Predict-and-Optimize Robust Unit Commitment with Statistical Guarantees via Weight Combination

TL;DR

This paper introduces a novel robust unit commitment framework that integrates forecasting and optimization with statistical guarantees, using weight combination of multiple predictions and neural network acceleration to improve power system reliability under uncertainty.

Contribution

It proposes an integrated forecasting-optimization approach with statistical guarantees, utilizing weight combination of diverse predictions and neural network surrogates for efficiency.

Findings

Improved UC performance through combined forecasts.

Statistical guarantees ensure robustness against uncertainty.

Case studies demonstrate effectiveness on IEEE systems.

Abstract

The growing uncertainty from renewable power and electricity demand brings significant challenges to unit commitment (UC). While various advanced forecasting and optimization methods have been developed to predict better and address this uncertainty, most previous studies treat forecasting and optimization as separate tasks. This separation can lead to suboptimal results due to misalignment between the objectives of the two tasks. To overcome this challenge, we propose a robust UC framework that integrates forecasting and optimization processes while ensuring statistical guarantees. In the forecasting stage, we combine multiple predictions derived from diverse data sources and methodologies for an improved prediction, aiming to optimize the UC performance. In the optimization stage, the combined prediction is used to construct an uncertainty set with statistical guarantees, based on which the robust UC model is formulated. The optimal robust UC solution provides feedback to refine the weight used for combining multiple predictions. To solve the proposed integrated forecasting-optimization framework efficiently and effectively, we develop a neural network-based surrogate model for acceleration and introduce a reshaping method for the uncertainty set based on the optimization result to reduce conservativeness. Case studies on modified IEEE 30-bus and 118-bus systems demonstrate the advantages of the proposed approach.