Collaborative Research: AMPS: Deep-Learning-Enabled Distributed Optimization Algorithms for Stochastic Security Constrained Unit Commitment

Kansas State University, Manhattan KS

Investigators

Abstract

The operational landscape of electric power systems is currently experiencing a profound transformation driven by various factors, including the integration of renewable energy sources, the need for a cleaner energy economy, and the urgency to address the climate crisis. This project aims to lay the mathematical groundwork necessary to harness the full potential of deep machine learning approaches in enhancing power system operations, particularly in relation to renewable energy, such as wind and solar generation. The research will develop a new suite of distributed optimization tools that will empower large-scale power system operations to manage uncertainty while incorporating renewable energy resources effectively. The new algorithms will potentially transform operational practices within the power system. At the same time, the results will increase public awareness and understanding among stakeholders, regulators, policymakers, and market participants. The successful completion of this project will enable power system operators to adopt cutting-edge algorithms that significantly enhance their operational practices with renewable generation. The project will provide training and outreach opportunities to students from both institutions, particularly those from underrepresented groups in STEM. The project aims to develop and validate deep-learning-enabled distributed stochastic algorithms. These algorithms will solve large-scale, stochastic security-constrained unit commitment problems within power systems. Specifically, the project will focus on the following objectives: (i) the design of a holistic, three-stage, deep neural network-based machine learning approach; (ii) the solution strategies based on the hybrid distributed parameter system control theory; and (iii) extensive validations of the proposed algorithms using large-scale real-world power system datasets. The research will advance the field by introducing innovative techniques to address the challenges associated with power system operation under uncertainty. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.