Computational Science for Grid Management

The power grid continues to increase in complexity, in good part by the growing use of distributed energy resources, for example, wind and solar power.   Compounding this complexity are the vastly increased dynamics that these renewables introduce as well as greater uncertainty surrounding supply and demand results when computationally modeling the integration of wind and solar power.

This project is helping to solve these challenges by developing and deploying new algorithms and solutions for grid optimization, uncertainty, and dynamics.  Key to this effort is an advanced framework that allows 10 times faster prototyping of computationally intense analyses as well as open source solutions that compute 100 times faster by harnessing parallelism.  The team is also identifying use cases where they can demonstrate the framework and solutions at scale.

The algorithms and solutions will be key to supporting efforts by utilities to assess renewable variability effects in operations and planning.  They will also be important to helping the Department of Energy understand the effects of renewable energy variability on reliability in the power grid.

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