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Optimal Selection of Distributed Energy Resources under Uncertainty and Risk Aversion
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. University College London, U.K..
2016 (English)In: IEEE transactions on engineering management, ISSN 0018-9391, E-ISSN 1558-0040, Vol. 63, no 4, 462-474 p.Article in journal (Refereed) Published
Abstract [en]

The adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. In order to overcome this barrier to investment, we develop a mean-risk optimization model for the long-term risk management problem of an energy consumer using stochastic programming. The consumer can invest in a number of generation technologies, and also has access to electricity and gas futures to reduce its risk. We examine the role of on-site generation in the consumer's riskmanagement strategy, as well as interactions between on-site generation and financial hedges. Our study shows that by swapping electricity (with high price volatility) for gas (with low price volatility), even relatively inefficient technologies reduce risk exposure and CO2 emissions. The capability of on-site generation is enhanced through the use of combined heat and power (CHP) applications. In essence, by investing in a CHP unit, a consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing its financial risk. Finally, in contrast to the extant literature, we demonstrate that on-site generation affects the consumer's decision to purchase financial hedges. In particular, while on-site generation and electricity futures may act as substitutes, on-site generation and gas futures can function as complements.

Place, publisher, year, edition, pages
2016. Vol. 63, no 4, 462-474 p.
Keyword [en]
Decisions under risk and uncertainty, management of new technologies, stochastic optimization, sustainability, technology selection
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-135438DOI: 10.1109/TEM.2016.2592805ISI: 000386243700011OAI: oai:DiVA.org:su-135438DiVA: diva2:1045222
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2016-11-29Bibliographically approved

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Siddiqui, Afzal S.
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