the suitability and accuracy of learning curve models for cost estimation are dependent on the development stages of PV systems. At each stage, different models exhibit different levels of closure in cost estimation. Furthermore, our analysis underscores the critical role of incorporating global technical sources into learning curve models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8005-:d:1297813
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/8005/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/24/8005/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John Paul Helveston & Gang He & Michael R. Davidson, 2022. "Quantifying the cost savings of global solar photovoltaic supply chains," Nature, Nature, vol. 612(7938), pages 83-87, December.
    2. Guo, Xiaopeng & Dong, Yining & Ren, Dongfang, 2023. "CO2 emission reduction effect of photovoltaic industry through 2060 in China," Energy, Elsevier, vol. 269(C).
    3. Xin-gang, Zhao & Wei, Wang & Ling, Wu, 2021. "A dynamic analysis of research and development incentive on China's photovoltaic industry based on system dynamics model," Energy, Elsevier, vol. 233(C).
    4. Johan Lilliestam & Marc Melliger & Lana Ollier & Tobias S. Schmidt & Bjarne Steffen, 2020. "Understanding and accounting for the effect of exchange rate fluctuations on global learning rates," Nature Energy, Nature, vol. 5(1), pages 71-78, January.
    5. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    6. Miremadi, I. & Saboohi, Y. & Arasti, M., 2019. "The influence of public R&D and knowledge spillovers on the development of renewable energy sources: The case of the Nordic countries," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 450-463.
    7. Aslani, Alireza & Helo, Petri & Naaranoja, Marja, 2014. "Role of renewable energy policies in energy dependency in Finland: System dynamics approach," Applied Energy, Elsevier, vol. 113(C), pages 758-765.
    8. Zhang, M.M. & Zhang, C. & Liu, L.Y. & Zhou, D.Q., 2020. "Is it time to launch grid parity in the Chinese solar photovoltaic industry? Evidence from 335 cities," Energy Policy, Elsevier, vol. 147(C).
    9. Franco Malerba & Maria Mancusi & Fabio Montobbio, 2013. "Innovation, international R&D spillovers and the sectoral heterogeneity of knowledge flows," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 149(4), pages 697-722, December.
    10. de La Tour, Arnaud & Glachant, Matthieu & Ménière, Yann, 2013. "Predicting the costs of photovoltaic solar modules in 2020 using experience curve models," Energy, Elsevier, vol. 62(C), pages 341-348.
    11. Guo, Xiaodan & Guo, Xiaopeng, 2015. "China's photovoltaic power development under policy incentives: A system dynamics analysis," Energy, Elsevier, vol. 93(P1), pages 589-598.
    12. Dong, Changgui & Zhou, Runmin & Li, Jiaying, 2021. "Rushing for subsidies: The impact of feed-in tariffs on solar photovoltaic capacity development in China," Applied Energy, Elsevier, vol. 281(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Grafström, Jonas & Poudineh, Rahmat, 2023. "No evidence of counteracting policy effects on European solar power invention and diffusion," Energy Policy, Elsevier, vol. 172(C).
    2. Grafström, Jonas & Poudineh, Rahmat, 2023. "Invention and Diffusion in the Solar Power Sector," Ratio Working Papers 364, The Ratio Institute.
    3. Libo Zhang & Qian Du & Dequn Zhou, 2021. "Grid Parity Analysis of China’s Centralized Photovoltaic Generation under Multiple Uncertainties," Energies, MDPI, vol. 14(7), pages 1-19, March.
    4. 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.
    5. Tang, Lei & Guo, Jue & Zhao, Boyang & Wang, Xiuli & Shao, Chengcheng & Wang, Yifei, 2021. "Power generation mix evolution based on rolling horizon optimal approach: A system dynamics analysis," Energy, Elsevier, vol. 224(C).
    6. Sommerfeldt, Nelson & Madani, Hatef, 2017. "Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part one – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1379-1393.
    7. Liu, Dunnan & Zhao, Weidong & Li, Zhihao & Xu, Xiaofeng & Xiao, Bowen & Niu, Dongxiao, 2018. "Can hydropower develop as expected in China? A scenario analysis based on system dynamics model," Energy, Elsevier, vol. 161(C), pages 118-129.
    8. Wang, Rong & Hasanefendic, Sandra & Von Hauff, Elizabeth & Bossink, Bart, 2022. "The cost of photovoltaics: Re-evaluating grid parity for PV systems in China," Renewable Energy, Elsevier, vol. 194(C), pages 469-481.
    9. Xin-gang, Zhao & Wei, Wang & Ling, Wu, 2021. "A dynamic analysis of research and development incentive on China's photovoltaic industry based on system dynamics model," Energy, Elsevier, vol. 233(C).
    10. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    11. 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.
    12. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    13. Zhang, Libo & Chen, Changqi & Wang, Qunwei & Zhou, Dequn, 2021. "The impact of feed-in tariff reduction and renewable portfolio standard on the development of distributed photovoltaic generation in China," Energy, Elsevier, vol. 232(C).
    14. Emanuele Massetti & Lea Nicita, 2010. "The Optimal Climate Policy Portfolio when Knowledge Spills across Sectors," CESifo Working Paper Series 2988, CESifo.
    15. Kwag, Kyuhyeong & Shin, Hansol & Oh, Hyobin & Yun, Sangmin & Kim, Tae Hyun & Hwang, Pyeong-Ik & Kim, Wook, 2023. "Bilevel programming approach for the quantitative analysis of renewable portfolio standards considering the electricity market," Energy, Elsevier, vol. 263(PD).
    16. Tom Broekel & Matthias Brachert, 2015. "The structure and evolution of inter-sectoral technological complementarity in R&D in Germany from 1990 to 2011," Journal of Evolutionary Economics, Springer, vol. 25(4), pages 755-785, September.
    17. Emanuele Bacchiocchi & Fabio Montobbio, 2010. "International Knowledge Diffusion and Home‐bias Effect: Do USPTO and EPO Patent Citations Tell the Same Story?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 112(3), pages 441-470, September.
    18. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    19. Behnam Zakeri & Samuli Rinne & Sanna Syri, 2015. "Wind Integration into Energy Systems with a High Share of Nuclear Power—What Are the Compromises?," Energies, MDPI, vol. 8(4), pages 1-35, March.
    20. Yuliang Yang & Chaoqun Cui, 2022. "Which Provincial Regions in China Should Give Priority to the Redevelopment of Abandoned Coal Mines? A Redevelopment Potential Evaluation Based Analysis," Sustainability, MDPI, vol. 14(23), pages 1-22, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8005-:d:1297813. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.