Thesis Title: Advances in Large-Scale Power System operations: Reconstruction, Reliability, Learning
Thesis Committee:
Dr. Pascal Van Hentenryck (advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Santanu Dey, School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Weijun Xie, School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Daniel Molzahn, School of Electrical and Computer Engineering, Georgia Institute of Technology
Dr. Spyros Chatzivasileiadis, Department of Wind and Energy Systems, Technical University of Denmark
Date and Time: Friday, May 5th, 9:00am (EST)
In-Person Location: CODA C1115 – Druid Hills
Meeting ID: 241 892 237 185
Passcode: 5egfRw
Abstract:
Modern Power System operations are based on large-scale optimization problems that are becoming increasingly more complex and subject to higher degrees of uncertainty with multiple components such as renewable generation, distributed energy sources, electrification of transportation and extreme weather. Frameworks based on Optimization under uncertainty and Machine Learning have the potential to facilitate and improve the Power Grid operation in multiple ways. The former in terms of cost reduction and enhancing system reliability, and the latter in faster generation of solutions and real-time risk assessment. The thesis presents advancements on the scalability of such methods to large-scale power networks and evaluates the impact and benefits of the methods in the operations.
The first part of the thesis addresses the availability of suitable power grid data for conducting modern research in Power Systems, access to which is limited by privacy concerns and the sensitive nature of energy infrastructure. This lack of data, in turn, hinders the development of modern research avenues such as machine learning approaches or stochastic formulations. To overcome this challenge, we propose a systematic, data-driven framework for reconstructing high-fidelity spatio-temporal consistent time series, using a combination of public and private set of data. The proposed approach, from geo-spatial information and generation capacity reconstruction to time-series disaggregation, is applied to the French transmission grid. Thereby, synthetic but highly realistic time series data, spanning multiple years with a 5-minute granularity, is generated at the bus level.
The second part of the thesis focuses on the impact of Reliability Assessment Commitment (RAC) processes in modern Power System operations. The recent growth of Renewable Energy sources and Distributed Energy sources has introduced significant operational uncertainty in front and behind the meter, increasing forecasting errors and reliability risks in the operations. Due to this fact, Independent System Operators (ISOs) execute day-ahead and intra-day RAC processes to address unforeseen changes in power grid conditions. Based on the operation pipeline of the Midcontinent Independent System Operator (MISO), we conduct a systematic analysis of the impact of RAC processes in MISO operations and propose a two-stage Stochastic Programming extension to MISO's deterministic day-ahead RAC process. To overcome the computational challenge of solving the stochastic problem, an accelerated version of the Bender's Decomposition algorithm is developed that is scalable to industry-sized instances. A novel computational analysis is conducted on the benefits of deterministic and stochastic RAC processes in modern large-scale power grid instances from MISO and the French Transmission System. These benefits are demonstrated both in terms of operational cost and power system-specific risk and reliability metrics.
The third part of the thesis proposes a novel Machine Learning (ML) approach for learning the behavior of the AC Optimal Power Flow problem (AC-OPF), a problem at the core of the operations, that features a fast and scalable training. It is motivated by the significant training time needed by existing ML approaches for predicting AC-OPF. The proposed approach is two-stage and exploits a spatial decomposition of the power network that is viewed as a set of regions. The first stage learns to predict the flows and voltages on the buses and lines coupling the regions, and the second stage trains, in parallel, the ML models for each region. The predictions can then seed a power flow model to eliminate the physical constraint violations, resulting in minor violations only for the operational bound constraints. Experimental results on the French transmission system (up to 6,700 buses) and large publicly available topologies (up to 9,000 buses) demonstrate the potential of the approach. Within a short training time, the approach predicts AC-OPF solutions with very high fidelity, producing significant improvements over existing centralized methods. The proposed approach opens the possibility of training ML models quickly to respond to changes in operating conditions.