Machine Learning-Augmented Algorithms: Theory and Applications in Energy and Sustainability

Citation

Christianson, Nicolas Henry (2025) Machine Learning-Augmented Algorithms: Theory and Applications in Energy and Sustainability. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/nyn2-q614. https://resolver.caltech.edu/CaltechTHESIS:06022025-224128103

Abstract

Uncertainty poses a significant challenge for decision-makers in energy and sustainability domains. The ongoing energy transition—characterized by increasing penetrations of variable renewable generation, deployment of novel grid assets like battery energy storage systems, and growing risks from climate-driven natural disasters—introduces new, multifaceted uncertainties that traditional operational methods struggle to accommodate. While artificial intelligence (AI) and machine learning (ML) hold significant promise for navigating this transition and improving the efficiency of energy system operation, their direct deployment to high-stakes energy and sustainability problems presents substantial risks. In particular, current AI/ML tools typically lack guarantees on reliability, robustness, and safety, and thus pose a risk of poor performance or catastrophic failure if deployed in the real world. To make progress on decarbonization while maintaining reliability, new approaches are needed to enable the design of AI- and ML-augmented algorithms that achieve near-optimal performance while providing rigorous guarantees on robustness and reliability when deployed in real-world energy and sustainability problems.

This thesis addresses this challenge from two complementary perspectives, seeking to bridge the gap between theoretical algorithmic insights and practical impact. In the first part, we develop learning-augmented algorithms that integrate black-box AI/ML "advice" into online optimization problems while ensuring provable, worst-case performance guarantees. We propose algorithms for several classes of problems—including cases with convex costs, nonconvex costs, and long-term deadline constraints—that obtain the provably optimal tradeoff between exploiting good AI performance and worst-case robustness. We demonstrate these algorithms' ability to improve operational efficiency in energy and sustainability domains through case studies on cogeneration power plant operation under high renewables penetration and carbon-aware workload shifting for geographically-distributed datacenters.

In the second part of this thesis, we move beyond the "black box" model of AI/ML to explore how risk-awareness and reliability can be integrated as primary design criteria in AI/ML model training and algorithm development more generally. We consider this objective along several avenues, introducing new theoretical and methodological approaches for risk-aware optimization and uncertainty quantification, designing new mechanisms for pricing general forms of uncertainty in electricity markets, and developing new frameworks for training machine learning models with provable reliability guarantees. Throughout, we emphasize connections with and applications to energy and sustainability problems ranging from grid-scale battery storage operation to power grid contingency analysis. Together, these approaches highlight the challenges facing and benefits to risk- and reliability-aware learning and decision-making.

Thesis Files