In France, thanks to the deployment of 37 million Linky smart meters, a vibrant open-source community has emerged, developing smarter, greener, and more open energy-management systems powered by Linky’s locally emitted data. Enedis, the main French DSO, now works alongside this community to accelerate the use of its meters’ data for the energy transition. Open hardware, open software, open data—all of this is key to meeting the challenges !
Presenting the Energy System Description Language (ESDL) open-source community, which is currently being built around the open standard ESDL and the ecosystem of open-source tools that work with ESDL. There is a dozen tools that are being used by several companies and initiatives to design energy hubs, heat networks and develop scenario's to best integrate new battery, hydrogen, solar and wind assets within grid with limited available capacity.
Akkudoktor-EOS (Energy Optimization System) is an open-source platform designed to generate highly optimized energy management plans for home energy management systems. Initially developed by Dr. Andreas Schmitz (“Akkudoktor”), EOS has been publicly available for just over a year and has already built a community of users who integrate it into their home automation environments.
At its core, EOS is a self-hosted server that calculates optimal schedules for batteries, electric vehicles, and household devices. These plans are derived from user configuration, real measurement data, and automatically retrieved or self-generated forecasts. EOS focuses on long-term optimization over a day or longer. The home automation system manages short-term control. Together, they combine strategic planning with real-time execution, delivering the best of both worlds in home energy management.
Common applications include optimizing consumption under dynamic electricity tariffs, ensuring cost-efficient EV charging, shifting flexible loads to cheaper periods, and connecting seamlessly with systems such as Home Assistant or NodeRED.
EOS stands out through its genetic algorithm, enabling optimization of any behavior that can be simulated—without the limitations of linear or convex models. Non-linear battery degradation, grid-stress signals, comfort models, or heat pumps with non-linear COP fields can be used directly, without artificial simplification. This makes EOS highly modular and flexible, allowing new components or physical models to be added and immediately included in the optimization.
Optimally planning the energy flows across multiple sites becomes more important, e.g. for orchestrating the aggregated flows due to grid congestion, or for implementing energy sharing. This approach can break bottlenecks and increase savings - as such, energy communities are an important topic for the European Commission.
In this talk, we present our ongoing work towards a Community Energy Management System (CEMS) with FlexMeasures. We discuss our architectural approach: optimizing the flows for each sites by themselves and then adding an orchestration layer on top. This approach is being tested in a project with TNO in the Netherlands. The goal is to manage neighbourhoods as well as commercial sites optimally.
In addition, we want to discuss how scalable any CEMS system can be, as many circumstances and conditions often vary, per site and per energy community. We chose our CEMS architecture approach for this reason, but versatility has been a design principle for FlexMeasures since the beginning. In this talk, we will showcase a complete example script of a setup orchestrating a few homes. This script is written with the FlexMeasures client and is also open source. FlexMeasures being 100% scriptable is a design choice that lets many developers built just what they need in energy intelligence.
This is also an opportunity to visit some fundamental improvements we have made in the last year in the documentation of FlexMeasures and its flexibility options - both for developers and users.
Solar energy is predicted to be the largest form of power generation globally by 2040 and having accurate forecasts is critical to balancing the grid. Unlike fossil fuels, renewable energy resources are unpredictable in terms of power generation from one hour to the next. In order to balance the grid, operators need a close estimate of when and how much solar and wind power will be generated on a given day.
Open Climate Fix (an open source AI company) developed and deployed PVNet, a large ML model which forecasts solar generation for the next 36 hours. The forecasts are used by the UK electricity grid operator for real-time decision making and for reserve planning. These forecasts can save 300,000 tonnes of CO₂ and £30 million per year.
But how do we have a global impact? We decided to build a lightweight solar forecast that works anywhere in the world, which we showcased last year at FOSDEM. Combining this with every country's solar capacity, we are able to produce a solar forecast for every country in the world. In this talk, we'll demo our Global Forecast and discuss how this forecast can support grid transition as well as open-source renewable energy projects all over the globe.
Open Climate Fix is an open-source not for profit company using machine learning (ML) to respond to the need for accurate renewable energy forecasts. Connecting energy industry practitioners with ML researchers doing cutting-edge energy modelling is our aim, and one way we seek to do this is by making much of our code open-source.
Storing energy reversibly is useful. For clean energy, electrochemical batteries are one of the most attractive options. Most battery technology is proprietary, hard to recycle, and complicated to manufacture. What if that wasn't the case?
We will present our collective and individual efforts with the Flow Battery Research Collective (https://fbrc.dev/) to build open-source batteries for stationary storage applications. This includes our flow battery work, such as efforts to build a larger-format cell with simple manufacturing techniques like laser cutting and FDM printing, as well as our different experiments with flow battery electrolytes based on zinc, iodine, iron, and manganese.
We will also cover our individual efforts to build conventional, non-flow flooded batteries based on water and the above elements (including this work by the speaker Daniel: https://chemisting.com/2025/05/23/a-low-cost-open-source-cu-mn-rechargeable-static-battery/). We will discuss the economic hurdles facing practical implementations of these systems.
As a student in electronics, I was already passionate about renewable energy. Then after many years of open-source software development, I am now finally starting to engage with the Energy community. By attending various events, meeting a whole range of inspiring people, hacking around existing projects and completing a blog posts series on Digital Substations and SEAPATH, I have made the first steps in this personal journey. It is already a very rewarding one and I believe many other developers would relate to it. Open source culture and renewable energy both contribute to a more sustainable world.
This lightning talk tells the story of how I became an active contributor in the Energy community.
Standards like OCPP and ISO 15118 describe how EV charging should work, yet real-world deployments often behave differently. This session explains why a full stack of tools, testing methods, and feedback loops is essential for true interoperability, and how the open-source EVerest ecosystem has become a practical integration point for these technologies. We will show how Software-in-the-Loop testing, Golden SUT validation, conformance tooling, virtual charger parks, testing-hackathons, and cloud-based remote debugging work together to close the gap between specification and reality. The talk demonstrates how open-source reference implementations can strengthen standards, improve certification tools, and reduce interoperability pain across the EV charging industry.
OpenLEADR-rs is an opinionated, open-source Rust implementation of the OpenADR 3.0 protocol, which is already being used for real-world pilots.
In this joint presentation, Stijn van Houwelingen (ElaadNL) and Maximilian Pohl (Tweede Golf) will kick things off with a quick primer on demand response: what it is, and why it’s essential to accelerate the energy transition. From there, they’ll dive into some design decisions behind OpenLEADR-rs.
Among the decisions explored: why Rust was a good choice for the protocol and why OpenLEADR-rs did not implement real-time updates yet.
Next, the focus shifts to adoption, specifically focusing on the use case of Grid-Aware Charging in the Netherlands.
The talk then wraps up with a look at what’s next for OpenLEADR-rs, including our effort to implement OpenADR version 3.1, how developers and organizations can get involved, and what early adopters can expect in terms of support and collaboration.
See how the open-source Transformer Thermal Model helps safely push the limits of the grid.
This to lighten the net congestion problem we have in the Netherlands.
We demonstrate how simulating hotspot and oil temperatures reveals new acceptable load limits.
By sharing this model openly, we are able to work with other TSOs and DSOs to benefit and strengthen sector-wide collaboration! Within this talk we want to show you our journey in going open source and how this strengtens our effort in lighten the net congestion problem with pushing the limits of the grid!
The model: https://github.com/alliander-opensource/transformer-thermal-model Github Discussions: https://github.com/alliander-opensource/transformer-thermal-model/discussions
The lack of global access to electricity, and the push towards renewable energies and electrification requires us to develop our grids. However, globally, data of the power grids are outdated, incomplete or closed off, which makes it challenging for us to effectively plan and research grid developments.
Therefore, we created an initiative called MapYourGrid where anyone can map, contribute and own the data of our grids. We created a fully open and free toolchain, combining developed and existing free tools and software, in order to empower people around the world to be able to map their grid. Instead of reinventing the wheel, we collaborated with existing communities and incorporated existing open-source tools, as this leads to higher quality workflows and higher community impact.
By mapping the world’s power grids, anyone can learn and understand the backbone of what lets us turn our lights on, as well as owning this valuable data. This can then be used by researchers, local communities and authorities, NGO’s and many more, to help solve pressing issues our world faces. MapYourGrid: https://mapyourgrid.org/
The electricity grid faces increasing complexity as solar panels, wind turbines, EVs, and heat pumps reshape both supply and demand patterns. Grid congestion has become one of the most pressing challenges for utilities navigating this transition. Accurate short-term load forecasting is essential—not only for congestion management, but also for transport forecasts, EV charging capacity estimation, and grid loss prediction.
OpenSTEF is an open-source Python package that provides accurate short-term forecasting for all of these use cases. As demand grows beyond congestion management, we have been working with the community on a major redesign to make it more flexible and easier to adopt across different contexts and user types—from researchers and small-scale teams to large-scale deployments within complex enterprise landscapes.
In this presentation, we will share the journey and architecture of the OpenSTEF V4 redesign, how we did it, the lessons we learned, and a sneak peek of the features of the current alpha release.
To learn more about OpenSTEF, visit: https://www.lfenergy.org/projects/openstef/
In this talk, we introduce µSolarVerter, our open-hardware micro-inverter designed to support decentralised solar production while giving users full control over the information their system generates. The project grew from a simple question: how do we make small-scale solar both understandable and adaptable, without locking people into a black box?
Our presentation will be split into three parts:
We’ll walk through the design process by following the things users interact with most: clear and accessible data, a flexible software layer, and a hardware platform that supports both without getting in the way. The story is about enabling insight and experimentation: good telemetry, open interfaces, and a control system that stays transparent, all backed by a converter designed to be efficient, safe and repairable without requiring deep power-electronics expertise. Hardware is there, but it’s not the centrepiece — it’s the foundation beneath a system meant to be explored, extended and trusted.
We'll show you how we made µSolarVerter as a platform, built with the intention of gathering a community around it. We will unveil our open documentation, open firmware and open data flows, making it easy to integrate with domotics systems like Home Assistant or openHAB, or to build your own dashboards and analysis tools. Users can experiment with their own production/load-matching logic, develop new applications, or simply keep their data fully local. The openness is practical: it’s there to be used, not just admired.
We will introduce our OwnTech beta program, inviting participants who want early access to the platform, to test it, review it, or contribute improvements. Whether you are interested in decentralised energy, open design, or taking a more active role in your own solar production, this is a chance to get involved from the start and help shape where the project goes next.
Project's repository: https://github.com/owntech-foundation/micro-inverter OwnTech's Forum: https://forum.owntech.org/ OwnTech's documentation center: https://docs.owntech.org/latest/
This talk explores the evolution of VeraGrid (formerly GridCal), a power-system simulation tool, over the past decade; From its humble and simple beginnings to a fully integrated software capable of performing all power-system calculations, ranging from electromagnetic transient (EMT) simulations to multi-year investment planning.
Throughout its development, the software has undergone seven major refactors to accommodate new functionalities. As the system evolved, the effort required for each refactor decreased, highlighting an organic, evolutionary structure shaped by practical needs and use cases.
I will discuss the challenges and insights gained during this evolution, focusing on how we integrated static network models, time-series data, transient analysis, and long-term planning. Additionally, I will cover how we overcame the challenges of multi-binary pitfalls and ensured seamless interaction between these diverse functionalities.
This session will provide an overview of how these continuous improvements have produced an open-source tool that bridges the gap between research, operational applications, and long-term infrastructure planning. See: https://github.com/SanPen/VeraGrid
PyPSA is an open-source Python framework for optimising and simulating modern power and energy systems, designed to scale well with large networks and long time series. It is made for researchers, planners and utilities with basic coding aptitude who need a fast, easy-to-use and transparent tool for power and energy system analysis.
The first public version was released in 2016 and has since gained many users and contributors from around the world, becoming one of the most widely used energy system modeling tools. In October 2025, version 1.0 was released, which now enables modeling under uncertainty with a two-stage stochastic programming framework. This allows for more realistic decision making by accounting for multiple possible futures with uncertain renewable generation, demand, and prices, rather than optimizing for a single expected scenario.
The talk will give a general overview of PyPSA and showcase the new stochastic programming functionality by solving an energy system planning problem under uncertainty. It is suitable for both experienced PyPSA users and newcomers to energy system modeling.
There is a vast ecosystem of open-source energy system modelling (ESM) tools. Hundreds of tools have been published to date, mostly originating from research organisations. However, few have gained enough traction to be considered by practitioners for infrastructure planning. If we are to make open-source the norm in decision making, we need to ensure it is possible to explore and compare the range of tools available.
This has not been possible. Until now.
In this talk, we introduce the Open Energy Modelling Tool Tracker (openmod-tracker), a platform that aggregates data on open ESM tool source code repositories and their development communities, created by Open Energy Transition with support from Breakthrough Energy GRIDS. We will share insights drawn from repository activity and user engagement, highlighting which tools demonstrate the strongest momentum and why these should be the focus of collaborative development efforts.
Complementing this, we present our open-source tool feature platform, designed to help practitioners select tools and developers identify feature gaps. Our goal is to expand the platform’s coverage and refine its taxonomy with input from the wider community. We see FOSDEM as an opportunity to kick-start this collaboration and invite you to join us in shaping the future of open-source energy modelling.
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 Scheme), 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—across time structures, scenario trees, and study data—into a complete optimization problem. This model-agnostic architecture enables rapid experimentation, lowers development and maintenance costs, and promotes true reusability: adding a new component requires no code, only data. The language is already supported in Antares Simulator and in the Python package GemsPy.
We present how GEMS could paves the way for interoperability between modelling tools, offering a neutral and extensible modeling layer that can be shared across the open-source energy modeling ecosystem.
When choosing observability platforms, we rarely consider their carbon footprint. Yet every metric collected, every log retained, and every dashboard query consumes energy and at scale, the environmental impact becomes significant. This talk explores the principles and real-world advantages of green observability. We’ll examine how open source observability ecosystems are beginning to address carbon awareness and promote more efficient data practices. Through examples, I’ll show how teams can reduce ingestion volume, lower storage requirements, improve performance and enhance reliability through green coding practices. By linking observability design choices to the Green Software Foundation’s principles, attendees will see how green observability supports a broader sustainable software strategy. They’ll also learn why sustainability in observability isn’t just an organizational obligation, it's a responsibility each engineer carries in the way we collect, store, and interpret data.
E-Paper technology is often highlighted for its reflective readability and near-zero static power consumption, making it an attractive choice in a world where digital displays are becoming increasingly ubiquitous. From public transport signage to smart meters and IoT devices, the number of deployed displays continues to grow—and with it, the cumulative energy they consume. A sustainable future does not require removing or avoiding displays, but rather designing and driving them intelligently.
If you work with E-Paper displays, you will inevitably encounter a situation where the manufacturer provides only partially documented driver code—or, in many cases, a binary blob packed with initialization parameters and so-called waveform lookup tables (LUTs). Experimenting with these values often leads to unwanted side effects such as ghosting, low contrast, long-term image retention, or even permanently damaged panels. A solid understanding of the physics behind E-Paper driving is essential for safely modifying LUTs and optimizing them for lower active energy usage through improved waveform design and voltage-generation strategies. In this talk, we break down the electrical and algorithmic principles that govern E-Paper operation and show how waveform LUTs influence update speed, ghosting behavior, image quality, and—critically—energy consumption.
Key Takeaways