A Partnership of UConn and Eversource

Eversource Energy Center



Author: Large-eddy Simulation of Cumulus Clouds



Georgios Matheou, Department of Mechanical Engineering, University of Connecticut

DOI: https://doi.org/10.1103/APS.DFD.2021.GFM.V0013

The representation of clouds in climate models is one of the largest sources of uncertainty in climate projections. High-resolution numerical modeling is used to elucidate the cloud physics and develop parameterizations for the representation of clouds in weather and climate models. Large-eddy simulation (LES) is currently the best technique to provide reliable and well-characterized cloud modeling. The video shows a simulation of a cumulus-topped trade wind atmospheric boundary layer observed during the Barbados Oceanographic and Meteorological Experiment (BOMEX). The video demonstrates the fidelity of modern computational methods for the simulation of clouds and atmospheric turbulence. These simulations help address important questions in environmental fluid dynamics and atmospheric science, and help improve weather forecasts and climate projections.

Author: Eversource and UConn Extend Energy Center Partnership

The goal is to make Connecticut a national leader in addressing climate change, clean energy, and more.

UConn Today story on Eversource and UConn extended partnership.

With an eye toward making Connecticut a national leader in clean energy, Eversource and UConn have extended their joint commitment to the Eversource Energy Center into 2028, with the energy company announcing Wednesday it will invest $14 million in the program during that time.

The extension, announced at a meeting of the UConn Board of Trustees, focuses on five research “pillars” – Resiliency, Reliability, Renewable Integration, Cyber Security and Community Engagement, Education, and Entrepreneurship. Through this partnership extension, Eversource will:

  • Support the operational UConn weather and outage forecasting and optimal restoration management system for Eversource’s Connecticut, Massachusetts, and New Hampshire service territories
  • Support offshore wind energy research for the Revolution Wind and South Fork wind farms currently being developed as a joint venture by Eversource and Ørsted
  • Expand the energy company’s substation flood early warning system to substations in Massachusetts and New Hampshire
  • Provide professional education to Eversource engineers through UConn’s Power Grid Modernization Graduate Certificate Program
  • Engage underrepresented minority undergraduate students in all areas of sustainable energy research

“We share Connecticut’s goal of a greener energy future and are always focused on innovative solutions that benefit our customers and advance clean energy,” says Eversource President and CEO Joe Nolan. “We also applaud UConn’s expansion of our agreement to include and promote a diversity and inclusion undergraduate research focus among the pillars. Creating pathways for historically underrepresented groups in the clean energy industry aligns with our increased focus on racial and social justice.”

The Eversource Energy Center, which got its start in 2015, is a dynamic partnership between UConn faculty, students, and Eversource colleagues in which state-of-the-art research, technology, and software aim to solve real-world challenges for electric customers where weather, climate, and energy intersect. Current research areas include projects on storm outage forecasting, tree and forest management, electric grid reinforcement, resiliency, climate change and flooding, geomagnetic disturbances, integration of renewable generation, and cyber security.

“It’s an honor to expand on the crucial work we are doing in collaboration with Eversource, which has benefited millions of people across the New England region,” says UConn Eversource Energy Center Director Emmanouil Anagnostou. “This collaboration is a model for how private industry and academia can work seamlessly, blending cutting edge research with real-world challenges where weather, resilience and energy intersect.”

The center’s efforts will help to make Connecticut a national leader in clean energy as it works to accelerate research and adoption of blended energy sources, including solar, wind, hydrogen, fuel cell, hydro, and legacy sources, and to further modernize the regional power grid. The extended partnership will also help make the Eversource electric distribution system among the most reliable through enhanced storm preparedness and emergency response, system resilience, vegetation management, and strategies to address climate change, while highlighting the UConn team as a national leader in these areas through basic science and applied research.

“Our on-going partnership with UConn and the center’s leading-edge research are helping us to further mitigate storm hazards, increase the reliability of the electric grid and secure a sustainable energy future for the people of Connecticut,” says Nolan. “This additional investment will further support the center’s advancements in multiple areas, further benefitting all of our customers.”

Author: Presentations from our 2021 Annual Workshop

The EEC recently hosted a workshop where principal investigators had the opportunity to present new research projects.

Below are links to each project presentation.



Power System Botnet Cyber Attack: Machine Learning Based Analysis and RTDS Simulation

Sean Youngblood, Lynn Pepin, Sung-Yeul Park, Fei Miao

Green Energy Development & Carbon Mitigation Potential of Forests and Working Lands

Anita Morzillo, Chandi Witharana, Zhe Zhu, Robert Fahey, Tom Worthley, and Charles Towe

Landowner Planning for Roadside Forest Management Given Multiple Stressors

Anita Morzillo, Emlyn Crocker, and Jacob Cabral

Near Real-time Monitoring of Roadside and Right-of-way (ROW) Forest Disturbances in CT

Kexin Song and Zhe Zhu

Evaluation of DER Integration on Distribution Protection with Hardware in the Loop Using Low Energy Signals

Kalinath K, PhD Student; Supervisor: Prof. Ha

Evaluating Biomechanical and Structural Response to Tree Trimming

Nicholas Cranmer, Robert Fahey, Chandi Witharana,Thomas Worthley, Amanda Bunce, Brandon Alveshere

Legacy and Shockwaves: A spatial analysis of strengthening resilience of the power grid in Connecticut

Adam Gallaher, Marcello Graziano, and Maurizio Fiaschetti

Hurricane Resilience Assessment for Power Distribution System Considering Tree Failure Risk and Topology

Jintao Zhang, Qin Lu, William Hughes, Wei Zhang, Amvrossios Bagtzoglou

Machine Learning Methods for Transmission Outage Prediction

Sita Nyame

A Pathway to Enable Sustainable Modern Power Systems: Optimal System Dispatch

Leila Chebbo

Snowfall Prediction using Integration of Numerical Weather Prediction and Machine Learning

Ummul Khaira, Marina Astitha and Diego Cerrai

Use of Multi-source Remote Sensing Data and Geospatial Modelling to Analyze Roadside Vegetation Risk on Utility Infrastructure

Durga Joshi, Harshana Wedagedara, Chandi Witharana

E.T.: Re-Thinking Self-Attention for Transformer Models on GPUs

Shaoyi Huang

Evaluation of the New OPM Winter Model

Xinxuan Zhang

Evaluating the Effectiveness of Resilience Improvement Efforts on the Electric Grid

Will Taylor


Author: Joint Seminar by the ECE Department & Eversource Energy Center at UConn Part of the ECE 6094 Power & Energy Seminar Series

Friday 12/10/2021 in ITEB 336(12:20-1:20 p.m.)

Also Available Live via Webex here


Chance Constrained Unit Commitment Approximation Under Uncertain Wind Energy


Dr. Claire Guo, Assistant Professor, Department of Information Systems and Decision Science, University of Baltimore


Abstract: With the increasing integration of renewable energy into power systems, the scheduling and operation of power systems becomes more challenging, in particular because of the intermittency of wind speed. Thus, appropriate optimization tools are needed to efficiently manage the operation of modern power systems. My research considers the unit commitment problem of power systems under high wind energy penetration. To mitigate the potential negative effects of inaccurate wind energy forecast, a chance constrained model is employed to guarantee that supply and demand for energy be balanced at each time period over a finite planning horizon. As joint chance constraint is known to be difficult to handle analytically, two approximation approaches, namely quantile-based and p-efficiency-based, are proposed and benchmarked against the well-known scenario-based approximation. The results indicate that the proposed schemes provide feasible solutions and tight upper bounds within a much shorter time frame than the scenario-based counterpart.

Bio: Dr. Claire Guo is an Assistant Professor in the Department of Information Systems and Decision Science at the University of Baltimore’s Merrick School of Business. Her research focuses on optimization modeling and solution algorithm development for large-scale optimization, stochastic optimization, and integer optimization, in applications on power systems and manufacturing systems. Prior to joining the University of Baltimore, Dr. Guo was a Postdoctoral Associate in Systems Engineering at Cornell University. She received her Ph.D. in Operations Research from Iowa State University and received her M.S. in Industrial Engineering from Texas A&M University. Dr. Guo currently serves on the Editorial Board of the Journals Production and Operations Management and Humanities & Social Science Communications (Operations Research track) and serves as a peer reviewer for many impactful journals. Her research works are published in leading journals including International Journal of Production Research, Computers and Operations Research, IEEE Transactions on Power Systems, and Operations Research Letters.

Author: 2021 Annual Meeting

We cordially invite you to attend the Eversource Energy Center Annual Workshop taking place on November 19th from 11:00 AM – 3:30 PM . In-person and remote access will be available. You can view the workshop agenda by clicking the date.

We will begin with welcome remarks from UConn and Eversource leadership, Center updates, and presentations by UConn faculty of the new projects initiated this year. The first part of the Workshop will include a keynote presentation by Ali Ghassemian, a program manager for the Division of Advanced Grid Research & Development, Office of Electricity (DOE). This will then be followed by two breakout sessions: Resilience and Reliability, which will be coordinated by Diego Cerrai and Robert Fahey, as well as Renewables Integration and Cyber/Physical Security, coordinated by Junbo Zhao and Mal Pena.

Meeting Link: http://s.uconn.edu/ipb317webex 

Author: Hurricane Henri Outage Forecast

OPM prediction:

August 22nd, 2021

Hurricane Henri

Outage Prediction Modeling group
Eversource Energy Center
University of Connecticut

Fourth prediction, released on: 08-21-2021 10:30 a.m. EDT

The Eversource Energy Center Outage Prediction Model at UConn is forecasting a very high impact from Hurricane Henri in Connecticut. The highest relative impact is expected in Eastern Connecticut, and the overall impact in Connecticut is expected to be between 10,000 and 20,000 damage locations.


Hurricane Henri is expected to make landfall in Connecticut (after a first landfall in Long Island). It is still not clear yet whether the hurricane will primarily hit Eastern, Central, or Western Connecticut. In any case, the impact in Connecticut appears to be very severe. The expected impact on the CT electric grid further increased with respect to yesterday’s update.

We are currently excluding a landfall in Eastern Massachusetts. However, this territory will be on the right (strong) side of the hurricane, despite not close to the center, and therefore will experience a moderate impact. A moderate to low impact is also expected in New Hampshire. The expected impact in NH and EMA decreased with respect to yesterday’s update.

For Western Massachusetts there is some uncertainty on the speed and on the weakening rate of the hurricane. If the hurricane slows down and weakens quickly in Connecticut, Western Massachusetts will have a moderate-high impact, while if the hurricane keeps moving North quickly, with a moderate weakening, the impact will be high also on this territory. An intermediate projection for WMA is provided.

Weather Predictions

With a landfall in Central Connecticut, wind gusts are expected to exceed 50 mph for several hours in CT and 45 mph in WMA and EMA.

Long lasting gusts exceeding 40 mph are also expected in New Hampshire.

Up to 5 inches of precipitation are expected in Connecticut and Western Massachusetts, while 2-4 inches are expected elsewhere.

Summary Table

Probability of TS Ranges

Author: NRT Inundation Maps of the European Flood

The UConn team at the Eversource Energy Cener has mapped European flood extent from Jul 15 to 17 spanning West Germany, France, Netherlands, and Luxembourg using a near-real-time tool, the Radar Produced Inundation Diary (RAPID, patent published), based on Sentinel-1 Synthetic Aperture Radar (SAR) satellite. The maps are also provided to the Dartmouth Flood Observatory (DFO). Original maps (created by Qing Yang) in GeoTiff format can be found via Amazon Web Service (AWS). Publications describing the algorithm and the CONUS system can be found online.

Author: Tropical Storm Elsa Outage Forecast


The Eversource Energy Center Outage Prediction Model at UConn is forecasting moderate impact from tropical storm Elsa for Connecticut. As shown at the storm outages maps, the highest impact is expected in Eastern Connecticut, and the overall impact in Connecticut is expected to be between 300 and 600 damage locations.

Author: Wind Whirls to Electricity: Predictive Modeling of Offshore Wind Power

By Georgios Matheou

Offshore wind is an abundant energy resource with significant environmental and economic benefits, but as a natural resource, it is variable. At the Eversource Energy Center the Marine Boundary Layer Modeling project aims to improve the design and operation of wind farms by better characterizing meteorological conditions at the wind-farm scale.

We are currently developing a high-fidelity, high-resolution computational model that is capable of simulating the atmospheric motions around individual wind turbines. To model the flow in the wind farm, we are extending UConn’s Large-Eddy Simulation (LES) model to include wind turbines. LES models are high resolution atmospheric models capable at simulating the wind, thermals, and clouds at spatial scales of a few meters. The newly developed model is able to capture the interaction of individual turbines with the turbulent atmosphere. The new model will allow us to investigate the dependence of overall power output on the environmental conditions and the characteristics of the wind farm, such as the type of wind turbines and their relative positioning.

A comprehensive modeling system is developed to capture the energy flow through the atmosphere. Energy initially enters the system by differential solar heating of the Earth’s surface. Regions near the equator receive more radiant solar heating compared to the regions near the poles. The energy enters the atmosphere through direct heating of the air at the surface and through evaporation in the form of latent heating of the atmosphere. This spatially variable energy flux drives the large-scale atmospheric circulation, weather patterns, and eventually the wind field at the location of the wind farm. Design and operation of the wind farm depends on the details of the wind field at small scales (less than 100 m) and near the surface. Modeling of the entire atmosphere at fine scale is not feasible. Thus, a hierarchy of modeling methods is used to track the energy flow through the system

Numerical weather predilection (NWP) modeling of the entire atmosphere provides information about the global weather patterns. NWP modeling is typically performed by government agencies and the model output is available to researchers. The output of the global model is used as input to a regional weather model which captures wind patterns at smaller scales (about 250 m). Subsequently, the output of the regional model is passed to the LES model that includes the interaction of the wind field with the wind farm. Through this modeling sequence, we are able to produce realistic predictions of energy output and the gather critical information related to equipment maintenance operations.

The wind turbines are converting the kinetic energy of the wind field to electrical power. Similar to the experience of a “bumpy” flight, the turbine blades move through a constantly fluctuating wind field. The fluctuating character of the wind field, or the characteristics of atmospheric turbulence, are important for the efficiency of energy extraction and the structural fatigue of the turbines. Accordingly, the accurate representation of atmospheric turbulence is important in the LES model. The model uses a sophisticated technique to generate a turbulent atmosphere upstream of the wind farm by using a pair of concurrent synchronized LES simulations. An auxiliary LES is carried out to generate a realistic turbulent atmosphere. Data from the auxiliary LES are used as an inflow condition to the main LES which includes the wind farm. 

The following figures show results from the main LES domain. The locations of the wind turbine disks are indicated with black lines. Movies correspond to a horizontal plane at the hub height and a vertical plane along the turbine axis. The color contours correspond to wind speed. The wind speed downstream of the wind turbines decreases as a result of the reduction of the kinetic energy as the atmosphere moves through the wind turbine. Essentially, the plots visualize the extraction of kinetic energy from the atmosphere. Also shown is the importance of atmospheric turbulence as small fluctuations interact with the turbines but also the interactions of the wakes between the turbines. For closely placed turbines as in the figure, the placement to reduce interference of the wakes with the downstream turbines is important because the wakes are regions of low wind speed, thus, less energy can be generated.



Author: New Power Engineering Faculty Joining ECE and the Center

The Electrical and Computer Engineering (ECE) Department and Eversource Energy Center (EEC) are happy to announce the recruitment of two new faculty as part of our cluster hire in the field of power systems engineering:



Dr. Zongjie Wang is currently research associate in Systems Engineering at Cornell University. She earned her Bachelors,Masters, and Ph.D. degrees in Electrical and Computer Engineering at Harbin Institute of Technology (HIT), China. Dr. Wang’s research interests focus on problems in modern power systems and renewable energy through leveraging dataanalytics, optimization and simulation techniques. Her projects include development of new algorithms  for  optimal  power  flow  in  power  systems  with  high  penetration  of renewable energy sources; bi-level optimization between transmissionand distribution systems; comprehensive modeling of distributed generators in active distribution systems; networkequivalent modeling of New York state power system topology; feasible power flow solutions in weakly-meshed activedistribution systems, sensitivity analysis of new extended bus types in power systems. Dr. Wang has a patent “online multi-period power dispatch problems” filed by U.S. in 2020. As an invited speaker, she gave one of her talks at the headquarter ofUS Federal Energy Regulatory Commission (FERC)’s Technical Meeting in DC. She is also a member of PSERC committee and hascollaborations with power system operators in the industries and other institutions, for example, New England ISO, New York ISO, MIT, OhioState University, Technical University of Denmark.


Dr. Junbo Zhao has been an Assistant Professor at Mississippi State University, Starkville, MS, USA since 2019. He received his Ph.D. degree from the Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA, in 2018. He was a Research Assistant Professor at Virginia Tech from May 2018 to August 2019. He also did a summer internship at Pacific Northwest National Laboratory from May to August 2017. He is currently the chair of the IEEE Task Force on Power System Dynamic State and Parameter Estimation and the IEEE Task Force on Cyber-Physical Interdependency for Power System Operation and Control, co-chair of the IEEE Working Group on Power System Static and Dynamic State Estimation, the Secretary of the IEEE PES Bulk Power System Operation Subcommittee. He has published three book chapters and more than 100 peer-reviewed journal and conference papers, where more than 50 appear in IEEE Transactions. His research interests are cyber-physical power system modeling, estimation, security, dynamics and stability, uncertainty quantification, renewable energy integration and control, robust statistical signal processing and machine learning. He serves as the editor of IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid and IEEE Power and Engineering Letters, the Associate Editor of International Journal of Electrical Power & Energy Systems, and the subject editor ofIET Generation, Transmission & Distribution. He is the receipt of best paper awards of 2020 IEEE PES General Meeting and 2019 IEEE PES ISGT Asia. He received the Top 3 Associate Editor Award from IEEE Transactions on Smart Grid and IEEE PES Outstanding Engineering Award in 2020.




Eversource Energy Center | Innovation Partnership Building: 159 Discovery Drive, Unit 5276, Storrs, CT 06269-5276 | E-Mail: eversourceenergycenter@uconn.edu