The UK power grid is becoming more interconnected and this, together with increased contribution from renewable energy sources, poses some challenges in the anticipation of energy flows. The volatility inherent to interconnections and renewable energy increases the uncertainty of physical energy flows, complicating the anticipation of internal congestion in the day-ahead market and resulting in more decisions needed within day by the control room to overcome congestions. This project focuses on the probabilistic forecasting of congestion after the clearing of the day-ahead market, assuming that the day-ahead scheduled flows are known at the forecasting time. By quantifying the possible deviations between the scheduled flows and the physical flows, the project will assess the impact of congestion across the network and predict the probabilistic risk of congestion on specific branches of the power grid. 

Benefits

This project will use historic data to quantify the probability spread of different congestion levels and provide operator visibility to support decision making. This data driven analysis will improve the congestion forecast accuracy and transparency within day, enabling control room engineers to better prioritise scenarios, improving anticipation of congestions, and helping to justify decisions. A better optimised system has the potential to lower constraint costs through reduced curtailment and redispatch, while also lowering carbon emissions and improving security of supply. The outcomes of this project will also feed into the Virtual Energy System.