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Authors: Joerg Bremer 1 and Sebastian Lehnhoff 2

Affiliations: 1 Department of Computing Science, University of Oldenburg, Uhlhornsweg, Oldenburg, Germany ; 2 R&D Division Energy, OFFIS – Institute for Information Technology, Escherweg, Oldenburg, Germany

Keyword(s): Privacy Preservation, Distributed Optimization, Multi-Agent Systems, Secret Sharing.

Abstract: Negotiation among agents that are controlling and orchestrating a set of distributed processes often relies on frequent data exchange to allow solution evaluation and thus convergence towards a joint solution. Solving decentralized coordination problems with coalitions of agents that exchange messages and information to build beliefs for problem solving, inevitably allows insight into other agents’ operational options. Keeping local information private is thus of utmost importance for a wide user acceptance of such algorithms. We present an extension to a distributed, self-organizing algorithm for energy scheduling in virtual power plants or energetic neighborhoods that keeps all information about possible operations of participating energy resources private. For calculations during optimization the algorithm relies on secret sharing and joint multi-party computations. We evaluate the algorithm against the original non privacy-preserving standard version and present some insights for future work. (More)

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Paper citation in several formats:
Bremer, J. and Lehnhoff, S. (2025). Privacy-Preserving Self-Organization in Distributed Energy Scheduling. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5; ISSN 2184-433X, SciTePress, pages 253-262. DOI: 10.5220/0013094900003890

@conference{icaart25,
author={Joerg Bremer and Sebastian Lehnhoff},
title={Privacy-Preserving Self-Organization in Distributed Energy Scheduling},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={253-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013094900003890},
isbn={978-989-758-737-5},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Privacy-Preserving Self-Organization in Distributed Energy Scheduling
SN - 978-989-758-737-5
IS - 2184-433X
AU - Bremer, J.
AU - Lehnhoff, S.
PY - 2025
SP - 253
EP - 262
DO - 10.5220/0013094900003890
PB - SciTePress

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