150 Western Avenue, Allston, MA 02134

Our electric grids are undergoing changes in both form and function, where renewable resources and new devices are creating systems that are more distributed, dynamic and uncertain. Modern AI and machine learning tools have the potential to transform the operation of these new energy systems. However, such algorithms typically do not provide guarantees on stability or safety, making them difficult to implement in practice. In this talk, I will describe how to bridge the gap between learning and the need to satisfy safety-critical constraints. Through applications in building energy management, frequency control, and optimal power flow problems, I will show how structured neural networks can leverage advances in machine learning and provide formal guarantees such as system stability and hard constraint satisfaction.

  • Samuel Collier
  • Tobias Egle

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