Abstract
The transformation of power systems, driven by decarbonization and the rapid growth of distributed energy resources (DER), requires new methods of coordination, flexibility, and real-time control. The aim of this study is to develop and analyze an integrated distributed energy management model that combines hierarchical dynamic optimization with artificial intelligence methods to enhance system efficiency, stability, and adaptability. The paper presents a systematic literature review (2009–2025), supported by NLP and text-mining techniques, demonstrating the evolution of concepts from communication-oriented solutions to advanced DERMS platforms. Based on these findings, a research model is proposed, built on a network graph and power balance equations, extended with machine-learning predictors for renewable energy generation and demand, AI-supported state estimation, and reinforcement learning for adaptive control under uncertainty. The proposed approach enables the development of digital twin representations while preserving physical constraints and ensuring operational system security.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.181
Artificial Intelligence in Distributed Energy Management: A Model for Optimizing Modern Power Systems
The transformation of power systems, driven by decarbonization and the rapid growth of distributed energy resources (DER), requires new methods of coordination, flexibility, and real-time control. The aim of this study is to develop and analyze an integrated distributed energy management model that combines hierarchical dynamic optimization with artificial intelligence methods to enhance system efficiency, stability, and adaptability. The paper presents a systematic literature review (2009–2025), supported by NLP and text-mining techniques, demonstrating the evolution of concepts from communication-oriented solutions to advanced DERMS platforms. Based on these findings, a research model is proposed, built on a network graph and power balance equations, extended with machine-learning predictors for renewable energy generation and demand, AI-supported state estimation, and reinforcement learning for adaptive control under uncertainty. The proposed approach enables the development of digital twin representations while preserving physical constraints and ensuring operational system security.
Recommended Citation
Bilan, Y., Rabe, M., Norek, T., Lopatka, A. & Bilan, S.(2026). Artificial Intelligence in Distributed Energy Management: A Model for Optimizing Modern Power Systems. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.181