LF Energy Summit Europe 2026

During the last years, the PowSyBl ecosystem has expanded in many directions around the core module dedicated to grid data management, allowing the collaboration of an increasing number of organisations. As the workhorse of power systems, Open Load Flow is leading the adoption cycle with live projects for load flow, security analysis or sensitivity analysis at many TSOs and Coordination Centers. The advanced optimization tool OpenRAO is also a leading operational remedial action optimizer in Europe. Beyond computation modules, many users also adopted PowSyBl thanks to its easiness of use in day-to-day scripting through the python interface, and to grid and substation visualization features. The technical ecosystem is also widening with projects like the dynamic simulation module Dynawo and the grid study platform GridSuite. These last projects are open-source and are an open invitation to start cooperation. The session will provide an overview of the ecosystem, providing, for each module, the main features and use cases, the maturity status, and the roadmap. Eventually, through some examples, the way PowSyBl governance allows to start new cooperations will be presented.

The power grid is changing fast, and the systems behind it are under pressure. More data, tighter timeframes, increasing congestion, and regulatory requirements are pushing traditional grid security tools beyond their limits. At TenneT Netherlands, legacy tooling was simply no longer fast and adaptable enough to keep up with this new reality.

By combining the open source PowSyBl framework with a cloud-native orchestration platform (ReFlow), TenneT rebuilt its grid security analysis from the ground up. The result is a step change in performance and capability: reducing calculation runtimes from minutes to seconds and enabling a fundamentally different way of working.

This session will show how we:

- Moved from monolithic legacy tooling to a modular, scalable architecture
- Used open source to accelerate delivery and avoid vendor lock-in
- Established what it takes to run open source in a mission-critical environment
- Strengthened operator support by providing an integrated view across analyses through modular design

We need the capability to thermally model all components in the chain of assets. The global energy transition is rapidly reshaping electricity systems, driven by decarbonization, electrification, and the integration of renewables. These developments introduce new operational challenges for grid operators, including increased congestion and the need to maximize existing grid capacity while ensuring reliability.

Building on our Transformer Thermal Model, we add two new members to the family of thermal models: Cable Thermal Model (CTM) and Switchgear Thermal Model (SGTM). These models estimate asset temperatures based on load, ambient conditions, and technical specifications. Together, they allow operators to identify bottlenecks, assess hidden capacity, and mitigate congestion risks. As this now enables operators to model most primary components in the grid, the applicability and value increase exponentially!

In this talk, we demonstrate how we apply these models in practice across multiple asset types, enabling higher asset utilization, optimal grid planning, improved monitoring, and more efficient system operation.

Understanding, simulating and predicting the behavior of overhead lines and their supporting structures (including electrical towers) is essential for securing grid maintenance and extending asset life. Phlowers provides tools to achieve this in a simple way, despite the complexity of the underlying algorithms.

Phlowers is an Open Source ecosystem that brings mechanical and thermal modeling of overhead lines to engineers, researchers and operators, including those working offline. In the future, it will integrate the modeling of supporting structures (pylons/towers) to simulate mechanical constraints and interactions within the grid.

The ecosystem currently includes Python libraries for simulations and an offline web application called Stellar. It enables users to customize models, integrate their own data and adapt tools to operational needs. It will also support large-scale batch computations to assess maintenance policies, simulate multiple scenarios and optimize long-term asset management strategies.

In this session, we will explore its architecture, use cases and contribution to innovation in grid asset management.

Energy systems are undergoing rapid transformation as sector coupling intensifies and variable renewable generation grows, creating a pressing need for flexible and transparent modeling tools. While many open-source frameworks offer rich features, extending them with new mathematical models typically requires writing custom software, a barrier for many analysts.

We present GEMS (Generic Energy Systems Modelling Schema), a high-level modelling language designed to make multi-energy system adequacy and planning studies both more expressive and more accessible. GEMS brings model definitions out of the codebase and into simple YAML configuration files, where users describe variables, parameters, and constraints using natural mathematical expressions. These expressions are parsed into abstract syntax trees and automatically expanded into a complete optimization problem. This model-agnostic architecture enables rapid experimentation, lowers development and maintenance costs, and promotes true reusability: adding a new model requires no code, only data. The language is already supported in Antares Simulator and in the Python package GemsPy.

Modern grid control systems require interoperable, scalable, and vendor-neutral data infrastructures. While CIM/CGMES has become the standard exchange format for power system models, many implementations still treat it primarily as a file format instead of a semantic data platform.

This session presents practical experiences from building modern utility control system components using RDF, SHACL, SPARQL, Apache Jena, and open-source tooling. We will show how semantic technologies can evolve from offline model exchange into high-performance, real-time knowledge graph infrastructures for power systems.

Topics include high-performance in-memory RDF graph processing, SHACL validation for operational grid models, lessons learned from Apache Jena internals, and the development of open-source tooling such as RDFArchitect and OpenCGMES.

The session shares real-world insights from developing software for transmission system operators and discusses how open semantic infrastructures can support interoperable and future-proof digital energy systems.

DyCoV (Dynamic Compliance Verification) is an Open-Source tool built by RTE for bringing transparency to the process of verifying grid-code compliance of the dynamic behavior of new generation facilities. It contemplates both *electric performance* (i.e., "does the behavior pass the requirements?") and *generic model validation* (i.e., "does the model match the actual behavior?").

The tool builds upon Dynawo, RTE's Open-Source dynamic simulator, which in turn uses Open Modelica for its RMS models, including WECC and IEC generic models. This provides a fully transparent process for all stakeholders, thus avoiding the pitfalls of proprietary models and black-box tools. In addition, DyCoV removes the drudgery of measuring, comparing, and extracting all required KPIs from curves: rise/settling times, FRT responses, point-wise diffs, etc.

The tool is built with Python and structured as a series of independent tests, each producing its own report in PDF. The current tests correspond to RTE's connection grid code (DTR document), but DyCoV design uses templating (Jinja) and configuration files to make it easily adaptable to other grid codes, avoiding code changes as much as possible.

Modern AI for power flow and OPF is on the rise, but it will still require large amounts of trustworthy training data and strong conventional solvers for validation (“solver‑certified AI”). An important application are adjacent‑state studies needed for N‑1 screening and decision planning. This session argues that classic algorithms will not vanish, but must be redesigned for batch computation: solving many related grid states faster than repeated Newton-style runs. We reshape ideas from perturbation theory into a load-flow solver pattern that reuses structure across scenarios and maps well to parallelizable hardware. We introduce the key mathematics behind the approaches including the holomorphic embedding load flow method (HELM) and related methods. We then show how to turn them into an engineering workflow that can outperform repeated Newton-Raphson solves in high-throughput studies. We close with an open roadmap: an industrial‑grade implementation under development at 50Hertz/Elia Group and a clear invitation to collaborate on open interfaces, benchmarks, and integration with LF Energy tooling.

Power Grid Model is a high-performance, open-source calculation library built for advanced distribution system analysis.

This joint presentation by Alliander and SOPTIM tells a story of open source collaboration and innovation between a Grid Operator and Vendor within the LF Energy Power Grid Model (PGM) project.

Alliander—maintainer of the Power Grid Model project—and SOPTIM—a key contributor—have met each other at a LF Energy summit, which has led to a productive partnership, including shared development efforts, knowledge exchange, and the introduction of new features that enhance the capabilities of the model.

Overall, the presentation provides a tangible example of how open collaboration within LF Energy can drive innovation, strengthen ecosystems, and accelerate the development of open-source solutions for the energy sector, while also diving into the technical part of new features that support the various use cases at Alliander and SOPTIM.

Grid operations are becoming fundamentally more complex. The classical DC load-flow worked well in the past, but its simplifying assumptions are increasingly violated in modern grids with high renewables, reactive power flows, and tighter operational margins.

DC+ is a voltage-sensitive linearization of the AC load flow that retains voltage magnitude, angles, and reactive power while remaining orders of magnitude faster than full AC solvers.
Conceptually, it corresponds to a single Newton step around the AC base case, capturing the direction and severity of violations far more accurately than classical DC, while scaling to millions of N‑1 evaluations per second on GPUs.

DC+ enables a new class of scalable, open optimization workflows:
Potential use cases of DC+ are:
- Fast contingency screening and ranking?
- Topology optimization with voltage awareness?
- Security constrained voltage Optimization?