Safe and Scalable Learning-Based Control: Theory and Application in Sustainable Energy Systems

Citation

Yu, Jing (2025) Safe and Scalable Learning-Based Control: Theory and Application in Sustainable Energy Systems. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/bgar-0602. https://resolver.caltech.edu/CaltechThesis:08192024-223132153

Abstract

From intelligent transportation systems to the smart grid, the next generation of cyber-physical systems (CPS) will substantially transform our society. It is vital that these systems are scalable and robust to uncertainties, with contextual awareness and fast adaptation. This dissertation presents progress towards addressing key challenges arising in the control of large-scale CPS, with a special focus on applications in sustainable energy systems.

Large-scale CPS such as the smart grid often consist of numerous interconnected and heterogeneous subsystems that must coordinate to achieve global objectives by exchanging information over a communication network. Therefore, the first part of this thesis focuses on developing control algorithms that handle crucial design requirements emerging from scalability and communication constraints, such as disturbance localization, communication delay conformation, and distributed implementation.

Sustainable energy systems are crucial for reducing greenhouse gas emissions and mitigating climate change. However, the inherent unpredictability and large uncertainties associated with renewable generation pose significant challenges for maintaining system stability and safety. Traditional control approaches, while robust and effective for known system models, often fall short when faced with the dynamic and uncertain nature of modern power systems. In the second part of the thesis, we address this challenge by integrating machine learning techniques with model-based control methods using uncertainty sets constructed from real-time data. In particular, we will introduce and provide convergence guarantees for a classic uncertainty set estimation method. Building on these uncertainty sets, we combine learning and control techniques to tackle core CPS control problems, such as adversarial stability certification for linear time-varying systems as well as networked systems under communication constraints where the system models are unknown.

The final part of this thesis applies the developed methodologies to address the voltage control problem in power distribution networks with unknown grid topologies. We will combine online learning techniques and a robust predictive controller to achieve provably finite-time convergence to safe voltage limits, despite uncertainties in network topology and load variations. Our case study on a Southern California Edison 56-bus distribution system demonstrates the effectiveness of this approach in nonlinear, partial observation, and partial control settings.

Thesis Files