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GridPACK™: An Open Source Framework for Developing High Performance Computing Simulations of the Power Grid

  • Objectives
  • Technical Approach
  • Accomplishments
  • Resources
  • About AGM at PNNL

Accomplishments

  1. Extensive review of build procedures and documentation with testing by independent evaluators. Feedback from the evaluators has been incorporated into the build documentation and the build itself has been simplified as much as possible.
  2. The contingency analysis calculation has been updated with features that provide basic statistical analyses of the results for different power flow variables. This capability provides a condensed summary of the results over the complete set of individual calculations in the contingency analysis.
  3. A dynamic simulation capability has been developed that couples DAE solvers with advanced variable time stepping integrators to speed up simulations. This simulation also contains detailed models of power grid devices such as generators, governors, and exciters. Preliminary results indicates that use of advanced time integrators leads to substantial performance improvements, enabling faster than real time simulation.
  4. Faster-than-real-time dynamic simulation has been achieved for WECC-size systems with detail models
  5. GridPACK has been heavily leveraged by other projects as a core function for power grid simulations
  6. Python wrappers have been added to some of the GridPACK functionality to enable integration with a reinforcement machine learning framework that is being used to apply machine learning to operation of the power grid. This is being used to develop surrogate models for automatic control of the power grid.

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