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arxiv: 2606.20230 · v1 · pith:O3XUJ3INnew · submitted 2026-06-18 · 💻 cs.SE

SysML Modeling of Digital Twins for Renewable Energy Communities

Pith reviewed 2026-06-26 16:19 UTC · model grok-4.3

classification 💻 cs.SE
keywords SysMLDigital TwinsRenewable Energy CommunitiesSAREF4ENERModel-Based Systems EngineeringBlock Definition DiagramsSemantic GapsSmart Energy
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The pith

SysML re-expression of a renewable energy community house yields two block diagrams and identifies gaps for SAREF4ENER to close in digital twin models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper attempts to establish the start of a model-based systems engineering workflow for digital twins of renewable energy communities. It begins with an industrially validated domain model and translates a representative house subset into SysML using Modelio, resulting in a device taxonomy diagram and a community organizational diagram. The authors then identify four semantic gaps that plain SysML cannot handle for this domain and sketch an import of the SAREF4ENER ontology as a reference package to address them. A reader would care because RECs combine varied devices, contracts, and data flows, and a structured modeling method could support consistent digital twin construction across such heterogeneity.

Core claim

Starting from an industrially-validated REC domain model, we re-express a representative house subset in SysML using the open-source Modelio tool, yielding two Block Definition Diagrams - a device taxonomy and a community organizational view. We then discuss four semantic gaps that plain SysML leaves open and sketch how the SAREF4ENER ontology could be imported as a reference package to close them. Combining SysML with SAREF-based semantics for smart-energy Digital Twins remains largely unexplored, and we position this paper as a first step along that line.

What carries the argument

Two SysML Block Definition Diagrams (device taxonomy and community organizational view) created from the REC domain model, together with the proposed import of SAREF4ENER as a reference package to address semantic gaps.

If this is right

  • An MBSE workflow for REC digital twins becomes feasible by re-expressing domain models in SysML.
  • Four semantic gaps in plain SysML for smart-energy contexts can be addressed by importing SAREF4ENER.
  • The approach supports handling heterogeneity of devices, contracts, and runtime data in REC digital twins.
  • SysML combined with SAREF semantics provides a foundation for engineering digital twins of energy sharing systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The diagrams could be extended as templates for modeling additional REC elements such as shared storage or grid connections.
  • SAREF4ENER integration might enable automated consistency checks across contracts and device data in the twin.
  • Applying the method to a full multi-house REC instance would test scalability beyond the single-house subset.
  • The modeling could connect to other energy system standards to improve interoperability in digital twin platforms.

Load-bearing premise

The industrially-validated REC domain model is complete and accurate enough to serve as a starting point for SysML re-expression, and importing SAREF4ENER will close the semantic gaps without introducing new inconsistencies.

What would settle it

If the created SysML diagrams fail to represent the actual devices and organizational structure of a working renewable energy community, or if importing SAREF4ENER produces modeling conflicts when applied to the digital twin.

Figures

Figures reproduced from arXiv: 2606.20230 by Andrey Sadovykh, Gabriela Lucas, Lu\'is Miguel Pinho, Mohammad Samadi.

Figure 1
Figure 1. Figure 1: SysML BDD of the household. Five concrete [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Representation of the EVCharger device in JSON format. semantics, flexibility profiles, and an active runtime link – is the subject of Section 5. Each device can be represented in a JSON format/structure (i.e., metamodel) in the implementation phase. This structure contains various features and properties defined for the de￾vice, which can be used to manage things in Eclipse Ditto [PITH_FULL_IMAGE:figures… view at source ↗
Figure 4
Figure 4. Figure 4: Digital Twin architecture for RECs, where the entire system is controlled through multiple modules. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

Renewable Energy Communities (RECs) are emerging as a key organizational model for local and global sharing of renewable generation, storage, and flexible loads. Engineering Digital Twins of RECs is made difficult by the heterogeneity of devices, contracts, and runtime data involved. In this paper, we take a first step toward a Model-Based Systems Engineering (MBSE) workflow for REC's Digital Twins. Starting from an industrially-validated REC domain model, we re-express a representative house subset in SysML using the open-source Modelio tool, yielding two Block Definition Diagrams - a device taxonomy and a community organizational view. We then discuss four semantic gaps that plain SysML leaves open and sketch how the SAREF4ENER ontology could be imported as a reference package to close them. Combining SysML with SAREF-based semantics for smart-energy Digital Twins remains largely unexplored, and we position this paper as a first step along that line.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper claims to take a first step toward a Model-Based Systems Engineering (MBSE) workflow for Digital Twins of Renewable Energy Communities (RECs) by starting from an industrially-validated REC domain model, re-expressing a representative house subset in SysML via the Modelio tool to produce two Block Definition Diagrams (a device taxonomy and a community organizational view), identifying four semantic gaps left open by plain SysML, and sketching how importing the SAREF4ENER ontology as a reference package could close those gaps.

Significance. If the re-expression accurately captures the domain model and the sketched gaps are correctly diagnosed, the work provides an initial exploration of combining SysML with SAREF-based semantics for smart-energy digital twins—an area noted as largely unexplored. This could lay groundwork for more systematic handling of device heterogeneity, contracts, and runtime data in REC digital twins, though the contribution remains descriptive rather than validated.

minor comments (2)
  1. The abstract and description indicate that concrete examples of the four semantic gaps and the resulting diagrams are central to the contribution, but without explicit section references or excerpts showing how the gaps are enumerated (e.g., in a dedicated discussion section), it is difficult to evaluate their technical specificity.
  2. The reliance on an 'industrially-validated REC domain model' as the unexamined starting point should be supported by at least a brief citation or summary of its validation process to strengthen reproducibility of the SysML re-expression.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and for recommending minor revision. The referee's summary accurately describes the manuscript's scope and contributions as an initial exploration of SysML combined with SAREF4ENER for REC digital twins.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is a descriptive modeling exercise that re-expresses an existing industrially-validated REC domain model into two SysML Block Definition Diagrams using Modelio and sketches four semantic gaps plus a possible SAREF4ENER import; it contains no equations, derivations, fitted parameters, predictions, or uniqueness claims. The central contribution is explicitly framed as a first step without any reduction of outputs to inputs by construction, self-citation load-bearing premises, or ansatz smuggling. All load-bearing elements (domain model completeness, ontology integration) are presented as assumptions or discussion points rather than derived results, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on an existing industrially-validated REC domain model whose accuracy is taken as given, plus the assumption that SAREF4ENER can be imported without conflict. No free parameters or invented entities are introduced.

axioms (1)
  • domain assumption The industrially-validated REC domain model accurately captures the heterogeneity of devices, contracts, and runtime data in renewable energy communities.
    The paper begins by re-expressing this model in SysML, making its validity a load-bearing premise for the diagrams produced.

pith-pipeline@v0.9.1-grok · 5691 in / 1276 out tokens · 28573 ms · 2026-06-26T16:19:54.008358+00:00 · methodology

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Reference graph

Works this paper leans on

33 extracted references

  1. [1]

    Digital twin technology for renewable energy, smart grids, energy storage and vehicle-to-grid integration,

    A. Q. Al-Shetwi, I. E. Atawi, M. A. El-Hameed, and A. Abuelrub, “Digital twin technology for renewable energy, smart grids, energy storage and vehicle-to-grid integration,”IET Smart Grid, 2025

  2. [2]

    What is a digital twin any- way? deriving the definition for the built environment from over 15,000 scientific publications,

    M. Abdelrahman, E. Macatulad, B. Lei, M. Quintana, C. Miller, and F. Biljecki, “What is a digital twin any- way? deriving the definition for the built environment from over 15,000 scientific publications,”Building and Environment, vol. 274, p. 112748, 2025

  3. [3]

    Us- ing model-based systems engineering to design system- based digital twins,

    M. V . P. Pessoa, A. P. L. Schuch, and J. M. Bezerra, “Us- ing model-based systems engineering to design system- based digital twins,” inProceedings of the INCOSE International Symposium, vol. 33, pp. 1299–1315, Wi- ley, 2023. 8-step MBSE approach, Eclipse Papyrus, Python parser for SysML to simulation

  4. [4]

    Model- driven engineering for digital twins: a systematic map- ping study,

    D. Lehner, J. Zhang, J. Pfeiffer, S. Sint, A.-K. Splettstößer, M. Wimmer, and A. Wortmann, “Model- driven engineering for digital twins: a systematic map- ping study,”Software and Systems Modeling, 2025

  5. [5]

    Modelio: Open-source modeling environ- ment

    Modeliosoft, “Modelio: Open-source modeling environ- ment.” https://www.modelio.org/. Accessed 2026-04-24

  6. [6]

    SysML 4 digital twins – utilization of system mod- els for the design and operation of digital twins,

    F. Wilking, C. Sauer, B. Schleich, and S. Wartzack, “SysML 4 digital twins – utilization of system mod- els for the design and operation of digital twins,” in Proceedings of the Design Society (DESIGN 2022), vol. 2, pp. 1815–1824, Cambridge University Press,

  7. [7]

    SysML diagrams → executable DT behavior code derivation

  8. [8]

    From engineering models to digital twins: Generating AAS from SysML v2 models,

    E. Ferko, L. Berardinelli, A. Bucaioni, M. Behnam, and M. Wimmer, “From engineering models to digital twins: Generating AAS from SysML v2 models,”The Journal of Systems and Software, vol. 233, p. 112688,

  9. [9]

    QVT-based transformation SysML v2 → AAS, EMF-compliant, 24 validated examples

  10. [10]

    Interoperability for smart appliances in the IoT world,

    L. Daniele, M. Solanki, F. den Hartog, and J. Roes, “Interoperability for smart appliances in the IoT world,” inThe Semantic Web – ISWC 2016, vol. 9982 ofLecture Notes in Computer Science, pp. 21–29, Springer, 2016. Original SAREF ontology paper, SAREF4EE extension for EEBus/Energy

  11. [11]

    Digital twin network for the IIoT using Eclipse Ditto and Hono,

    M. Kherbache, M. Maimour, and E. Rondeau, “Digital twin network for the IIoT using Eclipse Ditto and Hono,” IFAC-PapersOnLine, vol. 55, no. 8, pp. 37–42, 2022

  12. [12]

    Design of an ontology-driven constraint tester (ODCT) and ap- plication to SAREF and smart energy appliances,

    T. M. R. H. Chy, H. Lamboro, O. Genest, A. Kung, C. Rabrait, D. Sebilleau, and A. Gyrard, “Design of an ontology-driven constraint tester (ODCT) and ap- plication to SAREF and smart energy appliances,” in International Knowledge Graph and Semantic Web Con- ference, pp. 183–198, Springer, 2024

  13. [13]

    Leverag- ing SysML v2 to enhance system architecture decision- making based on process information,

    C.-P. Grunenwald, A. Dybov, and R. Stark, “Leverag- ing SysML v2 to enhance system architecture decision- making based on process information,” in2025 IEEE International systems Conference (SysCon), pp. 1–8, IEEE, 2025

  14. [14]

    Ten years of asset administration shell: Developments, research opportunities, and adoption challenges,

    L. Sakurada, F. De La Prieta, and P. Leitao, “Ten years of asset administration shell: Developments, research opportunities, and adoption challenges,”IEEE Access, 2025

  15. [15]

    Increasing interoperability between digital twin stan- dards and specifications: Transformation of DTDL to AAS,

    C. Schmidt, F. V olz, L. Stojanovic, and G. Sutschet, “Increasing interoperability between digital twin stan- dards and specifications: Transformation of DTDL to AAS,”Sensors, vol. 23, no. 18, p. 7742, 2023. HIGH: DTDL (Azure) to AAS (IEC 63278) transformation; interoperability between DT standards

  16. [16]

    Industry 4.0 renewable energy power plant ar- chitecture using next generation service interface linked data connectors,

    I.-A. Gal, L. Vl ˘ad˘areanu, V . Vl˘ad˘areanu, and D.-O. Melinte, “Industry 4.0 renewable energy power plant ar- chitecture using next generation service interface linked data connectors,” in2023 8th International Conference on Mathematics and Computers in Sciences and Indus- try (MCSI), pp. 155–160, IEEE, 2023

  17. [17]

    Extending the SAREF4ENER ontology with flexibility based on FlexOffers,

    F. Lilliu, A. Laadhar, C. Thomsen, and D. R. Recupero, “Extending the SAREF4ENER ontology with flexibility based on FlexOffers,” 2025

  18. [18]

    SARGON – SmArt eneRGy dOmain oNtology,

    M. Haghgoo, I. Sychev, A. Monti, and F. H. Fitzek, “SARGON – SmArt eneRGy dOmain oNtology,”IET Smart Cities, vol. 2, no. 4, pp. 191–198, 2020

  19. [19]

    Uni- fied, ontology-based information model for smart energy management systems,

    T. Ivanova, P. Kesova, Y . Belev, and I. Batchkova, “Uni- fied, ontology-based information model for smart energy management systems,” inProceedings of the IEEE In- ternational Conference on Automation and Informatics (ICAI), IEEE, 2025. M . S a m a d i , L . M . P i n h o, A . S a d o v y k h , G . L u c a s7

  20. [20]

    Multi-partner project: A model-driven engineering framework for federated digital twins of industrial systems (MATISSE),

    A. Bucaioni, R. Eramo, L. Berardinelli, H. Brune- liere, B. Combemale, D. E. Khelladi, V . Muttillo, A. Sadovykh, and M. Wimmer, “Multi-partner project: A model-driven engineering framework for federated digital twins of industrial systems (MATISSE),” inPro- ceedings of the Design, Automation and Test in Europe Conference (DATE), (Lyon, France), pp. 1–6, ...

  21. [21]

    A review of SHACL: from data validation to schema reasoning for RDF graphs,

    P. Pareti and G. Konstantinidis, “A review of SHACL: from data validation to schema reasoning for RDF graphs,”Reasoning Web International Summer School, pp. 115–144, 2021

  22. [22]

    Featherweight OCL: A proposal for a machine-checked formal seman- tics for OCL 2.5,

    A. D. Brucker, F. Tuong, and B. Wolff, “Featherweight OCL: A proposal for a machine-checked formal seman- tics for OCL 2.5,” 2015

  23. [23]

    Utilization of SysML system models for smart assembly using digital twins,

    F. Wilking, S. Kaup, M. Fett, S. Goetz, E. Kirchner, and S. Wartzack, “Utilization of SysML system models for smart assembly using digital twins,” inProceedings of NordDesign 2024, (Reykjavik, Iceland), pp. 61–70, Aug

  24. [24]

    SysML to discrete event simulation for assembly DT, manufacturing structure integration

  25. [25]

    Architect- ing a BIM-based digital twin platform for airport asset management: a model-based system engineering with SysML approach,

    B. Keskin, B. Salman, and O. Koseoglu, “Architect- ing a BIM-based digital twin platform for airport asset management: a model-based system engineering with SysML approach,”Journal of Construction Engineering and Management, vol. 148, no. 5, p. 04022018, 2022

  26. [26]

    Using SysML mod- els as digital twins for early validation of modular sys- tems and systems of systems,

    H. Wagner, L. Portenlänger,et al., “Using SysML mod- els as digital twins for early validation of modular sys- tems and systems of systems,” inProceedings of the IEEE International Systems Conference (SysCon), pp. 1– 8, IEEE, 2023

  27. [27]

    Semantic interoperability on IoT: Aligning IFC and Smart Application Reference (SAREF) sensor data models,

    E. D. Okonta, F. Rahimian, V . Vukovi ´c, and S. Ro- driguez, “Semantic interoperability on IoT: Aligning IFC and Smart Application Reference (SAREF) sensor data models,”Automation in Construction, vol. 177, p. 106328, 2025. IADOM approach for aligning saref:Sensor and IfcSensor, RDF/Protege modeling

  28. [28]

    Ontology-driven smart building semantics: An EnergyPlus-SAREF approach,

    U. Kırnapcı, M. B. Hekimo˘glu, S. Baghaee, and I. Ulu- soy, “Ontology-driven smart building semantics: An EnergyPlus-SAREF approach,” inProceedings of the IEEE Signal Processing and Communications Applica- tions Conference (SIU), IEEE, 2025

  29. [29]

    A survey on semantic modeling for building energy management,

    M. Aniakor, V . V . Cogo, and P. M. Ferreira, “A survey on semantic modeling for building energy management,” 2024

  30. [30]

    Empowering the Eclipse Arrowhead framework with a digital twin as a proxy service,

    A. Aziz, O. Schelén, U. Bodin, L. Römer, S. E. Jeroschewski, and J. Kristan, “Empowering the Eclipse Arrowhead framework with a digital twin as a proxy service,” inProceedings of the 22nd International Con- ference on Control, Automation and Systems (ICCAS), pp. 1716–1721, IEEE, 2022

  31. [31]

    OpenTwins: An open-source framework for the development of next- gen compositional digital twins,

    J. Robles, C. Martín, and M. Díaz, “OpenTwins: An open-source framework for the development of next- gen compositional digital twins,”Computers in Industry, vol. 152, p. 104007, 2023

  32. [32]

    Digital twin technology for renewable energy microgrids,

    K. E. Bassey, J. Opoku-Boateng, B. O. Antwi, A. Nti- akoh, and A. R. Juliet, “Digital twin technology for renewable energy microgrids,”Engineering Science & Technology Journal, vol. 5, no. 7, pp. 2248–2272, 2024

  33. [33]

    SysML as a common integration platform for co-simulations,

    A. Sadovykh, A. Bagnato, I. Quadri, A. E.-D. Mady, L. D. Couto, S. Basagiannis, and M. Hasanagic, “SysML as a common integration platform for co-simulations,” inProceedings of the 12th Central and Eastern Euro- pean Software Engineering Conference in Russia (CEE- SECR), pp. 1–5, ACM, 2016