Abstract
This study uses maximum entropy method to find an optimal combination of energy sources for electricity generation in Thailand. It sets three targets including unit cost, risk and pollution. In the optimization process, it forms three constraints according to these three targets. It solves the system following the guideline of Golan, Judge and Miller (1996). It analyses six scenarios of the targets. For the major results, it finds that hydropower, nuclear, wind and solar energy are major sources of electricity generation. The country cannot avoid adopting nuclear energy for its electricity generation in order to meet all the three targets that are optimal for its electricity generation and economic development.
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References
Bera, A.K., Park, S.Y.: Optimal Portfolio Diversification Using Maximum Entropy Principle. Econometric Reviews 27, 484–512 (2008)
Gartner, I.R.: Differentiated Risk Models in Portfolio Optimization: A Comparative Analysis of the Degree of Diversification and Performance in the Sao Paulo Stock Exchange (Bovespa). Pesquisa Operacional 32(2), 271–292 (2012)
Golan, A., Judge, G.G., Miller, D.: Maximum Entropy Econometrics: Robust Estimation with Limited Data. John Wiley & Sons (1996)
Hochreiter, R., Pflug, G.C., Wozabal, D.: Multi-stage stochastic electricity portfolio optimization in liberalized energy markets. Working Paper on Optimal Energy Portfolios, Department of Statistics and Decision Support System, University of Vienna (2005)
Inhaber, H.: Is Solar Power More Dangerous Than Nuclear? IAEA Bulletin 21(1), 11–17 (1982)
Jiang, Y., He, S., Li, X.: A Maximum Entropy Model for Large Scale Portfolio Optimization. In: Proceedings of the International Conference on Risk Management and Engineering Management 2008, pp. 610–615 (2008)
Liu, M.: Portfolio optimization in electricity markets. Electric Power Systems Research 77, 1000–1009
Ministry of Energy of Thailand. Thailand electricity generation by source in 2011. Ministry of Energy, Bangkok (2011)
Mitsubishi Corporation. Annual Report, Power Business (2012), http://www.mitsubishicorp.com
Park, S.Y.: Optimal Portfolio Diversification Using Maximum Entropy Principle. Chapter 3 in Sung Yong Park, Essays on Maximum Entropy Principles with Applications to Econometrics and Finance. ProQuest (2007)
Qin, Z., Li, X., Ji, X.: Portfolio selection based on cross-entropy. Journal of Computational and Applied Mathematics 228, 139–149 (2009)
Rebennack, S., Kallrath, J., Pardalos, P.M.: Energy Portfolio Optimization for Electric Utilities: Case Study for Germany. In: Energy, Natural Resources and Environmental Economics Energy Systems, pp. 221–246 (2010)
Rodriguez, J.: A New Portfolio Optimization Based on Entropy. Master thesis, Section of Mathematics, Faculty of Sciences, University of Geneva (2007)
Roeddner, W., Gartner, I.R., Rudolph, S.: Entropy-Driven Portfolio Selection: A Downside and Upside Risk Framework. Discussion Paper Number 437. Faculty of Economic Sciences, University of Hagen (2009)
Sovacool, B.K.: Valuing the greenhouse gas emissions from nuclear power: A critical survey. Energy Policy 36, 2940–2953 (2009)
Sudtasan, T., Suriya, K.: Nuclear power plant after Fukushima incident: Lessons from Japan to Thailand for choosing power plant options. The Empirical Econometrics and Quantitative Economics Letters 1(3), 1–8 (2012)
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Sudtasan, T., Suriya, K. (2014). Optimal Combination of Energy Sources for Electricity Generation in Thailand with Lessons from Japan Using Maximum Entropy. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Modeling Dependence in Econometrics. Advances in Intelligent Systems and Computing, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-319-03395-2_35
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DOI: https://doi.org/10.1007/978-3-319-03395-2_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03394-5
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