Sandia National Laboratories Advanced Grid Modeling Program
The electric power grid is the foundation of modern society. In fact, the National Academy of Engineering identified electrification as the greatest engineering achievement of the 20th century. (http://www.greatachievements.org/). Today’s grid is rapidly evolving. Increasing amounts of variable renewable generation like wind and solar PV are being deployed, including a significant amount of distributed generation. In addition, recent natural disasters and severe weather events have illustrated the fragility of the grid and the potential for long-term outages. Because of the increasing complexity of the electric power grid, advanced grid modeling (AGM) is required to work towards a future resilient and flexible electric power system. Sandia’s AGM program focuses on the following technical areas: grid stability; electric power system planning and operations; electric power system protection; threat modeling; resilience metrics and modeling; and machine learning/data analytics.
Power System Dynamic Performance
Power system stability refers to the ability of the electric power grid to quickly recover from disturbances. Small signal stability is concerned with the response of the power system to small disturbances where the system can be approximated by a linear model. All large electric power systems exhibit low frequency inter-area oscillations in the 0.1-1 Hz range. These are a concern because of their potential to cause power disruptions such as the 1996 West Coast Blackout, which was partially attributed to an undamped inter-area oscillation. Transient stability refers to the response of an electric power system to a large disturbance, like the loss of a large generator or transmission path. In this case, a nonlinear system model is required, and the goal is to maintain synchronism in the face of the large disturbance. Voltage stability is concerned with maintaining a constant voltage and avoiding operating states that might quickly result in voltage collapse. Sandia’s research focuses primarily on small signal stability, transient stability, and voltage stability.
Power System Operation, Planning & Economics
Power system operations and planning have a large impact on the economics and resilience of the electric power grid. Optimal power system operation can result in significant cost savings, especially in the face of uncertainty associated with variable renewable generation. The dispatch of generation can also impact the resilience of the grid to natural and manmade disasters. Finally, as the grid is evolving, new market designs are required to maintain efficient and stable operation of the grid. Sandia’s research focuses on advanced production cost modeling (PCM) which is the basis for power system operation, power system planning, and electricity market design.
Power System Relaying and Control
Electric power system protection is fundamental to the safe and economical operation of the electric grid. Without protection schemes very expensive equipment like generators and transformers could be easily damaged under various fault scenarios. As the grid is evolving to include more inverter-based and distributed generation new protection schemes are required to maintain safe and reliable operation. The fault induced response of inverter-based generation is fundamentally different than traditional generation. As inverter-based generation becomes more prevalent, much of the existing protection hardware will have to be updated or upgraded to maintain functionality. Sandia’s research focuses on developing new protection schemes that will be required for the future grid.
The electric power grid is vulnerable to a wide range of natural and manmade threats. Examples include extreme weather events, terrorist attacks, and even cyber-attacks. An accurate threat model is the foundation of all analytics to assess and mitigate vulnerabilities. Sandia’s threat modeling research focuses on developing statistical models of natural and manmade threats, as well as how these models can be used for resilience optimization. This research involves building models from historical data as well as performing device testing to identify failure modes and their associated probability density functions.
Resilience Metrics and Modeling
Resilience is defined as the ability of a system to withstand and quickly recover from low probability, high consequence events. Examples of these types of events include hurricanes and earthquakes as well as manmade disasters. This differs from reliability, which is the ability of the system to withstand and quickly recover from high probability, low consequence events. Reliability metrics are well defined, and utilities are often compensated for meeting (or penalized for exceeding) reliability metrics. On the other hand, there are no agreed upon metrics for evaluating grid resilience. Much of Sandia’s work focuses on defining appropriate resilience metrics, including metrics for defense critical energy infrastructure (DCEI). Another area of research is quantifying the impact to society of long-term outages and developing modeling tools to improve grid resilience.
Machine Learning and Data Analytics
The increased prevalence of sensors in the grid, ranging from phasor measurement units (PMUs) to advanced metering infrastructure (AMI) in distribution networks, has resulted in an explosion of grid data. Machine learning techniques are well suited for making inferences from large data sets. Examples include applying machine learning techniques to AMI data to identify model errors in distribution networks to using machine learning algorithms to improve the operation of protective relays. Machine learning and data analytics is an important component of Sandia’s Advanced Grid Modeling (AGM) program.
Sandia’s AGM program is supported by the U.S. Department of Energy Office of Electricity Advanced Grid Modeling Program under the guidance of Dr. Ali Ghassemian. Sandia National Laboratories is a multimission laboratory operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. Sandia Labs has major research and development responsibilities in nuclear deterrence, global security, defense, energy technologies and economic competitiveness, with main facilities in Albuquerque, New Mexico, and Livermore, California.
Bryan is an optimization modeler who has focused on power grid resiliency and microgrid cost analysis. In addition, he has a keen interest in scheduling problems. Read full bio here.
Michael Baca, Ph.D.
Michael Baca has extensive experience and background in the electric power industry. With Sandia, he has extensive experience in the areas of cyber security, energy surety, resilience, power modeling, and advanced microgrid analysis and design. Specifically, he has done project and technical work to analyze and develop advanced microgrid designs for the Department of Energy and the Department of Defense customers both for military facilities and commercial sites. Prior to working at Sandia, Mike worked with Bonneville Power Engineering for 10 years as an electrical test engineer where he commissioned several large power substations, and he worked with Intel for three years where he helped commission the distribution infrastructure for Fab 11X in Rio Rancho, New Mexico. He has an M.S. in electric power engineering and a Ph.D. in neuroscience from the University of New Mexico. Read full bio here.
Pedro Barba is a Member of Technical Staff in the Electric Power Systems Research Department at Sandia. His current research focuses on modeling traveling waves on transmission and distribution systems, and on optimization methods for energy storage systems. Before joining Sandia, he was a product design engineer at Delphi Automotive Systems, and then a power engineer at El Paso Electric. He served as Adjunct Professor at the College of Engineering and the College of Business Administration at The University of Texas at El Paso. Read full bio here.
Logan Blakely received his Master of Computer Science degree, specializing in Machine Learning, from Portland State University in 2018. His research focus is in machine learning applied to power systems challenges, particularly in the intersection merging physics domain knowledge with machine learning techniques. Read full bio here.
Raymond Byrne Ph.D.
Ray Byrne is the team lead for the Analytics and Regulatory Thrust Area of Sandia’s Energy Storage Systems Program. His research interests include optimal control of energy storage to maximize revenue and grid benefits, as well as control problems related to the grid integration of renewables. Ray is a fellow of the IEEE and a member of Eta Kappa Nu. He is a distinguished member of the technical staff at Sandia National Laboratories, where he has been employed since 1989. Ray holds a B.S. in electrical engineering from the University of Virginia, an M.S. in electrical engineering from the University of Colorado, Boulder, and a Ph.D. in electrical engineering from the University of New Mexico (control theory and electronics). He also received an M.S. in financial mathematics from the University of Chicago. Read full bio here
Hyungjin Choi Ph.D.
Hyungjin Choi received a Ph.D. in Electrical Engineering from University of Minnesota in 2017 focusing on convex optimization approaches for transient stability and uncertainty propagation in power systems. Before joining Sandia, he worked as a software engineer at Siemens Digital Grid on developing transmission network applications for energy management system and energy market management system products. From 2017-2018, he worked as a postdoctoral research associate at the University of Illinois, Urbana Champaign, on stability of a microgrid cybersecurity. Read full bio here.
Ryan T. Elliot
Ryan T. Elliott received the Ph.D. degree in Electrical and Computer Engineering in 2020 from the University of Washington, Seattle, WA, USA, where he studied in the Renewable Energy Analysis Lab (REAL). He is currently a Senior Member of Technical Staff with the Electric Power Systems Research Department at Sandia National Laboratories. In 2017, he earned an R&D 100 Award for his contributions to a real-time damping control system using phasor measurement unit (PMU) feedback. His research interests include renewable energy integration, wide-area measurement systems, and power system dynamics and control. Read full bio here.
Manuel Garcia received his B.S. and M.S. degrees from the University of California at Berkeley and a Ph.D. from the University of Texas at Austin where he held the Cockrell School of Engineering Fellowship. His dissertation is titled “Non-Convex Myopic Electricity Markets: the AC Transmission Network and Interdependent Reserve Types.” After earning his M.S. degree, Manuel was a researcher in the Theoretical Division at Los Alamos National Laboratory for two years. As a Ph.D. student, he had internships at Argonne National Laboratory, the Massachusetts Institute of Technology, and the Pontifical University of Chile. Read full bio here.
Kaitlyn Garifi Ph.D.
Kaitlyn’s research focuses on power system optimization spanning from building-to-grid interactions to the transmission grid. Some of her contributions include developing a chance constrained energy management algorithm for behind-the-meter residential applications, analyzing relaxed convex battery models using optimization theory, and solving large scale transmission grid resiliency optimization problems. Read full bio here.
Javier Hernández-Alvídrez was born in Delicias Chihuahua, México. He received his B.S. degree in electrical engineering and telecommunications from Monterrey Institute of Technology and Higher Studies at Monterrey NL, México; where he graduated with honors. He also holds a M.Sc. and Ph.D. degrees in electrical engineering from New Mexico State University at Las Cruces NM, USA. Read full bio here.
Matthew J. Hoffman
Matthew Hoffman has over 15 years of experience in project leadership and in simulation, optimization and data analytics of complex systems for national security. He received his MS in applied mathematics from the New Mexico Institute of Mining and Technology, where his work on hybrid (continuous-discrete) dynamical systems involved extensive improvements to a power systems dynamics research code. At Sandia he has focused on applied optimization research, and in 2015 he was named an INFORMS Franz Edelman Laureate for leading work “representative of the best applications of analytical decision making in the world” optimizing multi-billion-dollar strategic investment decisions for the US Army. Read full bio here.
Ronald C. Matthews Jr. Ph.D.
Ronald C. Matthews received his B.S. in Mathematics and M.S. in Mathematical Sciences from the University of West Florida in 2005 and 2007 respectively. He earned his M.S. and Ph.D. in Electrical Engineering from Michigan Technological University in 2012 and 2018 respectively. He joined Sandia National Laboratories in 2018 as a Postdoctoral Appointee and transitioned to R&D S&E, Electrical Engineer in 2020. His current work focuses on grid/microgrid protection, optimization, real-time modeling of microgrids, and controls. Read full bio here.
Karina Munoz-Ramos received her B.S. and M.S. in electrical engineering from the New Mexico Institute of Mining and Technology in 2007 and 2009, respectively. Since then, she has been at Sandia National Laboratories, where she is currently a Senior Member of Technical Staff. Read full bio here.
Tu A. Nguyen
Tu A. Nguyen is a Senior Member of the Technical Staff at Sandia National Laboratories. He is also a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and an editor of IEEE Transactions on Sustainable Energy. He received his B.S degree in Power Systems from Hanoi University of Science and Technology, Vietnam in 2007 and his Ph.D. degree in Electrical Engineering from Missouri University of Science and Technology in 2014. Before joining Sandia National Laboratories in September 2016, he worked as a Postdoctoral Research Associate at University of Washington. His research interests include energy storage analytics, microgrid modeling and analysis, and the integration of distributed resources into power grids. Read full bio here.
Brian J. Pierre, Ph.D.
Brian J. Pierre holds a B.S. degree in electrical engineering from Boise State University, Boise, ID, USA; and an M.S. and Ph.D. in electrical engineering from Arizona State University focused on electric power systems, Tempe, AZ, USA. He has prior experience at the NASA Glenn Research Center and Schweitzer Engineering Laboratories. He is a Senior Member of IEEE. He is presently a Senior Member of Technical Staff in the Power Systems Research Department at Sandia National Laboratories, Albuquerque, NM, USA. His research interests and expertise include power system modeling, power system resilience, power system optimization, power system controls, grid dynamics, and renewable energy integration. Read full bio here.
Matthew J. Reno, Ph.D.
Matthew J. Reno started working at Sandia National Laboratories in 2003 and is a senior member of technical staff in the Electric Power Systems Research Department. His expertise in quasi-static time series analysis of distribution grid feeders and circuit reduction methods has led to transformative changes to speed up feeder modeling. Matt received his Ph.D. in electrical engineering from Georgia Institute of Technology. Read full bio here.
David Schoenwald, Ph.D.
David Schoenwald is a Principal Member of the Technical Staff in the Electric Power Systems Research Department at Sandia National Laboratories. Dr. Schoenwald focuses on control system design to improve dynamic stability of electric power systems. He also develops performance standards for grid-scale energy storage applications. Before joining Sandia, he was with Oak Ridge National Laboratory, where he designed control systems for manufacturing applications. He was also an adjunct assistant professor in the Electrical Engineering Department, University of Tennessee, Knoxville, where he taught a graduate course on nonlinear control systems. Dr. Schoenwald received an R&D 100 award in 2017 for development of an inter-area oscillation damping controller for the western North American power grid. Read full bio here.
Andrea Staid Ph.D.
Andrea Staid is a Principal Member of Technical Staff in the Discrete Math & Optimization Department at Sandia. After receiving a BS in aerospace engineering from MIT, she worked for Pratt & Whitney Rocketdyne for a few years before pursuing a Ph.D. at Johns Hopkins. While at JHU, her research focused on data analysis and statistical modeling to support renewable energy integration, uncertainty modeling, and infrastructure risk and resilience. Since joining Sandia in 2016, she has continued along these lines of research while also developing a strong focus in threat and consequence modeling for adverse weather impacts to our infrastructure and probabilistic scenario creation. Read full bio here.
Rodrigo D. Trevizan Ph.D.
Rodrigo D. Trevizan is a Postdoctoral Appointee at Sandia National Laboratories. Rodrigo authored research papers on the subjects of control of energy storage systems and demand response for power grid stabilization, power system state estimation, and detection of nontechnical losses in distribution systems. Rodrigo received a B.S. and M.Sc. degree in Electrical Engineering from the Federal University of Rio Grande do Sul, Brazil, in 2012 and 2014, respectively, a M.Sc. in Power Systems Engineering from the Grenoble Institute of Technology (ENSE3) in 2011 and a Ph.D in Electrical Engineering from the University of Florida in 2018. Read full bio here.
Stephen J Verzi, Ph.D.
Stephen J. Verzi earned his PhD from the University of New Mexico in computer science, with a focus in neural network design and analysis. At Sandia National Laboratories. His professional experience in algorithm development and computational modeling spans many application domains: artificial intelligence, machine learning, information retrieval and multilingual text analysis, neural function including the hippocampus and its substructures, system dynamics models of human behavior via the theory of planned behavior expressed using the mathematics of qualitative choice theory, individual-based and dynamical systems modeling, resilience metrics and their application in critical infrastructure, hospital evacuation and neural network dynamics, game-theoretic and agent-based models of adversarial behavior, and most recently exploring neural-inspired computation via phase-coding with application to image and video processing including anomaly detection as well as the application of adversarial reinforcement learning in grid resilience stability modeling. Read full bio here.
Amanda Wachtel is a Senior Analyst in the Mathematical Analysis and Decision Sciences Department at Sandia National Laboratories. Her primary research areas include resilience, system-of-systems modeling and simulation, and developing computational capabilities for DoD, DOE, defense energy, and community applications. She developed the initial prototype of the Resilient Node Cluster Analysis Tool (ReNCAT) to assess critical infrastructure within cities and find dense areas of buildings capable of providing services during grid outages, and is now the lead analyst for the tool. Read full bio here.
Felipe Wilches-Bernal, Ph.D.
Felipe Wilches-Bernal is a senior research engineer in the Electric Power Systems Research Department at Sandia National Laboratories. Felipe’s work includes power system wide-area control and monitoring using phasor measurement units, studying the impact of communications on real-time control, and developing the future smart-grid. He also has experience analyzing the effects that high levels of wind energy penetration have on the bulk power system. Felipe obtained his Ph.D. in electric power and control at Rensselaer Polytechnic Institute in Troy, NY; his M.Sc. in control and signal processing at Université Paris-Sud XI in Orsay, France; and his B.Sc. in electrical engineering at Pontificia Universidad Javeriana in Bogota, Colombia. Read full bio here.
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