Slick Sheet: Project
The University of Minnesota (UMN) will develop a net-load management framework that rapidly identifies neighborhood-units to support grid infrastructure and enable ultrafast coordinated management. UMN’s project will rethink power recovery from near blackout conditions with a focus on rapid energization and maximizing power duration. This project’s approach could fundamentally change the way large contingencies are managed.

Slick Sheet: Project
Sandia National Laboratories will develop advanced core materials for grid-level electrical transformers, improving their efficiency and resiliency. Current transformers feature copper windings surrounding a magnetic core. The project team’s new core material seeks to increase electrical efficiency by at least 10% while enabling a 50% reduction in transformer size. The core will be robust, withstanding EMPs and GMDs that threaten today’s grid. Sandia will also develop additives that can be added to the oil in existing transformers as a retrofit as well as included in new transformers.

Slick Sheet: Project
ABB Inc. will design a low-cost, secure, and flexible next-generation grid service platform to improve grid efficiency and reliability. This technology will merge advanced edge computing, data fusion and machine learning techniques for virtual metering, and create a central repository for grid applications such as distributed energy resource (DER) control and others on one platform.

Slick Sheet: Project
The University of Oklahoma will develop a novel, zero-liquid discharge freeze system to remove dissolved salt from contaminated water, such as is produced by industrial processes like oil and gas extraction. The project will take advantage of the density difference between water and ice to extract pure ice from a salty brine, using a cooling approach that maximizes efficiency and avoids the need for energy-intensive evaporation methods. The system will operate under atmospheric pressure and be capable of treating highly concentrated/contaminated water.

Slick Sheet: Project
Siemens will develop an operator support system and grid planning functionality that enable a power system to operate with 100% inverter-based renewable generation from wind and solar. ReNew100 features automatic Controller Parameter Optimization and model calibration technologies that help ensure power system reliability as the generation mix changes. Successful test results will be a milestone toward the goal of a stable and reliable power system obtaining a majority of total electrical energy sourced from variable wind and solar.

Slick Sheet: Project
The University of Michigan will develop load-control strategies to improve grid reliability in the face of increased penetration of DERs and low-cost renewable generation. As the electricity generation mix changes to include more renewables and DERs, load shifting is essential. Today, there are few load-shifting strategies in use at grid scale that are capable of balancing current levels of intermittent energy production. The team will develop three testing environments to identify issues the grid faces with increased levels of energy from distributed and renewable generation.

Slick Sheet: Project
Pacific Northwest National Laboratory (PNNL) will construct an intelligent, real-time emergency control system to help safeguard the U.S. electric grid by providing effective and fast control actions to system operators in response to large contingencies or extreme events. PNNL’s scalable platform will utilize advanced machine learning techniques (deep-meta-reinforcement learning) as well as high-performance computing to automatically provide effective emergency control strategies seconds after disturbances or attacks.

Slick Sheet: Project
The Georgia Tech Research Corporation will design an autonomous, resilient and cyber-secure protection and control system for each power plant and substation on its grid. This will eliminate complex coordinated protection settings and transform the protection practice into a simpler, intelligent, automated and transparent process.

Slick Sheet: Project
PingThings will develop a national infrastructure for analytics and artificial intelligence (AI) on the power grid using a three-pronged approach. First, a scalable, cloud-based platform will store, process, analyze, and visualize grid sensor data. Second, massive open and accessible datasets will be created through (a) deploying grid sensors to capture wide-scale and localized grid behavior, (b) simulating and executing grid models to generate virtual sensor data, and (c) establishing a secure data exchange mechanism.

Slick Sheet: Project
The team led by Pacific Northwest National Laboratory (PNNL) will develop a High-Performance Power-Grid Optimization (HIPPO) technology to reduce grid resource scheduling times to within a fraction of current speeds, which can lead to more flexible and reliable real-time operation. The team will leverage advances in optimization algorithms and deploy high-performance computing technologies to significantly improve the performance of grid scheduling.