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 two-stage 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 worst-case forecast error for reserve deliverability.

Simulations on the RTS-GMLC system show that adding these scenarios to the day-ahead problem significantly reduces the frequency of congestion-driven reserve undeliverability.

Notably, the choice and number of scenarios dynamically adapt to the day-ahead schedule.