Just-In-Time Learning for Operational Risk Assessment in Power Grids
This addresses the problem of real-time risk assessment for power grid operators dealing with increased volatility from renewables, though it is incremental as it builds on existing methods with a new framework.
The paper tackles the computational burden of real-time operational risk assessment in power grids with high renewable generation by proposing JITRALF, a framework that uses machine learning to train risk surrogates, reducing computational requirements and enabling fast risk estimation.
In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and generation. The computational requirements of the forward uncertainty propagation problem, which must solve numerous security-constrained economic dispatch (SCED) optimizations, is a major barrier for such real-time risk assessment. This paper proposes a Just-In-Time Risk Assessment Learning Framework (JITRALF) as an alternative. JITRALF trains risk surrogates, one for each hour in the day, using Machine Learning (ML) to predict the quantities needed to estimate risk, without explicitly solving the SCED problem. This significantly reduces the computational burden of the forward uncertainty propagation and allows for fast, real-time risk estimation. The paper also proposes a novel, asymmetric loss function and shows that models trained using the asymmetric loss perform better than those using symmetric loss functions. JITRALF is evaluated on the French transmission system for assessing the risk of insufficient operating reserves, the risk of load shedding, and the expected operating cost.