Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty

TL;DR

This paper introduces a machine learning-based predictive prescription framework for unit commitment under net load uncertainty, leveraging contextual information to improve out-of-sample performance compared to traditional stochastic models.

Contribution

It proposes a novel framework that uses machine learning to adaptively weight scenarios based on covariates, enhancing decision quality in uncertain power system operations.

Findings

Outperforms traditional stochastic UC models in out-of-sample tests

Effectively incorporates weather and temporal information into UC decisions

Provides a systematic approach for covariate selection and hyperparameter tuning

Abstract

To take unit commitment (UC) decisions under uncertain net load, most studies utilize a stochastic UC (SUC) model that adopts a one-size-fits-all representation of uncertainty. Disregarding contextual information such as weather forecasts and temporal information, these models are typically plagued by a poor out-of-sample performance. To effectively exploit contextual information, in this paper, we formulate a conditional SUC problem that is solved given a covariate observation. The presented problem relies on the true conditional distribution of net load and so cannot be solved in practice. To approximate its solution, we put forward a predictive prescription framework, which leverages a machine learning model to derive weights that are used in solving a reweighted sample average approximation problem. In contrast with existing predictive prescription frameworks, we manipulate the weights that the learning model delivers based on the specific dataset, present a method to select pertinent covariates, and tune the hyperparameters of the framework based on the out-of-sample cost of its policies. We conduct extensive numerical studies, which lay out the relative merits of the framework vis-\`a-vis various benchmarks.