IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v168y2019icp370-379.html
   My bibliography  Save this article

Uncertainty quantification and scenario generation of future solar photovoltaic price for use in energy system models

Author

Listed:
  • Kim, Hansung
  • Cheon, Hyungkyu
  • Ahn, Young-Hwan
  • Choi, Dong Gu

Abstract

Recently, researchers have recognized the necessity of incorporating uncertainties into energy system models. This has led to the development of stochastic programming-based models. Such models require values of input parameters in the form of a scenario tree to handle the uncertainties. However, there are limited studies that have generated scenario trees for technical factors based on historical data and quantitative methods. This study shows that a scenario tree for a technical factor can be constructed based on quantitative methods and historical data. More specifically, the main contribution of this study is that it proposes an approach to not only quantify the uncertainty of future solar photovoltaic module price by considering the uncertainty in learning rate but also make it into a scenario tree. The approach comprises three steps: (1) stochastic process model estimation, (2) scenario tree generation, and (3) uncertainty quantification. In conclusion, an estimated multivariate autoregressive model can efficiently represent the uncertainty of solar photovoltaic module price. The moment matching method can be applied to generate an appropriate scenario tree for the price. The proposed approach can be applied to other technical factors, and it can help policy makers and practitioners to develop persuasive scenarios for technical factors.

Suggested Citation

  • Kim, Hansung & Cheon, Hyungkyu & Ahn, Young-Hwan & Choi, Dong Gu, 2019. "Uncertainty quantification and scenario generation of future solar photovoltaic price for use in energy system models," Energy, Elsevier, vol. 168(C), pages 370-379.
    • Handle: RePEc:eee:energy:v:168:y:2019:i:c:p:370-379
    DOI: 10.1016/j.energy.2018.11.075
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218322862
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.11.075?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Kanudia, Amit & Shukla, PR, 1998. "Modelling of Uncertainties and Price Elastic Demands in Energy-environment Planning for India," Omega, Elsevier, vol. 26(3), pages 409-423, June.
    2. van Vuuren, Detlef P. & Hoogwijk, Monique & Barker, Terry & Riahi, Keywan & Boeters, Stefan & Chateau, Jean & Scrieciu, Serban & van Vliet, Jasper & Masui, Toshihiko & Blok, Kornelis & Blomen, Eliane , 2009. "Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials," Energy Policy, Elsevier, vol. 37(12), pages 5125-5139, December.
    3. Allen C. Miller, III & Thomas R. Rice, 1983. "Discrete Approximations of Probability Distributions," Management Science, INFORMS, vol. 29(3), pages 352-362, March.
    4. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    5. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    6. 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.
    7. 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.
    8. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
    9. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
    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. Krukanont, Pongsak & Tezuka, Tetsuo, 2007. "Implications of capacity expansion under uncertainty and value of information: The near-term energy planning of Japan," Energy, Elsevier, vol. 32(10), pages 1809-1824.
    12. Mauleón, Ignacio & Hamoudi, Hamid, 2017. "Photovoltaic and wind cost decrease estimation: Implications for investment analysis," Energy, Elsevier, vol. 137(C), pages 1054-1065.
    13. 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.
    14. Min, Daiki & Chung, Jaewoo, 2013. "Evaluation of the long-term power generation mix: The case study of South Korea's energy policy," Energy Policy, Elsevier, vol. 62(C), pages 1544-1552.
    15. John Bistline & John Weyant, 2013. "Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach," Climatic Change, Springer, vol. 121(2), pages 143-160, November.
    16. Hu, Ming-Che & Hobbs, Benjamin F., 2010. "Analysis of multi-pollutant policies for the U.S. power sector under technology and policy uncertainty using MARKAL," Energy, Elsevier, vol. 35(12), pages 5430-5442.
    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. Wang, Jiangjiang & Huo, Shuojie & Yan, Rujing & Cui, Zhiheng, 2022. "Leveraging heat accumulation of district heating network to improve performances of integrated energy system under source-load uncertainties," Energy, Elsevier, vol. 252(C).
    2. Zhou, Kaile & Chu, Yibo & Hu, Rong, 2023. "Energy supply-demand interaction model integrating uncertainty forecasting and peer-to-peer energy trading," Energy, Elsevier, vol. 285(C).
    3. Hongtao Ren & Wenji Zhou & Hangzhou Wang & Bo Zhang & Tieju Ma, 2022. "An energy system optimization model accounting for the interrelations of multiple stochastic energy prices," Annals of Operations Research, Springer, vol. 316(1), pages 555-579, September.
    4. Kumar, Dinesh & Bahauddin Alam, Syed & Ridwan, Tuhfatur & Goodwin, Cameron S., 2021. "Quantitative risk assessment of a high power density small modular reactor (SMR) core using uncertainty and sensitivity analyses," Energy, Elsevier, vol. 227(C).
    5. Wang, Jing & Kang, Lixia & Huang, Xiankun & Liu, Yongzhong, 2021. "An analysis framework for quantitative evaluation of parametric uncertainty in a cooperated energy storage system with multiple energy carriers," Energy, Elsevier, vol. 226(C).
    6. Nemet, Gregory F. & Lu, Jiaqi & Rai, Varun & Rao, Rohan, 2020. "Knowledge spillovers between PV installers can reduce the cost of installing solar PV," Energy Policy, Elsevier, vol. 144(C).
    7. Stevović, Ivan & Mirjanić, Dragoljub & Stevović, Svetlana, 2019. "Possibilities for wider investment in solar energy implementation," Energy, Elsevier, vol. 180(C), pages 495-510.
    8. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
    9. Kim, Hansung & Lee, Hwarang & Koo, Yoonmo & Choi, Dong Gu, 2020. "Comparative analysis of iterative approaches for incorporating learning-by-doing into the energy system models," Energy, Elsevier, vol. 197(C).
    10. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
    11. Yiping Huang & Qin Yang & Jinfeng Liu & Xiao Li & Jie Zhang, 2021. "Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand," Sustainability, MDPI, vol. 13(14), pages 1-23, July.

    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. 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.
    2. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    3. 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).
    4. Choi, Donghyun & Kim, Yeong Jae, 2023. "Local and global experience curves for lumpy and granular energy technologies," Energy Policy, Elsevier, vol. 174(C).
    5. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    6. 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).
    7. Bistline, John E., 2015. "Electric sector capacity planning under uncertainty: Climate policy and natural gas in the US," Energy Economics, Elsevier, vol. 51(C), pages 236-251.
    8. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    9. 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.
    10. 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).
    11. 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.
    12. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    13. 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.
    14. Kavlak, Goksin & McNerney, James & Trancik, Jessika E., 2018. "Evaluating the causes of cost reduction in photovoltaic modules," Energy Policy, Elsevier, vol. 123(C), pages 700-710.
    15. 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.
    16. Chiara Modanese & Hannu S. Laine & Toni P. Pasanen & Hele Savin & Joshua M. Pearce, 2018. "Economic Advantages of Dry-Etched Black Silicon in Passivated Emitter Rear Cell (PERC) Photovoltaic Manufacturing," Energies, MDPI, vol. 11(9), pages 1-18, September.
    17. Taghizadeh-Hesary, Farhad & Yoshino, Naoyuki & Inagaki, Yugo & Morgan, Peter J., 2021. "Analyzing the factors influencing the demand and supply of solar modules in Japan – Does financing matter," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 1-12.
    18. De Cian, Enrica & Buhl, Johannes & Carrara, Samuel & Michela Bevione, Michela & Monetti, Silvia & Berg, Holger, 2016. "Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach," MITP: Mitigation, Innovation and Transformation Pathways 249784, Fondazione Eni Enrico Mattei (FEEM).
    19. Buchholz, Wolfgang & Dippl, Lisa & Eichenseer, Michael, 2019. "Subsidizing renewables as part of taking leadership in international climate policy: The German case," Energy Policy, Elsevier, vol. 129(C), pages 765-773.
    20. Grafström, Jonas & Poudineh, Rahmat, 2021. "A review of problems associated with learning curves for solar and wind power technologies," Ratio Working Papers 347, The Ratio Institute.

    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:energy:v:168:y:2019:i:c:p:370-379. 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.journals.elsevier.com/energy .

    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.