IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v32y2007i10p1809-1824.html
   My bibliography  Save this article

Implications of capacity expansion under uncertainty and value of information: The near-term energy planning of Japan

Author

Listed:
  • Krukanont, Pongsak
  • Tezuka, Tetsuo

Abstract

In this paper, we present the near-term analysis of capacity expansion under various uncertainties from the viewpoints of the decision-making process on the optimal allocation of investment and the value of information. An optimization model based on two-stage stochastic programming was developed using real data to describe the Japanese energy system as a case study. Different uncertainty parameters were taken into consideration by a disaggregate analysis of a bottom-up energy modeling approach, including end-use energy demands, plant operating availability and carbon tax rate. Four policy regimes represented as energy planning or policy options were also studied, covering business as usual, renewable energy target, carbon taxation and nuclear phase-out regimes. In addition, we investigated the role of various energy technologies and the behavior of the value of information with respect to the probability function of the worst-case scenario. This value of information provides decision makers with a quantitative analysis for the cost to obtain perfect information about the future. The developed model could be regarded as an applicable tool for decision support to provide a better understanding in energy planning and policy analyses.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:32:y:2007:i:10:p:1809-1824
    DOI: 10.1016/j.energy.2007.02.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544207000333
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2007.02.003?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. Spinney, Peter J & Watkins, G Campbell, 1996. "Monte Carlo simulation techniques and electric utility resource decisions," Energy Policy, Elsevier, vol. 24(2), pages 155-163, February.
    3. Birge, John R. & Rosa, Charles H., 1995. "Modeling investment uncertainty in the costs of global CO2 emission policy," European Journal of Operational Research, Elsevier, vol. 83(3), pages 466-488, June.
    4. Kanudia, Amit & Loulou, Richard, 1998. "Robust responses to climate change via stochastic MARKAL: The case of Quebec," European Journal of Operational Research, Elsevier, vol. 106(1), pages 15-30, April.
    5. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
    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. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    2. Kaya, Tolga & Kahraman, Cengiz, 2010. "Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul," Energy, Elsevier, vol. 35(6), pages 2517-2527.
    3. Chandra Ade Irawan & Peter S. Hofman & Hing Kai Chan & Antony Paulraj, 2022. "A stochastic programming model for an energy planning problem: formulation, solution method and application," Annals of Operations Research, Springer, vol. 311(2), pages 695-730, April.
    4. Huang, Yun-Hsun & Wu, Jung-Hua & Hsu, Yu-Ju, 2016. "Two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty," Energy, Elsevier, vol. 116(P1), pages 1145-1157.
    5. Sadeghi, Hadi & Rashidinejad, Masoud & Abdollahi, Amir, 2017. "A comprehensive sequential review study through the generation expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1369-1394.
    6. Dagoumas, A.S. & Panapakidis, I.P. & Papagiannis, G.K. & Dokopoulos, P.S., 2008. "Post-Kyoto energy consumption strategies for the Greek interconnected electric system," Energy Policy, Elsevier, vol. 36(6), pages 1980-1999, June.
    7. Cristóbal, Jorge & Guillén-Gosálbez, Gonzalo & Kraslawski, Andrzej & Irabien, Angel, 2013. "Stochastic MILP model for optimal timing of investments in CO2 capture technologies under uncertainty in prices," Energy, Elsevier, vol. 54(C), pages 343-351.
    8. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    9. 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.
    10. Mirkhani, Sh. & Saboohi, Y., 2012. "Stochastic modeling of the energy supply system with uncertain fuel price – A case of emerging technologies for distributed power generation," Applied Energy, Elsevier, vol. 93(C), pages 668-674.
    11. 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.
    12. Scott, Ian J. & Botterud, Audun & Carvalho, Pedro M.S. & Silva, Carlos A. Santos, 2020. "Renewable energy support policy evaluation: The role of long-term uncertainty in market modelling," Applied Energy, Elsevier, vol. 278(C).
    13. Büyüközkan, Gülçin & Karabulut, Yağmur, 2017. "Energy project performance evaluation with sustainability perspective," Energy, Elsevier, vol. 119(C), pages 549-560.
    14. Ioannou, Anastasia & Fuzuli, Gulistiani & Brennan, Feargal & Yudha, Satya Widya & Angus, Andrew, 2019. "Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling," Energy Economics, Elsevier, vol. 80(C), pages 760-776.
    15. Ozgur Demirta, 2013. "Evaluating the Best Renewable Energy Technology for Sustainable Energy Plannin," International Journal of Energy Economics and Policy, Econjournals, vol. 3(Special), pages 23-33.
    16. Carrión, Miguel & Domínguez, Ruth & Zárate-Miñano, Rafael, 2019. "Influence of the controllability of electric vehicles on generation and storage capacity expansion decisions," Energy, Elsevier, vol. 189(C).
    17. 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.

    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. Takanobu Kosugi, 2010. "Assessments of ‘Greenhouse Insurance’: A Methodological Review," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(4), pages 345-363, December.
    2. Li, Y.P. & Huang, G.H. & Chen, X., 2011. "An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty," Energy Policy, Elsevier, vol. 39(7), pages 4313-4324, July.
    3. 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.
    4. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    5. 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.
    6. 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.
    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. Gabriel, Steven A. & Vikas, Shree & Ribar, David M., 2000. "Measuring the influence of Canadian carbon stabilization programs on natural gas exports to the United States via a 'bottom-up' intertemporal spatial price equilibrium model," Energy Economics, Elsevier, vol. 22(5), pages 497-525, October.
    9. 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.
    10. Dong, Cong & Huang, Guohe & Cai, Yanpeng & Li, Wei & Cheng, Guanhui, 2014. "Fuzzy interval programming for energy and environmental systems management under constraint-violation and energy-substitution effects: A case study for the City of Beijing," Energy Economics, Elsevier, vol. 46(C), pages 375-394.
    11. Heinrich, G. & Howells, M. & Basson, L. & Petrie, J., 2007. "Electricity supply industry modelling for multiple objectives under demand growth uncertainty," Energy, Elsevier, vol. 32(11), pages 2210-2229.
    12. Parkinson, Simon C. & Djilali, Ned, 2015. "Long-term energy planning with uncertain environmental performance metrics," Applied Energy, Elsevier, vol. 147(C), pages 402-412.
    13. Chen, Huayi & Zhou, P., 2019. "Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?," Omega, Elsevier, vol. 89(C), pages 257-270.
    14. Mort Webster & Karen Fisher-Vanden & David Popp & Nidhi Santen, 2017. "Should We Give Up after Solyndra? Optimal Technology R&D Portfolios under Uncertainty," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(S1), pages 123-151.
    15. Brian C. O'Neill & Paul Crutzen & Arnulf Gr�bler & Minh Ha Duong & Klaus Keller & Charles Kolstad & Jonathan Koomey & Andreas Lange & Michael Obersteiner & Michael Oppenheimer & William Pepper & Warre, 2006. "Learning and climate change," Climate Policy, Taylor & Francis Journals, vol. 6(5), pages 585-589, September.
      • Brian C. O'Neill & Paul Crutzen & Arnulf Grübler & Minh Ha-Duong & Klaus Keller & Charles Kolstad & Jonathan Koomey & Andreas Lange & Michael Obersteiner & Michael Oppenheimer & William Pepper & Warre, 2006. "Learning and climate change," Post-Print halshs-00134718, HAL.
    16. Valentina Bosetti & Laurent Gilotte, 2005. "Carbon Capture and Sequestration: How Much Does this Uncertain Option Affect Near-Term Policy Choices?," Working Papers 2005.86, Fondazione Eni Enrico Mattei.
    17. Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
    18. Richard P. van Leeuwen & Annelies E. Boerman & Edmund W. Schaefer & Gerwin Hoogsteen & Yashar S. Hajimolana, 2022. "Model Supported Business Case Scenario Analysis for Decentral Hydrogen Conversion, Storage and Consumption within Energy Hubs," Energies, MDPI, vol. 15(6), pages 1-22, March.
    19. Zeyringer, Marianne & Fais, Birgit & Keppo, Ilkka & Price, James, 2018. "The potential of marine energy technologies in the UK – Evaluation from a systems perspective," Renewable Energy, Elsevier, vol. 115(C), pages 1281-1293.
    20. Aurélie Méjean & Franck Lecocq & Yacob Mulugetta, 2015. "Equity, burden sharing and development pathways: reframing international climate negotiations," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 15(4), pages 387-402, 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:eee:energy:v:32:y:2007:i:10:p:1809-1824. 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.