WKU receives $2 million award to advance energy and disaster resilience at Innovation Campus. * Wednesday, October 28th, 2026 Western Kentucky University has received a $2 million award from the Kentucky Energy and Environment Cabinet's Office of Energy Policy, funded by the U.S. Department of Energy's State Energy Program, to develop a resilient microgrid at the WKU Innovation Campus. The project will create a platform for research, education and disaster resilience, helping the campus support critical operations during power outages while improving everyday energy management. The microgrid will integrate solar power, battery energy storage, existing backup generators and advanced controls. A microgrid is a coordinated local energy system designed to operate alongside the utility grid and, when needed, independently during an outage. It is intended to support critical facilities at the Innovation Campus during outages, including WKU's Disaster Science Operations Center (DSOC), which works alongside Kentucky Emergency Management to coordinate disaster response. "This award gives us an opportunity to connect disaster preparedness with practical energy solutions," said Dr. Manmeet Singh, Assistant Professor in WKU's Department of Earth, Environmental, and Atmospheric Sciences within the WKU Ogden College of Science and Engineering, and the project's principal investigator. "Our goal is to build a system that helps sustain critical operations when the grid is disrupted, while creating opportunities for students to work on real challenges at the intersection of energy, data science, and community resilience." Researchers and students in the DSOC's AI Research (AIR) Lab will use the microgrid as a research platform, developing data and artificial intelligence applications that connect weather, energy use and emergency operations. "This microgrid gives our students and faculty a working energy system to study, test and learn from, connected to the center where we monitor severe weather and support emergency response across Kentucky," said Dr. Josh Durkee, Director of DSOC and University Meteorologist. "Through the DSOC's AI Research (AIR) Lab, our team will develop tools that link weather forecasting with energy and emergency decisions, so the center can keep operating when the next ice storm or tornado outbreak takes down the grid. Being selected for the first award under Kentucky's Resilient Microgrid Program reflects the work our students and faculty have put into making WKU a leader in disaster science." The project includes engineering and design, equipment procurement and installation, and testing to evaluate how solar generation, stored energy and backup generators work together. WKU will work with the Electric Power Research Institute (EPRI) on engineering analysis and design review, and will coordinate with Bowling Green Municipal Utilities and the Tennessee Valley Authority on interconnection requirements and potential participation in TVA's Peak Rewards demand response program. Beyond infrastructure, the project will include education and outreach for the campus, the local community and emergency response personnel, explaining the microgrid's capabilities and its potential role during severe weather and other emergencies. Students in engineering, environmental science and emergency management programs will have opportunities to work with the system and its data. "We want this project to serve as a platform for learning and applied research," Dr. Singh said. "Students can connect what they learn in the classroom with the practical questions communities face: how to prepare for outages, manage limited energy resources, and make infrastructure more resilient." That integration of student learning and campus operations reflects WKU's sustainability initiatives. "One of the things that distinguishes WKU's approach to sustainability is that we do not view campus operations, education, and research as separate efforts," said Dr. Leslie North, Director of WKU's Office of Sustainability. "We intentionally connect the sustainability work happening on our campus with opportunities for students and faculty to learn, conduct research, and engage with real-world challenges. This microgrid project reflects that approach by creating a platform to explore how solar energy production and storage can couple with advanced controls to strengthen energy and disaster resilience." State officials emphasize that the impact will extend far beyond the borders of WKU's campus. "For communities to become more resilient, we need disaster response locations that can be self-sustaining," said Kenya Stump, Executive Director of the Office of Energy Policy in the Kentucky Energy and Environment Cabinet. "These microgrid locations create safe spaces for community members to come for essential services, and they ensure community services continue regardless of the hazardous event." The award period runs from September 2026 through June 2028. Project team Dr. Manmeet Singh, Assistant Professor, Department of Earth, Environmental, and Atmospheric Sciences (principal investigator) Dr. Pallav Bera, Assistant Professor, School of Engineering and Applied Sciences Dr. Josh Durkee, Professor of Meteorology and Director, Disaster Science Operations Center Dr. Leslie North, Chair, Department of Earth, Environmental, and Atmospheric Sciences, and Director, Office of Sustainability Partner: Electric Power Research Institute (EPRI) This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the State Energy Program Award Number DE-EE-0010074.

GridWrap awarded $5.9M from California Energy Commission to expand grid capacity and strengthen grid resilience. Cleantech San Diego member and Southern California Energy Innovation Network Company GridWrap has been selected by the California Energy Commission (CEC) for a $5.9 million project to validate and commercialize its Composite WiRe Wrap technology under real-world grid operating conditions, demonstrate its performance and reliability at scale, and accelerate commercialization and utility adoption. Electricity demand is rising rapidly, driven in large part by the explosive growth of artificial intelligence (AI), hyperscale data centers, cloud computing and broader electrification. At the same time, much of the nation's transmission and distribution infrastructure is decades old and was not designed to accommodate today's growing and increasingly concentrated power demands. Wildfires, extreme heat, severe storms, and other climate-related events are placing additional strain on aging grid infrastructure and increasing the need for a more resilient electric system. Conventional grid upgrades, including reconductoring, rebuilding transmission lines, and undergrounding, can cost millions of dollars per mile and often take years to permit, engineer, and construct. These challenges are creating an urgent need for faster, more cost-effective solutions that can increase the capacity of existing power lines, accelerate the interconnection of AI and data center loads, and strengthen grid resilience without requiring utilities to completely rebuild existing infrastructure. Composite WiRe Wrap is GridWrap's proprietary, patented technology designed to be installed directly onto existing overhead conductors. The system is engineered to increase a power line's capacity by up to 2 times while reducing thermal sag by up to 75%, helping maintain greater clearance from vegetation and the ground and reducing wildfire ignition risk. The technology also mechanically reinforces existing conductors to improve resilience against ice, snow, wind, and other extreme weather conditions. Installed using an autonomous robotic system, Composite WiRe Wrap minimizes field crew requirements and service disruptions, providing utilities with a faster and more cost-effective solution to increase the capacity, reliability, and resilience of existing grid infrastructure. The system targets a total installed cost of roughly one-tenth that of conventional reconductoring, which could save utilities and ratepayers in California up to $12 billion. "Utilities are being asked to deliver more power on infrastructure that was not built for today's demand or today's weather. This project will give utilities independent, real-world proof on whether Composite WiRe Wrap technology can meet that need at a fraction of the cost of replacement," says Davoud Zamani, CEO of GridWrap. Throughout the project, GridWrap is working directly with utility stakeholders, including California's investor-owned utilities, and technical experts at INL and EPRI to define operational requirements, performance benchmarks, and monitoring priorities. This collaboration is designed to surface and address technology risks early and give utilities the independent, real-world validation they need to adopt Composite WiRe Wrap at scale.

NVIDIA DGX Station: What Research Teams can run locally. See how research teams use DGX Station for protein search, weather forecasts and model development, then check memory, power and the exact system before buying. Key takeaways * EPRI used Station for a 50-member GenCast ensemble timestep in about 150 seconds. That is a reason to test local weather or simulation iterations, not a speed promise for another model. * Protein-database search, a large model shared by a lab, and fine-tuning before edge deployment are other documented research uses. Each needs a different fit or timing test. * Station's 748 GB combines 252 GB of GPU HBM3e with 496 GB of Grace CPU memory. A workload near 700 GB does not run entirely in GPU HBM. * A buyer needs the Linux/Arm software stack, a 20 A circuit, and the exact OEM storage and support configuration settled before delivery. EPRI's researchers used a deskside DGX Station to run one timestep of a 50-member GenCast weather ensemble for electric-grid planning. In the test NVIDIA documents, that run took about 150 seconds; the case study estimates more than ten hours on a CPU-only server. That result belongs to EPRI's setup, but it shows why a research team might want this much compute within reach rather than waiting for a shared cluster slot. A DGX Station purchase still has to answer ordinary questions: Will its model fit at the precision Uniqcli Inc use? Can its team share the machine without slowing each other down? Can the room power and cool it? Here is how to answer those questions before requesting a quote. What research teams are doing with it. At Sungkyunkwan University School of Medicine, researchers load protein-structure databases, including an AFDB50 collection of more than 60 million structures, into memory for fast homology searches. Cornell computer-science professor Kilian Weinberger reports using the memory to train multibillion-parameter language models without waiting for scheduled compute. These are different jobs, yet both benefit from having large, accessible memory next to the researcher. The work is not limited to language models. Carnegie Mellon University's Robotics Institute reports fine-tuning a drone-detection model on Station, then deploying it to a Jetson Orin NX for real-time inference. EPRI uses Station for weather prediction and downscaling research that informs electric-grid planning. The useful question for a buyer is which stalled experiment, slow data step or queue of jobs the system would remove for their own team. NVIDIA's examples are customer-reported use cases, not promised results for another lab. Turn those examples into a buying test. * Protein search like Sungkyunkwan's: Record the database and index size, memory occupied during a representative query, and searches completed per hour. The deciding issue is whether the working set fits and remains responsive when researchers share the system. * Ensemble forecasting like EPRI's: Run one representative timestep with the planned ensemble size and downscaling stage. Record time to result and peak memory; EPRI's 150-second result was for its own 50-member timestep, not a general Station benchmark. * Fine-tuning like CMU's: Use the intended dataset and model, then record training time, checkpoint size and whether the exported model still meets its target device's latency and memory limits. Station compute does not remove the separate edge-deployment test. Understand the 748 GB before sizing a model. DGX Station pairs one Blackwell Ultra GPU with a 72-core Grace CPU through NVLink-C2C. The GPU has 252 GB of HBM3e; the CPU has 496 GB of LPDDR5X. Together they form a coherent 748 GB address space, but data in CPU memory does not run at the same speed as data in GPU HBM. A model's weights, context cache, training states and active users all compete for space and bandwidth. NVIDIA lists up to 20 PFLOPS of FP4 compute and support for models up to one trillion parameters. Those are platform limits under particular conditions, not a speed or model-fit guarantee. Ask the team to record the exact checkpoint, precision or quantization, context length, batch size and target latency. If the decision depends on a large model running without harmful offload or serving several people at once, request a test with those settings rather than extrapolating from parameter count. Plan the room and the software. NVIDIA rates Station at 1,600 W and requires a 20 A circuit. Have facilities check the circuit, outlet, heat load, clearance, delivery path and service access against the OEM unit in the quote. A deskside machine still needs an installation plan. The shipping Linux configuration uses Ubuntu with NVIDIA AI Developer Tools. ASUS lists that environment for its ExpertCenter Pro ET900N G3. NVIDIA says DGX Station for Windows is expected from OEMs in December 2026; a current Linux ASUS unit should not be treated as a Windows purchase. Check Arm support for your containers, licensed software and model runtime before placing the order. For a shared machine, also decide who controls user access, updates, backups and storage. Station includes ConnectX-8 networking, with up to 800 Gb/s of platform bandwidth, and NVIDIA now supports linking two Linux Stations through NVIDIA Sync. That peak does not specify the cables or switch in your room. Name the network it must join and ask the supplier to list the required transceivers and links. If visualization or simulation is part of the job, NVIDIA allows one additional RTX PRO GPU on supported configurations; confirm the card and OEM support in writing. Check the exact ASUS system. ASUS says its ExpertCenter Pro ET900N G3 is available to order worldwide and publishes several configurations. The base two-SSD version lists two 2 TB NVMe drives; other versions add drives, QSFP transceivers or an RTX PRO card. The Uniqcli catalog page below is a useful starting point, but its listing does not establish immediate stock or the lead time for your chosen configuration. Ask for the manufacturer's full configuration, usable storage after any mirroring, required network parts, OS image, warranty, support entitlement and a dated delivery estimate. If a lab needs a different storage layout or graphics option, put that requirement in the request before a price is assembled. MSI's XpertStation WS300 is another DGX Station-architecture option in the Uniqcli catalog. MSI's manufacturer page calls the listed WS300 part a barebones configuration, so request a complete bill of materials and clarify the installed storage, software and support before comparing it with the ASUS system. DGX Station systems in its catalog. Compare the configured ASUS system with MSI's request-pricing WS300 listing. Confirm the exact included components and current delivery terms. Explore the system and the larger alternative. Send a useful request for quote. * Describe the real workload: checkpoint, precision, context length, data volume and whether the job is training, fine-tuning or inference. * Give an acceptance target: time for a representative job, response latency, throughput or number of concurrent researchers. * State the site's power, room and network details, plus any required security or remote-management controls. * Specify storage, an optional RTX PRO card, software entitlement and support needs for the exact OEM build. * Ask for the complete delivered configuration, current lead time and who handles installation. Get a DGX Station configuration quote. Send your workload and site details. Uniqcli Inc can check the exact ASUS configuration, support terms and current lead time. Faq. Can a model that needs 700 GB run entirely in GPU memory? Do seven MIG partitions mean seven researchers each get a full DGX Station? What does facilities need to approve before delivery? Can Uniqcli Inc buy a Windows DGX Station now? Does the MSI WS300 listing include a complete ready-to-run system? Sources. Product information reviewed October 25, 2026. Confirm the offered configuration and current commercial terms before ordering. Ask AI to apply this guide "Use NVIDIA DGX Station: What Research Teams... on my own purchase"