Lessons learned from developing and using short-term congestion forecasts for active power to procure local services on flexibility markets

Short-term congestion forecasts enable Distribution System Operators to act proactively when the risk of congestion heightens, to optimize grid use or to mitigate risks related to security of supply and ageing assets. This paper highlights the lessons learned from developing and using short-term congestion forecasts for active power in the flexibility markets CoordiNet, later Uppsala flexibility market, and sthlmflex during the winter seasons 19/20 to 23/24. During these winters, incidents impacting the forecasts or flexibility markets have been documented and the quality of the forecasts has been recurrently calculated and evaluated. The main findings highlight the difference between poor forecast models and poor input data when analysing the forecast error. The capacity shortage in Uppsala and Stockholm was most significant during cold weather, and understanding when the forecast has value and should be accurate can be beneficial when evaluating the forecast quality as well as when improving the forecast models. Other findings include that to effectively manage the forecasts, there is a need for new roles, expertise, and systems to manage the flexibility and data flows.