A portfolio risk analysis on electricity supply planning

Abstract Conventional electricity planning selects from a range of alternative technologies based on the least-cost method without assessing cost-related risks. The current approach to determining energy generation portfolios creates a preference for fossil fuel. Consequently, this preference results in increased exposure to recent fluctuations in fossil fuel prices, particularly for countries heavily depend on imported energy. This paper applies portfolio theory in conventional electricity planning with Taiwan as a case study. The model objective is to minimize the “risk-weighted present value of total generation cost”. Both the present value of generating cost and risk (variance of the generating cost) are considered. Risk of generating cost is introduced for volatile fuel prices and uncertainty of technological change and capital cost reduction. The impact of risk levels on the portfolio of power generation technologies is also examined to provide some valuable policy suggestions. Study results indicate that replacing fossil fuel with renewable energy helps reduce generating cost risk. However, due to limited renewable development potential in Taiwan, there is an upper bound of 15% on the maximum share of renewable energy in the generating portfolio. In the meantime, reevaluating the current nuclear energy policy for reduced exposure to fossil fuel price fluctuations is worthwhile.

[1]  Tirso Leonardo Barreto Gómez,et al.  Technological Learning in Energy Optimisation Models and Deployment of Emerging Technologies , 2001 .

[2]  Shimon Awerbuch,et al.  APPLYING PORTFOLIO THEORY TO EU ELECTRICITY PLANNING AND POLICY-MAKING , 2003 .

[3]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[4]  R. L. Christie,et al.  Annual Report for 2005 , 2006 .

[5]  Reinhard Madlener,et al.  A Real Options Evaluation Model for the Diffusion Prospects of New Renewable Power Generation Technologies , 2008 .

[6]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[7]  Reinhard Madlener,et al.  Modeling technology adoption as an irreversible investment under uncertainty: the case of the Turkish electricity supply industry , 2005 .

[8]  S. Hayden Lesbirel,et al.  Diversification and Energy Security Risks: The Japanese Case , 2004, Japanese Journal of Political Science.

[9]  Socrates Kypreos,et al.  Endogenizing R&D and Market Experience in the "Bottom-Up" Energy-Systems ERIS Model , 2004 .

[10]  S. Fuss,et al.  Investing in energy conversion technologies - an optimum vintage portfolio selection approach , 2005 .

[11]  Jung-Hua Wu,et al.  Renewable energy perspectives and support mechanisms in Taiwan , 2006 .

[12]  Patrik Söderholm,et al.  Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies , 2007 .

[13]  Katherine T. McClain,et al.  Reducing the Impacts of Energy Price Volatility Through Dynamic Portfolio Selection , 1998 .

[14]  Patrik Söderholm,et al.  Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models , 2006 .

[15]  C. Wene.  Experience Curves for Energy Technology Policy , 2000 .

[16]  Leo Schrattenholzer,et al.  Learning rates for energy technologies , 2001 .