Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

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We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed ... continued below

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Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M. (Mathematics and Computer Science); Chicago), (Univ. of et al. October 9, 2009.

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We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

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  • Report No.: ANL/MCS-TM-309
  • Grant Number: DE-AC02-06CH11357
  • DOI: 10.2172/1009334 | External Link
  • Office of Scientific & Technical Information Report Number: 1009334
  • Archival Resource Key: ark:/67531/metadc831213

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  • October 9, 2009

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

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  • Dec. 12, 2016, 7:40 p.m.

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Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M. (Mathematics and Computer Science); Chicago), (Univ. of et al. Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming., report, October 9, 2009; United States. (digital.library.unt.edu/ark:/67531/metadc831213/: accessed October 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.