A Computational Framework for Uncertainty Quantification and Stochastic Optimization in Unit Commitment With Wind Power Generation

We present a computational framework for integrating a state-of-the-art numerical weather prediction (NWP) model in stochastic unit commitment/economic dispatch formulations that account for wind power uncertainty. We first enhance the NWP model with an ensemble-based uncertainty quantification stra...

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Veröffentlicht in:IEEE transactions on power systems Jg. 26; H. 1; S. 431 - 441
Hauptverfasser: Constantinescu, E M, Zavala, V M, Rocklin, M, Sangmin Lee, Anitescu, M
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Closed-loop
Computation
Construction
Distributed memory
economic dispatch
Economic forecasting
Economics
Mathematical models
Power generation economics
Power system modeling
Power system simulation
Predictive models
Stochastic processes
Stochasticity
Uncertainty
Unit commitment
Weather forecasting
wind
Wind energy
Wind power generation
ISSN:0885-8950, 1558-0679
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:We present a computational framework for integrating a state-of-the-art numerical weather prediction (NWP) model in stochastic unit commitment/economic dispatch formulations that account for wind power uncertainty. We first enhance the NWP model with an ensemble-based uncertainty quantification strategy implemented in a distributed-memory parallel computing architecture. We discuss computational issues arising in the implementation of the framework and validate the model using real wind-speed data obtained from a set of meteorological stations. We build a simulated power system to demonstrate the developments.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2010.2048133