Leveraging Machine Learning for Boosting Computational Performance (Slides)

With the growing scale and complexity of modern power grids, available data could provide useful information and insights into the underlying power grid, and machine learning methods could be valuable to help understand and reveal the relationship between system parameters and optimal operations, finally addressing optimal operation problems in a more efficient and accurate way. In this webinar, we will discuss two examples to apply data-driven and machine learning methods to power systems optimization problems for improving computational performance, including: (i) a data-driven approach by leveraging historically solved unit commitment (UC) instances to promptly solve new UC problems; and (ii) a closed-loop predict-and-optimize framework for deriving better forecasts that could lead to an enhanced UC solution quality.