Energy storage systems (ESS) and distributed energy resources (DER) provide the much needed flexibility and grid services in different time scales of operation for large-scale integration of renewable energy resources in the grid.
Our research focuses on developing models for scheduling and pricing ESS and DER services in power systems, which would enhance the utilization of growing flexibility resources in the grid operation.
We combine the accuracy of mathematical optimization methods with scalability and adaptability of machine learning algorithms for efficient, scalable and accurate utilization of the flexibility of fast-response resources for addressing the variability and uncertainty of renewable resources in the grid.
