Open Access
Open access
volume 11 issue 1 pages 100499

EnerChain: A Decentralized Knowledge Management Framework for Smart Energy Systems with Smart Manufacturing Agents via Blockchain Technology

Publication typeJournal Article
Publication date2025-03-01
scimago Q1
SJR1.215
CiteScore14.0
Impact factor
ISSN21998531
Abstract
Energy management can be designed from different perspectives including production, distribution, and consumption. Focusing on consumption perspective, manufacturing systems can be enhanced by enabling smart machines as agents which operate with their own knowledge representation models in a shopfloor. These agents can benefit from industry 4.0 enablers like IoT including sensors, controllers, and actuators. This paper focuses on how these agents can interoperate with each other and exchange knowledge to optimize energy consumption. Since different knowledge models may not be capable of interacting with other ones based on their different provider semantics. This paper explores the application of blockchain technology for secure, decentralized storage and sharing knowledge models in smart energy systems. The research introduces EnerChain as a blockchain-integrated and a decentralized application (DApp) system prototype that employs smart contracts for access management and conflict resolution. It also incorporates the InterPlanetary File System (IPFS) for efficient off-chain storage, addressing scalability concerns. The feasibility and practicality of this approach are demonstrated through the development of EnerChain. The findings highlight the significant potential of blockchain technology in facilitating efficient knowledge model management for smart shopfloors. Additionally, an operational scenario has been evaluated as a case study for the proposed conceptual model to illustrate how it can solve energy conflicts in a smart environment. An impact analysis at the end of this research shows that EnerChain can make annual 27.5 TWh reduction in residential energy consumption which yields to annual 7.8 million tonnes reduction in CO2 emissions and annual €8.25 billion financial benefits.
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GOST Copy
Mofatteh M. Y., Khadka U., Fatahi Valilai O. EnerChain: A Decentralized Knowledge Management Framework for Smart Energy Systems with Smart Manufacturing Agents via Blockchain Technology // Journal of Open Innovation: Technology, Market, and Complexity. 2025. Vol. 11. No. 1. p. 100499.
GOST all authors (up to 50) Copy
Mofatteh M. Y., Khadka U., Fatahi Valilai O. EnerChain: A Decentralized Knowledge Management Framework for Smart Energy Systems with Smart Manufacturing Agents via Blockchain Technology // Journal of Open Innovation: Technology, Market, and Complexity. 2025. Vol. 11. No. 1. p. 100499.
RIS |
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RIS Copy
TY - JOUR
DO - 10.1016/j.joitmc.2025.100499
UR - https://linkinghub.elsevier.com/retrieve/pii/S2199853125000344
TI - EnerChain: A Decentralized Knowledge Management Framework for Smart Energy Systems with Smart Manufacturing Agents via Blockchain Technology
T2 - Journal of Open Innovation: Technology, Market, and Complexity
AU - Mofatteh, Mohammad Yaser
AU - Khadka, Ujjwal
AU - Fatahi Valilai, Omid
PY - 2025
DA - 2025/03/01
PB - Elsevier
SP - 100499
IS - 1
VL - 11
SN - 2199-8531
ER -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@article{2025_Mofatteh,
author = {Mohammad Yaser Mofatteh and Ujjwal Khadka and Omid Fatahi Valilai},
title = {EnerChain: A Decentralized Knowledge Management Framework for Smart Energy Systems with Smart Manufacturing Agents via Blockchain Technology},
journal = {Journal of Open Innovation: Technology, Market, and Complexity},
year = {2025},
volume = {11},
publisher = {Elsevier},
month = {mar},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2199853125000344},
number = {1},
pages = {100499},
doi = {10.1016/j.joitmc.2025.100499}
}
MLA
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MLA Copy
Mofatteh, Mohammad Yaser, et al. “EnerChain: A Decentralized Knowledge Management Framework for Smart Energy Systems with Smart Manufacturing Agents via Blockchain Technology.” Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, no. 1, Mar. 2025, p. 100499. https://linkinghub.elsevier.com/retrieve/pii/S2199853125000344.