Constructing Deployment Scenarios for Reserve Deliverability via Adaptive Robust Optimization

TL;DR

This paper presents an adaptive robust optimization approach to improve reserve deliverability by constructing deployment scenarios that consider grid topology and schedule interactions, reducing congestion-related issues.

Contribution

It introduces a two-stage adaptive robust optimization model with a column-and-constraint algorithm to dynamically generate worst-case forecast error scenarios.

Findings

Significant reduction in congestion-driven reserve undeliverability.

Scenario selection adapts to the day-ahead schedule.

Method improves reserve deployment reliability.

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.