Data-Driven Modeling OF DERs For Extreme Weather Resilient Power Systems
Institution: University of Calgary | PI: Mostafa Farrokhabadi | Dates: May to August 2026
Abstract: The project aims to develop data-driven tools to enhance the resilience of power systems against extreme weather events by modeling the behavior and performance of Distributed Energy Resources (DERs). Leveraging advanced machine learning techniques and realistic data, the outcomes of this project will contribute to better decision-making and planning in energy systems, particularly in the face of challenges posed by extreme weather.
Heatwave and Extreme Cold Impacts on Electricity Load Profiles in Alberta
Institution: University of Alberta | PI: Petr Musilek | Dates: May to August 2026
Abstract: Climate-driven extreme temperature events, including prolonged heatwaves and extreme cold spells, are increasingly disrupting electricity demand patterns and challenging power system operation. This project investigates how extreme temperature conditions distort system-level electricity load profiles in Alberta using historical demand data from the Alberta Electric System Operator (AESO) combined with weather records. By comparing load behavior during normal conditions with periods of extreme heat and cold, the study quantifies changes in peak demand, load shape, and demand variability. The results provide insight into the impacts of temperature extremes on electricity demand and highlight implications for load forecasting accuracy, grid operation, and climate-resilient power system planning.
Informing Wildfire Mitigation Through Outage Risk Assessment for Utilities in British Columbia
Institution: UBC Vancouver | PI: Roland Stull | Dates: May to August 2026
Abstract: The goal is to develop an electric-grid outage-risk index for the different fire regimes of British Columbia. This project will expand upon an existing wildfire probability model developed by UBC Master’s student Mina Deshler. The approach is to include additional datasets that are relevant for the assessment of risk to the electric grid. The undergraduate student will integrate historic utility outage data, along with other relevant variables, into the existing wildfire probability model to identify risk areas for BC Hydro assets. The anticipated research outcome is the development of an outage risk index that combines the probability of wildfire occurrence with the potential impacts on grid infrastructure. The outage risk index will help inform wildfire mitigation planning and asset management for BC Hydro and potentially for other utilities in British Columbia.
AI Data Centers Load Models
Institution: UBC Okanagan | PI: W. John Braun | Dates: May to August 2026
Abstract: The Boychuk (2009) Fire Spread Model provides a stochastic framework for simulation. Its underlying assumptions have never been rigorously tested. By conducting smoldering fire experiments in a lab fumehood and recording with a thermal camera, we plan to examine the pattern of heat transport at the pixel level. An updated modernized version of the model will be implemented, likely in Python.
Community Experiences of Power Loss and Climate Hazards: A Scoping Review
Institution: University of Regina | PI: Amber Fletcher | Dates: May to August 2026
Abstract: This project seeks to hire an undergraduate student fellow to assist the team in conducting a scoping review on power loss and climate hazards in Canada and the United States. The student will conduct literature searches for existing evidence on community-level experiences during power loss resulting from snowstorms, wildfires, and floods. The purpose of the review will be to identify both themes and gaps in the literature on this topic. In addition to conducting the scoping review, the student will have the opportunity to gain hands-on experience in research design. They will help our faculty team design research tools (e.g., surveys) and prepare research ethics applications for WIRED sub-projects in our research laboratory, and also help design future sub-projects associated with the WIRED Centre. This would be a unique combination of independent research and research project training for the student.
Evaluation of wildfire mitigation actions of utility firms in the Western US Region
Institution: University of Utah | PI: Divya Chandrasekhar | Dates: May to August 2026
Abstract: This study examines the extent to which utility companies have adopted wildfire mitigation strategies as well as the challenges and opportunities of pursuing such actions. The project entails conducting focus groups and interviews with representatives from utility firms, energy planning consultancy firms, and regulatory agencies within the western region of the United States to identify actions taken within seven domains of mitigation action: grid hardening and design, vegetation management, situational awareness, asset management and inspection, long-term data tracking and planning, wildfire emergency coordination and outreach, grid response operations, and public safety power shutoff. Focus groups will also assess key challenges faced by various stakeholders in pursuing these actions, such as resource constraints (e.g., financial, insufficient staffing or expertise, data availability, and technological), geographic or environmental constraints, regulatory hurdles, stakeholder opposition, and coordination challenges. These outcomes of this study will contribute to a more consistent understanding of wildfire mitigation practices and support improved decision-making in the power utility sector.
Monitoring Personal Exposure to Urban Heat and Wildfire Smoke Pollution using Wearable Sensors
Institution: University of Utah | PI: Alexandra Ponette-Gonzalez | Dates: May to August 2026
Abstract: Outdoor heat and wildfire smoke air pollution are growing threats to human health in cities across the American West, and they are even more harmful when they occur simultaneously. To protect people, communities need to know what individuals are feeling and breathing on the ground and as conditions change from hour to hour. Our project seeks to understand the combined impact of urban heat and particles from wildfire smoke and pollution on urban residents, and how cooling centers can make communities safer.
Although we focus on Salt Lake City, the approach and results from this project can guide other places with similar geography and plans that address both heat and wildfire smoke air pollution events. By measuring what people experience in the moment and by assessing the role of cooling centers during these events, we aim to produce clear, actionable guidance that helps communities reduce the combined risks of heat and wildfire smoke pollution. These health hazards can reinforce each other, so practical steps that lower both at once can have outsized benefits for health.
Indoor Environmental Quality Assessment and Energy-Efficient Retrofit Evaluation of an Existing Research Laboratory Building
Institution: University of Utah | PI: Ajla Aksamija | Dates: May to August 2026
Abstract: Research laboratory buildings are among the most energy-intensive buildings on university campuses due to their high energy demands, complex building systems and the need to maintain controlled indoor environmental conditions. Many existing laboratory buildings, constructed prior to current building performance standards, continue to operate with aging mechanical systems that may struggle to maintain indoor environmental quality (IEQ) and energy efficiency. Despite their critical role in supporting research activities, comprehensive performance-based assessments of these aging facilities remain limited. Therefore, a clear understanding of the relationship between indoor environmental performance and building energy consumption is essential for improving the energy efficiency of aging laboratory facilities.
This project investigates the IEQ and energy performance of the Merrill Engineering Building, a mid-century research building located at the University of Utah. The overarching aim is to identify key performance gaps and recommend targeted retrofit strategies. To achieve this aim, the project utilizes monitored data from three engineering laboratories located on the basement and third-floor levels, including hourly measurements of indoor air temperature, relative humidity, carbon dioxide (CO₂), and fine particulate matter (PM2.5), with data monitoring ongoing since March 2025. In addition to analyzing indoor environmental trends, the project incorporates actual building energy consumption data and simulations to evaluate energy use patterns and overall operational performance.
Preliminary IEQ results indicate floor-level differences in thermal and humidity variability, with extended periods of underheating in the basement laboratory, intermittent CO₂ spikes during occupied periods on the third-floor laboratory, and consistently low PM2.5 concentrations in both spaces. Building on this initial IEQ assessment, the project will integrate IEQ measurements with actual energy performance analysis to identify building performance trends, seasonal variations, operational inefficiencies, and potential opportunities for targeted retrofit strategies. The research will provide data-informed insights into how aging laboratory infrastructure performs and contribute to decision-making aimed at improving occupant comfort and energy efficiency in existing campus research buildings.
Impacts of Pyroconvection on Fire Spread Rates
Institution: University of Utah | PI: Dereek Mallia | Dates: May to August 2026
Abstract: Extreme wildfire behavior in the western United States can be impacted by pyroconvection, which can dramatically accelerate fire spread, generate erratic winds, and promote rapid transitions from manageable incidents to large, high-impact events. We will examine how pyroconvective processes modify near-surface wind fields and fire–atmosphere coupling to influence fire spread rates in conjunction with NASA’s Injected Smoke and PYRocumulonimbus Experiment (INSPYRE). This project seeks to quantify how plume dynamics are coupled with surface fire behavior by integrating airborne observations, satellite products, and high-resolution modeling. We propose to incorporate high-frequency surface meteorological observations from power utility networks across California. These data provide a dense, underutilized observational resource capable of resolving localized wind enhancements and turbulence associated with pyroconvection. We will also develop a machine learning–based forecasting framework to estimate the probability that point ignitions will transition into megafires using a new wildfire database generated for all wildfires in California from 2003 to 2020.
Experimental and Theoretical Refinements of Heat and Mass Transport in Hot Water-Surfactant Nanofluids for Enhanced Oil Recovery
Institution: University of Utah | PI: Samira Shiri | Dates: May to August 2026
Abstract: Enhanced oil recovery (EOR) techniques are essential for maximizing hydrocarbon extraction, particularly in mature and challenging reservoirs where conventional methods become inefficient. Chemical EOR, specifically surfactant-based approaches, has shown significant potential in improving oil displacement by altering wettability and reducing interfacial tension. Recent advancements in nanotechnology have introduced nanoparticle-enhanced surfactants (NES), which offer improved stability, enhanced oil mobility, and reduced adsorption losses on reservoir rock. However, a critical knowledge gap remains in understanding the interfacial phenomena involved in the complex interplay of energy and mass transport in nanofluid–surfactant systems, particularly in reactive reservoirs, limiting their applicability to real-world EOR processes.
The proposed research aims to address key knowledge gaps related to nanoparticle transport, interfacial interactions, and energy transfer in porous networks by integrating experimental analysis with theoretical modeling. This work will establish:
1) how nanoparticle properties—such as size, shape, material, and concentration—govern retention, mobility, and thermal behavior in different reservoir conditions;
2) how surfactant-modified nanoparticles interact with reservoir surfaces to alter wettability, reduce interfacial tension, and enhance oil displacement efficiency;
3) how reservoir grain geometry, pore connectivity, and surface properties influence energy distribution, fluid displacement dynamics, and the overall efficiency of nanofluid formulations in complex porous networks.
The findings will contribute to optimizing nanofluid formulations tailored to specific reservoir characteristics, ultimately enhancing oil recovery efficiency while reducing uncertainties in field-scale applications. This research will serve as a foundation for the next generation of nanotechnology driven EOR strategies, promoting more efficient and sustainable hydrocarbon extraction methods.
Power, Water, Land, and Policy Requirements for Data Center Development in the Western States
Institution: University of Utah | PI: Masood Parvania | Dates: May to August 2026
Abstract:This project investigates the interconnected power, water, land, and policy requirements that shape data center development across the Western United States. As demand for digital infrastructure accelerates, data centers require substantial electrical capacity, reliable water supplies for cooling, and large tracts of appropriately zoned land, all of which vary significantly across states with diverse resource constraints and regulatory environments. Through comparative analysis of regional utility availability, water-resource limitations, land‑use planning frameworks, and state and local policy landscapes, this research identifies the key factors influencing site feasibility and long‑term operational sustainability. The project aims to provide a clear characterization of regional challenges and opportunities to support future data center planning that balances economic growth with responsible resource stewardship in the Western states.
Digital Twin Tool for Forecasting HVAC Electricity Demand
Institution: University of Calgary | PI: Hamidreza Zareipour | Dates: May to August 2026
Abstract: Electricity demand forecasting is central to reliable power system planning. Utilities use it to estimate future needs for generation, transmission, and distribution. Traditional long-term forecasting has mostly relied on historical demand data and statistical trend extrapolation, which works reasonably well under stable conditions but is becoming less reliable as demand patterns change. Two major drivers are reshaping electricity use: climate change and electrification. More frequent temperature extremes are increasing heating and cooling needs, while wider adoption of technologies such as heat pumps and air conditioners is making electricity demand more temperature-sensitive. Existing methods for modeling them into demand forecasting do not address the spatiotemporal diversity and complexity of these devices at large-scale adoption. This project addresses that gap by developing a simplified digital twin for residential heat pumps and air conditioners.The goal is to create a transparent, adaptable, and computationally efficient model that estimates electricity use based on the key physical relationships driving heating and cooling demand, without becoming an overly complex engineering simulation. The model will then be combined with adoption trends for these technologies, using public data, policy reports, and regional statistics to estimate current and future uptake to generate 8760 load shapes for adoption scenarios.
