Dynamic Formulation for Multistage Stochastic Unit Commitment Problem

TL;DR

This paper introduces a dynamic multistage stochastic unit commitment model that uses a state-space approach to better handle uncertain and evolving power load scenarios, improving decision-making in power markets.

Contribution

It presents a novel dynamic formulation with a state-space model for commitment variables and algorithms for constructing scenario trees from real load data.

Findings

Effective scenario tree algorithms for real net-load data

Improved decision-making under load uncertainty

Enhanced modeling of future power system states

Abstract

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.