OpenMod workshop Freiburg 2026 - Lightning Talks and Poster Contributions

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OpenMod workshop Freiburg 2026 - Lightning Talks and Poster Contributions

This topic is to collect lightning talks and poster contributions for the Freiburg workshop scheduled for 28–29 September 2026. The freiburg-workshop-2026 tag lists all topics relevant to this event.

About

Lightning talks are short presentations of up to 5 minutes intended to introduce a project, idea, dataset, challenge, or research question to the community and stimulate informal discussions during the workshop.

Posters could showcase research projects, tools, datasets, case studies, or ongoing work. Poster sessions will be organised during the workshop.

Participants are encouraged to combine a lightning talk with a poster presentation to allow for more detailed exchanges during the poster sessions. Standalone poster or lightning talk contributions are also welcome.

Indicating your Topic

Please reply to this topic to propose your topic by 20.07.2026.

:zero:

**Contribution type:** lightning talk, poster, or both
# Title title title
**Presenter(s):**
**Description:** Brief summary of the topic, project, research question, tool, or dataset you would like to present.
**Background:** Additional background information or context.
**Optional links:** Repository, documentation, project website, paper, etc.

For questions and everything else than contributions - look at the main thread:

OpenMod workshop Freiburg 2026 - Registration and Call for Participation

OpenMod workshop Freiburg 2026 - Registration and Call for Participation Planning and organisation for the 14th European Open Energy Modelling Workshop in Freiburg, Germany are underway. This page contains basic information, registration details, and the call for participation. The freiburg-workshop-2026 tag lists all topics relevant to this event. Facts Dates: Monday – Tuesday; 28–29 September 2026 Location: Freiburg, Germany Venue: University of Freiburg, Faculty of Engineering Format: In-p…

[sina_hs]

Contribution type: Lightning talk and Poster

Modelling Heat Decarbonisation Pathways in Luxembourg Using PyPSA

Presenter: Sina Hassanzadeh Saraei

Description:
This work presents the development of a multi-scale heating system modelling framework for Luxembourg based on the open-source energy system modelling tool PyPSA. The project focuses on the residential heating sector and integrates a national-level representation of heating demand with a detailed description of heating supply technologies, including heat pumps, district heating systems, and gas boilers. The model represents Luxembourg’s heating sector across different spatial resolutions, while allowing flexible aggregation of demand nodes. Heating supply options are modelled with varying degrees of technical and spatial detail, enabling the analysis of individual technologies as well as hybrid heating configurations.

The optimisation framework determines the heating technology mix, including heat pumps (HP), district heating (DH), and gas boilers (GB), required to satisfy heating demand under different technical, economic, and policy constraints. In addition to purely cost-optimal scenarios, the framework allows predefined shares or minimum deployment levels of specific technologies to be imposed. This flexibility enables the representation of real-world constraints related to social acceptance, technical limitations, infrastructure capacity, and challenges associated with achieving full decarbonisation. The framework can also mimic co-optimisation effects at sub-national and municipal levels. For example, additional constraints or incentives can be introduced to promote district heating deployment in specific communes or regions where local subsidies, infrastructure availability, integration of renewable and waste heat sources, or urban planning strategies favour particular heating technologies.

Therefore, the modelling framework is developed as a flexible platform to support analyses of Luxembourg’s heating transition, including technology mix optimisation, heat pump electrification potential, district heating expansion, gas phase-out pathways, infrastructure constraint assessment, and demand-side flexibility evaluation. The contribution focuses on the modelling methodology, including workflows for integrating heating technologies into PyPSA, representing hybrid heating systems, and implementing scenario-based constraints on technology shares. The framework is designed as a reusable open modelling platform for Luxembourg-specific sector coupling modelling.

Background:
Open energy system modelling frameworks such as PyPSA are increasingly used to study decarbonisation pathways and future energy system transitions. However, heating system analysis often requires a higher level of technological and spatial detail than is typically included in national energy system models, particularly when assessing electrification impacts, district heating potential, and infrastructure constraints at local scale.

Luxembourg is a particularly relevant case study due to the importance of residential heating in its emissions profile and the diversity of feasible decarbonisation options, including heat pumps, district heating, and residual gas-based systems. The country’s transition pathway is strongly influenced by electricity network constraints, urban density patterns, and the feasibility of large-scale deployment of alternative heating technologies. This work therefore develops a multi-scale modelling approach in which detailed heating technology representation is embedded within a national optimisation framework. The resulting model provides a foundation for analysing not only cost-optimal heating transitions, but also constrained and policy-driven scenarios reflecting real-world implementation challenges and regional heterogeneity in Luxembourg’s heating sector.

[davquete]

Contribution type: Lightning talk and Poster

A Multi-Scale PyPSA-Based Electricity System Model for Luxembourg: Combining European System Representation with a Detailed National Grid Model

Presenter: David Romero-Quete

Description:
This work presents the development of a multi-scale electricity system modelling framework for Luxembourg based on the open-source platforms PyPSA and PyPSA-Eur. The project combines a continental-scale European electricity system representation with a highly detailed transmission network model for Luxembourg and neighbouring regions in Germany, France, and Belgium.

The proposed methodology aims to bridge the gap between large-scale European electricity system models and detailed national transmission studies. The framework integrates a simplified representation of the wider European system using bidding-zone aggregation and NTC-based interconnections, while modelling Luxembourg and its neighbouring transmission infrastructure with significantly higher spatial and technical detail. This includes substations, transformers, transmission lines, and cross-border interconnections.

The resulting hybrid framework enables simultaneous representation of regional system interactions and local operational constraints within a unified optimisation environment. The model is being developed as a flexible platform to support multiple types of analyses related to Luxembourg’s electricity transition. Initial applications include first-approximation Resource Adequacy Assessment (RAA) studies, transmission bottleneck identification, renewable integration studies, storage and flexibility assessment, electrification scenarios, and cross-border dependency evaluation.

Beyond adequacy-oriented applications, the framework is intended to support broader analyses aligned with Luxembourgish and European decarbonisation objectives, including increasing renewable penetration, electrification of demand sectors, storage integration, flexibility deployment, and transmission infrastructure reinforcement planning.

Particular emphasis is placed on representing Luxembourg’s unique characteristics within the interconnected European electricity system. Due to the country’s relatively small domestic generation fleet and strong dependency on neighbouring systems, realistic modelling of cross-border exchanges and transmission constraints is essential for evaluating future operational and planning challenges.

The contribution focuses primarily on the modelling methodology itself, including workflows for integrating detailed transmission data into PyPSA-Eur, combining different spatial resolutions within a single optimisation framework, and creating a reusable open modelling platform for Luxembourg-specific electricity system studies. Future sector-coupling developments can subsequently build upon this electrical modelling foundation.

Background:
Open electricity system modelling frameworks such as PyPSA and PyPSA-Eur are increasingly used to analyse decarbonisation pathways and future power system evolution at continental scales. However, national-level studies often require substantially higher technical and spatial resolution than what is typically available in large European models, particularly for analyses involving transmission constraints, infrastructure adequacy, operational flexibility, and electrification impacts.

Luxembourg represents a particularly relevant case study because of its strong electrical interconnection with neighbouring countries and its ongoing energy transition objectives. The country’s future electricity pathways are closely linked to cross-border exchanges, renewable integration, electrification trends, storage deployment, and regional infrastructure evolution. Capturing these interactions requires modelling approaches capable of representing both detailed local infrastructure and broader European system conditions.

This work therefore explores a hybrid modelling strategy in which a detailed Luxembourg transmission network is embedded within a wider PyPSA-Eur framework. The resulting model serves as a foundation for future analyses related not only to adequacy and operational resilience, but also to broader questions associated with decarbonisation, flexibility, and long-term electricity infrastructure planning.

[matteo_berthelin]

Contribution type: both lightning talk and poster

Characterizing the value of energy storage technologies in future grids

Presenter: Mattéo Berthelin

Description: Combining publicy available historical data and Energy pathways to 2050 prospective study from the French grid operator RTE, a simple PyPSA model is built to characterize and compare the economical value of energy storage technologies in grids with high renewable penetration. Optimal durations, dispatch, LCOS and others are assessed using several years of data and under different electrical production mix.

Background: This work is done within the framework of my PhD in co-tutelle between University Le Havre Normandy (ULHN) and Universtity of Balearic Islands (UIB) on large-scale grid storage and offshore wind energy development.

Optional links: A journal paper published during my intership on the coupling between a wind farm and a hydrogen storage. Although methodology has changed, what I want to present is in the same spirit and is the continuation of this work.

• DOI: https://doi.org/10.1155/er/1182085

[MScholten]

Contribution type: Lightning talk and Poster

Quantifying Storage Cost–Resilience Trade-offs in Renewable Island Systems with Rolling-Horizon Multi-objective Bayesian Optimisation

Presenter: Mirabelle Scholten

Description:
Storage planning in power systems must account for both routine operation and performance under extreme weather disruptions, yet cost efficiency and resilience are rarely optimised within a unified framework. This challenge is particularly acute for non-interconnected island systems, which cannot rely on neighbouring grids or emergency imports when disruptions occur, making self-sufficiency during extreme events a planning necessity rather than an optional objective. A further methodological concern arises when storage dispatch is optimised with perfect foresight. Depending on model structure, the dispatcher can charge storage in advance of a simulated hazard, systematically overstating storage availability and operational value during the event relative to what a real operator with limited forecasting information could achieve.

To address these issues, this contribution presents a PyPSA-based framework that embeds rolling-horizon dispatch within a multi-objective Bayesian optimisation (MOBO) loop. MOBO handles the outer sizing optimisation, proposing storage capacity and siting configurations to evaluate. PyPSA solves the dispatch of storage, generation, and network flows jointly within each RH window for both routine and cyclone simulations, and the resulting cost and expected energy not served (EENS) values update the surrogate model that guides subsequent MOBO iterations. Rolling-horizon dispatch is applied consistently across routine and cyclone simulations and operational decisions are made over a bounded forecast horizon, while the realised state of charge is carried forward between successive steps, ensuring that storage availability reflects operationally realistic information conditions rather than perfect foresight. Storage configurations are evaluated against two objectives: total system cost, covering investment and operational costs across normal and cyclone conditions; and cyclone-related EENS, a probabilistic measure of unserved load computed as the expectation of energy not served across the Monte Carlo outage realisations. Because each full evaluation couples RH dispatch with stochastic outage sampling, direct enumeration of the trade-off space would be computationally intractable. MOBO addresses this through sample-efficient surrogate modelling of the objective landscape, directing evaluations toward the Pareto front without exhaustive search, while also accommodating the noise introduced by Monte Carlo sampling and the non-convex structure of the trade-off surface.

The resilience assessment relies on a dedicated hazard module that combines representative historical cyclone tracks from IBTrACS with Monte Carlo component-outage realisations and RH dispatch on the stressed transmission network. Component exposure, failure probabilities, and recovery dynamics are parameterised from historical event records and published fragility and restoration data, with distribution-level disconnections represented explicitly in the network topology passed to PyPSA.

The framework is applied to a 2050, 100% renewable electricity scenario for La Réunion, in which generation technology and capacity are held fixed while storage capacity and siting are the decision variables optimised by MOBO. Results show that utility-scale storage can reduce the storage-addressable component of cyclone EENS, but that marginal resilience gains diminish where residual disruptions are driven by distribution-feeder outage floors or binding transmission constraints.

For the openmod community, the contribution demonstrates how operational realism and hazard-based resilience assessment can be integrated directly into storage investment evaluation within an open modelling chain. The framework is currently under active development and will be made publicly available. We welcome discussion on transferable hazard-module interfaces, transparent modelling assumptions, and comparable resilience metrics across open energy system models.

Background:
This framework is being developed as part of an ongoing PhD project at Université Marie & Louis Pasteur within the FEMTO-ST institute and in collaboration with the ENERGY-Lab at the University of Réunion. The research is supervised by Robin Roche, Dominique Grondin, and Halima Ikaouassen.

[tseibold]

Contribution type: Lightning talk and Poster

Beyond Continuous Expansion: Graph-Based Candidate Networks for Infrastructure Planning in Large Scale Energy System Models

Presenter: Toni Seibold

Description:
Large-scale open energy system models commonly represent transport infrastructure through continuous linear expansion. While being computationally efficient, it can produce many small-capacity segments and provides limited insight into discrete corridor choices.
This contribution presents a graph-based topology-generation workflow that creates plausible candidate networks before they are evaluated in a sector-coupled energy system model. The method combines spatial candidate graphs, source and sink weighting, length budgets, and perturbation steps to generate ensembles of alternative transport topologies.

Background:
The workflow is applied to the planning of early-stage CO₂ transport infrastructure in Germany.
In this case study, candidate pipeline networks are generated for different sink access options, including shipping from Germany to the Northern Lights project in Norway and interconnections to Denmark and the Netherlands.
The topology ensemble varies network length, source prioritization and perturbations of high-potential source regions.
These candidate networks are then evaluated in a sector-coupled PyPSA-DE model for 2035 to assess which corridors are repeatedly selected, how strongly they are utilized, and how sensitive system outcomes are to topology choice.

Optional links:

GitHub - toniseibold/pypsa-de at co2_network_v2

[etienne.ott]

Contribution type: Lightning talk and Poster

QuaSi — A new toolbox for energy system simulation on the scale of buildings and districts

Presenter: Etienne Ott

Description:

The simulation of energy systems is well established in both the general field of engineering and the openmod community. Many open source tools exist to facilitate this, varying in focus, purpose and suitability to a specific use case. For simulations on the scale of buildings and districts, particular considerations have to be observed. Typically the simulations ought to help make decisions in the early planning stage of a construction or renovation project, when only limited information is available. At the same time the decisions are carried forward into more detailed planning processes and thus the used tools need to be able to take details into account, while maintaining the flexibility to quickly compare different scenarios.

The development of QuaSi started with the goal in mind to better support this gap between early, less detailed results and more advanced planning stages, when details start to matter where other established tools are less robust. One focus are the operational strategies and control schemes of the components of an energy system, which include complex and non-linear effects that occur in real energy systems. Another is the flexibility to model a large variety of energy-related components in almost any energy flow configuration.

QuaSi currently consists of five major components:

• GenSim - Generates energy demand profiles for heating, cooling, lighting and electric devices based on thermal building simulation using OpenStudio™ and EnergyPlus™. A library of pre-configured building types and parameter values based on various standards is included and an Excel-GUI is available.

• SoDeLe - An easy-to-use tool to calculate energy profiles from photovoltaic systems with different orientations and different PV modules, based on python-pvlib. Ships with an Excel-GUI and a CLI for automatic workflows.

• ReSiE - The simulation engine for the calculation of power, heat and other energy flows in an energy system, including post-processing to perform an economic analysis of the simulation results. Can also perform multi-objective black-box optimisation. Its main strength and weakness is the novel mathematical approach based on aspects of systems analysis, agent-based simulation and graph theory, which can cover non-linear control mechanisms and imposes no limit on the complexity of component models.

• SUSI & SIMON: Two web-based UI tools that enable users to work with ReSiE in lieu of a full GUI.

We present an overview of the QuaSi toolbox and discuss future development and tools.

Background:

The QuaSi project is scheduled to have its first full release of the major components in August 2026. We would appreciate the opportunity to introduce the results of this muilti-year project and how it will continue to be developed and used. While a lightning talk cannot possibly cover every aspect we would like to talk about, we hope to give a brief overview and invite those interested to learn more, either during the workshop or afterwards.

Optional links: The project has a website with current news and further links at https://quasi-software.org

[robertovercellino]

Contribution type: lightning talk and poster

Bottom-Up Simulation of AI Data Center Load Profiles for Energy Infrastructure Planning

Presenter: Roberto Vercellino

Description: The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the energy footprint of data centers. However, existing power consumption data is largely proprietary and reported at varying resolutions, creating challenges for estimating whole-facility energy use and planning infrastructure. In this work, we leverage a novel simulation platform developed at the National Laboratory of the Rockies (NLR – formerly NREL), to develop data-driven representative whole-facility load profiles. These profiles will help discuss key differences in compute workloads, hardware, scale, load profile characteristics and infrastructure requirements between different AI data center archetypes such as training, colocation and inference.

Background: I (Robi Vercellino) have been a research scientist at NREL since 2022. The focus of my work has been energy modeling, optimization and techno-economic analysis of energy systems, with particular attention to behind-the-meter resources and microgrids, electrified transportation and AI data centers. I am relocating to Milano, Italy in August 2026, and am looking forward to introducing myself to OpenMod, and the open-source energy research community in Europe (and beyond).

Optional links:

• Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
• Dataset of Generative AI Workload Power Profiles

[floschei]

Contribution type: Lightning talk and Poster

Addressing Spatial Resolution Challenges in Wind Resource Assessment for Energy System Modeling

Presenter: Florian Scheiber

Description:
Future energy systems increasingly rely on weather-driven variable renewable energy sources. As a result, the accuracy, resolution, and statistical consistency of meteorological inputs have become key considerations in energy system modelling (ESM). In particular, wind power estimates strongly depend on local wind speed characteristics, including both distributional properties and temporal variability. However, widely used meteorological datasets such as ERA5 often provide insufficient spatial detail for assessing wind resources at scales relevant to future ESMs. At the same time, higher-resolution datasets are frequently limited in temporal coverage, geographical extent, or accessibility.

This work discusses current challenges associated with deriving validated, transparent high-resolution wind resource inputs for energy system applications. Building on ERA5 reanalysis data as a commonly used starting point, we present an approach for estimating statistical properties and time series of wind speed at 250 m resolution. Preliminary results suggest that methodological choices can influence derived wind speed characteristics.

Beyond the methodological aspects, the contribution highlights the broader challenge of translating meteorological datasets into model-ready renewable energy inputs. Throughout this process, assumptions and uncertainties are introduced that often remain implicit, despite their relevance for subsequent power generation estimates and downstream energy system analyses. The contribution aims to foster discussion on transparent and reproducible workflows, uncertainty awareness, and the role of meteorological preprocessing within the open energy modelling community.

Background:
The contribution addresses the interface between meteorological datasets and energy system modelling. In addition to model structure and optimisation methods, the derivation of renewable generation inputs plays an important role in shaping model assumptions and results. The presented work aims to contribute to a transparent discussion of assumptions, uncertainties, and preprocessing workflows involved in transforming meteorological data into model-ready inputs.

[Justine_Duv]

Contribution type: lightning talk & poster

Allocation of low-carbon power capacities in the world: a fair energy transition within planetary boundaries

Presenter: Justine Duval

Description: Decarbonizing the global economy requires large-scale deployment of low-carbon electricity sources, including photovoltaic (PV) and wind power, with two main challenges:

1. The relevance of deploying variable renewable energy sources (vRES), depends not only on their potential, which is geographically uneven, but also on electricity demand patterns, driven by diverse usages and climatic conditions.

2. Meanwhile, unlimited expansion of renewable capacity risks breaching biophysical limits and leading to the hoarding of resources by the wealthiest.

The following question thus emerges: How can we optimize the global deployment of variable renewable energy to maximize energy service while respecting certain planetary boundaries?

Background: In my PhD, I develop an optimization model to size variable renewable capacities at the country-scale based on the temporal match between vRE supply and demand at hourly resolution. Unlike most energy models, our approach is not techno-economic but aims at minimizing residual demand, and therefore answers the upstream question of physical relevance of vRES development. The model is constrained by a sustainable space obtained from the Planetary Boundaries framework.

Optional links: This is part of the EQUALS project.

Admin note: This work also related to a breakout group proposal.

[dhanssen]

Contribution type: Lightning talk and Poster

Identifying energy consumption associated with heating residential buildings: a case study from Flanders, Belgium

Presenter: Dimitri Hanssens

Description:
According to Eurostat, buildings (both residential and commercial) account for around 40% of final energy consumption in Europe, with space and water heating representing the largest share. In response, and in line with the Energy Efficiency Directive (EU) 2023/1791, European cities are required to develop decarbonisations plans for the heating sector to contribute to the EU’s objective of achieving climate neutrality by 2050.

Most existing building decarbonisation plans rely on conservative demand models based on rigid and often opaque assumptions. Yet demand estimation forms the foundation of decarbonisation pathway optimisation, meaning that the resulting conclusions depend heavily on these underlying assumptions.

Thermal energy demand can be estimated using indirect approaches, such as surveys and modelling, direct approaches based on metered data, or a combination of the two. Traditionally, models have relied on a physical representation of building envelopes and their associated energy systems. However, when analyses are conducted over longer time horizons and larger geographical scales, such as cities or countries, simplifying assumptions are often required to capture the characteristics of the entire building stock. To better ground these assumptions in reality, physics-based models can instead be informed by direct consumption measurements, increasingly available at high temporal resolution with the widespread deployment of smart meters. While access to these data is restricted by GDPR requirements, anonymised datasets are, in certain cases, made publicly available.

Before the heating-specific component can be estimated from metered demand profiles, data-driven models should also require information on the heating equipment installed in each dwelling (boiler, heat pump, auxiliary heating, etc.), which is rarely available for the entire building stock. This work aims to address this gap by using open-access datasets and exploiting the weather-dependent characteristics of energy consumption to infer the heating equipment present in individual dwellings. Focusing on the residential sector in Flanders (Belgium) as a case study, we propose and discuss a model that identifies this equipment from electricity and gas load profiles. Based on this characterisation, the corresponding heating demand is estimated together with its associated uncertainty.

The proposed project (still under development) is part of a broader effort to improve the modelling of heating (and cooling) demand at the urban scale. It primarily serves as a testbed combining socio-economic data from Eurostat, meteorological data from the Royal Meteorological Institute of Belgium (RMI), and consumption data from the Flemish DSO Fluvius. The resulting analysis is expected to contribute to the broader discussion on the role of consumption data to support more robust urban decarbonisation plans. The objective is not to introduce methodological innovations per se, but rather to apply and combine existing approaches to foster discussion within the community.

Background:
This research is carried out within SWIFFT, an interdisciplinary decarbonisation research collective launched in October 2025, bringing together the Université Libre de Bruxelles (ULB) and the Vrije Universiteit Brussel (VUB).

Optional links:
A GitHub repository gathering the project’s tools with preliminary results (currently under construction) is available at: https://github.com/burn-research/LOADid

[etienne.cuisinier]

Contribution type: Lightning talk & Poster

Modelling VRE forecast errors in large-scale energy system planning studies: Are we planning for enough flexibility means?

Presenter: Etienne Cuisinier

Description: Long-term energy system optimisation models are widely used to derive cost-optimal investment pathways at continental scales; Forecast uncertainty is often simplified in planning studies, including in detailed operational models. As a result, capacity portfolios that appear adequate in multi-weather year assessments may still face short-term flexibility shortfalls when forecast based schedules are exposed to realised VRE generation deviations.

This work introduces an approach using cascading models that propagate variable renewable energy (VRE) forecast errors from day-ahead scheduling to intraday and real time flexibility assessment. Starting from a capacity-expansion framework, the adequacy of the derived capacity portfolio is evaluated with a scheduling layer driven by day-ahead VRE forecasts across multiple weather years; using spatiotemporally correlated forecast errors. The resulting forecast-error-driven imbalances (FEDI) are decomposed into intraday-adjustable and post-gate-closure components, and are evaluated against the residual upward and downward margins available from flexibility means (dispatchable units, transmission lines, VRE curtailment, flexible demand). The proposed framework provides a transparent, tractable stress-testing layer to go beyond usual adequacy concepts when planning for European energy systems with high VRE shares.

The results show that portfolios with low loss-of-load expectation (LOLE) under perfect-foresight multi-weather-year adequacy assessment can still experience non-negligible hours of insufficient flexibility once forecast errors are represented explicitly.

Future work include a more detailed modelling of Balancing and Area & Frequency Control steps; as well as feedback to the capacity expansion model.

Background: This PhD project is hosted at DTU and is part of the MuESSLi project within Cresym; involving 5 PhDs from DTU, TU Delft and UP Comillas. It is funded by RTE, Total Energies and NaTran.
Authors: Mohammadhassan Bahmani, Thomas Heggarty, Etienne Cuisinier, Yalin Huang, Matti Koivisto.

Optional links:

• MuESSLi project: https://cresym.eu/muessli/
• The cascade of models (so called “Balancing Tool Chain”) used in this work relies on the open-source modelling tool Balmorel: Balmorel — Balmorel 5.0 documentation
• The open-source tool CoRES is used to derive spatiotemporally correlated forecast errors for VREs: https://corres.windenergy.dtu.dk/

[wonsun.song]

Contribution Type: Lightning Talk & Poster

Bridging Planning and Operation: A Coupled PyPSA and pandapipes Framework for District Heating Digital Twins

Presenter: Wonsun Song

Description: To design reliable, cost-optimal carbon-neutral urban environments, energy system planning must transition to bottom-up, physics-informed optimization, particularly in the heating sector, where thermal energy cannot be easily transported over long distances.

However, a critical bottleneck persists in current open-source toolchains: while macro-level capacity planning tools (such as PyPSA or oemof) are highly effective for energy system design, they lack the physical detail of real-world district heating networks. Conversely, detailed thermo-hydraulic tools like pandapipes are purely simulation-oriented. Uniting these two domains remains a major open challenge, primarily due to the severe computational complexity of embedding nonlinear thermo-hydraulic equations into large-scale capacity and dispatch optimization solvers.

To address this challenge and bridge the gap between optimization and simulation, this contribution presents a three-stage framework designed for the spatial planning, capacity sizing, and validation of district heating networks:

• Spatial Grid Topology Optimization: We implement a mixed-integer linear programming (MILP) model to determine cost-optimal spatial pipeline routing across building blocks, using virtual network flow constraints to prevent isolated network islands.

• Spatiotemporal Capacity Sizing: Utilizing a customized implementation of PyPSA, we co-optimize sector-coupled capacities (electricity generators, heat pumps, CHP, batteries) and 8760-hour dispatch of these assets.

• Thermo-Hydraulic Operational Validation: We couple the optimization outputs with a physical solver loop in pandapipes to verify mass flows, pressures, and thermal dynamics across the heating grid.

We apply this framework into a real-world heating system. This builds upon our prior study of the Freiburg-Dietenbach district (6900 households), which focused on the simultaneous design and control optimization of the district’s non-linear seasonal thermal energy storage system.

Background: This contribution is developed by greenventory GmbH, a high-tech energy-IT spin-off from Fraunhofer ISE and the KIT that provides municipal utilities and urban planners with high-resolution digital twins and decision-support software to design and operate climate-neutral energy systems.

Optional Links:

• Company Platform: greenventory.de

• Frameworks: PyPSA Github | pandapipes Github | NOSTES Github

• Reference Paper: Song, W., Harzer, J., Jung, C., Sander, L., & Diehl, M. (2025). Novel numerical method for simultaneous design and control optimization of seasonal thermal energy storage systems. Energy, 337, 138580. https://doi.org/10.1016/j.energy.2025.138580

[dlr_jk]

Contribution type: Lightning talk and Poster

Agent-based Modelling Competing Flexibility Options with AMIRIS

Presenter: Johannes Kochems

Description:
This work presents a novel method for simulating the competition between flexibility options within the open agent-based electricity market model AMIRIS. To simulate competition among flexibility agents, they are equipped with an estimate of the merit-order to be able to assess both, their own price implication as well as those of competitors. As a proxy for evaluating the price impact, so-called dispatch multipliers are used. These are calculated from past awards of all flexibility units compared to the dispatch of the unit of interest. To smoothen out fluctuations, a moving average approach is used. The method has been developed for a generic representation of flexibility. So far, it has been applied to storages as well as sector coupling flexibility options, such as flexible uni- and bidirectional charging, electric vehicles and heat pumps. An extension for flexible electrolyzers is currently under development.

Background:
Flexibility is gaining increased attention in a future electricity system dominated by variable renewable energy sources. For instance, stationary battery systems as well as sector coupling flexibility from electric vehicles and heat pumps are expected to show quite a steep ramp-up. However, in order to evaluate business opportunities of these technologies, their competition and potential profit saturation needs to be accounted for. This is non-trivial for agent-based dispatch models as competition needs to be accounted for when creating a marketing schedule. The contribution presents a novel approach to address this challenge.

This research was funded by the German Federal Ministry for Economic Affairs and Energy, the
CETPartnership, the European Partnership under Joint Call 2022 for research proposals, co-funded by the European Commission (GA N°101069750) and with the funding organisations listed on the CETPartnership website.

Optional links: A journal paper that describes the method has been published. Another conference paper is about to be published.

• https://doi.org/10.1016/j.est.2025.120054

[JulietteGerbaux]

Contribution type: Lightning talk and poster

From Code to Models-as-Data : GEMS, a High-Level Language for Energy System Modelling

Presenters: Thomas Bittar, Juliette Gerbaux

Description:
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.

Background:
Energy systems practitioners predominantly rely on Object-Oriented Modelling Environments (OOMEs), such as PyPSA or Antares Simulator, which provide modular, domain-specific components for defining optimization problems. However, these tools often lack expressiveness, as the abstract mathematical equations governing the behavior of physical assets are embedded directly within the source code. Conversely, Algebraic Modelling Languages (AMLs) such as JuMP and Pyomo offer high expressiveness but lack a domain-specific layer that would enable model reusability and accessibility for analysts without expertise in optimization.
As energy systems evolve rapidly, so do their modelling requirements. There is therefore significant value in defining a framework capable of providing, in a self-contained manner, a complete description of an energy system, spanning from the abstract mathematical equations of physical assets to the instantiation of components within a system, together with their associated data. Such a framework would foster transparency and reusability across tools.
We present how GEMS could pave the way for interoperability between modelling tools, offering a neutral and extensible modelling layer that can be
shared across the open-source energy modeling ecosystem.

Optional links:

• GEMS documentation : https://gems-energy.readthedocs.io/en/latest/
• GEMSPy : GemsPy: Gems, a framework for generic modelling in energy systems
• PyPSA to GEMS converter : PyPSA-to-GEMS-Converter: PyPSA to GEMS models converter

[guillaume.maistre]

Contribution type: Lightning talk and poster

Framework Interoperability benefits through the PyPSA-to-GEMS converter

Presenters: Dušan Paripović, Guillaume Maistre

Description:

The Antares Simulator Team has developed a new modeling language, GEMS (Generic Energy Modeling Scheme), which decouples model definition from problem solving. This separation brings two benefits: flexibility, since models are described in editable YAML libraries rather than hard-coded, and stability, since the mathematical formulation is handled independently of how each model is defined. One of GEMS’s core design goals is interoperability, which motivated the development of the PyPSA-to-GEMS Converter. It’s an open-source Python package that exports a PyPSA network into a GEMS study format.

Once converted, a study can be run for several purposes: multi-scenario simulation through the two open-source GEMS interpreters (Antares Modeler and GemsPy), and also investment optimisation with Antares Xpansion (Antares’ investment solver). We present a benchmark comparing these interpreters against the native PyPSA solver on two axes: runtime, where first results indicate a speed-up over the original PyPSA interpreter, and result consistency by verifying the objective values. This presentation shows the conversion approach and headline results and current conversion limitations.

Background:

This work is carried out within the Antares Simulator Team at RTE (the French electricity transmission system operator), as part of a broader effort to make the Antares/GEMS ecosystem interoperable with the wider open energy modeling community. GEMS is developed as an open modeling language with public YAML model libraries; the PyPSA-to-GEMS Converter is released under an open-source licence (MPL-2.0) and is at an early stage (v0.0.1), with active development ongoing. It bridges the widely used PyPSA and proven Antares, both open-source energy modeling tools.

Optional links:

• Converter: GitHub - AntaresSimulatorTeam/PyPSA-to-GEMS-Converter: PyPSA to GEMS models converter · GitHub
• GEMS documentation: https://gems-energy.readthedocs.io

[eantonini]

Contribution type: Lightning talk and poster

The Open Energy Benchmark

Presenters: Siddharth Krishna

Description:

The Open Energy Benchmark is an open and reproducible platform for evaluating optimization solvers on real-world energy system planning problems. It compares four open-source
solvers (CBC, GLPK, HiGHS, and SCIP) and a proprietary baseline (Gurobi) across 213 linear and mixed-integer problems from 13 energy modelling frameworks. Solvers are assessed under standardized computational settings using metrics such as runtime, memory consumption, solve success, and other established solution quality indicators. Among the recent findings, the new HiPO method in HiGHS increased the number of problems that can be solved by open-source solvers by 7%, completing complex models with millions of variables up to 15 times faster. The lightning talk will introduce the benchmark, highlight its main findings, and outline opportunities for researchers and modelling teams to contribute new problems and evaluate solver performance on their applications.

Background:

Optimization solvers are central to energy system modelling, but their performance can vary substantially depending on model formulation, problem size, temporal and spatial resolution, and solver configuration. Comparisons based on small or synthetic test cases may therefore provide limited guidance for practical modelling applications. The Open Energy Benchmark addresses this gap by testing solvers on a transparent and continuously expanding collection of problems derived from established energy modelling frameworks. The current benchmark includes linear and mixed-integer problems spanning a wide range of sizes and formulations. Rather than identifying a universal “best” solver, the project aims to improve understanding of solver performance, scaling choices, solver options, and what is feasible to solve with open-source and proprietary solvers.

Optional links

• https://openenergybenchmark.org/
• GitHub - open-energy-transition/solver-benchmark: A benchmark of (MI)LP solvers on energy modelling problems · GitHub
• The State of Solvers for Energy Planning - Results of the 2025 Open Energy Benchmark | Zenodo

[Tinhinane]

Contribution type: Lightning talk and Poster

Modeling flexibility from linepack storage in multi-energy modelling

Presenters: Nikolaos POLITIDIS, Tinhinane MEZAIR

Description:
The concept of linepack storage refers to the quantity of gas contained within pipelines, which serves as an inherent source of flexibility for gas networks and, potentially, future hydrogen networks. The main challenge addressed in this work is to represent this flexibility within the Antares Simulator, a widely used multi-energy planning tool that does not natively support explicit pressure modeling or nonlinear equations.

To overcome these constraints, we treat linepack as a constrained storage, with minimum and maximum values determined by gas flow and pipeline pressure limits. The relationship between gas flow and usable linepack is pre-calculated and then linearized for integration into the simulator. This allows us to quantify the flexibility offered by linepack, even though some dynamic effects are necessarily simplified. The implementation involves linking gas flows to the storage node, applying binding constraints, and periodically resetting the storage level.

This approach is applied in a case study of a French industrial hub. Initial results indicate that incorporating hydrogen linepack into the planning model reduces reliance on underground storage by 30%.

Background:
This research was undertaken in the context of the PlaneTerr project, which aims to advance multi-energy planning and sector coupling in France.

Optional links:

• Rossi, B. & Carpentier, F. & Martin, F.. (2014). Optimizing linepack usage and intraday operations of a gas transmission network, a new approach on GRTgaz network. 1. 231-238.

• RTE, short cycle storage in Antares: Short term storage · ANTARESSimulatorTeam/ANTARES_Simulator · Discussion #1133 · GitHub

[clizbe]

Contribution type: Lightning Talk

Manta: an enterprise-grade open-source energy modelling application

Presenters: Lauren Clisby, Siddharth Krishna, Max Parzen

Open energy modelling frameworks (such as PyPSA, Calliope, GenX, SpineOpt, and TulipaEnergyModel) are accelerating in popularity (see the openmod-tracker), but a major roadblock to becoming the industry standard is the lack of a modern, unified graphical user interface.

This presentation introduces an initiative to build Manta: an end-to-end enterprise FOSS application to enable TSOs, DSOs, academics, and other professionals to better adopt open energy modelling frameworks. We’ll talk about the initial version we’re building around PyPSA and our plans to expand to multi-model support. Participants will learn more about our vision, roadmap, team, and how they can get involved.

Background: The project is an open-source initiative by Open Energy Transition.

Optional links: GitHub repo will be added

[kajetannow]

Contribution type: both lightning talk & poster

Beyond Default: How to Calibrate Your Availability Profiles with Different Wind Turbines

Presenter: Kajetan Nowak
(EDIT: Dominik Madej will be also present at the poster session)

Description:

The modelling of wind availability profiles is a simple, yet tricky to master, process. Have you ever wondered why default assumptions generated unexpected results? What if the default setting doesn’t apply in your case?

In this talk I will discuss how different turbine models affect final results. I’ve made renewables profiles in Atlite using the ERA5 dataset and compared them to the historical generation based on ENTSO-e. I’ll explain why Vestas_112_3MW profile was problematic in Instrat’s cases. I’ll quickly discuss other wind turbines, especially their capacity factors. I’ll show you what works for the modelling of Poland, and may apply to your area. I’ll explain why you shouldn’t be scared of profile smoothing.

This presentation will be accompanied by its own repository, and hopefully pull requests into other open modelling projects.

Background: This work was done within the statutory activity of Instrat. It contributed to province-level profiles in the PyPSA-PL model, our paper called Grids Fit-For-Purpose and our collaboration with the TGE (Polish Energy Exchange). Special thanks to the former & current members of Instrat team, especially Patryk Kubiczek, Michał Grabka, Michał Smoleń, Wojciech Przedlacki, Agata Miazga, Małgorzata Domińska, and Michał Hetmański.

Optional links:

• PyPSA-PL: https://github.com/instrat-pl/pypsa-pl

• Grids Fit-For-Purpose: https://instrat.pl/en/grids-and-res/