Constructing Deployment Scenarios For Reserve Deliverability Via Adaptive Robust Optimization

Network congestion often hinders the deployment of reserves needed to balance forecast errors during real-time operations. A pertinent idea to tackle this challenge involves adding deployment scenarios of spatial distributions of forecast errors as contingencies to the day-ahead problem. However, current approaches disregard the effect of grid topology and the day-ahead schedule on the induced congestion and, consequently, reserve deliverability. In this work, we formulate a twostage adaptive robust optimization problem to jointly consider interactions between day-ahead and real-time operations and forecast errors. Using a column-and-constraint algorithm, we iteratively construct deployment scenarios by finding the worstcase forecast error for reserve deliverability. Simulations on the RTS-GMLC system show that adding these scenarios to the dayahead problem significantly reduces the frequency of congestiondriven reserve undeliverability. Notably, the choice and number of scenarios dynamically adapts to the day-ahead schedule.