Correlated wind-power production and electric load scenarios for investment decisions

L. Baringo and A.J. Conejo

Applied Energy, 2013, vol. 101, issue C, 475-482

Abstract: Stochastic programming constitutes a useful tool to address investment problems. This technique represents uncertain input data using a set of scenarios, which should accurately describe the involved uncertainty. In this paper, we propose two alternative methodologies to efficiently generate electric load and wind-power production scenarios, which are used as input data for investment problems. The two proposed methodologies are based on the load- and wind-duration curves and on the K-means clustering technique, and allow representing the uncertainty of and the correlation between electric load and wind-power production. A case study pertaining to wind-power investment is used to show the interest of the proposed methodologies and to illustrate how the selection of scenarios has a significant impact on investment decisions.

Keywords: Clustering; Correlated scenarios; Electric load; Investment; Stochastic programming; Wind-power (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (47)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:101:y:2013:i:c:p:475-482

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DOI: 10.1016/j.apenergy.2012.06.002

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