LF Energy Summit 2024

The energy transition is causing both a rapid growth in net grid loads and fundamental changes in the patterns of power supply and distribution. This leads to congestion on the electricity grid, where some businesses have to wait longer to get their requested connection. One way to mitigate net congestion is by curtailing generation during peak situations. Requests for curtailment have to be sent out by grid operators a day ahead, and thus require accurate load forecasts. In this project demo, I will show how Alliander, a Dutch Distribution System Operator, forecasts grid congestion using openSTEF, an open source Python package for Short Term Energy Forecasting. I will share how we tuned machine learning models to predict peak situations well. If you are also dealing with grid congestion or energy forecasting, please join this talk to exchange experiences and best practices.