Development and wide-spread adoption of sustainable, renewable energy sources is a key global need to halt climate change and ensure a productive future. At the University of Vermont, we have a strong focus on power systems and smart grids, and are working with local and national industries and federal agencies (DOE, NASA etc.) to address the many challenges that arise during large-scale implementation of sustainable energy systems. Vermont is the first state-wide smart grid in the nation, and it is host to a variety of small companies leading development of renewable energy solutions. Our faculty working in this area encompasses experts from a variety of backgrounds, including Electrical & Biomedical Engineering, Mechanical Engineering, and Computer Science.

Large-scale adoption of sustainable, renewable energy systems is key to mitigating the effects of global climate change. We invite you to join us in discovering how to shift our society to a sustainable energy future.

Core Faculty

Mads Almassalkhi, Electrical & Biomedical Engineering

Dr. Almassalkhi is an Assistant Professor at the University of Vermont and co-founder of startup company Packetized Energy. His research interests lie at the intersection of power systems, mathematical optimization, and control systems and focuses on developing scalable algorithms that improve responsiveness and resilience of power systems.


Faculty Profile, Lab Website

Research Spotlight: CORE Systems Lab
In the CORE Systems Lab, our goal is to advance our fundamental understanding of how power and energy systems can actively and reliably respond to changing market and grid conditions and enable a clean energy future. Towards this objective, our group seeks to develop impactful grid-aware control and optimization algorithms that coordinate distributed, networked grid and energy resources. Our group's work, thus, investigates modeling and control of fleets of distributed energy resources (DERs) as virtual energy storage (VES), optimization of DERs in transmission and distribution systems, and real-time validation of algorithms in a practical context.

Luis Duffaut Espinosa, Electrical & Biomedical Engineering

Dr. Espinosa is an Assistant Professor at the University of Vermont. His research interests lie in the intersection of estimation theory, signal processing, control theory and learning technologies with emphasis in approaches that are non-parametric and model-free for applications in power systems, monitoring of environmental systems, quantum control and general modelling of nonlinear systems.

Faculty Profile, Lab Website

Research Spotlight: AIRLab
The Autonomous and Intelligent systems Research Laboratory (AIRLab) has as its primary objective contributing to the development and deployment of autonomy technologies. Particularly, the laboratory is at the crossroads of the fields of systems and control engineering, signal processing, and estimation. Our team currently works in projects related to planning and formation of autonomous systems, localization and mapping, fault tolerant state estimation, free model learning technologies for navigation in harsh environments, constraint management of autonomous systems, and control of unmanned vehicles such as UAVs, among others.

Paul Hines, Electrical & Biomedical Engineering

Dr. Hines is a Professor and the L. Richard Fisher chair in the Department of Electrical Engineering at the University of Vermont. He is also Co-Founder and CEO of Packetized Energy, a clean energy startup company. The mission of Dr. Hines' group is to understand the complexity of electricity and to use that understanding to make energy systems work better (cleaner, more reliable and less costly) through innovative research. 

Faculty Profile, Lab Website

Research Spotlight: Harnessing Smart Grid Data to Enable Resilient and Efficient Electricity
The objective of this research is to harness Smart Grid data (Big Data) to enable more resilient and efficient electricity. Three research sub-projects contribute to this goal. Project 1 combines a new "Random Chemistry" computational algorithm with complex networks methods to find patterns of vulnerability in power systems, and uses the results to reduce cascading failure blackout risk. Project 2 transforms smart grid data into actionable information about the health of a power grid by looking at statistical properties in data from grid sensors. Projects 1 and 2 seeks to make power grids more resilient to fluctuations from renewable generation or weather events. Project 3 uses crowdsourcing to identify trends affecting residential energy consumption through a web-based energy efficiency social network.

Jeffrey Marshall, Mechanical Engineering

Dr. Marshall is a Professor and Associate Dean of Research in CEMS at the University of Vermont. He performed research in fluid mechanics and particulate flows, with a focus on vortex flows and adhesive particulate flows. His recent projects involve nanoparticle diffusion, biofilm simulation, renewable energy systems, turbulent particle agglomeration, obscurent cloud dynamics, particle transport in gas turbine engines, and cold-regions sensor systems.

Faculty Profile, Faculty Website

Research Spotlight: Soiling of Solar PV Panels
Soiling of solar PV panels accounts for significant energy losses in dusty, dry regions of the Earth. This is a particularly important issue for solar energy supply during extraterrestrial exploration on dusty planets and moons, such as on our moon and on Mars, where dust particles are often electrically charged and difficult to remove from the panels. We are working with NASA to develop electrostatic dust shields, which automatically and touchlessly transport particles off solar panels using electrodes embedded in the panels subject to oscillating electrical charges.

Hamid Ossareh, Electrical & Biomedical Engineering

Dr. Ossareh joined the University of Vermont from Ford Motor Company, where he worked as a Research Engineer on advanced automotive control systems. He is currently investigating optimal constraint-aware control algorithms with applications in power and automotive systems. He earned his Ph.D. in electrical engineering systems (control theory) from the University of Michigan in 2013 and his B.A. Sc. from the University of Toronto in 2008.

Faculty Profile, Faculty Website

 

Affiliated Research

Jeff Frolik, Electrical & Biomedical Engineering

Dr. Frolik is a Professor and Chair of the Department of Electrical and Biomedical Engineering. Dr. Frolik's research focuses on sensor networks, wireless communications, and distributed control. This work has resulted in over 150 peer-reviewed publications, 8 patents, and has involved over 30 graduate students. The work has been supported by industry and by NSF, DOE, NASA, and DOT.

Faculty Profile, Faculty Website

Safwan Wshah, Computer Science

Dr. Wshah is currently investigating machine learning algorithms to be applied in Energy, Transportation and Healthcare fields. He joined UVM CEMS from PARC (Palo Alto Research Center), where he worked as a research scientist in the fields of machine learning/deep learning, computer vision, and image/video processing.


Faculty Profile, Lab Website

Affiliated Centers

    
Complex Systems Center

 

Selected Courses in Energy & Grid Power Systems

EE 217 Smart Grid

Smart Grid: Using information/communication technology to modernize electric power/energy systems, including generation, transmission, distribution and consumption. Electricity physics/economics/policy; renewable energy; energy storage; demand response; energy efficiency; distributed generation; advanced metering infrastructure; distribution automation; microgrids; synchrophasors; HVDC and FACTS systems.

EE 218 Power Electronics

An introduction to the field of power conversion using power electronics devices. Topics include Energy and Power, AC-to-DC Converters, DC-to-DC Converters, DC-to-AC Converters, Elements of Control and Design of Power Converters, Applications of Power Electronics in Renewable Energy and Microgrids. Simulations and experiments illustrate concepts. Final project related to renewable energy.

ME 236 Renewable Energy Harvesting

Covers the engineering fundamentals of different renewable energy technologies, including wind power, tidal power, solar power, biomass, hydropower, etc. Focus placed on the mathematical derivation and application of small scale vibration energy harvesting technologies.

ME 238 Energy Systems Engineering

Engineering assessment of both potentially sustainable and unsustainable practical primary energy systems. Examination of options of meeting demand and impacts on the environment.