Dynamic Formulation for Multistage Stochastic Unit Commitment Problem

As net-load becomes less predictable there is a lot of pressure in changing decision models for power markets such that they account explicitly for future scenarios in making commitment decisions. This paper proposes to make commitment decisions using a dynamic multistage stochastic unit commitment formulation over a cohesive horizon that leverages a state-space model for the commitment variables. We study the problem of constructing scenario tree approximations for both original and residual stochastic process and evaluate our algorithms on scenario tree libraries derived from real net-load data.