Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.
Abstract
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.
- Authors:
-
- Mathematics and Computer Science
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1009334
- Report Number(s):
- ANL/MCS-TM-309
TRN: US201107%%745
- DOE Contract Number:
- DE-AC02-06CH11357
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- ENGLISH
- Subject:
- 17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; ARCHITECTURE; FORECASTING; IMPLEMENTATION; POWER SYSTEMS; PROGRAMMING; SAMPLING; SENSITIVITY ANALYSIS; VELOCITY; WEATHER; WIND POWER
Citation Formats
Constantinescu, E M, Zavala, V M, Rocklin, M, Lee, S, Anitescu, M, Univ. of Chicago), and New York Univ.). Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.. United States: N. p., 2009.
Web. doi:10.2172/1009334.
Constantinescu, E M, Zavala, V M, Rocklin, M, Lee, S, Anitescu, M, Univ. of Chicago), & New York Univ.). Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.. United States. https://doi.org/10.2172/1009334
Constantinescu, E M, Zavala, V M, Rocklin, M, Lee, S, Anitescu, M, Univ. of Chicago), and New York Univ.). 2009.
"Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.". United States. https://doi.org/10.2172/1009334. https://www.osti.gov/servlets/purl/1009334.
@article{osti_1009334,
title = {Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.},
author = {Constantinescu, E M and Zavala, V M and Rocklin, M and Lee, S and Anitescu, M and Univ. of Chicago) and New York Univ.)},
abstractNote = {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.},
doi = {10.2172/1009334},
url = {https://www.osti.gov/biblio/1009334},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Oct 09 00:00:00 EDT 2009},
month = {Fri Oct 09 00:00:00 EDT 2009}
}