These are the most recent journal articles, blogs, publications & news from the Grid Foresight team.
For an up-to-date list of all journal articles visit Dr. Zareipour’s Google Scholar listing.
If you would like a copy of a paper, for personal use only, please contact us.
Reliable smart charging requires forecasting the charging behavior of EVs. Deep learning algorithms could present a solution. However, deep neural networks (DNNs) require a large number of samples for training. Although there are available datasets for EV charging, such data…
AI Data Centers and the Grid: Where the Research and Practice Gaps Still Are
Energy storage systems are increasingly recognized as uniquely flexible grid assets. Technically, they can inject and absorb power rapidly, respond to contingencies, support voltage, reduce overload risk, and help defer or complement conventional wires investments. That broad capability is part…
The Rise of Distributed Energy Resources Electric power systems (EDS) are changing rapidly. The growth of distributed energy resources (DERs), such as rooftop solar panels, battery storage, and small-scale generators, is transforming the traditional EDS. Consumers are no longer passive users but are becoming prosumers who…
Heatwaves, cold snaps, tornadoes, earthquakes, are you hearing these terms in the news more often, or have personally experienced an impactful weather event? In 2025, Alberta alone faced some serious weather events that disrupted communities, forced evacuations, ravaged infrastructures, and…
Quantum computing has been marketed as the next great computational revolution. A technology capable of reshaping materials science, leading to drug discovery, disrupting cryptography, helping in discovering new batteries, climate mitigation strategies, optimizing global energy systems [1-6], or supposedly cracking…
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…
The Optimal Power Flow (OPF), as an optimization problem, is indispensable for the economic and secure operation of power distribution networks. The OPF is crucial for achieving a multitude of operational objectives including: Solution techniques for the OPF problem have…
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…
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…
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…
The emergence of the sharing economy has fundamentally altered paradigms of resource utilization, shifting the emphasis from individual ownership to collective access. This transformation, evident across domains such as transportation, housing, and digital infrastructure, is increasingly penetrating the energy sector.…
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…
In this article, I review the research in the 2024 NeurIPS conference paper, From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection, by Wang et al. The paper explores integrating event analysis into LLM-based time-series…
Photovoltaic (PV) systems are key to the transition toward clean energy, but their real-world performance is shaped by complex environmental interactions. For researchers working on the physics of solar energy systems, understanding how variables like temperature, humidity, aerosols, and wind…
Recent advancements in the field of Natural Language Processing (NLP) and Large Language Models (LLMs) have opened new avenues for processing qualitative data. This development is particularly interesting for power systems as they have an enormous amount of untapped qualitative…
Author: Hamidreza Zareipour Although all research presentations share a common goal of conveying content, the most effective ones are tailored to the specific context and audience. These factors can drastically shape both the content and style of your talk. Presenting…
This study explores the impact of weather features on short to medium electricity load prediction across diverse geographical locations. Using hourly load data, we evaluated the effectiveness of several feature selection methods, including Mutual Information (MI), Principal Component Analysis (PCA), Lasso, and Heatmap correlation. We benchmarked…
Transformers, powered by scaling laws, have transformed the landscape of AI, but their performance in time series forecasting is less definitive. This post explores the effectiveness of transformers, the challenges of scaling, and the rise of specialized foundation models like…
This article introduces emissions response, the widespread use of real-time emissions factors in electricity grids as a signal for a dynamic response to facilitate decarbonization and system efficiency. Many publications have suggested dynamic approaches to addressing systemwide emissions, reducing emissions…
Degradation of energy capacity and energy efficiency of lithium-ion battery energy storage systems depend on their operation conditions and are key factors in their business case feasibility. Using traditional energy reservoir models with energy throughput methods to quantify degradation leads…
In this paper, a new deep learning-based two-stage dataset-clustering/temporal-clustering method is proposed for time aggregation in renewable energy-integrated power systems. In this way, for the first time, the representative period, including one or more representative days, is obtained using a…
The integration of wind energy into the power grid has been on the rise, becoming an increasingly important source of electricity. However, the intermittency of wind energy output, particularly the wind power ramps (the rate at which wind energy output…
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…
This IEEE paper, ‘Demand Charge Management Based on Battery Aggregation of Commercial and Passenger Electric Fleet Vehicles’, proposes a load management platform to help industrial and commercial electricity customers assess the feasibility of demand charge management through battery aggregation of…
Wholesale electricity markets are designing market participation models for hybrid resources that consist of energy storage and generation. This IEEE paper investigates the strategy behind two proposed market-participation models of a hybrid resource. The first is co-located hybrid resource, where…
Harnessing Grid Integration Opportunities for Accelerated Adoption: This paper proposes a multistage investment planning framework for the fleet transition problem, with a particular focus on the capacity of the electric fleet’s aggregated battery to generate revenue for the fleet owner…
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…
Overcoming the Cold-Start Problem in EV Charging Forecasting The rapid growth of electric vehicle (EV) adoption has been imposing pressure on distribution systems [1]. Voltage drop and congestion of feeders are the most common issues at the distribution networks level…
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…
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…
