OpenMod workshop Freiburg 2026 - Lightning Talks and Poster Contributions

Workshop
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22

:two: :zero:

Contribution type: lightning talk

Open-source Gas Infrastructure Data for Energy System Decarbonization

Presenter: Thomas Klatzer (Contributors: Marco Quantschnig, Yannick Werner, Sonja Wogrin)

Description: Achieving climate neutrality requires a fundamental transformation of energy systems, where electrification will be complemented by renewable gases to decarbonize hard-to-abate sectors and enable long-duration energy storage. Decision-making in this transition is supported by energy system optimization models, which require detailed infrastructure data as input. However, despite previous research efforts and datasets such as SciGRID_gas, a comprehensive, openly available, and reproducible dataset of Europe’s gas infrastructure is still lacking. To close this gap, this work presents an open and transparent European gas infrastructure dataset – compiled through a reproducible workflow – together with the interactive QGas toolkit for visualization, modification and extension of gas infrastructure datasets.

The dataset provides a comprehensive, geographically resolved, and topology-consistent representation of Europe’s gas transmission network (280,000 km) enriched with technical infrastructure data. The network topology is based on a georeferenced representation of the ENTSOG Capacity Map. We develop a graph-based route reconstruction framework for automated, similarity-based pipeline data assignment from open sources such as OpenStreetMap and Global Energy Monitor. We perform topology-aware feature engineering and train machine learning models (ExtraTrees classifiers) to approximate pipeline diameters. Designed for energy system optimization models, the dataset is envisioned to serve as a strong foundation for evaluating future gas, hydrogen, and CO2 infrastructure pathways, thereby supporting decision-making throughout the energy transition.

QGas is an interactive toolkit for visualizing, creating, and collaboratively extending georeferenced gas infrastructure datasets. QGas integrates GIS-based geometry editing with topology-preserving graph operations in a unified web-based environment, enabling users to digitize infrastructure plans, edit network elements, manage attributes, and perform topology-consistent modifications while maintaining a georeferenced representation of the system. The toolkit is implemented using a modular architecture based on Python, JavaScript, and the Leaflet mapping library.

Optional links:

QGas on GitHub:https://github.com/IEE-TUGraz/QGas
QGas preprint: QGas: Interactive Gas Infrastructure Toolkit
QGas application paper: Mapping Austria’s Natural Gas and Hydrogen Infrastructure Plans
European gas infrastructure dataset preprint: follows

OpenMod workshop Freiburg 2026 - Schedule & Program
23

:two: :one:

Contribution type: Poster only

EU Cyber Resilience Act and exemptions for free and open source software (CRA × FOSS)

Presenter: Robbie Morrison

Description: This poster covers the intersection between free and open source software projects (FOSS) and the new EU Cyber Resilience Act (CRA). This legislation seeks to boost the quality and security of software throughout its lifecycle.

The material presented is tailored to the needs of FOSS projects that support systems‑based public interest analysis and specifically energy policy.

Topics comprise: CRA scope, CRA implementation, examples of CRA coverage, open source software definition, criteria for exempt status, example of FOSS exemptions, open source when not exempted by the CRA, closure.

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OpenMod workshop Freiburg 2026 - Schedule & Program
25

:two: :two:

Contribution type: Lightning talk and Poster

Capacity Withholding in Electricity Markets: A Policy Function Approximation of Equilibrium Problems with Equilibrium Constraints

Presenter: Jan Ot Pinya Urgell

Description: Electricity markets are prone to oligopolistic concentration, giving some participants the ability to move prices away from marginal-cost levels through economic or physical capacity withholding. This kind of strategic behaviour raises clearing prices, displaces efficient generation, and reduces consumer welfare. Representing it typically requires bi-level or equilibrium formulations (MPEC/EPEC), but these are difficult to solve at the scale of realistic, multi-firm, multi-technology portfolios, which has kept most existing studies limited to single plants or technologies. Agent-based modelling offers another way to represent this kind of decentralized, strategic decision-making, but scaling it to systems with many interacting agents raises similar computational and convergence challenges.

This work presents a Policy Function Approximation (PFA) approach for integrating strategic capacity withholding into large-scale Energy System Optimization Models. The methodology first formulates the strategic bidding problem of a single profit-maximizing firm as a Mathematical Program with Equilibrium Constraints (MPEC), then extends it to a multi-firm Equilibrium Problem with Equilibrium Constraints (EPEC). To avoid repeatedly solving the computationally expensive MPEC step, the PFA learns, for each strategic firm, a piecewise-linear mapping from system load to that firm’s strategic dispatch deviation. This mapping is constructed by evaluating the firm’s MPEC over a representative set of load points and interpolating the resulting dispatch-withholding observations; heuristics are added to handle out-of-sample load levels and discontinuities in the underlying bidding response.

Background: This work is carried out at TU Delft within the MuESSLi (Multi-Energy System Smart Linking) project, which builds data-driven proxies of energy system models to enable their coupling across sectors and actors. MuESSLi spans five interlinked PhD topics across TU Delft, DTU and UP Comillas, together with industrial partners RTE, TotalEnergies and NaTran.

Optional links: This work is part of a PhD conducted within CRESYM (Collaborative Research for Energy SYstem Modeling), cresym.eu/muessli . A conference paper describing the method is currently in preparation.

OpenMod workshop Freiburg 2026 - Schedule & Program
26

:two: :three:

Contribution type: Lightning talk and Poster

An agent-based approach on investment modelling guided by profitability and common well-being indicators

Presenter: Agustín Cortés Hähnle

Description:

Current energy system modeling primarily emphasizes on aspects such as cost-efficiency, emissions reduction and profitability, often overlooking public interests like land use, job creation, and acceptance. With this work, we contribute to close this gap, thereby focusing on the actors’ perspective. In the project ENERGOOD, we expanded the AMIRIS agent-based market model to consider common well-being, defined by a set of quantified indicators, as an additional degree of freedom for agents to decide on investments. In our simulation, agents make simultaneous investment decisions based on an endogenous long-term power price forecast. We replaced the standard net present value metric with a multi-criteria assessment that combines profitability with well-being indicators. This allows for flexible weighting of factors, enabling an investigation into how these priorities influence technology choices and the incentives needed to encourage well-being-oriented behavior. This enables us to present differences in investment trajectories when prioritizing well-being versus classical profitability. Additionally, we analyze the profit and common well-being levels of investors with varying preferences and indicator weights within the overall electricity system.

Background:

This work has received funding by the German Federal Ministry for Economic Affairs and Energy under grant agreement number (FKZ): 03EI1091A.

Optional links:

OpenMod workshop Freiburg 2026 - Schedule & Program
27

:two: :four:

Contribution type: lightning talk & poster

Human-readable mathematics for energy system modelling

Presenter: @brynpickering

Description:

Energy system modelling (ESM) tools are usually linear optimisation wrappers; they process user data into optimisation problems using internally defined mathematical expressions.
Most ESM tools share a common set of these expressions but there is an increasing need for bespoke mathematical constraints, to reflect the complexity of real systems.
Defining this “custom” math is undertaken in the language-specific API of an ESM tool in most cases, creating a barrier to porting math to different contexts and to defining the math in the first place.
Meanwhile, the “core” math is often embedded in the tool source code and therefore completely disconnected from the data that is used to create the model.
In this talk, I will present a human-readable, YAML-based mathematics syntax which we have implemented in Calliope and linopy.
It can be used to store the core and custom math of a model alongside its data, ultimately increasing model transparency and reproducibility.

Optional links:

NOTE: this session could also be a 1.5hr breakout session dedicated to training in use of the math syntax and getting participants to define their own custom constraints.

OpenMod workshop Freiburg 2026 - Schedule & Program
28

:two: :five:

Contribution type: lightning talk and poster

Speaking Each Other’s Language: How to Pythonize the Java-Based Framework FAME

Presenter: Felix Nitsch

Description:
We are developing a native Python interface for the Java-based modelling framework FAME. The goal is to make FAME’s existing features completely accessible from Python, without the need of programming (or understanding) any Java. This shall substantially lower the entry barrier for the modelling community to using FAME. We share our current thinking on the design of this Java-Python coupling approach, and welcome feedback from the community. In addition, we present details on the technical implementation and performance benchmarks from our proof-of-concept implementation.

Background:
FAME is the open Framework for distributed Agent-based Models of Energy systems. At its core, FAME provides powerful features for developing and executing comprehensive agent-based simulations in Java. It has been designed to lower the barrier for building and maintaining complex agent-based models such as AMIRIS. FAME’s features range from convenient input and output data handling, rapid model execution, and out-of-the-box parallelisation to comprehensive metadata annotation. Today, however, FAME requires programming of agents and actions in Java, while most open modellers are skilled with Python. The present work aims to bridge this gap and thus allow more modellers to benefits from its capabilities.

Optional links:

OpenMod workshop Freiburg 2026 - Schedule & Program
29

:two: :six:

Contribution type: lightning talk and poster

ENETRA-Project — a webtool for modelling and planning optimized inland port energy systems

Presenters: Moritz Schiel, Manuel Wahl

Description:

The web tool enables companies operating in inland port areas and their stakeholders to model their specific energy systems on a map. It not only displays the current situation, but also provides future scenarios for comparison. Investment decisions are optimised by taking into account changing infrastructure, energy sources and technologies. This is underpinned by an OEMOF-based model incorporating components for mobility and logistics.

Background:

The development is done in the research project ENETRA, which is funded by the German Federal Ministry for Transport. The project partners Reiner Lemoine Institut (RLI) on the research side together with BEHALA, the operator of ports in Berlin, aim to develop robust scenarios for the energy transition of the Westhafen.

The project is also supported by Reiner Lemoine Foundation, BEHALA, bayernhafen, DeltaPort and the Bundesverband öffentlicher Binnenhäfen.

Optional links:

OpenMod workshop Freiburg 2026 - Schedule & Program
30

Dear Agustín, this projects looks very interesting; A quick question (for now or September :slight_smile: ): What is the typology of agents used? Thanks, best,
Etienne

31

:two: :seven:

Contribution type: lightning talk and poster

Data-driven sector reduction using differentiable optimization layers

Presenter: Tim Graulich

Description:

Energy system models are becoming increasingly complex as they aim to represent additional system dynamics at higher spatial and temporal resolutions. A key driver of this trend is the growing importance of sector coupling, which requires the simultaneous modeling of multiple sectors. This substantially increases model size and solution times, creating significant barriers to adopt multi-sectoral studies. In particular, when stakeholders require high resolution and uncertainty quantification in a specific sector, extending the same level of detail to additional sectors often becomes computationally infeasible. Thus, a simpler and computationally lighter way is required to include additional sectors in energy system models. One common strategy is spatially aggregate nodes in the additional sectors. However, aggregating models generally creates errors between the full and aggregated model, thus affecting the model outcomes. Although previous work has investigated how to reduce these errors, such as through additional constraints or iterative resolution refinement, no existing methods explicitly account for predefined accuracy measurements while optimizing the aggregation itself to minimize induced errors. In this study we propose a data-driven workflow that utilizes parametrized aggregation models and differentiable optimization layers, to minimize case-oriented metrics between the full and aggregated model. In a case study, we develop a reduced spatial representation of the European H2 sector, while trying to keep electrolyzer operation as close as possible to the original system. Using congestion aware clustering and learned bounds on the aggregated system, we minimize the error and compare it to naive clustering approaches.

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.

Optional links:

OpenMod workshop Freiburg 2026 - Schedule & Program
32

:two: :eight:

Contribution type: lightning talk

Comparison of Atlite vs PVLib/Windpowerlib — how to accurately model renewables generation?

Presenter: Florian Maurer

Description: We compare Atlite and PVLib/Windpowerlib for German solar and onshore wind in 2023 on a shared ERA5 cutout, from plant/farm scale (Campus Jülich PV, Kelmarsh SCADA) up to TSO zones and national ENTSO-E feed-in.

Holding weather fixed separates conversion and fleet choices from the weather product. Main findings: at plant scale the libraries agree closely; national wind gaps shrink once turbine/hub are aligned; national solar remains high vs public-grid feed-in (behind-the-meter and other residuals). Class aggregation also makes plant-level stacks competitive on runtime.

Background: Open energy models (e.g. AMIRIS, ASSUME, PyPSA-style workflows) often treat weather-to-power as a solved input, yet library and fleet assumptions can still move market and system results.

Optional links:

OpenMod workshop Freiburg 2026 - Schedule & Program
33

:two: :nine:

Contribution type: Lightning talk and Poster

Smart-Linking of Power, Gas, and Hydrogen Models via Optimization Proxies

Presenter: Emad Nematbakhsh

Description:

The investment and operation of multi-carrier energy networks is extremely complex. Hard-linking these systems is computationally intensive, whereas soft-linking struggles to fully capture their interactions. This work introduces an intermediate “smart-linking” solution that overcomes these limitations. We create a proxy of the gas and hydrogen networks and embed it as a set of optimization constraints within the electricity network model, transforming the multi-level problem into a solvable single-level optimization. To accurately capture the varying operational contexts that affect coupling variables, we apply factor selection to include both local and global features. Ultimately, this allows the electricity system operator to analyze network issues at high speed and in a scalable manner.

Background:

Supervised learning methods typically train proxies on pre-solved datasets, which imposes a massive computational burden for realistic multi-carrier systems. To address this, our methodology utilizes decomposition methods (such as Benders decomposition) to generate training data. This iterative process generates a rich trajectory of solutions rather than just a single final endpoint. By intelligently sampling operationally meaningful intermediate states and transition regions, the proxy achieves superior accuracy across a wider operating space compared to blind, pre-defined sampling strategies.

Optional links:

34

:three: :zero:

Contribution type: lightning talk and poster

Introducing the ETHOS Energy Transformation Pathway Optimization Model Suite

Presenter: Jonas Becker, Johannes Behrens

Description:

Energy systems analysis requires a diverse set of tools to comprehensively evaluate complex transformation pathways. Robust assessments depend on methods for data generation and processing, renewable energy potential estimation, energy system optimization, as well as socio- and macroeconomic analysis. We present a harmonized model suite – ETHOS – that integrates these functionalities within a consistent analytical framework. The ETHOS suite comprises tools ranging from automated data extraction using natural language processing to state-of-the-art renewable potential assessments and energy system optimization models, among others. We give an overview of the suite’s building blocks and how they interoperate, including:

  • Data & metadata: NLP-based extraction of techno-economic data from literature (Quinex), residential and industrial load profile generators (LoadProfileGenerator, PeNALPS), and a nationwide building database (BUILDA)
  • Renewable potential assessment: land eligibility (GLAES), globally validated generation profiles (RESKit), and reproducible workflow managers (REFLOW, PASSION)
  • Energy system design: the optimization framework FINE, together with temporal aggregation (tsam)
  • Socio-economics: macro-economic assessment (DEMARCO) and discrete-choice mobility modelling (MODE.behave)

Background:

ETHOS is developed at the Institute of Climate and Energy Systems (ICE-2, Jülich Systems Analysis), Forschungszentrum Jülich. Most modules are available as open source (Forschungszentrum Jülich - Jülich Systems Analysis (ICE-2) · GitHub), and further development is ongoing. The suite has been applied in national and European transformation studies, in a study designing cost-optimal energy systems for all ~11,000 German municipalities, and in support of several IEA reports (Global Hydrogen Review, Latin America Energy Outlook, Ukraine energy system).

Optional links:

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OpenMod workshop Freiburg 2026 - Schedule & Program
35

:three: :one:

Contribution type: lightning talk & poster

Open-Source Intelligence for Spatially Granular Energy Poverty Identification in the Netherlands

Presenter: Despoina Kassandra Georgiadi

Description: This work presents an open-source methodology for identifying energy poverty at sub-neighbourhood resolution by combining freely available geospatial, building and socioeconomic datasets into a composite multi-indicator index. The pipeline integrates 3DBAG (Basic Registration of Addresses and Buildings, LOD1.2 building geometry), EP-Online (Dutch energy label register), ERA5 Copernicus reanalysis (hourly meteorological data) and CBS datasets (Central Statistics Bureau income, energy consumption and demographics at PC6 postcode level).

Insights are presented within a digital twin interface to ensure that the findings are accessible and beneficial for decision-makers. This work operates on the “silver lining” of data granularity, aiming for insights that are at a high enough resolution to be actionable for neighborhood-level interventions, yet adequately aggregated to ensure individual privacy.

The work demonstrates how open, privacy-respecting data can advance objective metrics of energy poverty, enabling policymakers to design mitigation initiatives in an informed and targeted way that ensures no household is left behind in the energy transition.

Background: This research is developed at the department of Electrical Sustainable Energy at TU Delft within the REFORMERS project, which explores how technologies today can transform local energy systems into systems that are able to meet their communities’ energy needs. Among its activities the project develops a replicable digital twin architecture and services for its flagship valley of Alkmaar in the Netherlands.

Optional links:

OpenMod workshop Freiburg 2026 - Schedule & Program
36

:three: :two:

Contribution type: Lightning talk and Poster

Water Value Computation with Surrogate-Assisted Dynamic Programming for Long-Duration Storage in Energy System Prospective Studies

Presenter: Ruiqi Zhang

Description:
Energy system prospective studies for adequacy assessment and long-term planning typically simulate a full operational year at hourly resolution over many weather and load scenarios. Expected operating cost and reliability metrics such as LOLE and EENS are obtained by averaging over these sampled years.

These simulations almost always assume perfect foresight over the year. For long-duration storage this is too optimistic: it lets the operator schedule the reservoirs against the entire year’s realization and overstates the flexibility that storage actually provides. We therefore introduce imperfect foresight through stochastic dynamic programming: the year is split into 52 weekly decision stages, each week’s dispatch is solved at hourly resolution under only week-ahead information, and a value function carries the expected future worth of stored energy between weeks (its slope is the water value).

Because long-duration technologies shift energy across seasons and years, how a one-year model values stored energy at year-end is a central modelling question. A zero terminal value drains the storage toward year-end. Return-to-target penalties or specialized end-of-year water values fix the boundary with parameters that are based on expert knowledge. A pre-defined discount factor removes the boundary by devaluing energy far in the future.

Building these value functions is computationally demanding. In the primal (discretized) dynamic program, the weekly dispatch must be evaluated for every combination of storage state, weekly control, and scenario across all 52 weeks, and the number of state–control combinations grows exponentially with the number of storage units. Since the weekly operating cost is the quantity evaluated over and over, we replace it with a surrogate: a learned approximation of the weekly operating-cost function, trained once and then evaluated cheaply in place of the full dispatch solve.

We illustrate this on a stylized three-area adequacy case (France, Germany, Switzerland, one long-duration reservoir per area), comparing how the different year-end treatments change water values, reservoir trajectories, operating cost, and reliability metrics, and also the surrogate performance in accelerating the computation. The lightning talk introduces the problem and the modelling architecture and the poster details the information passed between weekly subproblems and preliminary comparisons of the terminal treatments.

Background:
This PhD project is hosted at TU Delft 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. The model applied in this project is implemented using GemsPy, developed by RTE.

Optional links:

  1. MuESSLi project: https://cresym.eu/muessli/.
  2. GemsPy, a framework for generic modelling in energy systems: GitHub - AntaresSimulatorTeam/GemsPy: Gems, a framework for generic modelling in energy systems · GitHub.
OpenMod workshop Freiburg 2026 - Schedule & Program
37

:three: :three:

Contribution type: Lightening talk and Poster

Bringing a Baltic energy system model into SpineOpt

Presenter: Kārlis Baltputnis

Description:
This contribution presents the Baltic case study of the Mopo project. After disconnecting from the BRELL ring and synchronising with Continental Europe in February 2025, the Baltic energy system must balance itself independently while integrating a growing share of variable renewable generation. The case study examines large-scale wind integration under spatial constraints, including military-related development restrictions in selected regions. Wind and solar time series are simulated at sub-national and sub-hourly resolution to capture the intermittency patterns relevant for planning in each Baltic country.

An existing Baltic energy system model, originally developed in the open source Backbone framework, is translated via Spine Toolbox into the INES data specification and then converted into a SpineOpt model. Alongside the translation, the model content is substantially updated to reflect the current state and planned development of the Baltic energy system, and new high-resolution renewable generation data from CorRES is incorporated. The contribution covers the modelling workflow, the translation experience, scenario results for regional wind integration including the role of storage in balancing and reserve provision, and lessons learned on interoperability.

Background:
Mopo is a Horizon Europe project developing an open-source, validated toolset for energy system modelling, combining component tools for creating model-ready datasets with tools for scenario and workflow management and long-term planning. The Baltic case study, led by Riga Technical University, serves as a practical test of the INES specification as a translation layer between independently developed modelling frameworks.

Optional links:

OpenMod workshop Freiburg 2026 - Schedule & Program
38

:three: :four:

Contribution type: poster

Modelling future green-vs-grey steel trade and the effect of various carbon policy regimes

Presenter: Philip Horster

Description:

This poster presents model results from an economic Armington Trade Model assessing the impact of various climate policy regimes on future green-vs-grey steel trade. In a 2035 projection, green and grey steel are treated as separate, yet largely substitutable goods. The baseline scenario is without any carbon prices, to which the following policies are introduced sequentially: a CO2 price in the EU, a carbon border tax, export rebates, and the addition of other regions to the area subject to these policies.

The resulting scenarios are then compared across multiple indicators: the value of steel production in a given region, global carbon emissions of the steel sector, and changes in regional production, consumption and trade patterns of green and grey steel.

There are two main contributions: First, the poster aims to inform to what extent climate policies can shape the transition to a decarbonised steel sector. Second, it provides an example of how the results of open energy models can support answering research questions of interdisciplinary nature, using the methodological toolkit of adjacent disciplines (e.g. economics)

Background:

As data inputs, it utilises the regional projections of various open models: the production cost curves for green steel (PyPSA-based), steel demand (REMIND-MFA), and the carbon intensity of fossil production (REMIND).

OpenMod workshop Freiburg 2026 - Schedule & Program
40

:three: :six:

Contribution type: Lightning talk and Poster

NPAP - Network Partitioning and Aggregation Package

Presenter: Benjamin Stöckl

Description:
The Network Partitioning and Aggregation Package (NPAP) is an open-source, standalone Python library designed to standardize the reduction of spatial complexity in network graphs for optimization models. A core design principle of NPAP is the explicit decoupling of the spatial reduction pipeline into two distinct stages: network partitioning (which maps nodes to disjoint clusters) and network aggregation (which reduces the topology based on that mapping). Built on a modular strategy design pattern, NPAP enables users to independently swap, customize, or extend partitioning and aggregation algorithms without altering the core codebase.

While its fundamental architecture is general-purpose, NPAP features specialized tools tailored for power system optimization. It introduces advanced physical constraints critical for large-scale transmission networks, notably AC-island awareness and voltage-aware clustering. These features automatically detect and independently partition AC grids connected solely by DC links and actively prevent the assignment of nodes with distinct voltage levels to the same cluster. This preserves critical physical infrastructure details, such as multiple distinct voltage levels and their connecting transformers, ensuring more accurate investment decisions in aggregated grids.

Background:
As optimization models representing real-world networks exhibit high complexity, they frequently become computationally intractable. To regain computational efficiency, modelers widely use spatial aggregation. However, a significant research gap exists: existing spatial reduction tools are typically tightly embedded into specific modeling frameworks, which heavily limits their independent reusability and makes cross-framework comparisons difficult.

To maximize the impact of our solution and make the tool available on a large scale, we are currently integrating the package into the well-established PyPSA framework via an open pull request. This ongoing integration has already been used to successfully demonstrate NPAP’s scalability on the full pan-European high-voltage transmission network. In our European case study, NPAP demonstrated that voltage-aware aggregation preserves up to 70% of the transformer expansion costs in the aggregated model compared to the full grid model, significantly outperforming traditional voltage-unaware methods.

Optional links:
NPAP is available on GitHub. Additionally a preprint, which is currently under review at the Journal of Open Source Software (JOSS), is available on arXiv. Another preprint, which was accepted for the EEM conference, showcases the advantages of the voltage-aware aggregation methods over typical voltage-unaware methods.

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OpenMod workshop Freiburg 2026 - Schedule & Program
41

:three: :seven:

Contribution type: lightning talk and/or poster

Building a Hydrogen Platform with Programmable Grid Simulation to evaluate Electrolyser systems

Presenter: Niels Springer, PTB

Description:
To address various scientific questions regarding the dynamic market ramp-up of hydrogen technologies, PTB is setting up a modular hydrogen research platform. The following modules will be realised in the start-up phase:

  • Power grid simulator: representation of realistic energy generation processes (e.g. wind, PV), reproducible dynamic operation.
  • PEM electrolyser: production of green and high-purity hydrogen
  • Gas treatment: drying and compression of the hydrogen as well as gas analysis

The modular set-up allows for future modules to be used at the site, such as electrolysers of other types, battery storage or fuel cells.

In the context of open energy modelling, we’re interested in finding future project partners. With the hydrogen platform, including the programmable grid simulation, we’re providing a research site to generate accurate and reliable data on electrolyser operation. This includes different power input situations, as well as incomplete/noisy inversion scenarios and intermittent supply. We’re open to projects looking to validate modelling, as well as generating data for use in new models or machine learning applications.

Background:
PTB is the National Metrology Institute of Germany with scientific and technical service tasks. PTB measures with the highest accuracy and reliability – metrology as the core competence. The hydrogen platform is an interdisciplinary project of seven PTB divisions.

Optional links:

42

:three: :eight:

Contribution type: lightning talk and poster

MicroGridsPy: A Multi-Resolution and Multi-Energy Framework for Bottom-Up Energy Access Planning

Presenter: Alessandro Onori, Politecnico di Milano - SESAM Research Group

Description:

MicroGridsPy is an open-source modelling ecosystem for bottom-up energy planning in remote and underserved areas. It supports robust and transparent decision-making when electrification strategies must balance technical feasibility, economic viability, social priorities and environmental impacts. The ecosystem integrates stochastic demand assessment, renewable resource characterization, long-term capacity expansion, dispatch simulation, and LV/MV network design with load-flow validation. Its energy-planning model optimizes renewables, storage, dispatchable generators and grid interaction using hourly typical-year or multi-year formulations, while accounting for uncertainty, demand growth, phased investments, reliability, emissions and other externalities.

The contribution will focus on two developments within my ongoing PhD research.

The first is a multi-resolution planning architecture that soft-links spatial network design with long-term energy-system optimization. The topology module generates and compares alternative electrification architectures, including full LV networks, hybrid grid and stand-alone solutions, and MV backbones supplying local LV service zones. Candidate networks are built from georeferenced customers and roads, sized through iterative conductor and transformer reinforcement, and validated through AC load-flow analysis. This allows network costs, voltage limits, losses, service obligations and priority users to inform long-term planning without requiring a single, computationally prohibitive integrated model.

The second is a multi-energy modelling mapping with the aim of extending electricity-access planning to water, heating and cooling, biomass and productive uses of energy. The objective is to capture synergies and trade-offs such as flexible water pumping, biomass allocation between electrical and thermal services, and productive activities acting as anchor loads.

Background:

Mini-grid planning involves decisions at very different spatial and temporal scales. Distribution-network design depends on individual customer locations, settlement morphology, road infrastructure and electrical constraints, whereas energy-system optimization typically considers hourly operation, multi-year demand evolution, technology replacement and uncertain future conditions. At the same time, energy access cannot be adequately assessed through electricity supply alone. Households and communities require a wider set of energy services, including clean cooking, water pumping, heating, cooling and energy for income-generating activities. These services interact with electricity demand, local biomass resources, infrastructure requirements and environmental outcomes. Treating them separately may therefore overlook important synergies, resource competition and unintended trade-offs.

This work is being developed within my PhD research at Politecnico di Milano and builds on the modular MicroGridsPy ecosystem. The research investigates how open-source tools operating at different resolutions can be connected without creating an excessively large or opaque integrated model. More broadly, it explores how energy-access planning can progress from electricity-only least-cost optimization towards comprehensive, uncertainty-aware and socially informed infrastructure planning that accounts for multiple energy carriers, services and sustainability dimensions.

Optional links:

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OpenMod workshop Freiburg 2026 - Schedule & Program
43

:three: :nine:

Contribution type: lightning talk and poster

Modeling investment decision-making in the steel industry under deep uncertainty

Presenter: Philipp Wächter

Description:
Investment decision-making in the steel industry is characterized by an inconvenient combination of long asset lifetimes and deep uncertainty. Parameters such as future H2 prices, CO2 prices, and CCS availability strongly influence the correct choice in steelmaking technology yet remain highly uncertain. Thus, these investment decisions carry considerable risk which investors must navigate.

In this contribution, we explore how modeling investment decision-making under deep uncertainty can provide insight into the steel industry transformation. We investigate which technology choices remain robust across a wide range of scenarios, and which allow steelmakers to retain strategic flexibility.

Using the open-source TEAM framework (Techno-Economic Assessment and Manipulation), we conduct techno-economic assessments of 14 steelmaking routes, modeled as a graph of technologies and products. Cost and performance data are drawn from and extended within POSTED (Potsdam Open-Source Techno-Economic Database). Parameter sweeps show how strongly the cheapest route depends on CO2, H2 and fossil prices and CCS assumptions.

A Monte Carlo analysis over all input parameters yields cost and regret distributions for each route, evaluated with risk metrics such as Conditional Value at Risk (CVaR). Preliminary results show widely overlapping distributions and no single cheapest route; an uncertain CO2 price mainly widens the downside risk of CO2-intensive routes.

We also share conceptual work that builds on the model’s graph structure: flexible feedstock supply for DRI plants, the high-grade iron ore bottleneck, and time-dependent pathways from 2026 to 2050, where the initial investment is fixed and later decisions adapt myopically in each scenario, to identify robust initial choices.

Background:
POSTED (Potsdam Open-Source Techno-Economic Database) is a public database of techno-economic data on energy and climate-mitigation technologies, developed at the Potsdam Institute for Climate Impact Research (PIK) as part of the Ariadne project. TEAM (Techno-Economic Assessment and Manipulation) is a companion framework for performing techno-economic assessments using such data. Together, they provide a consistent, open-source pipeline for curating technology cost and performance data and carrying out energy and climate-mitigation analyses.

Optional links:

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OpenMod workshop Freiburg 2026 - Schedule & Program