Large-scale stochastic mixed-integer programming algorithms for power generation scheduling

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.

Año de publicación:

2016

Keywords:

Fuente:

scopus