IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v37y2009i11p4987-4996.html
   My bibliography  Save this article

Technology learning for renewable energy: Implications for South Africa's long-term mitigation scenarios

Author

Listed:
  • Winkler, Harald
  • Hughes, Alison
  • Haw, Mary

Abstract

Technology learning can make a significant difference to renewable energy as a mitigation option in South Africa's electricity sector. This article considers scenarios implemented in a Markal energy model used for mitigation analysis. It outlines the empirical evidence that unit costs of renewable energy technologies decline, considers the theoretical background and how this can be implemented in modeling. Two scenarios are modelled, assuming 27% and 50% of renewable electricity by 2050, respectively. The results show a dramatic shift in the mitigation costs. In the less ambitious scenario, instead of imposing a cost of Rand 52/t CO2-eq (at 10% discount rate), reduced costs due to technology learning turn renewables into negative cost option. Our results show that technology learning flips the costs, saving R143. At higher penetration rate, the incremental costs added beyond the base case decline from R92 per ton to R3. Including assumptions about technology learning turns renewable from a higher-cost mitigation option to one close to zero. We conclude that a future world in which global investment in renewables drives down unit costs makes it a much more cost-effective and sustainable mitigation option in South Africa.

Suggested Citation

  • Winkler, Harald & Hughes, Alison & Haw, Mary, 2009. "Technology learning for renewable energy: Implications for South Africa's long-term mitigation scenarios," Energy Policy, Elsevier, vol. 37(11), pages 4987-4996, November.
  • Handle: RePEc:eee:enepol:v:37:y:2009:i:11:p:4987-4996
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301-4215(09)00479-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    2. 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.
    3. Winkler, Harald, 2005. "Renewable energy policy in South Africa: policy options for renewable electricity," Energy Policy, Elsevier, vol. 33(1), pages 27-38, January.
    4. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    5. C. Harmon, 2000. "Experience Curves of Photovoltaic Technology," Working Papers ir00014, International Institute for Applied Systems Analysis.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Chunyu & Zheng, Xinrui & Yang, Haibin & Tang, Waiching & Sang, Guochen & Cui, Hongzhi, 2023. "Techno-economic evaluation of energy storage systems for concentrated solar power plants using the Monte Carlo method," Applied Energy, Elsevier, vol. 352(C).
    2. Olatayo, Kunle Ibukun & Wichers, J. Harry & Stoker, Piet W., 2020. "The advanced and moderate-growth development paths for the viability and future growth of small wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    3. Koo, Jamin & Park, Kyungtae & Shin, Dongil & Yoon, En Sup, 2011. "Economic evaluation of renewable energy systems under varying scenarios and its implications to Korea's renewable energy plan," Applied Energy, Elsevier, vol. 88(6), pages 2254-2260, June.
    4. Li, Yuqiang & Liao, Shengming & Rao, Zhenghua & Liu, Gang, 2014. "A dynamic assessment based feasibility study of concentrating solar power in China," Renewable Energy, Elsevier, vol. 69(C), pages 34-42.
    5. Morgan Bazilian & Patrick Nussbaumer & Hans-Holger Rogner & Abeeku Brew-Hammond & Vivien Foster & Shonali Pachauri & Eric Williams & Mark Howells & Philippe Niyongabo & Lawrence Musaba & Brian Ó Galla, 2011. "Energy Access Scenarios to 2030 for the Power Sector in Sub-Saharan Africa," Working Papers 2011.68, Fondazione Eni Enrico Mattei.
    6. Sofia, Daniele & Gioiella, Filomena & Lotrecchiano, Nicoletta & Giuliano, Aristide, 2020. "Cost-benefit analysis to support decarbonization scenario for 2030: A case study in Italy," Energy Policy, Elsevier, vol. 137(C).
    7. Kim, Seunghyok & Koo, Jamin & Lee, Chang Jun & Yoon, En Sup, 2012. "Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors," Energy, Elsevier, vol. 44(1), pages 126-134.
    8. Shih, Yi-Hsuan & Tseng, Chao-Heng, 2014. "Cost-benefit analysis of sustainable energy development using life-cycle co-benefits assessment and the system dynamics approach," Applied Energy, Elsevier, vol. 119(C), pages 57-66.
    9. Guta, Dawit Diriba & Börner, Jan, 2015. "Energy security, uncertainty, and energy resource use option in Ethiopia: A sector modelling approach," Discussion Papers 207697, University of Bonn, Center for Development Research (ZEF).
    10. Hernández-Moro, J. & Martínez-Duart, J.M., 2015. "Economic analysis of the contribution of photovoltaics to the decarbonization of the power sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1288-1297.
    11. Visser, Henning & Thopil, George Alex & Brent, Alan, 2019. "Life cycle cost profitability of biomass power plants in South Africa within the international context," Renewable Energy, Elsevier, vol. 139(C), pages 9-21.
    12. Walwyn, David Richard & Brent, Alan Colin, 2015. "Renewable energy gathers steam in South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 390-401.
    13. Musango, Josephine K. & Brent, Alan C., 2011. "Assessing the sustainability of energy technological systems in Southern Africa: A review and way forward," Technology in Society, Elsevier, vol. 33(1), pages 145-155.
    14. Thiam, Djiby-Racine & Benders, René M.J. & Moll, Henri C., 2012. "Modeling the transition towards a sustainable energy production in developing nations," Applied Energy, Elsevier, vol. 94(C), pages 98-108.
    15. Zijie Yang & Dong Huang & Yuqing Zhao & Wenqian Wang, 2022. "A Bibliometric Review of Energy Related International Investment Based on an Evolutionary Perspective," Energies, MDPI, vol. 15(9), pages 1-21, May.
    16. Xiaoru Zhuang & Xinhai Xu & Wenrui Liu & Wenfu Xu, 2019. "LCOE Analysis of Tower Concentrating Solar Power Plants Using Different Molten-Salts for Thermal Energy Storage in China," Energies, MDPI, vol. 12(7), pages 1-17, April.
    17. Hernández-Moro, J. & Martínez-Duart, J.M., 2012. "CSP electricity cost evolution and grid parities based on the IEA roadmaps," Energy Policy, Elsevier, vol. 41(C), pages 184-192.
    18. Parrado, C. & Marzo, A. & Fuentealba, E. & Fernández, A.G., 2016. "2050 LCOE improvement using new molten salts for thermal energy storage in CSP plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 505-514.
    19. Hernández-Moro, J. & Martínez-Duart, J.M., 2013. "Analytical model for solar PV and CSP electricity costs: Present LCOE values and their future evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 119-132.
    20. Lin, Boqiang & He, Jiaxin, 2016. "Learning curves for harnessing biomass power: What could explain the reduction of its cost during the expansion of China?," Renewable Energy, Elsevier, vol. 99(C), pages 280-288.
    21. Vinny Motjoadi & Pitshou N. Bokoro & Moses O. Onibonoje, 2020. "A Review of Microgrid-Based Approach to Rural Electrification in South Africa: Architecture and Policy Framework," Energies, MDPI, vol. 13(9), pages 1-22, May.

    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. 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.
    2. 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.
    3. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    4. Strupeit, Lars, 2017. "An innovation system perspective on the drivers of soft cost reduction for photovoltaic deployment: The case of Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 273-286.
    5. Harashima, Taiji, 2012. "A Theory of Intelligence and Total Factor Productivity: Value Added Reflects the Fruits of Fluid Intelligence," MPRA Paper 43151, University Library of Munich, Germany.
    6. Tu, Qiang & Mo, Jianlei & Betz, Regina & Cui, Lianbiao & Fan, Ying & Liu, Yu, 2020. "Achieving grid parity of solar PV power in China- The role of Tradable Green Certificate," Energy Policy, Elsevier, vol. 144(C).
    7. Arthur van Benthem & Kenneth Gillingham & James Sweeney, 2008. "Learning-by-Doing and the Optimal Solar Policy in California," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 131-152.
    8. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    9. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
    10. 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.
    11. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    12. Harashima, Taiji, 2009. "A Theory of Total Factor Productivity and the Convergence Hypothesis: Workers’ Innovations as an Essential Element," MPRA Paper 15508, University Library of Munich, Germany.
    13. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    14. Berry, Stephen & Davidson, Kathryn, 2016. "Improving the economics of building energy code change: A review of the inputs and assumptions of economic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 157-166.
    15. Harashima, Taiji, 2011. "A Model of Total Factor Productivity Built on Hayek’s View of Knowledge: What Really Went Wrong with Socialist Planned Economies?," MPRA Paper 29107, University Library of Munich, Germany.
    16. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    17. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.
    18. 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).
    19. Chen, Xiaoguang & Khanna, Madhu, 2012. "Explaining the reductions in US corn ethanol processing costs: Testing competing hypotheses," Energy Policy, Elsevier, vol. 44(C), pages 153-159.
    20. Bolinger, Mark & Wiser, Ryan, 2009. "Wind power price trends in the United States: Struggling to remain competitive in the face of strong growth," Energy Policy, Elsevier, vol. 37(3), pages 1061-1071, March.

    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:eee:enepol:v:37:y:2009:i:11:p:4987-4996. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.