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Stochastic Security for Operations Planning With Significant Wind Power Generation

Bouffard, F., Galiana, F.D.·2008·IEEE Transactions on Power Systems

Abstract

In their attempt to cut down on greenhouse gas emissions from electricity generation, several countries are committed to install wind power generation up to and beyond the 10%-20% penetration mark. However, the large-scale integration of wind power represents a challenge for power system operations planning because wind power 1) cannot be dispatched in the classical sense; and 2) its output varies as weather conditions change. This warrants the investigation of alternative short-term power system operations planning methods capable of better coping with the nature of wind generation while maintaining or even improving the current reliability and economic performance of power systems. To this end, this paper formulates a short-term forward electricity market-clearing problem with stochastic security capable of accounting for nondispatchable and variable wind power generation sources. The principal benefit of this stochastic operation planning approach is that, when compared to a deterministic worst-case scenario planning philosophy, it allows greater wind power penetration without sacrificing security.

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Metadata

Authors
Bouffard, F., & Galiana, F. D.
Venue
IEEE Transactions on Power Systems
Published
2008-05
Volume
23
Issue
2
Pages
306-316
ISSN
  • 0885-8950 (ISSN-L)
  • 1558-0679
OpenAlex
W2135131968
Cited by
713
References
0
FWCI
26.30
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stochastic Security for Operations Planning With Significant Wind Power Generation. https://app.cochat.ai/papers/10.1109/tpwrs.2008.919318
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Bouffard, F., & Galiana, F. D. (2008). Stochastic Security for Operations Planning With Significant Wind Power Generation. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2008.919318

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.