Dynamic Reserve: Data-Driven and Market-Based Uncertainty Management for Grid Operations

This paper describes the Midcontinent Independent System Operator’s (MISO) dynamic reserve by using advanced analytics to manage operational uncertainty for bulk power system operations. Motivated by growing system variability from renewable integration, electrification, large Artificial Intelligence (AI)-driven loads, and extreme weather, MISO developed a dynamic reserve framework supported by Machine Learning (ML) models. The paper focuses on three key innovations including net uncertainty quantification across different operation timeframes, ML-based net uncertainty forecasting and dynamically setting reserve requirements in MISO’s energy and ancillary service co-optimized markets.