Methodology for distributed optimization of flexible energy resources through semi-automated model transformation and deployment

DOI
URI
Scopus ID
Project
Publisher
IEEE
Series or journal
IEEE Open Journal of the Industrial Electronics Society
ISSN
Periodical volume
6
First page
1682
Last page
1703
Is part of
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Alternating direction method of multipliers
distributed optimization
multiagent system
scalability
dtec.bw
Abstract
Effectively utilizing flexible energy resources requires optimizing their operation over time to balance dynamic demand and fluctuating supply from volatile renewable sources. Traditionally, this has been achieved through centralized optimization models, which suffer from scalability limitations, single points of failure, and limited flexibility when applied to decentralized and dynamically changing environments. Distributed models offer a promising alternative, providing enhanced flexibility, robustness, and computational efficiency by enabling parallel processing and reducing coordination delays. Thus, this work presents a methodology for the semi-automated transformation of centralized optimization models into distributed architectures, leveraging containerized multiagent systems to achieve scalable and efficient optimization across multiple computing units. A case study involving 120 electrolyzers distributed across up to five optimization agents, including both homogeneous and heterogeneous configurations, demonstrates that the distributed approach accelerates computation time by a factor up to 27.42 compared to a centralized model while accepting a solution quality deviation of only 2.2%. The optimization integrates site-wide and real-time optimization, ensuring adaptability to fluctuating renewable energy availability and improving system resilience. This combination enables long-term strategic planning while allowing real-time adjustments to maximize renewable energy utilization. The findings highlight the benefits of distributed optimization in modular energy systems and confirm that containerized multiagent architectures enhance scalability and computational efficiency, making the approach well-suited for real-world applications in decentralized and modular energy networks.
Description
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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Published version
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