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Cited by 45Systems Engineering Methodologies and ApplicationsDigital Platforms and EconomicsUniversity-Industry-Government Innovation Models

Business ecosystem modeling- the hybrid of system modeling and ecological modeling: an application of the smart grid

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Ma, Zheng·2019·Energy Informatics
DOI 10.1186/s42162-019-0100-4
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Abstract

Business ecosystem is popularly used to investigate a complex social system with the business perspective, and particularly contributes to the understanding of actors and their relations in the innovation research. However, the aspect of business ecosystem modeling is limited discussed in the literature, although the importance has emerged significantly in recent years due to the emphasis on cross-disciplinary research and digitalization with artificial intelligence. Therefore, this paper proposes a framework for business ecosystem modeling with the discussion of system engineering and ecological modeling. The domain of smart grid is selected to demonstrate how system engineering, especially standards and ontologies contribute to the business ecosystem modeling. The proposed framework of the business ecosystem modeling includes three parts and nine stages that combines theories from system engineering, ecology, and business ecosystem. Part I-Business ecosystem architecture development includes four stages which aims to identify a target business ecosystem and its elements (actors, roles, and interactions). Part II-Factor analysis includes two stages to identify potential changes (and the dimensions of the changes) in the ecosystem. Part III- Ecosystem simulation and reconfiguration aims to use simulations to investigate the transition of an ecosystem and the re-configurated ecosystem. The framework not only provides a systematic approach for modeling a business ecosystem but also provides a methodological foundation for research on the aspect of complex systems in the business ecosystem field.

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  • The multi-agent simulation framework, augmented by a business ecosystem modeling approach, captures micro-level charging decisions and macro-level capacity constraints.

    “By embedding each EV user, the aggregator, and the DSO as separate agents, the model captures micro-level charging decisions and macro-level capacity constraints.”

    From A Multi-Agent, Laxity-Based Aggregation Strategy for Cost-Effective Electric Vehicle Charging and Local Transformer Overload Prevention (2025) · cites this paper as [20]

    The Ecosystem Modeling section (page unavailable) describes agent-based models as addressing disaggregated parts and micro-level interactions, while system dynamics represents aggregated systems. The Conclusion (page unavailable) proposes a business-ecosystem framework that includes simulation, but our copy does not describe a model with EV users, an aggregator, and a DSO as separate agents, or show it capturing charging decisions and capacity constraints. (LLM, conf=0.83)

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Cited by

Sorted by citations

Authors
Ma, Z.
Affiliations
University of Southern Denmark (DK)
Corresponding author
Ma, Zheng
ORCID
Ma, Zheng
Venue
Energy Informatics
Publisher
Springer Science and Business Media LLC
Published
2019-11-21
Volume
2
Issue
1
ISSN
2520-8942 (ISSN-L)
DOI
10.1186/s42162-019-0100-4
OpenAlex
W2990790201
Cited by
45
References
3
Citations by year
2026: 3 · 2025: 7 · 2024: 7 · 2023: 4 · 2022: 7 · 2021: 12 · 2020: 5
FWCI
3.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Energy Informatics (publishedVersion, cc-by)
  • University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Systems Engineering Methodologies and Applications
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › University-Industry-Government Innovation Models
Keywords
smart grid, systems engineering, ecological modeling, complex systems, ecosystem modeling, actor interaction, digitalization

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