EPCN: Learning power grids from limited measurements: fundamental limits and practical algorithms

California Institute Of Technology, Pasadena CA

Investigators

Abstract

This project develops methods to make the management of future power grids more efficient, secure, and robust. The development of an intellectual basis and practical algorithms for future energy systems is an important challenge and a goal of this project. Specifically, network topology and line parameters are critical information for the management of power systems. Even though errors in such information can significantly impact system operation, there is relatively little work in identifying them, especially when measurements are limited both temporally and spatially. This problem is even more severe in medium-voltage and low-voltage distribution grids, not only because the lack of monitoring infrastructure today, but also because topology may change more frequently in response to changes in load or solar generation. Our goal is to develop a theory and algorithms for topology and line parameter identification. We will focus on cases where measurements are available only at limited locations or/and for a limited time period. These settings are difficult but realistic, as, e.g., most distribution systems today have measurements from substations where a distribution grid interfaces with the bulk transmission grid, and from smart meters at end users, but not much measurements in between. Future applications may have to make identification decisions in near real-time based on limited numbers of samples. The proposed research consists of three thrusts. Thrust 1 (Learning with limited spatial measurements) focuses on the theory and algorithms when not all network nodes are observable, but sufficient samples can be collected over time from the observable nodes before an estimation has to be made. Thrust 2 (Learning with limited temporal measurements) focuses on the case where all network nodes are observable, but only a limited number of samples are available for identification. Thrust 3 (Integrated learning with limited measurements) integrates the theory and algorithms developed in Thrusts 1 and 2 into an overall identification system that is applicable where measurements are limited both spatially and temporally. The methods developed in the proposal are applicable to other network systems (social, communications, transportation, financial). The project integrates research with education including curriculum development and linking to entrepreneurship. 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.