Solving Multistage Stochastic Linear Programming via Regularized Linear Decision Rules: An Application to Hydrothermal Dispatch Planning

TL;DR

This paper introduces a regularized linear decision rule approach using AdaLASSO to improve multistage stochastic linear programming solutions, specifically applied to hydrothermal dispatch planning, enhancing out-of-sample performance and model simplicity.

Contribution

The paper proposes a novel regularized LDR method based on AdaLASSO for MSLP, addressing overfitting issues and improving out-of-sample results in hydrothermal dispatch planning.

Findings

Significant reduction in model complexity with fewer non-zero coefficients.

Improved out-of-sample cost performance.

Enhanced spot-price profile accuracy.

Abstract

The solution of multistage stochastic linear problems (MSLP) represents a challenge for many application areas. Long-term hydrothermal dispatch planning (LHDP) materializes this challenge in a real-world problem that affects electricity markets, economies, and natural resources worldwide. No closed-form solutions are available for MSLP and the definition of non-anticipative policies with high-quality out-of-sample performance is crucial. Linear decision rules (LDR) provide an interesting simulation-based framework for finding high-quality policies for MSLP through two-stage stochastic models. In practical applications, however, the number of parameters to be estimated when using an LDR may be close to or higher than the number of scenarios of the sample average approximation problem, thereby generating an in-sample overfit and poor performances in out-of-sample simulations. In this paper, we propose a novel regularized LDR to solve MSLP based on the AdaLASSO (adaptive least absolute shrinkage and selection operator). The goal is to use the parsimony principle, as largely studied in high-dimensional linear regression models, to obtain better out-of-sample performance for LDR applied to MSLP. Computational experiments show that the overfit threat is non-negligible when using classical non-regularized LDR to solve the LHDP, one of the most studied MSLP with relevant applications. Our analysis highlights the following benefits of the proposed framework in comparison to the non-regularized benchmark: 1) significant reductions in the number of non-zero coefficients (model parsimony), 2) substantial cost reductions in out-of-sample evaluations, and 3) improved spot-price profiles.