Constructing Deployment Scenarios for Reserve Deliverability via Adaptive Robust Optimization

Abstract

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.

Figures (5)

• Figure 1: Schematic of the 5-bus system. Red color indicates line congestion in DA DSW schedule. Orange color indicates RT line congestion given the DSW decisions, causing deliverability problems.
• Figure 2: Deployment scenarios found for the 5-bus system ($\alpha=0.95$). Grey points indicate the sampled scenarios, used to construct the uncertainty set.
• Figure 3: Grid topology of RTS-GMLC 2019 System. Red color indicates line congestion after DSW is solved and orange color indicates RT line congestion given the DSW decisions, for the illustrative period. Bold dashed lines are used to initialize ADM.
• Figure 4: Deployment scenarios for $\texttt{EXT}$ and $\texttt{CCG}$for the illustrative period. Dashed lines indicate $\hat{\rho}^+$, $\hat{\rho}^-$. The $\times$ marker indicates realized errors.
• Figure 5: DA schedule for a selected period, aggregated per zones.