Optimization Under Uncertainty for Energy Infrastructure Planning: A Synthesis of Methods, Tools, and Open Challenges

TL;DR

This paper reviews recent methods and tools for energy infrastructure planning under uncertainty, highlighting advances in optimization, machine learning integration, and identifying research gaps.

Contribution

It synthesizes recent developments in stochastic, robust, and distributionally robust optimization for energy planning and explores emerging machine learning techniques.

Findings

Categorized modeling needs and identified research gaps.

Reviewed integration of machine learning with optimization methods.

Highlighted emerging directions like surrogate modeling and probabilistic forecasting.

Abstract

Energy infrastructure planning under uncertainty has become increasingly complex as electrification, interdependence between energy carriers, decarbonization, and extreme weather events reshape long-term investment decisions. This paper surveys recent advances at the intersection of generation and transmission expansion, and optimization under uncertainty, with a focus on stochastic programming, robust optimization, and distributionally robust optimization. We then categorize modeling needs along the axes of modeling fidelity, uncertainty characterization, and solution methods to identify dominant modeling features and trace research gaps. We further examine emerging directions at the interface of optimization and machine learning, including surrogate modeling, learning uncertainty sets, probabilistic forecasting, and synthetic scenarios, and discuss how these tools can be embedded within infrastructure planning models.