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The natural hedge of a gas-fired power plant
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. University College London, UK.
2016 (English)In: Computational Management Science, ISSN 1619-697X, E-ISSN 1619-6988, Vol. 13, no 1, 63-86 p.Article in journal (Refereed) Published
Abstract [en]

Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price.

Place, publisher, year, edition, pages
2016. Vol. 13, no 1, 63-86 p.
Keyword [en]
electricity markets, risk management, stochastic programming
National Category
Information Systems
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-111861DOI: 10.1007/s10287-014-0222-xOAI: diva2:776816
Available from: 2015-01-08 Created: 2015-01-08 Last updated: 2016-01-27Bibliographically approved

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Siddiqui, Afzal
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