Title: Synergizing Machine Learning and Optimization: Scalable Real-time Risk Assessment in Power Systems

Date: April 5th

Time: 10:05 AM – 12:00 PM ET

Location: Coda C1115

Meeting Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NDY2NDg1NWItYjhmNi00ZTc1LTlkYTMtNDMwNTNmZGE2MmJh%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22542de754-961d-41d3-b565-320666599892%22%7d

Wenbo Chen

Machine Learning PhD Student

H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Committee

Pascal Van Hentenryck (Advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology

Alan Erera, School of Industrial and Systems Engineering, Georgia Institute of Technology

Yao Xie, School of Industrial and Systems Engineering, Georgia Institute of Technology

Siva Maguluri, School of Industrial and Systems Engineering, Georgia Institute of Technology

Daniel K. Molzahn, School of Electrical and Computer Engineering, Georgia Institute of Technology

Abstract

The integration of renewable energy introduces increased uncertainties in power systems. These uncertainties bring new types of risk and motivate the Independent System Operators (ISOs) in the US to perform risk analysis in real time. However, traditional optimization-based risk assessment is not practical given the tight time budget of real-time operation as it requires systematically solving a sequence of large-scale optimization instances for thousands of load and renewable scenarios. Additionally, day-to-day operations often involve numerous instances. This cumulates a large dataset and gives the opportunity to shift the computational burden from online to offline through machine learning. These challenges and opportunities have motivated the thesis to develop optimization proxies, differentiable programs to learn the input-output mapping of underlying optimization, to enable real-time risk assessment by the principled integration of Machine Learning (ML) and optimization.

First, this thesis focuses on the practicality of developing optimization proxies for industrial-size Security-Constrained Economic Dispatch (SCED) problems, a foundational building block in US energy market clearing. Motivated by a principled analysis of the market-clearing optimization and simulation process in a realistic US energy market pipeline, the thesis proposes a novel just-in-time ML pipeline that addresses the main challenges incurred by the variability in load, renewable output, and production costs, as well as the combinatorial structure of commitment decisions. Second, the thesis presents a novel End-to-End Learning and Repair (E2ELR) architecture to unifiedly improve the feasibility and scalability. E2ELR combines deep learning with closed-form, differentiable optimization layers, thereby integrating learning and feasibility in an end-to-end fashion. The results demonstrate that the E2ELR achieves state-of-the-art performance, with optimality gaps that outperform other baselines by at least an order of magnitude. Finally, the thesis presents the first real-time risk assessment framework for large-scale power systems with high granularity e.g., at the level of generators and transmission lines.