Preventing Congestion in the Electricity Grid using OpenSTEF and Power Grid Model

With the growing adoption of renewable energy sources and the electrification of industries, congestion in the electricity grid has become a critical challenge. This paper explores innovative approaches to managing grid congestion through predictive and analytical tools. <br/><br/>OpenSTEF, an open-source automated machine learning pipeline, provides short-term energy forecasts to anticipate energy production and demand across the grid. Power Grid Model offers a high-performance, open-source calculation engine for distribution grid simulations under varying conditions, supporting power flow, state estimation and short-circuit analysis.<br/><br/>This paper explains the technical aspects of OpenSTEF and Power Grid Model, detailing their functionalities, performance and scalability. It examines their potential applications across various grid configurations, describing how these tools enable distribution system operators (DSOs) to address potential congestion points and contingency scenarios. By combining OpenSTEF's predictive insights with Power Grid Model's simulation capabilities, DSOs can mitigate grid overload risks, enhance renewable energy integration and create a more adaptive, resilient energy infrastructure.<br/><br/>A case study featuring Alliander, a Dutch DSO, demonstrates the practical application of these tools. The study details how Alliander leverages OpenSTEF to generate short-term electricity forecasts and uses Power Grid Model at scale for distribution power systems analysis, grid simulations and what-if scenarios. These tools allow Alliander to identify potential congestion points and plan for contingencies effectively, ensuring grid stability amid increasing renewable energy adoption and electrification of energy consumption.<br/><br/>By demonstrating the successful implementation of these open-source software tools, the paper underscores their critical role in supporting the energy transition.