Title: Interprocedural Binary Analysis
Speaker: Matt Revelle
Voiland College of Engineering and Architecture
April 2024
CHE 598 Seminar: Flow Behavior of Aspherical Particles
Buildings are the biggest consumers of electrical energy in the USA – according to the US Energy Information Administration (EIA), buildings account for 40% of total electricity consumed during the year 2020; out of which commercial and residential buildings account for 18% and 22% electricity consumption respectively. As a result, buildings become a potential asset for energy usage optimization from the point of view of electric grids. However, energy usage optimization is not the only problem that buildings lead to, the coupled problems of providing Quality of Service (QoS) to the occupants and supporting the grid with ancillary services also need to be dealt with for the complete utilization of buildings as an asset to the electric grid. The solution strategies for these problems arise from the confluence of diverse fields of Power Systems, Thermal systems, Control Systems, and Machine learning. This talk will provide a conceptual framework to come up with solutions for utilizing buildings as a grid-edge resource at scale.
Stop by to meet Interim Dean Partha Pande and have a quick snack break in your day!
The intricacies of operating and maintaining modern electric power systems are surging due to increased demand, integration of renewable energy resources, and the necessity for a reliable, resilient, and safe power infrastructure. To navigate these complexities, a new generation of data-driven solutions are coming to the fore that are enabled by high-fidelity synchronized transient measurements. These advanced analytics solutions offer a detailed view of grid conditions, which are instrumental in addressing the new requirements for grid modernization.
In this talk, we will delve into notable applications of synchronized transient measurements. We will examine the transformative impact of the solutions enabled by these measurements, illustrating how they can be used to detect and respond to transient disturbances with greater fidelity, and support the incorporation of distributed energy resources. By leveraging this new paradigm, utilities can achieve a forward-looking view of grid conditions, enabling them to anticipate issues and react more effectively to prevent power outages and asset failures. The implications for grid management in terms of strategic planning and change management will also be a key focus area.
Join us for the 2024 Voiland College of Engineering and Architecture Student Excellence Awards. This ceremony will recognize outstanding students from across the college, followed by a reception that also features a student poster session.
NNSA Graduate Fellowship Program (NGFP) information session for prospective applicants.
CySER Virtual Seminar – From Phishing to Floods: Effective and Timely Risk Communication Messages are Imperative
Is there a way to consistently finish projects on time, within budget and without compromising on the scope? Join Engineering and Technology Management (ETM) guest speaker Dr. Efrat Goldratt- Ashlag as she discusses the TOC approach to managing projects.
This webinar focuses on the newer and feasible approaches for the management and control of the electric grid with renewables. Reliable and efficient operation of the electric grid with advanced control and management of the electric distribution system with renewable energy resources (RERs) such as distributed RER clustering/unified control, stacked control of energy storage, and optimal reconfiguration and resilient control framework for real-time photovoltaic dispatch, will be the main topic of discussion. Further, operational methods including newer management and control tools are presented with a special emphasis on utility-scale functions. Finally, evolving techniques and pathways of electric grid management that integrate data sets generated from sensors and meters are also discussed with a special emphasis on the overall reliability and resiliency of the electric grid with renewable energy resources.
The goal of an optimal power flow (OPF) is to determine the “best” way to operate a power system. Usually “best” = minimizing operating cost or system power loss, while operational and engineering constraints are satisfied.
This talk will present a different programming perspective for physics-informed machine learning (PIML) of dynamical system models, learning to optimize, and learning to control methods. We will discuss the opportunity to develop a unified PIML framework by leveraging the conceptual similarities between these distinct approaches. Specifically, we introduce differentiable predictive control (DPC) as a sampling-based learning to control method that integrates the principles of parametric model predictive control (MPC) with physics-informed neural networks (PINNs). We also show how to use recent developments in control barrier functions and neural Lyapunov functions to obtain online performance guarantees for learning-based control policies. We demonstrate the performance of these PIML methods in a range of simulation case studies, including modeling of networked dynamical systems, robotics, building control, and dynamic economic dispatch problem in power systems.