The rapidly changing nature of energy generation in the United States has raised many questions about grid reliability. Recognizing the importance of these questions, the U.S. Department of Energy (DOE) is investing in research.
On August 7, the DOE’s Office of Electricity Delivery and Energy Reliability (OE) announced a nearly $900,000 investment in early stage research to address the risk and uncertainty of power systems. The funding will enable academic institutions in three states to perform research in wholesale market operations, transmission system design and demand-side participation. The states were identified for the transformative nature of their electricity markets. All of the projects involve research focused on the unpredictability of renewable energy sources (RES).
In Pennsylvania, $175,000 will go to researchers at Penn State University to develop and demonstrate a novel computational method for solving the optimal transmission plan under uncertainty in a large power system network.
In Utah, $357,000 will help fund a project at the University of Utah to develop mathematical models to study the variations of load and RES and efficiently deploy the ramping capability of flexible resources to compensate for the uncertainty in markets caused by variable sources, like solar and wind.
Finally, in Virginia, $360,000 will help pay for a project at Virginia Tech to develop a probability-based model for cost-effective integration of renewables into the electricity grid. The tool will allow independent system operators and regional transmission operators to consider renewable energy as an option for their expansion plans and help them calculate the capacity credit for each new solar/wind farm that comes online.
OE funding for each of the three projects is provided on a cost-share basis. The grants are part of the Energy Department’s Grid Modernization Initiative (GMI), a comprehensive effort to help shape the future of the nation’s grid and solve the challenges of integrating conventional and renewable sources with energy storage and smart buildings.