The FOSDEM 2025 Energy Devroom featured a presentation by Jaap Schouten and Thijs Baaijen from Alliander, a Dutch distribution system operator. Their talk, titled “Empowering the Energy Transition thru Fast & Flexible Network Simulation,” highlighted the importance of open source tools in tackling the challenges of modern power grids. Here’s a summary of the key takeaways from their presentation, and the full video follows at the end.
The Journey to Open Source Network Simulation
- The LF Energy Power Grid Model project originated five years ago at Alliander, where Schouten and Baaijen developed a Python library to model and simulate energy grids.
- Initially an internal tool, it gained traction among different teams within the company and evolved into an open source initiative.
- Now, this tool is officially launched as within the LF Energy ecosystem, aimed at improving data-driven decision-making for power grid management.
Core Components of the Simulation Framework
- The Power Grid Model simulation framework builds on PyPower and PowerModels.jl, leveraging a high-performance load flow engine written in C++.
- It integrates data science capabilities using Python, NumPy, and Rust-based graph processing for efficient network analysis.
- The tool enhances accessibility for data scientists by simplifying interactions with network models and streamlining complex calculations.
Key Functionalities for Grid Management
- Short-Term Forecasting: The tool enables real-time simulations for grid operators to predict network congestion based on load flow calculations.
- Long-Term Planning: It helps model future grid scenarios (e.g., for 2050 or 2060) by simulating the impact of renewable energy integration and load growth.
- Dynamic Grid Management: The software allows for real-time modifications, such as adding or removing network nodes and assessing congestion risks.
- Outage & Maintenance Planning: By simulating various failure scenarios, the tool supports strategic decision-making to maintain grid stability.
- Flexibility & Market Interaction: The system assists in managing short-term electricity markets by forecasting supply-demand imbalances and proposing interventions.
Technical Innovations & Data Science Applications
- The framework introduces a Python wrapper around PowerModels.jl to improve usability for data scientists.
- It supports both array-based (NumPy) and graph-based (Rust) representations of network models, facilitating diverse analytical approaches.
- Advanced filtering and state estimation methods enable better handling of incomplete or uncertain data.
Future Prospects & Community Collaboration
- The project is open to contributions, encouraging collaboration with researchers, grid operators, and software developers.
- The team is focused on expanding the tool’s capabilities, particularly in areas like AI-driven grid optimization and real-time monitoring.
- Alliander’s internal use cases provide a strong foundation for broader adoption within the LF Energy ecosystem and beyond.
