As the electricity grid transitions to using an increasing amount of renewable energy, aspects of its operation will change in order to manage variability in spatio-temporal mismatches in supply and demand. These mismatches can lead to power grid congestion due to limitations in flows between areas. Numerous approaches for dealing with this are either already operational or are actively being researched. These solutions take a variety of forms such as physical technologies (batteries, dynamic power line ratings), algorithms (optimal power flow solvers) and electricity market design (virtual bidding, ancillary services).

Machine Learning can play a role in all of these. Our focus is on electricity markets and using ML to forecast the divergence between Day-Ahead and Real-Time nodal prices. This helps to close the gap between expected conditions used for planning and those actually experienced, leading to lower emissions and increased reliability of the power grid. As other approaches are also trying to minimize this divergence, this becomes a task of forecasting the forecast error of other market participants, or at least their limitations in mitigating diverging conditions. This is further complicated by feedback loops between approaches acting at different system levels and the continuing evolution of the grid's underlying behaviour and control mechanisms.

In addressing these challenges, we employ a spectrum of approaches, from physics-aware models including electrical constraints of the grid, to physics-agnostic models which help identify the inherent uncertainty and biases in planning for grid reliability.