Open-Source Congestion Management: Future-Proofing Solutions for the Energy Grid
TL;DR (Dateline: October 2025 – LF Energy Summit)
At the 2025 LF Energy Summit, experts from Accenture and Artelys — including Jannis Kahlen (Accenture, Netherlands), Nicolas Omont (Artelys, France), Gladys Leon (Artelys España Soluciones Analíticas S.L., Spain), and Jordanno de Assuncao Paiva (Accenture, Germany) — presented the session “Open-Source Congestion Management: Future-Proofing Solutions for the Energy Grid.”
System Operators (SOs) are at the forefront of the energy transition and face the challenge of increasing grid load. This cannot be solved by building more infrastructure alone; instead, optimized congestion management strategies are required. Many SOs still depend on proprietary software or manual processes that limit scalability and flexibility.
The presentation demonstrated how open source tools (OST) can address these challenges. By combining and integrating multiple OSTs, operators can develop scalable, cost-efficient congestion management solutions that leverage community-driven innovation. The speakers explored how different open source tools can be combined into modular architectures, enabling efficient customization, interoperability, and targeted code extensions.
Accenture and Artelys showcased both strategic and practical insights — with Accenture focusing on solution architecture and tool integration, and Artelys highlighting customization and implementation pathways for open source congestion management systems.
Event Overview
The session focused on how open source tools can improve congestion management across increasingly complex and decentralized power systems.
As renewable integration, electrification of transport and heating, and distributed energy resources grow, System Operators (SOs) must manage more dynamic load flows and grid bottlenecks. Rather than expanding physical infrastructure, the speakers advocated for data-driven, modular congestion management solutions built on open standards and open source technology.
Understanding Congestion Management
Congestion management refers to the operational techniques used by SOs to detect, prevent, and mitigate grid bottlenecks while ensuring reliable power flow.
Key strategies include:
- Redispatch: Adjusting power plant generation schedules.
- Curtailment: Limiting renewable generation output.
- Demand Response: Encouraging flexible energy use.
- Policy Switching and Flex Contracts: Dynamic configuration and market-based mitigation.
The speakers cited Germany as a reference case:
- 45% increase in remedial action volume within four years.
- €2.5 billion spent in 2023 on congestion-related measures.
These figures underscore the need for automation, optimization, and open collaboration.
The Five Core Modules of Congestion Management
1. Data Management and Orchestration
Collect and standardize operational and forecast data using tools like:
- Grid eXchange Fabric (GXF)
- RTDIP for real-time data pipelines
- Apache Spark for transformation
- Apache Airflow for orchestration
2. Forecasting
Predict consumption and generation patterns for proactive congestion planning.
Open source options include OpenSTEF, widely used for renewable and load forecasting.
3. Grid Simulation and Analysis
Use open tools for power flow, model merging, and network analysis, e.g.:
- pypowsybl
- pandapower
- Power Grid Model
Future efforts aim to merge TSO and DSO models, enabling coordinated visibility and analysis for congestion detection.
4. Optimization and Mitigation Strategy Selection
Identify the most efficient congestion solutions — balancing cost, reliability, and flexibility.
Tools mentioned:
- POM (Power Optimization Model) and HiGHS (open source optimizers)
- Ample and Nitro (commercial solvers developed by Artelys)
5. Real-Time Activation
Deploy optimal congestion solutions using automation and control frameworks, tailored to market design and regulatory contexts.
Why Open Source Matters
The speakers emphasized that open source collaboration future-proofs congestion management by ensuring freedom from vendor and license constraints.
Open source tools:
- Enable modularity and vendor neutrality
- Improve transparency and customization
- Can coexist with or replace commercial solutions
- Foster collaboration and innovation across the energy ecosystem
They encouraged SOs to move beyond the traditional “make or buy” dichotomy toward a third path: collaborate.
Practical Examples: Building with Open Source
Drawing on Artelys and Accenture’s project experience, Gladys Leon presented a two-step workflow for congestion management projects:
Step 1 – Grid Digitalization and Data Quality
- Ensure data completeness and consistency from a power system perspective.
- Validate models and perform load flow simulations using pypowsybl and Open Load Flow.
- Integrate state estimation and OpenSTEF forecasting for accurate grid visibility.
Step 2 – Congestion Detection and Resolution
- Apply optimization engines to identify secure, cost-effective mitigation strategies.
- Combine probabilistic forecast data with optimization algorithms.
- Validate final actions through security analysis (e.g., pypowsybl security module).
Projects demonstrated how open source frameworks can integrate with commercial solvers to balance complexity, performance, and flexibility.
Addressing Real-World Integration
When asked about model integration and harmonization across operators, the speakers emphasized:
- pypowsybl supports multiple data formats (CGMES, UCTE, PSS/E).
- Custom importers can be developed collaboratively to handle additional formats.
- Harmonization depends on capacity building and community engagement — not a one-size-fits-all approach.
“You need to harmonize tools and processes — not just data models.
That’s where open source flexibility really matters.” — Gladys Leon, Artelys España Soluciones Analíticas S.L.
Key Takeaways
- Congestion management is a global challenge, critical for the energy transition.
- Open source tools empower System Operators to innovate faster and reduce costs.
- Collaboration across TSOs, DSOs, and vendors is essential for interoperability.
- Community-driven development ensures tools remain adaptable and future-proof.
Watch the Full Session
FAQ
Q1: What is congestion management?
The process of detecting and resolving grid bottlenecks through redispatch, curtailment, or flexibility actions.
Q2: Who presented this session?
Experts from Accenture and Artelys — Jannis Kahlen, Nicolas Omont, Gladys Leon, and Jordanno de Assuncao Paiva.
Q3: Which LF Energy tools were discussed?
pypowsybl, Grid eXchange Fabric (GXF), RTDIP, and OpenSTEF, among others.
Q4: Why use open source for congestion management?
To ensure transparency, interoperability, and flexibility without vendor lock-in.
Q5: What’s the main challenge?
Integrating diverse open source and commercial tools into unified, scalable workflows.
Keyword Cluster
Open Source Congestion Management, LF Energy, Accenture, Artelys, pypowsybl, OpenSTEF, Grid Simulation, TSO-DSO Collaboration, Power System Optimization, Digital Energy Infrastructure
About LF Energy
LF Energy, part of the Linux Foundation, accelerates the energy sector’s digital transformation through open source collaboration, driving interoperability, innovation, and sustainable grid modernization.
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Last updated: October 10, 2025
