Machine Learning-Enabled Large-Scale Capacity Expansion Planning under Uncertainty

TL;DR

This paper introduces AutoSCEP, an automated method for large-scale capacity expansion planning under uncertainty that efficiently estimates costs and improves decision-making accuracy.

Contribution

AutoSCEP automates scenario and horizon selection, trains surrogate models for cost approximation, and enhances large-scale planning efficiency and accuracy.

Findings

Achieves 2% optimality gap on reduced models

Attains 8% gap on large models, outperforming existing methods

Enables high-resolution uncertainty modeling at system scale

Abstract

Capacity expansion planning under uncertainty requires selecting a scenario count and representative operational horizon to estimate average production costs. Small choices risk unreliable plans, while large choices become intractable. We propose AutoSCEP, an automated, statistically grounded procedure that, for a fixed plan, selects the minimum sufficient scenario count and horizon length to estimate production costs to a given precision. Using these estimates, we train linear and neural surrogates to approximate expected production costs for arbitrary plans, and embed the surrogates within the planning model. On the continental-scale EMPIRE system, AutoSCEP attains 2% optimality gap on a reduced model and 8% gap on a large model, outperforming parallel progressive hedging under equal wall-clock budgets that include data generation, training, and solve times. Where the reduced model's optimum is available, investment patterns broadly align with the benchmark. Our approach enables high-resolution uncertainty modeling at realistic system scales.