Argonne National Laboratory

Large-Scale Stochastic Mixed-Integer Programming Algorithms for Power Generation Scheduling

TitleLarge-Scale Stochastic Mixed-Integer Programming Algorithms for Power Generation Scheduling
Publication TypeBook Chapter
Year of Publication2015
AuthorsKim, K, Zavala, VM
Other NumbersANL/MCS-P5382-0715
AbstractThis 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 to schedule 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 provided using a modified IEEE 118-bus system with uncertain wind power generation.  
PDFhttp://www.mcs.anl.gov/papers/P5382-0715.pdf