A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation.

Journal Article · Tue Feb 01 00:00:00 EST 2011 · IEEE Trans. Power Syst.

DOI:https://doi.org/10.1109/TPWRS.2010.2048133· OSTI ID:1011827

Constantinescu, E M; Zavala, V M; Rocklin, M; Lee, S; Anitescu, M ^[1]

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1. Mathematics and Computer Science

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.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: DE-AC02-06CH11357
• OSTI ID: 1011827
• Report Number(s): ANL/MCS/JA-65362; TRN: US201109%%646
• Journal Information: IEEE Trans. Power Syst., Vol. 26, Issue 1 ; Feb. 2011
• Country of Publication: United States
• Language: ENGLISH

Related Subjects

17 WIND ENERGY
24 POWER TRANSMISSION AND DISTRIBUTION
COMPUTER ARCHITECTURE
FORECASTING
IMPLEMENTATION
OPTIMIZATION
POWER SYSTEMS
WEATHER
WIND POWER
PARALLEL PROCESSING