Power Plant Valuation
Figure 4: Optimal Moments to Start – Valuation
down from GBP 109.2 to 107.4 million. However, with fewer starts the plant is eventually affected with a value going below GBP 100 million for 2 allowed starts.
Non-Perfect Foresight: Least-Squares Monte Carlo The above results may not be a fair assessment
of the value that can be extracted from this power plant. The dispatch optimisation has been carried out using perfect foresight about the price levels in the rest of the year. While operators may be able to have a quite precise assessment of prices in the next 24-48 hours, assessing spread levels and start-stop frequency a couple of weeks or months out, is more challenging. The Least-Squares Monte Carlo approach provides a
Source: KYOS Energy Consulting
methodology to introduce a degree of non-perfect foresight. Its impact is most pronounced when the number of allowed starts per year is the only limiting factor. So, there are no explicit start costs or minimum run-times. Figure 4 shows that there is a decline in value when the optimal moments to start cannot be selected with perfect foresight. The more stringent the start limitation, the more difficult it is to operate optimally, ultimately leading to a value decline of around GBP 6 million if only 10 starts per year are allowed. The implemented Least-Squares Monte Carlo approach
provides an estimate of the plant’s continuation value using only spot price information. Alternative specifications, incorporating forward price data, reduces the GBP 6 million slightly, but by never more than GBP 1 million in our test setups.
Conclusion Gas-fired power plants have generally higher marginal production costs than other production units. Yet, what
Using LSMC methodology ... the value loss due to non-perfect foresight may amount to 5% of the total plant value
motivates their investment is the combination of lower investment costs (per MW capacity) and their flexibility to vary production. With a rising penetration of wind power, and especially offshore wind, the price fluctuations in the hourly markets are expected to become more extreme. Nevertheless, the flexibility of gas-fired plants is not unlimited as each start is associated with explicit and implicit costs. In this article we
worldPower 2010
showed how such costs can be introduced in an investment analysis. We also showed that there is considerable real option value in the hourly production flexibility. However, this flexibility is likely to be overstated when assuming perfect foresight in the optimal dispatch decision. Using the Least-Squares Monte Carlo methodology, our calculations demonstrate that the value loss due to non- perfect foresight may amount to 5% of the total plant value. This is a fundamentally new outcome and worth the extra modelling effort. ■
By Cyriel de Jong, Dirk van Abbema, Henk Sjoerd Los & Hans van Dijken:
KYOS Energy Consulting and Erasmus University Rotterdam. KYOS Energy Consulting is an independent consultancy firm offering specialised advice on trading and risk management in energy markets. KYOS advises energy companies, end-users, financial institutions, policy makers and regulators.
www.kyos.com
Footnote: 1. Realistic Power Plant Valuations, WorldPower 2009, pp. 48-53.
Download from
www.commodities-now.com/worldpower References:
• Boogert, A., and C. de Jong, 2008, “Gas Storage Valuation Using a Monte Carlo Method”, Journal of Derivatives, Vol. 15, No. 3, pp. 81- 98.
• Tseng, C.L., and G. Barz, 2002, “Short-term generation asset valuation: a real options approach.” Operations Research, Vol. 50,
• Deng, S.J., and Z. Xia, 2006, “A Real Options Approach for Pricing Electricity Tolling Agreements”, International Journal of Information
No. 2, pp. 297-310.
• Los, H.S., C. de Jong and H. van Dijken, 2009, “Realistic power plant valuations: How to use cointegrated power and fuel prices”,
Technology & Decision Making, Vol. 5, No. 3, pp. 421-436.
• Clewlow, L. J. Lujun Liu, D. Meador, R. Sobey and C. Strickland, 2009, “Valuing generation assets using Monte Carlo Simulations”,
WorldPower 2009, pp. 48-53. Energy Risk, September, pp. 56-61. 53
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