Optimization Under Uncertainty for Energy Infrastructure Planning: A Synthesis of Methods, Tools, and Open Challenges
It provides a structured overview for researchers and practitioners in energy systems planning, but is incremental as it synthesizes existing work without new results.
This survey reviews optimization under uncertainty methods for energy infrastructure planning, focusing on stochastic programming, robust optimization, and distributionally robust optimization. It categorizes modeling needs and identifies research gaps, particularly at the intersection of optimization and machine learning.
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.