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

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  • Baringo, L.
  • Conejo, A.J.

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.

Suggested Citation

  • Baringo, L. & Conejo, A.J., 2013. "Correlated wind-power production and electric load scenarios for investment decisions," Applied Energy, Elsevier, vol. 101(C), pages 475-482.
    • Handle: RePEc:eee:appene:v:101:y:2013:i:c:p:475-482
    DOI: 10.1016/j.apenergy.2012.06.002
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    References listed on IDEAS

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    1. Baringo, L. & Conejo, A.J., 2011. "Wind power investment within a market environment," Applied Energy, Elsevier, vol. 88(9), pages 3239-3247.
    2. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    3. Frederic H. Murphy & Yves Smeers, 2005. "Generation Capacity Expansion in Imperfectly Competitive Restructured Electricity Markets," Operations Research, INFORMS, vol. 53(4), pages 646-661, August.
    4. Zhou, Ying & Wang, Lizhi & McCalley, James D., 2011. "Designing effective and efficient incentive policies for renewable energy in generation expansion planning," Applied Energy, Elsevier, vol. 88(6), pages 2201-2209, June.
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