LF Energy Summit Europe 2026

Note: Food and beverages will not be provided for this workshop.

Dynawo is an Open-Source, production-level dynamic simulator developed by RTE, now in use for operations planning processes. It is also the reference tool for validating the compliance of new projects to grid code requirements in France. Dynawo comes with an extensive library of RMS models written in Modelica, including a complete suite of generic models (WECC, IEC) for inverter-based resources.

This Tutorial will benefit anyone who is interested in using and/or extending Dynawo's *model library* but is finding that Dynawo (the *simulator*) has a steep learning curve. In particular, we will be presenting new tools designed for facilitating the uptake of Dynawo's models, demonstrating a few use-cases:
* Usage within pure Modelica: solving the initialization problem
* Usage of Dynawo via PyPowSyBl: construction, manipulation, and visualization of (large) study cases
* Parameter-sweep studies
* Small-stability analysis

The tools take the form of Python and Julia Notebooks, designed to be flexible and easy to change by end-users. The session will also emphasize *understanding* these tools to encourage the customization and promote new creative solutions.

This hands-on tutorial will introduce VeraGrid’s open-source dynamic simulation capabilities for modern power system studies. Participants will learn how to build, run, and interpret dynamic simulations using both GUI-based and scripting workflows, making the session accessible to users with different levels of coding experience.

The tutorial will cover RMS simulation, EMT simulation, and small-signal analysis at both RMS and EMT levels. Attendees will work with out-of-the-box dynamic model templates and see how these can be extended into customized control builds for specific applications. They will also create simulation events and visualize their effect on system behaviour.

The tutorial features five presentations from experts contributing to the LF Energy CoMPAS project.

1.Driving CoMPAS Adoption with Better UX
Enhancing user onboarding, reducing training effort and minimizing errors throughout the engineering process.

2. Building an substation specification with CoMPAS
The presentation is focused on the role of top down engineering in Alliander's software defined substation initiative, their steps so far using CoMPAS, their lessons learned and their Outlook.

3. Quality You Can Trust: Raising the Bar for SCL Engineering
Introducing a new validation rules concept for higher SCL quality and efficiency.

4. Designed to Extend: How Developers Shape CoMPAS
Highlighting the design principles, extension points and best practices that enable developers to adapt CoMPAS to specific use cases, without compromising maintainability, upgradability or standards alignment.

5. Extend CoMPAS in Minutes: Build Your First Plugin (Live Coding Session)
By showcasing a real end-to-end example the session aims to demystify the development process, reduce entry barriers and empower new contributors to actively participate in the CoMPAS community.

Detecting low-carbon technologies (LCTs) from smart meter data, and disaggregating their individual consumption profiles, is becoming increasingly important as electrification reshapes electricity systems. This interactive workshop will explore emerging approaches for technologies such as EVs, heat pumps, solar PV, and batteries from Advanced Metering Infrastructure (AMI), or smart meter, data using open and reproducible methods.

Hosted by researchers from the Centre for Net Zero, the session will combine presentations with collaborative, hands-on experimentation using open datasets and starter notebooks. Participants will explore machine learning and demand disaggregation techniques, discuss benchmarking and evaluation challenges, and consider the operational value of LCT detection for forecasting, flexibility, and network planning.

The workshop is designed to foster collaboration between researchers, industry practitioners, and open-source contributors working at the intersection of energy systems and data science. Whilst content is evolving, the focus will remain on practical experimentation, transparent methodologies, and community learning using open tools and datasets.

This hands-on tutorial introduces our end-to-end framework released through the OpenGridFM project, enabling the development and deployment of GridFM models for steady-state grid analysis.
Participants will work with notebooks to generate synthetic but realistic power-flow and optimal-power-flow datasets using gridfm-datakit, including load variation, generator-dispatch variation, topology perturbations, and configurable grid cases. They will then use gridfm-graphkit to train and evaluate GENCO, a unified graph neural solver, in a low-code environment and compare its performance against classical solvers. For advanced participants, the tutorial will also explain how to implement, integrate, and benchmark custom model architectures using the framework and the provided benchmarking datasets.
The tutorial is designed for power-system practitioners, researchers, and developers who want to contribute to OpenGridFM or apply its tools in their own workflows. No prior experience with the project is required; familiarity with Python and basic power-system concepts is helpful.

OpenSTEF 4 introduces a redesigned, modular architecture that makes it easier to build and adapt energy forecasting solutions to your specific context. Rather than prescribing a single way of to integrate OpenSTEF in your setup, version 4 gives users the flexibility to create forecasting pipelines that fit their data, workflows, and IT environment.

In this hands-on tutorial, participants will explore the new architecture and learn how to assemble forecasting workflows using OpenSTEF's modular building blocks. You'll discover how to customize data preparation and feature engineering, integrate your preferred tools, experiment with different modeling approaches, and adapt forecasting setups to your own use case.

The tutorial provides a practical introduction to the newly developed capabilities of OpenSTEF 4, including support for ensemble forecasting, benchmarking and backtesting through STEF BEAM, and integration of deep learning and foundation models. Participants will work through real examples and gain insight into how

OpenSTEF can support everything from experimentation to production-scale forecasting.

Who should attend?
Whether you are a researcher exploring new forecasting methods, a data scientist building operational models, an organization looking to scale forecasting capabilities, or whether you are already experienced with forecasting (potentially through using OpenSTEF 3), this session is for you.