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

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  • 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
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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, Open Access Journal, vol. 4(10), pages 1-33, October.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

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