| Issue |
E3S Web Conf.
Volume 638, 2025
International Conference on Electronics, Engineering Physics and Earth Science (EEPES 2025)
|
|
|---|---|---|
| Article Number | 02009 | |
| Number of page(s) | 12 | |
| Section | Renewable Energy and Green Technologies | |
| DOI | https://doi.org/10.1051/e3sconf/202563802009 | |
| Published online | 16 July 2025 | |
Intelligent agent orchestration for climate-resilient energy systems: Autonomous management of renewable integration and distributed resources
1 Democritus University of Thrace, Department of Forest and Natural Environment Sciences, 1st km of Drama - Mikrochori, Drama, 66100, Greece
2 Democritus University of Thrace, School of Chemistry, St. Lucas, Kavala, 65404, Greece
3 Democritus University of Thrace, Hephaestus Laboratory, School of Chemistry, Faculty of Sciences, St. Lucas, Kavala, 65404, Greece
* Corresponding author: kkravari@affil.duth.gr
Achieving aggressive greenhouse gas emission goals necessitates a radical change in our energy systems towards a swift transition towards renewable energy sources (RES) and distributed energy resources (DERs) diffusion. However, the intermittent nature of RES and decentralized characteristics of DERs pose challenges in the context of grid stability, efficiency of optimising energy consumption, and increasing climate resilience against weather-related effects. This article proposes a distributed multi-agent system (MAS) architecture for next-generation energy systems’ smart management with the aim of enhancing climate resilience by means of autonomous RES and DERs integration. Smart agents for different energy assets like solar farms, wind farms, battery storage, electric vehicles, microgrids, and demand response aggregators will be part of MAS. These agents will work together independently to forecast energy demand and supply under various climatic conditions, perform local energy control optimization, enhance grid stability under climate stresses, and allow high integration of RES. The proposed MAS is expected to aid in developing more sustainable, resilient, and climate-resilient energy systems by virtue of support from distributed intelligence and adaptive control methods. A simulation case study, focusing on the Greek energy sector and its susceptibility to climate effects, will show the effectiveness of the suggested MAS in enhancing energy system resilience as well as renewable energy optimization.
© The Authors, published by EDP Sciences, 2025
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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