| Issue |
EPJ Web Conf.
Volume 372, 2026
Advanced Power Systems (APS 2026)
|
|
|---|---|---|
| Article Number | 07006 | |
| Number of page(s) | 5 | |
| Section | Power and Energy Systems | |
| DOI | https://doi.org/10.1051/epjconf/202637207006 | |
| Published online | 11 June 2026 | |
https://doi.org/10.1051/epjconf/202637207006
Trust-by-design monitoring and energy management for renewable microgrids using distributed ledger technology
Technical University of Cluj-Napoca, Electrical Engineering Department, 400114 Cluj-Napoca, Romania
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 11 June 2026
Abstract
As renewable generation becomes increasingly deployed at the local level, the reliability of microgrids depends not only on physical infrastructure but also on the credibility of the measurement data driving energy control decisions. In conventional Energy Management Systems (EMS) architectures, monitoring is implicitly assumed to be correct, even though no mechanism exists to verify the authenticity or integrity of the received data. This gap can lead to suboptimal or misleading control actions, especially in distributed environments involving multiple stakeholders. This paper introduces a trust-by-design approach in which monitoring and energy management processes are natively supported by a lightweight Distributed Ledger Technology (DLT) layer embedded within the EMS. Rather than relying on external trust assumptions, the proposed mechanism ensures built-in traceability and tamper-evidence, enabling independent validation of the microgrid’s operational history. A simple renewable microgrid with battery storage is used as a demonstrative case study to show how a DLT-based ledger can safeguard measurement integrity and control decisions without adding technical complexity to the EMS itself. The results demonstrate that verifiable data flows and tamper detection significantly enhance the transparency and robustness of EMS architectures, while enabling future extensions towards predictive or AI-assisted control strategies.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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