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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Published in:	IEEE transactions on power systems Vol. 26; no. 1; pp. 431 - 441
Main Authors:	Constantinescu, E M, Zavala, V M, Rocklin, M, Sangmin Lee, Anitescu, M
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2011 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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.
AbstractList	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.
Author	Constantinescu, E M Zavala, V M Rocklin, M Sangmin Lee Anitescu, M
Author_xml	– sequence: 1 givenname: E M surname: Constantinescu fullname: Constantinescu, E M email: emconsta@mcs.anl.gov organization: Math. & Comput. Sci. Div., Argonne Nat. Lab., Argonne, IL, USA – sequence: 2 givenname: V M surname: Zavala fullname: Zavala, V M email: vzavala@mcs.anl.gov organization: Math. & Comput. Sci. Div., Argonne Nat. Lab., Argonne, IL, USA – sequence: 3 givenname: M surname: Rocklin fullname: Rocklin, M organization: Dept. of Comput. Sci., Univ. of Chicago, Chicago, IL, USA – sequence: 4 surname: Sangmin Lee fullname: Sangmin Lee organization: Courant Inst. of Math. Sci., New York Univ., New York, NY, USA – sequence: 5 givenname: M surname: Anitescu fullname: Anitescu, M email: anitescu@mcs.anl.gov organization: Math. & Comput. Sci. Div., Argonne Nat. Lab., Argonne, IL, USA
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SubjectTerms	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
Title	A Computational Framework for Uncertainty Quantification and Stochastic Optimization in Unit Commitment With Wind Power Generation
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