The electric energy system is currently undergoing a period of unprecedented transformations. On the one hand, large-scale deployment of technologies such as rooftop solar and smart building management systems have the potential to make the power system more efficient, sustainable and reliable. On the other hand, achieving this promise has proven to be far from trivial, as many capabilities remain unused. Two primary systems of interest are the heating, ventilation, and air conditioning (HVAC) systems of commercial and industrial buildings, and distributed energy resources in the power distribution system. A fundamental challenge in controlling these systems is that their behaviors are often governed by complex dynamics with unknown parameters. For instance, the relationship between temperature setpoints in different zones and the HVAC power consumption is governed by a set of nonlinear high dimensional partial differential equations, whose parameters depend on detailed building characteristics that are difficult to measure in practice. Similarly, the distribution system is governed by nonlinear AC power flow equations, but since they are typically not monitored, their topology and line parameters are either not known or severely outdated.
This CAREER proposal addresses this challenge by leveraging the significant amounts of measurement data that are now becoming available. Fundamentally different from many existing AI applications, the physical laws governing the behaviors of these systems---laws of thermodynamics for heat transfers in buildings and power flow equations---are well studied, but the system parameters are not known and cannot be easily measured. The goal of this project is to provide algorithms with provable guarantees that combine physical laws with data to safely and efficiently operate these energy systems. Specifically, we present a model-based framework that uses structured neural networks to achieve both model tractability and representability, by designing them to be convex from input to output. This project will tightly integrate research and education by working with the campus sustainability office and the local utility, thus training a generation of professionals qualified both in power systems and machine learning.
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.
