Wildfire smoke is no longer just an air-quality concern; it’s becoming a real operational factor for modern electric grids. As photovoltaic (PV) systems continue to integrate into distribution networks, feeder performance becomes increasingly dependent on weather and environmental conditions. Under clear skies, PV panels inject substantial power into the grid and reshape how voltages behave…
Across the energy sector, storage has long been recognized as a cornerstone of grid reliability and renewable integration. Yet, the conversation about its environmental role often overlooks how they can actively contribute to carbon reduction. Our recent research, accepted for presentation at the Hawaii International Conference on System Sciences (HICSS) 2026, introduces a framework for…
A Physics-Informed Neural Network (PINN) is a machine learning architecture designed to solve scientific problems by harmonizing data with physical laws. Its core innovation lies in training a neural network not only to fit observed data but also to adhere to known governing equations, typically ordinary or partial differential equations. This is achieved by incorporating…
Competitive electricity markets are inherently complex. They require the integration of technically feasible operations, grounded in physical laws, with economically viable solutions that incentivize investments. These interactions aim to ensure a reliable, efficient, and technologically flexible market design. However, the operation of these markets is neither trivial nor perfect. Over time, they are exposed to…
This post summarizes our recent open-access publication in Energies.Read the full article here. Why Focus on Long-Term Load Forecasting? Long-term load forecasting (LTLF) is a crucial tool for planning and operating electric power systems. Accurate forecasts help system operators and planners make informed decisions about investments, operations, and policy. However, LTLF faces major challenges due…
This paper proposes a hybrid deep actor-critic framework for the optimal operation of a phase-changing soft open point (PCSOP) in an unbalanced distribution network. The framework combines algorithmic features of off-policy reinforcement learning and imitation learning. The proposed method comprises a policy-guiding module based on the PCSOP physics and an adaptive dynamic experience replay buffer.…
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Price spikes are unexpected and abrupt extreme prices whose value can be several orders of magnitude higher than normal electricity prices. Moreover, due to their stochastic nature, price spikes are short-lived extreme price variations observed in the short-term operation of all…
The U.S.-Canada Center on Climate-Resilient Western Interconnected Grid brings together leading experts in power engineering, climate, forestry, data analysis, policy and social sciences from a network of 35 partners. This new interdisciplinary center is aimed at fortifying the region’s power infrastructure against the floods, high winds, drought, even cold snaps that are also taking a…
The University of Calgary and the University of Utah will establish and co-lead the U.S.-Canada Center on Climate-Resilient Western Interconnected Grid through $5M funding by the U.S. National Science Foundation (NSF) and $3.75M funding by the Natural Sciences and Engineering Research Council of Canada (NSERC). Commonly known as “the Western Interconnection,” it is one of two…