Abstract
This chapter presents a stochastic unit commitment model for power systems and revisits parallel decomposition algorithms for these types of models. The model is a two-stage stochastic programming problem with first-stage binary variables and second-stage mixed-binary variables. The here-and-now decision is to find day-ahead schedules for slow thermal power generators. The wait-and-see decision consists of dispatching power and scheduling fast-start generators. We discuss advantages and limitations of different decomposition methods and provide an overview of available software packages. A large-scale numerical example is presented using a modified IEEE 118-bus system with uncertain wind power generation.
The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne’’). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
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Acknowledgments
This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. We gratefully acknowledge the computing resources provided on Blues, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. We thank Julie Bessac for providing wind speed prediction data.
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Kim, K., Zavala, V.M. (2016). Large-Scale Stochastic Mixed-Integer Programming Algorithms for Power Generation Scheduling. In: Martín, M. (eds) Alternative Energy Sources and Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-28752-2_18
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DOI: https://doi.org/10.1007/978-3-319-28752-2_18
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