# Hydrogen internal combustion engine CHP in decentralized energy systems: Towards TEA-LCA in an open-source framework

Paul Robineau, Naqib Salim, Maria Boltoeva, Maurice Kettner, Gaétana Quaranta, Nicolas Arbor

## ► To cite this version:

Paul Robineau, Naqib Salim, Maria Boltoeva, Maurice Kettner, Gaétana Quaranta, et al., Hydrogen internal combustion engine CHP in decentralized energy systems: Towards TEA-LCA in an open-source framework. 2025. hal-05410000

HAL Id: hal-05410000

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Preprint submitted on 10 Dec 2025

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Input Data

<figure><img src="image_2.png" /></figure>

CBE Clima Tool

Weather
data

Heat &
Electricity
demand
data

Stadt
Offenburg

<figure><img src="image_3.png" /></figure>

TEA model of H₂-ICE-CHP
Decentralized Energy System

OpenModelica

Electrical system

<figure><img src="image_4.png" /></figure>

Hourly
resolution
over a year

4 Families
of 7 Scenarios
(28 Total)

<figure><img src="image_5.png" /></figure>

Heat system

<figure><img src="image_6.png" /></figure>

ICE-CHP

<figure><img src="image_7.png" /></figure>

Hydrogen system

Comprehensive scenario LCA
for 3 System Boundaries

<figure><img src="image_8.png" /></figure>

Brightway

+

<figure><img src="image_9.png" /></figure>

Monte Carlo sampling
Contribution Analysis
Global Sensitivity Analysis

Towards TEA-LCA
in an open-source framework
<figure><img src="image_10.png" /></figure>

# Hydrogen internal combustion engine CHP in decentralized energy systems:
## Towards TEA-LCA in an open-source framework

Paul Robineau¹*, Naqib Salim², Maria Boltoeva¹, Maurice Kettner², Nicolas Arbor¹, Gaetana Quaranta¹

¹ Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France

2 Institute of Refrigeration, Air-Conditioning and Environmental Engineering, Karlsruhe University
of Applied Sciences, Germany

*Corresponding author

E-mail addresses and ORCID IDs:

paul.robineau@iphc.cnrs.fr, 0000-0002-2198-3689 (P. Robineau)

muhamad_naqib.md.salim@h-ka.de (N. Salim)

maria.boltoeva@iphc.cnrs.fr, 0000-0002-0330-8153 (M. Boltoeva)

maurice.kettner@h-ka.de, 0000-0001-6718-6119 (M. Kettner)

nicolas.arbor@iphc.cnrs.fr, 0000-0001-6457-1767 (N. Arbor)

quaranta@unistra.fr, 0000-0003-4867-0030 (G. Quaranta)

## KEYWORDS

Environmental Impact, Life Cycle Assessment, Hydrogen, Energy systems, Techno Economic Analysis, Cogeneration, Combined Heat and Power

## HIGHLIGHTS

* Towards OpenModelica-TEA and Brightway2-LCA integration for H2-ICE-CHP systems

* Real hourly demand data of Offenburg (Germany) municipality buildings used

* PV overproduction and system boundaries strongly shape environmental assessment

* H₂ sub-system shows low GHG but relevant burdens in other environmental categories

* Open-source workflow for transparency in local energy policy planning

## ABSTRACT

Hydrogen-based decentralized energy systems (H₂-DES) are gaining prominence as potential solutions for local decarbonization of heat and power supply. This paper presents a life cycle assessment (LCA) of a H₂-DES using an internal combustion engine for combined heat and power (ICE-CHP), grounded on a techno-economic analysis (TEA) model implemented in OpenModelica. The study addresses the relative underexploration of H₂-ICE-CHP technologies in the LCA literature, offering a comprehensive analysis based on real hourly demand data of buildings in Offenburg (Germany), a municipality involved in the Franco-German Interreg CO₂InnO project. Scenarios families were developed with varying configurations and dimensioning (PV, wind, battery, H₂ storage). The results of the TEA model were interfaced with the Brightway2 framework for LCA through custom Python-based routines and manual implementation. Monte Carlo simulations and global sensitivity analysis were conducted in the Activity-Browser software to evaluate uncertainty and parameter influence. Results show that impacts are strongly dependent of the scenario family and the system boundaries. The H₂ sub-system components showed modest impacts to climate change but not in other environmental categories, while PV significantly affected environmental assessment overall, depending on excess electricity consideration. These findings highlight the necessity of careful system design and boundary comparison in policy and environmental reporting. This work also contributes methodologically towards the development of an open-source TEA-LCA workflow. While still requiring manual data adaptations, it forms the foundation for future automation efforts. The approach offers actionable insights for local policymakers and researchers aiming for transparent, reproducible, and multi-criteria sustainability assessments of H₂-DES.
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# 1. INTRODUCTION

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Hydrogen has attracted growing attention in recent years, both for its current industrial uses – which require decarbonization – and its potential role in decarbonizing heavy transport, heating networks, and inter-seasonal electricity storage (Sadeq et al., 2024). Strategic divergences exist within the European Union (EU), notably between France and Germany. France, with its already low-carbon electricity mix, prioritizes decarbonizing hydrogen used in industry and agriculture. In contrast, Germany’s diverse industrial interests promote a broader range of hydrogen applications (Quitzow & Zabanova, 2024). At the EU level, efforts to establish a pan-European hydrogen market have led to initiatives such as the European Hydrogen Backbone (EHB, 2025), which brings together gas operators pursuing energy transition. Within this context, the Interreg CO2InnO project serves as a Franco-German cross-border living laboratory aimed at comprehensively assessing decentralized energy systems (DES) using hydrogen ($H_2$), particularly for heat decarbonization (CO2InnO, 2025). It explores $H_2$ use in high-efficiency combined heat and power (CHP) plants, for which several alternative technologies are available (Yu et al., 2023). In particular, one of the objectives of the CO2InnO project is to develop tools for stakeholders (e.g., local authorities, NGOs, companies) to test various DES configurations and assess their technical feasibility, economic viability, and environmental impact.

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Numerous environmental studies have examined $H_2$-based DES, employing diverse scopes, applications, and methodologies (Yue et al., 2021; Eriksson & Gray, 2017; Valente, 2017; Zhang et al., 2025; Le et al., 2024a, 2024b; Sharma et al., 2023; Song et al., 2023; Sharma et al., 2020). While $H_2$ use is often considered climate-neutral at the usage stage, life cycle assessments (LCA) enable to reveal the whole picture of environmental impacts throughout the life cycle and don’t limit the assessment to $CO_2$ emissions, also taking into account abiotic resources and water consumption, human and ecological toxicity, etc (Zhao et al., 2020; Gerloff et al., 2021). Most LCA studies focus on fuel cells (FCs)-based DES due to their high efficiency and relative novelty (Chadly et al., 2022; Gandiglio et al., 2022; Peppas et al., 2021; Rossi et al., 2020; Zhang et al., 2017; Ren et al., 2016), typically evaluating impact reductions relative to fossil-fuel-based systems (Bionaz et al., 2022; Gandiglio et al., 2022). In contrast, $H_2$-based DES using internal combustion engines (ICE) remain underexplored, despite advantages such as greater contamination tolerance, technological maturity, and lower reliance on scarce materials (Stępień et al., 2021).

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Moreover, there is a growing trend towards integration of techno-economic analysis (TEA) with life cycle assessment (LCA). While LCA is the most comprehensive method for environmental assessment, it typically focuses on technology comparison using standardized functional units (e.g., “1 kWh of electricity produced”). TEA, often grounded in process and flow simulations, complements LCA by enabling more context-specific and operationally relevant assessments. Their combination is seen as a path toward more transparent, localized, and multi-criteria evaluations (Ferdous et al., 2023). However, integrated TEA-LCA approaches face methodological challenges, including limited access to technical, economic, and territorial data, as well as a lack of integrated operational tools (Ribon, 2020; Mahmud et al., 2021). Notably, recent integrated TEA-LCA propositions for $H_2$-based DES rely on proprietary software (Sharma et al., 2023; Le et al., 2024), potentially hindering both research progress and accessibility for resource-constrained users.

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The present work thus aims to conduct a TEA-grounded LCA of a $H_2$-ICE-CHP based DES to fill a gap in the contemporary literature, and contribute to the development of open-source approaches for combined TEA-LCA. It is built on a case study of the Offenburg municipality (Baden-Württemberg, Germany), which provided real data regarding several buildings energy requirements, with hourly resolution over a year. This municipality is a partner of the CO2InnO project, representative of small to medium cities wishing to decarbonize their electricity and mostly their heat demand, but which will not be connected to the future hydrogen backbone before 2035 at least. A simulated energy system has been developed in OpenModelica (Fritzson et al., 2020) following a TEA approach (Beerlage et al. 2024). Based on the different outlined scenarios, we conduct a comparative LCA taking into account the multiple possible pathways of energy and mass throughout the different system components, using dedicated Python procedures for flows’ reconstruction and the Brightway2 open-source framework for LCA, which is also Python-based (Mutel, 2018).

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## 2. GOAL AND SCOPE OF THE STUDY

### 2.1. OBJECTIVE & METHODOLOGY

The simulated energy system developed in OpenModelica (Fritzson et al., 2020) using a TEA framework (Beerlage et al., 2024) was used to generate several quantitative scenarios with hourly resolution of flows over a year. Based on the results outlined, a comparative cradle-to-grave LCA (compliant with ISO 14040/44) was conducted to evaluate the environmental impacts of the DES configurations identified in the TEA study. Mass and energy balances for each scenario were reconstructed from OpenModelica outputs using custom Python procedures within Jupyter Notebooks – see **Appendix**. The LCA was implemented using the Brightway2 open-source framework (Mutel, 2018) via the Activity Browser GUI (Steubing et al., 2020). The background system was modeled using the ecoinvent 3.10 database with the “allocation, cut-off by classification” approach. Life cycle impact assessment (LCIA) was initially performed using the EU-recommended EF v3.1 method, which includes 16 environmental indicators (Andreasi Bassi et al., 2023). For clarity and relevance, five key indicators were selected for detailed analysis – **Table 1**. Uncertainty analysis was conducted through Monte Carlo simulations, followed by global sensitivity analysis (GSA) to identify key contributors to result variability.

<table><caption>**Table 1.** Environmental indicators used to assess the decentralized ‘stand-alone’ energy system based on H₂ ICE-CHP.</caption><thead><tr><td>Environmental category</td><td>Impact indicator</td></tr></thead><tbody><tr><td>Climate change</td><td>Global Warming Potential (GWP100)</td></tr><tr><td>Ecotoxicity (freshwater)</td><td>USEtox / Ecotoxicity: freshwater (ECOTOX-FW)</td></tr><tr><td>Human toxicity (carcinogenic)</td><td>USEtox / Human toxicity: carcinogenic (HT-C)</td></tr><tr><td>Resource consumption</td><td>Abiotic Depletion Potential: ultimate reserves (ADP-UR)</td></tr><tr><td>Water use</td><td>User Deprivation Potential (UDP-WU)</td></tr></tbody></table>

### 2.2. FUNCTIONAL UNIT

The functional unit, defined as the "quantified performance of a product system used as a reference unit in life cycle assessment," is set as "one year of operation of the H₂ ICE-CHP-based DES, calibrated to meet heat demand." This definition reflects that the system's decentralized character is primarily related to heat supply, while electricity exchange with the grid remains readily achievable. The annual heat and electricity demand respectively amounted to 1,410 MJ and 297.3 MWh.

### 2.3. SYSTEM BOUNDARIES

The investigated system, illustrated in **Figure 1**, consists of three interconnected subsystems: electrical, thermal, and hydrogen. As the central element, the heating system was dimensioned based on economic optimization.

Several scenarios were designed to assess how variations in hydrogen storage (H2S) capacity affect the feasibility and efficiency of the H₂ ICE-CHP-based DES (Beerlage et al., 2024). H2S was selected as a key variable due to its practical and economic constraints, including large volume requirements and high capital costs, which present major barriers in real-world implementation. By varying storage volumes, the study sought to identify an optimal size that balances hydrogen production and consumption without increasing emissions or inefficiencies. Given the system's heat-oriented design, the primary goal was to meet thermal demand; electricity production via photovoltaic (PV) and wind turbines (WT) was primarily intended to support hydrogen generation rather than grid supply. This focus on heat demand influenced both control strategies and the sizing of key components such as the CHP unit and hydrogen storage. Scenario variables are detailed in **Table 2**.
<figure><img src="image_11.png" /><figcaption>**Figure 1.** Overview of the decentralized ‘stand-alone’ energy system based on H₂ ICE-CHP. Adapted from (Beerlage et al. 2024).</figcaption></figure>

<table><caption>**Table 2.** Overview of the scenarios specifications for the decentralized ‘stand-alone’ energy system based on H₂ ICE-CHP.</caption><thead><tr><th>Scenario</th><th>ICE-CHP<sub>el</sub><br>(kW)</th><th>ICE-CHP<sub>th</sub><br>(kW)</th><th>PV<br>(MWp)</th><th>WT<br>(MWp)</th><th>BAT<br>(kWh)</th><th>GSHP<br>(kW)</th><th>TES<br>(m³)</th><th>PEMEL<br>(MW)</th><th>H2S<br>(m³)</th></tr></thead><tbody><tr><td>PV + WT + BAT</td><td></td><td></td><td>0,9</td><td>0,5</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>WT + BAT</td><td>38</td><td>53,7</td><td>0</td><td>0,5</td><td>500</td><td>197</td><td>5</td><td>0.5</td><td>1000, 500,<br>100, 50,<br>30, 10, 1</td></tr><tr><td>PV + BAT</td><td></td><td></td><td>1,8</td><td>0</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PV</td><td></td><td></td><td></td><td></td><td>0</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Given the system's ability to exchange electricity with the grid, multiple system boundaries can be defined for impact assessment, each yielding distinct results and interpretations. To ensure a comprehensive evaluation, three boundary scenarios were considered. In the ‘Load’ (L) boundary, only the flows directly meeting the heat and electricity demands of the Offenburg buildings are included. The ‘Load + Excess’ (LE) boundary also accounts for the environmental burden of the electricity overproduction which is not consumed by the buildings. Lastly, the ‘Load + Excess - Grid(DE)’ (LEG) boundary consequently assumes that the excess electricity is exported to the GRID for other uses. It replaces an equivalent amount of electricity from the German electricity mix, thereby potentially crediting the system with avoided emissions.
# 3. INVENTORY ANALYSIS

## 3.1. DATA ORIGIN AND UNCERTAINTIES

As part of the CO2InnO project, the city of Offenburg (Germany) provided hourly heat and electricity demand data over one year for five buildings. These buildings were modeled as a single system with shared infrastructure, based on simplifying assumptions agreed upon by project partners. Weather inputs were sourced from the open-source CBA Clima tool (Betti et al., 2024), using data from nearby Strasbourg (France) due to the absence of a dedicated Offenburg dataset. Mass and energy flows derived from simulations depend on quality of the OpenModelica model. Since the system design centers on a ICE-CHP unit and a proton-exchange membrane electrolyzer (PEMEL), these components' models were validated in the original TEA study (Beerlage et al., 2024). The average relative deviation was ~10% between model and lab measurements for the PEMEL, and 1-4% compared to manufacturer specifications or the Hofner model for the ICE-CHP unit.

Uncertainty analysis incorporated the existing uncertainty data from the ecoinvent v3.10 database for the background system. As most ecoinvent processes required scaling to align with the TEA-modeled system components, additional uncertainties were introduced using triangular distributions and pedigree matrix-based methods for modified flows – Sections 3.3-3.6. Monte Carlo simulations were performed with 1,000 iterations for each scenario. It is important to note that the Activity Browser does not include uncertainty data for characterization factors (CFs); thus, uncertainty propagation in the results reflects only the background and adapted foreground systems.

## 3.2. MASS/ENERGY FLOWS RECONSTRUCTION

The OpenModelica model generates hourly outputs for a representative year, detailing electricity, heat, hydrogen production and consumption among other variables. However, due to the model's mathematical structure, it does not directly provide component-level input and output flows, which are required for LCA calculations. To address this, a Python script embedded in a Jupyter Notebook was developed to extract, process, and validate the data, enabling reconstruction of the necessary flows – see Appendix. The TEA model outputs incorporate the system's control logic (Beerlage et al., 2024). Thus, flow attribution can be performed on each hourly output of the model using a simple proportionality method as described in Eq. 1:

$$ flow(Source \rightarrow Sink) = \frac{Source}{Supply} \cdot \frac{Sink}{Demand} \cdot \min(Supply, Demand) \quad (Eq.1) $$

With:

* Source: starting point of the given flow

* Sink: ending point of the given flow

* Supply: sum of all source productions of the given flow

* Demand: sum of all sink consumptions of the given flow

The reconstructed flows, integrated over the year, are displayed as Sankey diagrams in Figure 2. As the system is primarily dimensioned to continuously meet heat demand, scenarios including PV exhibit substantial electricity overproduction, with 99.3–99.8% of PV-generated electricity exported to the grid (“PV→GRID” flow). Similar trends are observed for other electricity sources: 52.6–67.1% of WT electricity and 36.9–42.1% of ICE-CHP electricity is also surplus. H2S has limited effect on electricity overproduction, except in the 1 m³ case, where ICE-CHP overproduction increases to 74%. In contrast, scenarios without PV show significantly reduced overproduction, ranging from 0.8–24.4% for WT and 0.4–9.7% for ICE-CHP. In all cases, electricity overproduction increases as H2S decreases. Direct H₂ use in the ICE-CHP without intermediate storage follows the same inverse relationship with H2S, with values ranging from 27.9–48.0% in scenarios comprising PV and 14.8–65.5% in non-PV scenarios. These reconstructed mass and energy balances enable detailed LCA modeling of the system’s operational phase for each scenario. They also incorporate the efficiency assumptions from the TEA model in OpenModelica. Accordingly, the generic operational data in the ecoinvent datasets used for the
LCA (see **Sections 3.3 to 3.6**) were replaced when necessary to ensure consistency with the system behavior as modeled in the TEA approach.

<figure><img src="image_12.png" /><figcaption>**Figure 2.** Overview of the flows balance in the energy system for the different families of scenarios, based on the case of H2S = 50 m³. Flows' widths are scaled per flow type to make proportions inside a type of flow more readable.</figcaption></figure>

## 3.3. INTERNAL COMBUSTION ENGINE CHP

This central component was modeled using the "heat and power co-generation unit, X kW electrical, lean burn (CH)" activities from the ecoinvent database, with X values of 50, 200, 500, and 1,000 kWel. These datasets are extrapolations from an empirical dataset corresponding to a real 160 kWel ICE-CHP unit fueled by natural gas (Heck, 2007). The "lean burn" configuration was selected to avoid the inclusion of a catalytic converter, which is absent from the referenced datasets. The associated infrastructure datasets – for "common components" and for "electricity/heat only" components – share identical flow structures across capacities, enabling us to apply a scaling approach following Zhang et al. (2017), as formalized in Eq. 2:

$$ \frac{c_2}{c_1} = \left(\frac{x_2}{x_1}\right)^b \quad (\text{Eq.2}) $$

With:

* $C_1$: known flow of component 1

* $C_2$: unknown flow of component 2

* $X_1$: capacity (here, kW) of system 1

* $X_2$: capacity (here, kW) of system 2

* $b$: scaling factor

Having multiple reference datasets enabled separate fitting of each flow's evolution to determine the extrapolation method used in constructing these datasets. Using **Eq. 3**, all coefficients of determination (R²) exceeded 0.999, indicating an excellent fit:

$$ C(X) = a \cdot X^b \quad (\text{Eq.3}) $$
Where:

* C: flow value

* a: regression coefficient across the datasets (not just the ratio of 2 X values)

* X: capacity (here, kW)

* b: scaling factor

The determined a and b values were used to derive the flow values for the infrastructure dataset of our 38 $kW_{el}$ (+53.7 $kW_{th}$) ICE-CHP, allowing consistency with the existing datasets. The "storage production, 10,000 l" ecoinvent activity of the original datasets was excluded.

The ecoinvent database heat and power production activities apply an exergy-based allocation, assigning a larger share of environmental impact to electricity due to its higher exergy value. Given the heat-led nature of our studied system, an energy-based allocation was deemed more appropriate. When considering electricity expressed in kWh and heat in MJ, OpenModelica simulations indicate a relatively stable hydrogen consumption split of 83.93% to electricity and 16.07% to heat, which was used to allocate flows accordingly. Lubricating oil flow was estimated based on a typical value of 0.025 [0.01-0.05] × 10⁻³ kg/MJ of fuel input, which translates into 2.997 [1.1988 - 5.994] 10⁻³ kg / kg H₂. Biosphere flows related to natural gas in the original dataset were removed, except for nitrogen oxides (NOx) and particulate matter (PM). Although H₂ combustion can increase NOx emissions, ultra-lean conditions (λ = 2.6) at 1500 rpm in the model allow to postulate very low NOx emissions (Sterlepper et al., 2021; Verhelst & Wallner, 2009; Heffel, 2003), justifying a conservative emission assumption of 10-50 ppm. PM emissions, primarily originating from lubricating oil (Worton et al., 2014), were scaled using the PM-to-lubricating oil ratio from the ecoinvent dataset. Due to CHP modulation mode in the OpenModelica model, annual electricity and heat yields vary by scenario, dynamically affecting ICE-CHP unit consumption based on an assumed 25-year lifetime.

Since the ICE-CHP is the endpoint of H₂ mass flows, this component completely integrates the modeling of the PEMEL and H2C components through its fuel supply – see **Section 3.6**. PEMEL and H2C thus don't appear directly in the contribution analysis in **Section 4.2**, unlike the H2S component modeled as an infrastructure with an annual unit consumption.

## 3.4. ELECTRICAL SYSTEM

### 3.4.1. PV – Photovoltaic

We adopted a mixed approach regarding PV construction/decommission of 0.9 and 1.8 MWp PV installations present in the investigated scenarios. Indeed, flows associated to the mounting system and the panel wafers are expected to vary linearly with installed capacity. We controlled this assumption by the analysis of the following ecoinvent activities when normalized to 1 kWp:

* "photovoltaic slanted-roof installation, 3 kWp, multi-Si, panel, mounted, on roof (CH)"

* "photovoltaic plant construction, 570 kWp, multi-Si, on open ground (GLO)"

The normalized flow ratios for the two sub-components of the PV system were both 0.987, indicating only a 1.3% flow reduction as installed capacity increased from 3 to 570 kWp. For these flows, linear scaling was applied. In contrast, other PV sub-components exhibited non-linear scaling with capacity, and each was adjusted using a dedicated b coefficient, following the method of Zhang et al. (2017) – see **Appendix**. Additionally, the "market for electricity, low voltage" dataset for Switzerland (CH) was replaced with its German (DE) equivalent. All base datasets used already include end-of-life processes.

The resulting PV electricity production process was based on the "electricity production, photovoltaic, 3 kWp slanted-roof installation, multi-Si, panel, mounted (low voltage, DE)" ecoinvent activity. We adapted the flow for the consumption of our 0.9 or 1.8 MWp PV unit based on the annual yields provided by the OpenModelica simulation, while keeping the same projected lifetime (30 years). The "market for tap water (EUR, CH excluded)" activity, representing the cleaning of the PV panels, was scaled linearly.
### 3.4.2. WT - Wind turbine

The construction and decommissioning of WT was modeled using the ecoinvent activity "wind power plant construction, 800 kW (fixed + moving parts, GLO)" as a reference. To adapt this process to a 500 kW WT, scaling was performed following a phenomenological approach adapting the method of Zhang et al. (2017), described in the previous section. A scaling factor $b = 0.83$ was determined through interpolation (Eq. 3) of impact scores derived from three ecoinvent datasets:

* "wind power plant construction, 800 kW (fixed+moving parts, GLO)"

* "wind turbine construction, 750 kW (GLO)"

* "wind turbine construction, small-scale, 6 kW (DE)"

This factor ensures consistency between the impact results of resulting 500 kW dataset and the trends observed in the available ecoinvent datasets. One WT unit of 500 kW is thus considered equivalent to 0.677 WT unit of the 800 kW WT. A triangular uncertainty was set on this coefficient based on the linear scaling case ($b = 1$) and the estimation from the comparison of exact technosphere matches for the 6, 750, 800 kW WT datasets ($b = 0.74$).

The operational electricity generation by the WT was modeled using the "electricity production, wind, <1 MW turbine, onshore (high voltage, DE)" activity. The unit consumption was adjusted to match the annual electricity yield from the 500 kW WT unit simulated in OpenModelica, assuming a 20-year service life. Voltage transformation losses from high to low voltage were accounted for using a coefficient of 0.9635, based on the "electricity voltage transformation" processes available in ecoinvent.

### 3.4.3. BAT - Battery

Battery production was modeled using the activity "market for battery, Li-ion, NMC811, rechargeable, prismatic (GLO)", linearly scaled to reflect a 500 kWh BAT. This scaling was based on the reported specific energy density of 0.149 kWh/kg. End-of-life treatment was approximated using the "market for used Li-ion battery (GLO)" activity, scaled identically.

Since no explicit use-phase activity exists in the ecoinvent database, we developed a custom use-phase model derived from the OpenModelica simulation results. As energy losses are already accounted for, our use-phase modeling calculates the unit consumption per kWh stored and retrieved (Porzio & Scown, 2021). This was done through a scenario-wide analysis of battery-related variables, including state of charge (SoC) evolution and charge/discharge behavior. The SoC evolution was relatively consistent within scenario families, with values typically ranging between 0.19 and 0.69. Although extreme SoC values (e.g., 0.10 and 0.90) were occasionally reached, the majority of operation remained in the mid-range window, yielding an average usable capacity of 49.7 ± 0.3% of the installed BAT. Similarly, distributions of charge/discharge rates (C-rates) showed little variance across scenarios, with a standard deviation of 0.33 ± 0.06. However, average C-rate values differed: "WT + BAT" scenario family showed a mean C-rate of 0.14, while those including PV had higher values, between 0.36 and 0.38. This difference reflects the increased use of storage capacity in PV scenarios due to the system's electricity overproduction. These patterns correspond to low to moderate operational stress, suggesting favorable battery longevity. Therefore, we adopted a projected lifetime of 2600 ± 1000 full equivalent cycles to 80% of initial capacity, which is consistent with high-end estimates for NMC811 chemistries (Preger et al., 2020; Ecker et al., 2014). Assuming full cycles defined between 10% and 90% SoC, the expected total energy throughput over the battery's life was calculated to define the unit consumption per kWh discharged, with a triangular uncertainty.

## 3.5. HEATING SYSTEM

### 3.5.1. GSHP - Ground source heat pump

Heat pump production was modeled using the ecoinvent datasets "heat pump production, brine-water, 10 kW (CH)" and "borehole heat exchanger production, 150 m (CH)", linearly scaled to reflect a 197 kW GSHP in the absence of data for scaling. When applicable, process geographies were replaced with German equivalents. The
heat pump dataset includes manufacturing and partial end-of-life (plastics disposal via "market for waste plastic, mixture"), which was removed. A separate "treatment of heat pump" activity was created, covering copper, steel, refrigerant, and PVC waste flows. The borehole component was assumed to remain in place at end-of-life due to limited data and practical constraints. Heat production modeling used the "heat production, borehole heat exchanger, brine-water heat pump 10 kW (CH)" activity, supplemented with the added disposal flow. Electricity use per MJ of heat was taken from the OpenModelica simulation.

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### 3.5.2. TES – Thermal energy storage

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This system component is absent from the flow exchanges in the scenarios modeled in the OpenModelica simulation, despite being present as an available infrastructure. We thus decided to consider it absent in our study.

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## 3.6. HYDROGEN SYSTEM

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### 3.6.1. PEMEL – Proton exchange membrane electrolyzer

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The electrolyzer component was based on the LCI from Gerloff (2021), harmonizing previous datasets and scaled for a 1 MW system using Zhang et al. (2016) method described in **Section 3.3**. We applied the same approach to construct and decommission our 0.5 MW PEMEL, using scaling coefficients of 0.7 for Balance of Plant and 0.88 for Stack flows. Hydrogen production was also based on Gerloff (2021), using the premise library version with uncertainties (Sacchi et al., 2022). Variations accounted for different electricity sources from the OpenModelica model. Electricity consumption per kg H₂ was taken from the simulation, with a 10% triangular uncertainty reflecting deviations in Beerlage et al. (2024). Water use in the model includes only stoichiometric needs, while Gerloff’s LCI accounts for additional real-world use (cooling, etc.), increasing water consumption by 55%. We thus scaled OpenModelica water consumption accordingly.

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### 3.6.2. H2C – Hydrogen compressor

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The compressor component was modeled using the cradle-to-grave LCI from Ghandehariun & Kumar (2016), with German (DE) flows applied when possible. Electricity consumption per kg of H₂ compressed was adjusted based on OpenModelica simulation data. Since electricity demand depends on the hourly state of hydrogen storage, the annual apparent electricity consumption (kWh/kg H₂ compressed) varies across scenarios.

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### 3.6.3. H2S – Hydrogen storage

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Hydrogen is stored as gas at a maximum of 80 bar in various sizes – **Table 1**. The LCI is based on the ecoinvent "storage production, 10,000 l" activity, representing 10 m³. This dataset includes end-of-life flows and assumes 100,000 hours of operation (about 11–12 years). We updated this to 20 years to align with the OpenModelica simulation and derived annual unit consumption accordingly. Other storage sizes were linearly scaled.

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# 4. RESULTS AND DISCUSSION

## 4.1. LCIA MONTE CARLO RESULTS

**Figure 3** summarises the results of the impacts calculated for the different 28 scenarios, per scenario family and per impact perimeter considered. H₂ storage capacities (H2S) within each scenario family evolve as described previously in **Section 2.3, Table 2**.

### 4.1.1. ‘Load’ boundary Monte Carlo

In this case, only the flows directly fulfilling the heat and electricity demand of the Offenburg buildings are considered. Due to uncertainties, no clear conclusion can be drawn regarding human toxicity (carcinogenic) or ecotoxicity (freshwater) impacts across scenarios. These impacts mostly hover near zero, except for the unrealistic H2S = 1000 m³ cases, which show greater result variability. For climate change and resource consumption, ‘WT + BAT’ scenarios consistently show higher impacts than those with PV. The highest impacts occur at H2S = 1 and 1000 m³, while capacities between 10 and 100 m³ yield the lowest, with no significant differences within this range in the same scenario family. Water use impacts follow a similar pattern, with ‘PV + WT + BAT’ scenarios generally having the lowest values. Since the 10–100 m³ H2S range is the only realistic option, it can be concluded that H2S dimensioning in real conditions likely has minimal effect on environmental performance, with scenario family playing a larger role. This is supported by subsequent cases showing near-flat impact trends and differences mainly due to scenario families and uncertainties.

### 4.1.2. ‘Load + Excess’ boundary Monte Carlo

When the system’s excess electricity – actually produced but not used by the Offenburg buildings – is included in the environmental assessment, the overall scenario family ranking is reversed. The ‘WT + BAT’ scenarios now show the lowest environmental impacts across all categories and exhibit less result variability than PV-including alternatives, due to low electricity overproduction reported in **Section 3.2**. This family uniquely displays negligible human toxicity (carcinogenic) and ecotoxicity (freshwater) impacts and is also the only one where the unrealistic H2S = 1000 m³ scenario causes a notable increase in both impact and uncertainty. Within PV-including scenarios, two subgroups emerge: those with 0.9 MW installed PV (‘PV + WT + BAT’) and those with 1.8 MW (‘PV + BAT’ and ‘PV’). The latter group produces almost identical results across categories, except for slightly lower resource use impacts in the BAT-excluded scenarios. Meanwhile, the ‘PV + WT + BAT’ family occupies a middle ground in both impact level and dispersion. As noted in **Section 3.2**, PV-heavy scenarios generate considerable electricity surpluses fed into the GRID. These findings support a second key conclusion: in H2 ICE-CHP-based DES, how excess electricity is accounted for can fundamentally alter the comparative evaluation of scenarios.

### 4.1.3. ‘Load + Excess - Grid(DE)’ boundary Monte Carlo

When excess electricity is assumed to be fed into the GRID and replaces electricity that would otherwise be produced by the German electricity mix, the resulting avoided impacts are considered. In this third assessment boundary, the environmental burden of the electricity overproduction is subtracted from the total (‘Load + Excess’) by modeling these kWh as if they had been generated using the national mix. Because the German grid mix is currently associated with high impacts across all categories, PV-including scenarios – producing large electricity surpluses – lead to substantial avoided impacts, especially in climate change, resource consumption, and water use. Among these, scenarios based solely on PV (in addition to the ICE-CHP) result in greater avoided impacts than the ‘PV + WT + BAT’ family, due to higher installed PV capacity. However, this benefit comes at the cost of increased result dispersion. In particular, the human toxicity (carcinogenic) and ecotoxicity (freshwater) categories exhibit wide uncertainty ranges nearly centered around zero, making the interpretation of avoided impacts in these categories less reliable.
<figure><img src="image_13.png" /><figcaption>**Figure 3.** Environmental impacts with uncertainties of the H₂-based energy system, by scenario family. Within each family, progression follows decreasing H₂ storage capacity in m³ (1000, 500, 100, 50, 30, 10, 1). The rows represent the environmental category tested. Columns represent the impact boundary. The solid lines represent the median values of the Monte Carlo simulations. The dotted lines delimit the quartiles ± 25% around the median values.</figcaption></figure>

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### 4.1.4. LCIA results synthesis

The Table 3 provides a synthetic presentation of the global results (scenarios aggregated) for the realistic H2S values – i.e. H2S = 10–100 m³. It highlights how the system boundaries considered highly shift the interpretation that can be made on the environmental impacts.

<table><caption>**Table 3.** Overview of the LCIA results for the decentralized ‘stand-alone’ energy system based on H₂ ICE-CHP.</caption><thead><tr><th>Impact Indicator</th><th>Unit</th><th>Load</th><th>Load + Excess</th><th>Load + Excess - Grid (DE)</th></tr></thead><tbody><tr><td>GWP100</td><td>(kg CO₂ eq) [· 10³]</td><td>67.5 ± 33</td><td>832 ± 129</td><td>-96532 ± 5526</td></tr><tr><td>ECOTOX-FW</td><td>(CTUe) [· 10³]</td><td>633 ± 465</td><td>80287 ± 24462</td><td>-418538 ± 385035</td></tr><tr><td>HTOX-C</td><td>(CTUh) [· 10⁻⁴]</td><td>4.8 ± 2.5</td><td>152 ± 86</td><td>-2360 ± 2471</td></tr><tr><td>ADP-UR</td><td>(kg Sb eq)</td><td>1.36 ± 0,21</td><td>10.6 ± 2,2</td><td>-1311.3 ± 361,1</td></tr><tr><td>UDP-WU</td><td>(m³ world eq deprived) [· 10³]</td><td>34 ± 7</td><td>6228 ± 2819</td><td>-20072 ± 3927</td></tr></tbody></table>

## 4.2. CONTRIBUTION ANALYSIS

**Figure 4** summarises the results of the contribution analysis for the different 28 scenarios, per scenario family and per impact perimeter considered. H₂ storage capacities (H2S) within each scenario family evolve as described previously in **Section 2.3, Table 2**.

### 4.2.1. ‘Load’ boundary contributions

Excluding excess electricity from the assessment enables a focused analysis of subsystem contributions to meeting the heat and electricity demands of Offenburg’s buildings. In this context, the ICE-CHP component – grouped with PEMEL and H2C due to the modeling structure – shows its lowest contribution (~3%) in the climate change and human toxicity (carcinogenic) categories, and its highest (~38%) in human toxicity (carcinogenic), ecotoxicity (freshwater), and water use. Within the realistic H2S = [10–100] m³ range, ICE-CHP contributions remain stable across PV-including scenarios (normalized standard deviation, N<sub>std</sub> = 2–5%), except for human toxicity (carcinogenic), where variability increases to ~21%. In contrast, this category’s N<sub>std</sub> drops to ~2% in the ‘WT + BAT’ family, while other families range between 8–13%.

Most of other subsystems (PV, WT, BAT, GSHP, GRID) follow similar patterns in this realistic H2S interval. Their contribution variability is highest in the human toxicity (carcinogenic) category (N<sub>std</sub> = 12–31%), with larger deviations linked to PV presence. For the remaining environmental categories, variability remains low (N<sub>std</sub> = 1–8%) across scenarios. PV reaches its maximum contribution (~16%) in water use and its minimum (~0.3%) in resource consumption. WT, by contrast, has a minimum contribution (1–2%) consistent across categories – except for a ~3% minimum contribution in resource consumption – and its maximum of ~23% is found in human toxicity (carcinogenic). BAT, GSHP, and GRID reach their lowest contributions (0.6–2%) in this same environmental category. BAT share peaks (~33%) in resource consumption, while GSHP and GRID show maximum contributions in climate change (~47% and ~59%, respectively).

H2S minimal contribution is consistently below 0.3% across several environmental categories but reaches a maximum of ~92% in human toxicity (carcinogenic). Even in the realistic H2S [10–100] m³ interval, contribution variability remains high with complex interplays. For resource consumption, N<sub>std</sub> changes between ~59% (with BAT) and ~83% (without BAT). For human toxicity (carcinogenic), N<sub>std</sub> depends mostly on PV, increasing from ~59% in ‘WT+BAT’ scenario family to ~71% in PV-including scenarios. In contrast, variability for climate change, ecotoxicity (freshwater), and water use is relatively stable, with N<sub>std</sub> around ~81%, ~76%, and ~79%, respectively.

A synthesis of the results for the realistic interval of H2S values is given in **Table 4**. Overall, ICE-CHP, GSHP and GRID tend to be key contributors to the impact across all environmental categories, while PV, WT, BAT, and
H2S reach significant contribution in only one or two categories out of five, each of them exhibiting a different configuration.

<table><caption>**Table 4.** Average contributions (in percentages) to the impact on the H2S = [10–100] m³ interval, across families of scenarios for the ‘Load’ boundary.</caption><thead><tr><th>Contributor</th><th>GWP100</th><th>ECOTOX-FW</th><th>HTOX-C</th><th>ADP-UR</th><th>UDP-WU</th></tr></thead><tbody><tr><td>ICE-CHP</td><td>12.3 ± 3.6</td><td>33.2 ± 6.4</td><td>29.0 ± 6.3</td><td>21.4 ± 3.0</td><td>29.0 ± 7.2</td></tr><tr><td>PV</td><td>1.0 ± 0.2</td><td>10.0 ± 2.2</td><td>2.9 ± 1.4</td><td>0.5 ± 0.1</td><td>13.1 ± 2.4</td></tr><tr><td>WT</td><td>3.0 ± 0.1</td><td>6.6 ± 1.7</td><td>15.1 ± 2.6</td><td>4.0 ± 0.7</td><td>3.4 ± 1.1</td></tr><tr><td>BAT</td><td>4.9 ± 2.0</td><td>8.8 ± 2.4</td><td>6.9 ± 1.3</td><td>23.4 ± 10.7</td><td>17.1 ± 6.6</td></tr><tr><td>GSHP</td><td>30.4 ± 3.0</td><td>20.6 ± 2.6</td><td>17.4 ± 3.9</td><td>26.2 ± 2.3</td><td>20.0 ± 3.1</td></tr><tr><td>H2S</td><td>2.9 ± 2.5</td><td>6.0 ± 4.5</td><td>23.7 ± 16.9</td><td>0.5 ± 0.3</td><td>2.0 ± 1.5</td></tr><tr><td>GRID</td><td>48.6 ± 8.2</td><td>22.7 ± 8.8</td><td>15.1 ± 7.3</td><td>32.1 ± 13.0</td><td>24.6 ± 11.9</td></tr></tbody></table>

### 4.2.2. ‘Load + Excess’ boundary contributions

The reversal in scenario ranking between the L and L+E boundaries stems from differing levels of electricity overproduction. In the ‘WT + BAT’ cases, excess electricity is limited, contributing only 20–25% to the total impact. This keeps results relatively stable across boundaries. In contrast, PV-based scenarios generate far more electricity than required, with overproduction electricity sometimes representing up to 99% of the total impact. **Table 5** summarizes outcomes for the realistic H2S values.

<table><caption>**Table 5.** Average contributions (in percentages) to the impact on the H2S = [10–100] m³ interval, per families of scenarios for the ‘Load + Excess’ boundary. Families only comprising PV (apart from the ICE-CHP) were aggregated due to the proximity of the results.</caption><thead><tr><th>Scenario family</th><th>Contributor</th><th>GWP100</th><th>ECOTOX-FW</th><th>HTOX-C</th><th>ADP-UR</th><th>UDP-WU</th></tr></thead><tbody><tr><td rowspan="2">PV + WT + BAT</td><td>Load</td><td>9.8 ± 0.2</td><td>1.3 ± 0.1</td><td>6.1 ± 1.2</td><td>14.6 ± 0.3</td><td>0.90 ± 0.02</td></tr><tr><td>Excess</td><td>90.2 ± 0.2</td><td>98.7 ± 0.1</td><td>93.9 ± 1.2</td><td>85.4 ± 0.3</td><td>99.10 ± 0.02</td></tr><tr><td rowspan="2">WT + BAT</td><td>Load</td><td>98.5 ± 1.2</td><td>95.8 ± 3.3</td><td>92.9 ± 5.8</td><td>96.1 ± 3.0</td><td>96.3 ± 2.8</td></tr><tr><td>Excess</td><td>1.6 ± 1.2</td><td>4.2 ± 3.3</td><td>7.1 ± 5.8</td><td>3.9 ± 3.0</td><td>3.7 ± 2.8</td></tr><tr><td rowspan="2">PV + BAT &amp; PV</td><td>Load</td><td>4.1 ± 0.1</td><td>0.41 ± 0.02</td><td>1.3 ± 0.4</td><td>7.2 ± 0.2</td><td>0.32 ± 0.01</td></tr><tr><td>Excess</td><td>95.9 ± 0.1</td><td>99.6 ± 0.02</td><td>98.7 ± 0.4</td><td>92.8 ± 0.2</td><td>99.68 ± 0.01</td></tr></tbody></table>

### 4.2.3. ‘Load + Excess - Grid(DE)’ boundary contributions

When avoided impacts are considered, the ratio of the H₂-ICE-CHP-based DES impacts to avoided German mix impacts ranges from 120–200 for climate change, 4–10 for freshwater ecotoxicity, 14–23 for human toxicity (carcinogenic), 130–160 for resource use, and 3–7 for water use. Even in the context of a French case study, these benefits are likely to occur, despite the French electricity mix being significantly cleaner. Indeed, the French/Germany electricity mix impact ratios for these environmental categories are respectively 0.22; 0.41; 0.53; 0.85; 1.42 (ecoinvent v3.10), suggesting an environmental benefit in the order of magnitude of 27–44 for climate change, 2–4 for freshwater ecotoxicity, 7–12 for human toxicity (carcinogenic), 110–136 for resource use, and 4–10 for water use. However, these results should be considered with caution. As the electricity mix gets cleaner, the decline of the environmental burden will induce a reduction of the avoided impacts as calculated here. This shift could be explored in future prospective LCAs, pending availability of national scenarios in tools like the premise library (Sacchi et al. 2022). Moreover, large electricity overproduction may strain the grid if demand isn’t present when transfer occurs. Although the TEA OpenModelica simulations had hourly resolution, the impact assessment integrates results annually and assumes uninterrupted transfer to the grid throughout the year.
<figure><img src="image_14.png" /><figcaption>**Figure 4.** Subsystem contributions to the impact of the H₂-based energy system, by scenario family. Within each scenario family, progression is by decreasing H₂ storage capacity in m³ (1000, 500, 100, 50, 30, 10, 1). The rows represent the environmental category tested. The columns represent the impact boundary. In the “Load + Excess - Grid (DE)” column, the avoided impacts (negative values) of GRID are normalized to the sum of “Load + Excess”.</figcaption></figure>

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### 4.2.4. Contribution analysis synthesis

Within the 'Load' boundary, the GRID component's dominance across environmental categories suggests that system-level optimization should prioritize reducing reliance on the German electricity mix year-round. Due to substantial electricity overproduction at certain times, two complementary strategies are proposed. For short-term LOAD demand fluctuations, increased short-term storage is needed, implying a larger BAT component. For seasonal variations, greater long-term storage is required, necessitating an expanded H₂ system. Indeed, fully using electricity overproduction first requires scaling up the PEMEL to produce more H₂. Given the low environmental impact of H2S (as shown in Table 4), maximizing its capacity is recommended. Stored H₂ quantity can be further increased by raising compression from 80 to 200 bar, following industrial norms, with additional electricity required sourced from overproduction. Using the supplementary H₂ would also require a larger ICE-CHP. Future TEA simulations with OpenModelica should examine these scenarios for feasibility and enable further LCA comparisons, advancing multi-criteria and multi-boundary optimization.

## 4.3. GLOBAL SENSITIVITY ANALYSIS

The Activity Browser software has a dedicated module for global sensitivity analysis (GSA) computations which is based on the δ-measure (Cucurachi et al. 2021, Borgonovo & Apostolakis, 2001). The δ value quantifies how much the variation of a given process is able to move the results distribution. This module was used to perform GSA upon the Monte Carlo simulations for each scenario and impact indicator, with a cut-off value of 0.01 for both the technosphere and biosphere.

### 4.3.1. Rank correlation and sets similarity

An initial analysis of GSA results used Kendall's τ coefficient (Kendall, 1938) to assess how process δ values rank across scenarios. Most τ values fell within [-0.1, 0.2], indicating weak correlation in process influence across scenarios. Two exceptions were water use and ecotoxicity (freshwater) in the L+E boundary, though their complex patterns are beyond this study's scope. Overall, the low correlation is likely due to many processes having similar δ values, allowing small differences to shift their ranking between scenarios.

We thus used a cruder indicator, the Jaccard similarity index (Jaccard, 1912), insensitive to ranking, to map sensitivity across scenarios. Figure 5 highlights clear patterns of similarity and divergence. Under the L boundary, most scenarios within the same family show low similarity in process-level sensitivity, except for those in the H2S = 10–100 m³ interval, which display higher overlap – consistent with Section 4.2.1's findings of minimal variation in contributors. Boundaries expansion (L+E and L+E-G) reveal a clearer separation of the 'WT + BAT' scenarios from the other families, driven by the role of PV and GRID in contribution variability, as discussed in Sections 4.2.2 and 4.2.3.
<figure><img src="image_15.png" /><figcaption>**Figure 5.** Jaccard similarity across scenarios for the hydrogen-based energy system. Values near 1 indicate strong overlap between GSA outputs by pairs of scenarios, while values near 0 indicate low overlap.</figcaption></figure>

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### 4.3.2. Spread of δ values and maximums

Given the sensitivity of contributing processes and their δ values, it is useful to examine both the maximum and range (max–min) of δ, by process and overall. **Table 6** summarizes global statistics across boundaries, scenario families, and impact indicators. Overall, aggregation choices yield similar δ value statistics.

<table><caption>**Table 6.** Global statistics of the δ values resulting from the GSA. Values are scaled by a $10^2$ factor for readability.</caption><thead><tr><th></th><th>Aggregation</th><th>Mean</th><th>Median</th><th>Std</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td rowspan="3">Boundary</td><td>Load</td><td>6.7-7.5</td><td>6.3-7.0</td><td>1.7-6.6</td><td>1.3-2.8</td><td>13.2-70.1</td></tr><tr><td>Load + Excess</td><td>6.8-13.7</td><td>6.6-7.4</td><td>1.7-21.2</td><td>1.1-4.1</td><td>13.6-78.0</td></tr><tr><td>Load + Excess - Grid (DE)</td><td>6.8-7.9</td><td>6.6-7.2</td><td>1.7-4.9</td><td>1.3-3.3</td><td>12.6-47.2</td></tr><tr><td rowspan="4">Scenario Family</td><td>PV + WT + BAT</td><td>6.7-11.8</td><td>6.3-7.2</td><td>1.7-18.4</td><td>1.3-3.2</td><td>12.7-76.3</td></tr><tr><td>WT + BAT</td><td>6.8-7.2</td><td>6.6-7.0</td><td>1.7-3.0</td><td>1.4-2.5</td><td>13.5-48.6</td></tr><tr><td>PV + BAT</td><td>6.8-13.5</td><td>6.6-7.3</td><td>1.7-21.2</td><td>1.8-4.1</td><td>12.6-78.0</td></tr><tr><td>PV</td><td>6.8-13.7</td><td>6.5-7.4</td><td>1.7-21.2</td><td>1.1-3.7</td><td>13.6-77.6</td></tr><tr><td rowspan="5">Impact Indicator</td><td>GWP100</td><td>6.7-7.9</td><td>6.3-7.1</td><td>2.1-4.9</td><td>1.1-2.8</td><td>22.2-48.6</td></tr><tr><td>ECOTOX-FW</td><td>7.0-9.0</td><td>6.8-7.4</td><td>1.7-6.3</td><td>1.3-4.1</td><td>13.1-35.2</td></tr><tr><td>HTOX-C</td><td>6.8-7.2</td><td>6.7-7.1</td><td>1.7-2.1</td><td>1.5-2.9</td><td>12.6-27.2</td></tr><tr><td>ADP-UR</td><td>6.8-7.2</td><td>6.6-6.9</td><td>2.1-4.0</td><td>1.4-2.7</td><td>23.7-41.4</td></tr><tr><td>UDP-WU</td><td>6.8-13.7</td><td>6.6-7.2</td><td>2.2-21.2</td><td>1.4-3.7</td><td>23.3-78.0</td></tr></tbody></table>

Greater dispersion of δ values is found in the L+E boundary, especially in PV-based scenarios and particularly for the water use category. A standout flow is the biosphere flow "Water // electricity production, from 0.9/1.8 MW PV" representing PV panel cleaning, which shows the highest δ values (up to 0.70–0.80), while the other flows are limited to δ values ≤ 0.50. Furthermore, this flow has low δ spread (δ = 0.74–0.78) within L+E boundary, making it the clear dominant source of results variability in water in this case. Its influence remains under the L+E-G boundary (δ = 0.16–0.38), and becomes highly fluctuant within the L boundary (δ = 0.43–0.70), reflecting the differing role of PV across scenarios and boundaries. Actual needs for panel cleaning are highly susceptible to local conditions of the PV infrastructure, thus field experiments should allow better tailoring of the LCA model. Outside this exception, processes with high δ values often show the widest spreads, as seen in **Figure 6**. The technosphere flow "reinforcing steel // storage production, 10,000 l" (H2S annual unit consumption) has the broadest δ range and second-highest δ values, with spreads of 0.42 (climate change) and 0.34 (water use), and δ peaks of 0.46-0.48 and 0.36-0.39, respectively. This is mainly because the dataset is old, which leads to a wide distribution of possible values within the uncertainties quantified by the pedigree matrix approach. Other influential elements include flows tied to the German electricity mix (e.g., "CO₂ [...] lignite/hard coal" "distribution network", "copper cathode", "Anthracene") or GSHP production, which we modeled as produced in Germany thus also related to the German mix. The uncertainty in the German mix dataset stems from the inherent complexity of large-scale systems like a national electricity grid. It is further compounded by the use of annual temporal averaging for national mixes that includes diverse electricity generation technologies, which is associated to high variations of impact throughout the year. In this context, using a representative dynamic dataset of the annual German electricity mix could reduce the associated uncertainties.

Thus, the top two contributors to LCIA variability (water use for PV panel cleaning & steel for H2S unit production) are driven by the foreground scenario, while most others are linked to background data uncertainties. A few dominant processes, whose importance varies along foreground scenarios, shape variability in water use, climate change, and resource consumption. Conversely, human toxicity (carcinogenic) and ecotoxicity (freshwater) indicators depend on a broader set of processes with individually smaller influence.
<figure><img src="image_16.png" /><figcaption>**Figure 6.** Processes with the highest spread of δ values across scenarios and impact indicators, per scenario family and boundary. Prefixes T/B respectively indicate a technosphere or biosphere flow.</figcaption></figure>

## 4.4. TEA-LCA OPEN-SOURCE CONTRIBUTION

As outlined in the **Introduction section**, this study also investigated the integration or combined use of techno-economic assessment (TEA) and life cycle assessment (LCA) for localized, multi-criteria evaluations using open-source tools. A recent publication synthesized ten key recommendations for research software in the energy domain (Ferenz et al., 2025). The publication identify a typical profile of energy researchers, which corresponds to the authors of the present study – scientists from diverse disciplinary backgrounds without formal training in software engineering. Among the Ferenz et al. recommendations, numbers 2, 4 9, and 10 pertain to medium to long-term software development practices and are thus outside the scope of the current work. The remaining recommendations are directly relevant and addressed in this section. These include: (1) reusing and extending existing software when possible; (3) ensuring openness and findability of the software; (5) considering reusability of research software; (6) maintaining a simple architecture; (7) providing thorough documentation; and (8) conducting adequate software testing.

A widely accessible overview of process simulators used in engineering and research is available on Wikipedia (2025). However, only ~8% of listed tools are fully open-source, and even fewer support full interoperability (3). For TEA-LCA integration, tools must be general-purpose and flexible (5), which to the authors’ knowledge narrows viable candidates to Pyomo and OpenModelica. Given in-house expertise and time constraints, the TEA model was developed using the Modelica language and OpenModelica compiler (1). The resulting simulation model is modular and scalable (6), supporting integration of diverse energy producers, consumers, and storage technologies. It is openly available on GitHub with initial documentation (3, 7) – see **Appendix**. Nonetheless, OpenModelica presents specific challenges. Internally, limited documentation and programming support (7) led to unexplained compiler failures and debugging difficulties. Externally, integration with platforms like Brightway or Activity Browser is feasible but hampered by language incompatibilities, complicating workflows (6).

To address interoperability issues (1, 5, 6), this work contributes modular Python procedures that: clean and preprocess OpenModelica outputs; extract relevant variables for the H₂-based ICE-CHP DES model; allocate energy and mass flows across any number of modeled scenarios – see **Section 3.2**. These procedures are embedded in Jupyter Notebooks, supporting internal documentation and reproducibility with verification computations available within (7). They were tested across scenarios of increasing complexity to ensure robustness (8). All materials are openly available via GitHub – see **Appendix (3)**. Furthermore, **Sections 3.3–3.6** describe how scaling factors were derived for most infrastructure components to align LCI datasets with the ecoinvent database, with thorough documentation available from the repositories linked in **Appendix (7)**. Although not open-source,
ecoinvent was selected for its general applicability and widespread use in LCA (1, 3). However, due to limitations in Activity Browser documentation (7) and programming capacity, modifying infrastructure and operational flows in LCA still requires manual input. Future work by more proficient developers could build on the current Python modules to enable automatic integration between OpenModelica and the Activity Browser GUI for Brightway, potentially via scenario-specific foreground flow-exchange files (1, 5).

A long-term, more robust solution would likely involve the development of a fully Python-based TEA-LCA modeling framework (6), following precedents in biorefinery simulation (Shi & Guest, 2020). This would support a transition toward Pyomo, a Python-native optimization library well-aligned with Brightway2 (1, 3). Pyomo underpins 18% of open-source tools listed by the Open Energy Modelling Initiative (Openmod, 2025), including the very recent AdOpT-NET0 (Wiegner et al., 2025), which targets multi-energy system optimization. Exploring its integration with Brightway2 offers a promising avenue for future TEA-LCA research (1, 5).
# 5. CONCLUSIONS

This study provides a thorough LCA of a H₂-based DES using an ICE-CHP, covering building demands under multiple scenarios based on real data of the Offenburg city (Germany). By combining TEA modeling in OpenModelica with customized Python procedures and a transparent LCA approach with Brightway/Activity Browser, we have contributed to the effort towards reproducible frameworks for open source multi-criteria sustainability assessment of complex energy systems at the local scale. Two specific methodological contributions call for further optimization: (a) flow allocation routines, to use when flows between system's components are not explicit outputs of the TEA simulation, and (b) scaling factors for adapting infrastructure sizes in new scenarios – focusing on consistency with the ecoinvent database.

Wide distributions of results are observed, even though 50% of the values are comparatively more tightly clustered. Through Monte Carlo sampling for uncertainty computation and Global Sensitivity Analysis, we found an unexpected weight of PV panel cleaning in the water use category. This process showed the highest δ values in the global sensitivity analysis, highlighting its outsized influence on results variability. The GSA also revealed that foreground scenario structure (e.g., presence of PV or sizing of H2S) and background database uncertainty jointly shape impact variability with distinct influences through lack of: regionalization (water for PV cleaning); recent data (H2S production); and dynamic accounting (German electricity mix contributors). A key insight from the GSA was that different environmental categories are dominated by different patterns: climate change, water use, and resource consumption are driven by a small number of dominant processes, while toxicity-related categories are shaped by a more diverse and diffuse set of contributors. The Jaccard similarity index allowed us to track how scenario family structure and selected system/impact boundaries correlates with the importance of these parameters in scenario comparisons.

Uncertainty and sensitivity elements considered, several findings emerge. The ICE-CHP system, incorporating PEMEL and H2C, contributed modestly to climate change category but can have an unneglectable burden in other environmental categories, highlighting that local actors should not only strive for CO₂ eq emissions reduction at the optimum cost, which is often their main focus, but also pay attention to other environmental categories. Unrealistic scenarios excepted, the hydrogen storage component generally had a negligible environmental footprint – especially in the climate change, ecotoxicity, and human toxicity categories.

Scenario family composition (PV, WT, BAT) was found to be a far more influential parameter than hydrogen storage size. In particular, the inclusion of PV infrastructure consistently led to higher electricity overproduction, which significantly altered environmental impacts depending on whether or how excess electricity was considered (boundary influence). When electricity overproduction is not considered, WT-based systems perform worse across impact categories. Conversely, when including overproduction, the environmental burden of PV becomes more apparent due to its disproportionate contribution linked to electricity overproduction that exceeds local demand. However, if this surplus is assumed to replace conventional German electricity in external uses, PV-comprising scenarios lead to the most avoided impacts, especially in categories like climate change and resource use. These benefits are contingent on the current impact of the electricity mix and postulate total transfer throughout the year, which may not be true depending on the grid stability due to increasing intermittent renewable energies. Based on our present findings, development policies of this kind of decentralized energy systems should thus be careful of system/impact boundaries selected when environmental benefits are reported and plan installations carefully to also anticipate and minimize pressure on the grid.

To conclude, while expected real-world applications of H₂ ICE-CHP systems appear environmentally interesting under realistic storage sizing, the environmental performance is highly dependent on scenario design, electricity overproduction consideration, and underlying database assumptions. The methodological open-source workflow developed here in the context of the Interreg CO2InnO project – though still limited by requiring manual adaptation for LCA – should be scaled and automated in future research. Developing a fully Python-based bridge between OpenModelica and Activity Browser, or a new integrated tool, would allow streamlined and automated analysis for broader TEA-LCA analysis. Our findings are relevant for the TEA-LCA community as well as for actors of local energy policies.
677

# APPENDIX

678

The TEA model developed (Beerlage et al., 2024) is available at the following GitHub URL:

679

* https://github.com/IKKUengine/CO2InnO-H2-CHP-Demonstrator

680

The Jupyter Notebooks embedding the Python modular procedures for TEA model results processing and reconstruction of inter sub-systems' flows for LCA models are available at the following GitHub and Zenodo URLs, with the Zenodo repository hosting the full datasets at all stages, and the GitHub repository hosting only the cleaned and processed datasets:

681

682

683

684

* https://github.com/Paul-Robineau/Processing-CO2InnO-H2-CHP-Demonstrator-outputs

685

* https://zenodo.org/records/16919026

686

The Jupyter Notebooks embedding the Python modular procedures for LCA data processing and visualization are available at the following GitHub and Zenodo URLs, both hosting the full datasets:

687

688

* https://github.com/Paul-Robineau/LCA-ScenarioResults-from-CO2InnO-H2-ICE-CHP-Demonstrator

689

* https://zenodo.org/records/17020672
GLOSSARY

<table>
  <tbody>
    <tr>
      <td>690</td>
      <td></td>
    </tr>
    <tr>
      <td>691</td>
      <td>BAT</td>
    </tr>
    <tr>
      <td>692</td>
      <td>Battery.</td>
    </tr>
    <tr>
      <td>693</td>
      <td>CF</td>
    </tr>
    <tr>
      <td>694</td>
      <td>Characterization factor. A numerical factor derived from a characterization model (i.e. impact indicator) in LCA, applied to the quantities of energy/material inputs or emission of a system, to quantify the associated impacts with respect to a given environmental category.</td>
    </tr>
    <tr>
      <td>695</td>
      <td></td>
    </tr>
    <tr>
      <td>696</td>
      <td></td>
    </tr>
    <tr>
      <td>697</td>
      <td>DES</td>
    </tr>
    <tr>
      <td>698</td>
      <td>Decentralized energy systems. Systems that operate mostly independently from conventional grids, often in relatively small scale and focusing on supply from clean energy sources.</td>
    </tr>
    <tr>
      <td>699</td>
      <td></td>
    </tr>
    <tr>
      <td>700</td>
      <td>EF</td>
    </tr>
    <tr>
      <td>701</td>
      <td>Environmental Footprint (in LCA). Global life-cycle impact assessment (LCIA) method recommended by the European Union. Includes 16 environmental indicators.</td>
    </tr>
    <tr>
      <td>702</td>
      <td></td>
    </tr>
    <tr>
      <td>703</td>
      <td>GRID</td>
    </tr>
    <tr>
      <td>704</td>
      <td>Interconnected network for electricity exchange between producers to consumers, here referring to the national German electricity grid.</td>
    </tr>
    <tr>
      <td>705</td>
      <td></td>
    </tr>
    <tr>
      <td>706</td>
      <td>GSHP</td>
    </tr>
    <tr>
      <td>707</td>
      <td>Global sensitivity analysis. Traditional or local sensitivity analysis study how the variation of one uncertain input within a model influences the output, while keeping the others at their original values or distribution of values. A global sensitivity analysis applies this logic to all inputs with uncertainties to compare them and study the complete variability behavior of the model.</td>
    </tr>
    <tr>
      <td>708</td>
      <td></td>
    </tr>
    <tr>
      <td>709</td>
      <td></td>
    </tr>
    <tr>
      <td>710</td>
      <td></td>
    </tr>
    <tr>
      <td>711</td>
      <td>GSHP</td>
    </tr>
    <tr>
      <td>712</td>
      <td>Ground-source heat pump.</td>
    </tr>
    <tr>
      <td>713</td>
      <td>H<sub>2</sub></td>
    </tr>
    <tr>
      <td>714</td>
      <td>Dihydrogen. Commonly referred to as hydrogen outside of chemistry research for simplicity.</td>
    </tr>
    <tr>
      <td>715</td>
      <td>H2C</td>
    </tr>
    <tr>
      <td>716</td>
      <td>Hydrogen compressor.</td>
    </tr>
    <tr>
      <td>717</td>
      <td>H2S</td>
    </tr>
    <tr>
      <td>718</td>
      <td>Hydrogen storage.</td>
    </tr>
    <tr>
      <td>719</td>
      <td>ICE-CHP</td>
    </tr>
    <tr>
      <td>720</td>
      <td>Internal conversion engine for combined heat and power. Combined heat and power (also called cogeneration) converts chemical energy from fuel into both electrical (el) and thermal (th) energy. In ICE, the chemical energy of the fuel is converted through combustion, as opposed to the pure chemical conversion used in fuel cells.</td>
    </tr>
    <tr>
      <td>721</td>
      <td></td>
    </tr>
    <tr>
      <td>722</td>
      <td></td>
    </tr>
    <tr>
      <td>723</td>
      <td>LCA</td>
    </tr>
    <tr>
      <td>724</td>
      <td>Life-cycle assessment. This approach usually focus on assessing average environmental impacts of a system, across multiple impact categories, relative to a unit of reference. The materials and emissions inventory of the assessed system are considered throughout the complete life-cycle. This typically allows comparison of technological alternatives for a given function, aiming to achieve sustainability.</td>
    </tr>
    <tr>
      <td>725</td>
      <td></td>
    </tr>
    <tr>
      <td>726</td>
      <td></td>
    </tr>
    <tr>
      <td>727</td>
      <td></td>
    </tr>
    <tr>
      <td>728</td>
      <td>LCI</td>
    </tr>
    <tr>
      <td>729</td>
      <td>Life-cycle inventory. Phase of inventory during an LCA, where material/energy flows and emissions of a system are quantified relative to the functional unit used for the study.</td>
    </tr>
    <tr>
      <td>730</td>
      <td></td>
    </tr>
  </tbody>
</table>
731 LCIA

732 Life-cycle impact assessment. Phase of assessment during an LCA, where impact indicators (e.g. global warming potential) are used to apply CFs to the LCI to quantify the impact of a system to an environmental category (e.g. climate change).

733 warming potential) are used to apply CFs to the LCI to quantify the impact of a system to an environmental category (e.g. climate change).

734 category (e.g. climate change).

735 L

736 Load boundary. System boundary limited to the flows directly involved in meeting the electricity and heat demand of the buildings considered.

737 demand of the buildings considered.

738 LE

739 Load + Excess boundary. System boundary taking into account all the flows involved in the system by adding the electricity overproduction sent to the grid to flows directly involved in meeting the electricity and heat demand of the buildings considered.

740 the electricity overproduction sent to the grid to flows directly involved in meeting the electricity and heat demand of the buildings considered.

741 demand of the buildings considered.

742 LEG

743 Load + Excess – Grid (DE) boundary. System boundary taking into account all the flows involved in the system but considering the electricity overproduction sent to the grid as replacing production from the German electricity grid for external users.

744 but considering the electricity overproduction sent to the grid as replacing production from the German electricity grid for external users.

745 electricity grid for external users.

746 N<sub>std</sub>

747 Normalized standard deviation. In the relevant sections of the article, N<sub>std</sub> value corresponds to the standard deviation normalized by the mean of the distribution considered.

748 deviation normalized by the mean of the distribution considered.

749 PEMEL

750 Proton-exchange membrane electrolyzer.

751 PV

752 Photovoltaic.

753 TEA

754 Techno-economic analysis. This approach usually focus on assessing specific technical and economic feasibility of a system, often using process simulation software to model the operational phase, which constitutes the base of the assessment.

755 of a system, often using process simulation software to model the operational phase, which constitutes the base of the assessment.

756 of the assessment.

757 TES

758 Thermal energy system.

759 WT

760 Wind turbine.
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895
## 896 AUTHORS CONTRIBUTION

897 **P. Robineau:** Conceptualization, Methodology, Data curation, Investigation, Formal analysis, Validation,
898 Visualization, Software, Writing—original draft, Writing—review and editing. **N. Salim:** Data curation, Software,
899 Validation, Writing—review and editing. **M. Boltoeva:** Conceptualization, Funding acquisition, Resources,
900 Validation, Software, Writing—review and editing, Supervision. **N. Arbor:** Conceptualization, Funding
901 acquisition, Resources, Software, Validation, Writing—review and editing, Supervision, Project administration.
902 **M. Kettner:** Funding acquisition, Validation, Writing—review and editing, Supervision. **G. Quaranta:**
903 Conceptualization, Methodology, Funding acquisition, Resources, Software, Validation, Writing—review and
904 editing, Supervision.
905 All authors have read and agreed to the published version of the manuscript.

## 906 DECLARATION OF COMPETING INTEREST

907 The authors declare that they have no known competing financial interests or personal relationships that could
908 have appeared to influence the work reported in this paper.

## 909 FUNDING

910 This work is part of the CO2InnO project, which is funded by the European Union (Interreg Upper Rhine 2021–
911 2027, A1.3).

## 912 GENERATIVE AI IN SCIENTIFIC WORK

913 After algorithmic conceptualization, the authors used ChatGPT (OpenAI) to help in the writing of the Python
914 procedures used to treat the OpenModelica simulation outputs and for data analysis and visualization of the LCA
915 results. The Python procedures were tested on minimal examples and contains validation steps. After writing the
916 original draft of this manuscript, the authors also used ChatGPT (OpenAI) to help improve readability of
917 some sections – without altering the factual content. The authors reviewed and edited the manuscript as needed
918 and take full responsibility for the content of the published article.

## 919 DATA AVAILABILITY

920 All data supporting the findings of this study are available within the paper or in the supplementary information
921 provided through the repositories linked in the **Appendix**.

## 922 ACKNOWLEDGEMENT

923 The authors acknowledge the co-funding by Interreg through the CO2InnO project, the Offenburg municipality
924 (Baden-Wurttemberg, Germany), for providing real data of buildings energy requirements, the contribution of
925 Florian Beerlage in the development of the OpenModelica simulation model, and the help of Jonathan Collin in
926 the design the Python procedures.