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Breakthrough Supply Chains: New Book by Iakovou and Co-Authors

Professor Eleftherios “Lefteris” Iakovou, a Professor of Engineering Technology and Industrial Distribution, and co-authors Christopher Gopal, Gene Tyndall, Wolfgang Partsch have published a book entitled “Breakthrough Supply Chains: How Companies and Nations Can Thrive and Prosper in an Uncertain World.” This book is A timely guide to rethinking and reinventing supply chains with breakthrough thinking […]

Integrating decentralized energy storage with power generation

Texas A&M University researchers have developed a computational model to study how to optimally integrate and operate batteries to store excess energy from renewable sources at individual power plants. The findings of this study were published in the Energy and Environmental Science journal. Energy sources such as solar and wind are not always available, but with […]

Review Article Published on Carbon Capture, Utilization and Storage

Faruque Hasan, an Associate Professor in the Artie McFerrin Department of Chemical Engineering and the Assistant Director of Decarbonization for the Texas A&M Energy Institute, along with his doctoral student Manali Zantye and Postdoctoral Research Associate Kazi Monzure-Khoda, co-authored an article titled, “Challenges and Opportunities in Carbon Capture, Utilization and Storage: A Process Systems Engineering Perspective” that was published in the October 2022 issue of Computers & Chemical Engineering by Elsevier. This work provides an overview of technology and systems integration challenges, advances, and opportunities in the area of carbon capture, utilization, and storage (CCUS).

Targeted Demand Response Reduces Price Volatility of Electric Grid

To reduce the energy load across the entirety of the state’s grid, traditional demand response studies focus on reducing the energy load in high population centers such as Houston and Dallas. However, Dr. Le Xie, professor in the Department of Electrical and Computer Engineering at Texas A&M University, and his team found that focusing on a few strategic locations across the state outside of those high-population areas is much more cost-effective and can have a greater impact on the price volatility of the grid. A machine learning algorithm is utilized to strategically select these demand response locations based on a synthetic Texas grid model.