Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Forecasting Renewable Energy Using Delphi Survey and the Economic Evaluation of Long-Term Generation Mix

델파이 활용 신재생 에너지 수요예측과 장기전원 구성의 경제성 평가

  • Koo, Hoonyoung (School of Business, Chungnam National University) ;
  • Min, Daiki (School of Business, Ewha Womans University)
  • Received : 2013.01.05
  • Accepted : 2013.03.11
  • Published : 2013.06.15
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Abstract

We address the power generation mix problem that considers not only nuclear and fossil fuels such as oil, coal and LNG but also renewable energy technologies. Unlike nuclear or other generation technologies, the expansion plan of renewable energy is highly uncertain because of its dependency on the government policy and uncertainty associated with technology improvements. To address this issue, we conduct a delphi survey and forecast the capacity of renewable energy. We further propose a stochastic mixed integer programming model that determines an optimal capacity expansion and the amount of power generation using each generation technology. Using the proposed model, we test eight generation mix scenarios and particularly evaluate how much the expansion of renewable energy contributes to the total costs for power generation in Korea. The evaluation results show that the use of renewable energy incurs additional costs.

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References

  1. Bar-Lev, D. and Katz, S. (1976), A portfolio approach to fossil fuel procurement in the electric utility industry, The Journal of Finance, 31(3), 933-947. https://doi.org/10.1111/j.1540-6261.1976.tb01935.x
  2. Birge, J. R. and Louveaux, F. (1997), Introduction to Stochastic Programming, Springer, New York.
  3. Celiktas, M. S. and Kocar, G. (2010), From potential forecast to foresight of Turkey's renewable energy with Delphi approach, Energy, 35, 1973-1980. https://doi.org/10.1016/j.energy.2010.01.012
  4. Chang, K. (2010), An study on indicator selection of renewable energy business evaluation, POSRI, 10(1), 116-140.
  5. Chung, J. and Min, D. (2012), The evaluation of long-term generation portfolio considering uncertainty, Journal of the Korean Operations Research and Management Science Society, 37(3), 135-151. https://doi.org/10.7737/JKORMS.2012.37.3.135
  6. Czaplicka-Kolarz, K., Stanczyk, K. and Kapusta, K. (2009), Technology foresight for a vision of energy sector development in Poland till 2030. Delphi survey as an element of technology foresighting, Technological Forecasting and Social Change, 76(3), 327-338. https://doi.org/10.1016/j.techfore.2008.05.007
  7. Deutch, J. M., Forsberg, C. W., Kadak, A. C., Kazimi, M. S., Moniz, E. J., Parsons, J. E., Du, Y., and Pierpoint L. (2009), Future of nuclear power: update of the MIT 2003, MIT Energy Initiative.
  8. Hart, E. K. and Jacobson, M. Z. (2011), A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables, Renewable Energy, 36, 2278-2286. https://doi.org/10.1016/j.renene.2011.01.015
  9. Huang, Y. H. and Wu, J. H. (2008), A portfolio risk analysis on electricity supply planning, Energy Policy, 36, 627-641. https://doi.org/10.1016/j.enpol.2007.10.004
  10. Jansen, J. C., Beurskens, L. W. M. and Tilburg, X. V. (2006), Application of portfolio analysis to the Dutch generating mix, Energy Research Council of Netherlands.
  11. KERI (2010), Amendment of the Korean Feed-in-Tariffs in 2011, Report I-2010-0-029, Korea Electrotechnology Institute.
  12. Kim, G., Ahn, Y., and Yoon, I. (2009), An economic analysis of the real investment projects with a variety of risk analysis techniques, J. of Advanced Engineering and Technology, 2(4), 289-297.
  13. Kim, S. (2005), Incorporating the power generation capacities of new and renewable energy into long-term electricity supply planning, Korea Ministry of Industry and Energy.
  14. Kim, S. (2009), A Study on Power-Mix Optimization by applying Portfolio theory, Master thesis, Korea Polytechnic University.
  15. Korea Ministry of Knowledge Economy (2010), The 5th basic plan for long-term electricity supply and demand, Korea Power Exchange.
  16. KPX (2009), KPX Knowledge Power 2009, Korea Power Exchange.
  17. Lee, K., Boo, K., and Lee, C. (2005), Promotion of renewable electricity market, Report, Korea Energy Economics Institute.
  18. Lee, J., Han, S., and Kim, B. (2009), A study of the long-term fuel mix with the introduction of Renewable Portfolio Standard, The Transactions of the Korean Institute of Electrical Engineers, 58(3), 467-477.
  19. Lee, T. and Chuang, M. (2012), Foresight for Public Policy of Solar Energy Industry in Taiwan : An Application of Delphi Method and Q Methodology, Proceedings of PICMET 2012 : Technology Management for Emerging Technologies, IEEE, 60-67.
  20. Moon, Y., D. Rho, S. Cho (2011), Re-examination of Energy Mix Policy : Scenario Analysis of Renewable Energy Expansion in Power Generation Sector, Korea Energy Economic Review, 10(2), 169-186.
  21. Nguyen, N. T. and M. Ha-Duong (2009), Economonic potential of renewable energy in Vietnam's power sector, Energy Policy, 37, 1601-1613. https://doi.org/10.1016/j.enpol.2008.12.026
  22. Park, J., Wu, L., Choi, J., Cha, J., El-Keib, A., and Watada, J. (2009), Fuzzy theory-based best generation mix considering renewable energy generators, IEEE International Conference on Fuzzy Systems.
  23. Shapiro, A. and Homem-de-Mello, T. (2001), On The Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs, SIAM Journal on Optimization, 11(1), 70-86.
  24. Shapiro, A., Homem-de-Mello, T., and Kim, J. (2002), Conditioning of convex piecewise linear stochastic programs, Mathematical Programming, 94, 1-19. https://doi.org/10.1007/s10107-002-0313-2
  25. Sun, H. and Tan, Z. (2012), Models of risk measurement and control in power generation investment, Systems Engineering Procedia, 3, 125-131. https://doi.org/10.1016/j.sepro.2011.11.017
  26. Van Zon, A. and Fuss, S. (2005), Irreversible investment and uncertainty in energy conversion : a Clay-Clay vintage portfolio selection approach, Working Paper, University of Maastricht/UNU-MERIT.
  27. Vithayasrichareon, P. and MacGill, I. F. (2012), A Monte Carlo based decision-support tool for assessing generation portfolio in future carbon constrained electricity industries, Energy Policy, 41, 374-392. https://doi.org/10.1016/j.enpol.2011.10.060

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