ERI: Intelligent Modeling and Parameter Selection in Distributed Optimization for Power Networks

Board Of Trustees Of Illinois State University, Normal IL

Investigators

Abstract

This Engineering Research Initiation (ERI) grant will contribute to the advancement of national prosperity, economic welfare, and national security by funding research that addresses the challenge of optimizing large-scale networked systems such as nation’s power system, which is rapidly evolving due to the growing prevalence of renewable energy sources and electric vehicles. This award supports research examining the development of innovative algorithms enabling multiple agents to communicate and collaborate effectively in solving complex network optimization problems while safeguarding individual privacy. By integrating mathematical optimization and engineering, the results of this cross-disciplinary project will benefit academic and industrial researchers, data scientists, and policymakers. The educational and outreach activities seek to inspire STEM students, especially those from underrepresented groups, to explore data-driven methodologies in science and engineering through initiatives like summer research programs. This research creates intelligent modeling and parameter selection methods to address important challenges in distributed optimization (DO), a promising approach for a broad class of complex networked systems such as the quickly evolving modern power networks. This research designs innovative partitioning techniques to improve the slow convergence of DO algorithms. This approach not only simplifies the customization of DO by reducing the number of sub-problems but also imposes desirable structures on the sub-problems. The PI devises adaptive and learning-based strategies to address the pervasive challenge of parameter selection for DO algorithms using both classical parameter selection techniques and learning-based methods, such as physics-informed deep learning. The research will also examine the robustness of DO algorithms to data uncertainty and communication errors. Results will be disseminated through open-source optimization software packages, facilitating the real-world implementation of these innovative algorithms. 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.