AI-Based Assistance Systems in Smart Distribution Grids
- The increasing decentralization and digitalization of energy distribution systems pose significant challenges to the management of operational and maintenance processes. The integration of renewable energy sources and the resulting dynamic grid conditions necessitate more adaptive, efficient, and technologically advanced workforce management (WFM) solutions. This work presents a systematic approach to modeling WFM processes tailored to the needs of modern smart grids. Based on a detailed evaluation of modeling languages and a requirements analysis informed by industry workshops, Business Process Model and Notation (BPMN) was identified as the most suitable formalism. An iterative development process was established, combining user story specification, process modeling, validation, and equirement refinement. The resulting WFM processes integrate real-time reactivity and AI-based decision support mechanisms. These include intelligent task classification, personnel selection, and resource planning, enabling human operators to make faster, more consistent decisions in dynamic grid environments. The work lays the foundation for a digitalized, flexible WFM-System that addresses the future demands of sustainable and secure energy distribution.
| Author: | Sijmen Robert BoersmaORCiD, Kajan KandiahORCiD, Cansu KahveciORCiD, Philip Song, Xuan-Anh Nguyen, Max-Ferdinand StrohORCiD, Wolfgang BoosORCiDGND |
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| DOI: | https://doi.org/10.1109/ICTMOD66732.2025.11371880 |
| Parent Title (English): | 2025 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) |
| Subtitle (English): | Developing a Workforce Management System |
| Publisher: | IEEE |
| Place of publication: | Piscataway (NJ) |
| Document Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2026/02/09 |
| Date of first Publication: | 2026/02/09 |
| Release Date: | 2026/05/22 |
| Tag: | 03 AI-based Assistance Systems; Decision Support; Knowledge Management; Predictive Maintenance; Smart Distribution Grids; Workforce Management |
| Page Number: | 6 S. |
| Note: | AProSys: AI-driven assistance and prognosis systems for the sustainable deployment in the intelligent distribution grid
The aim of the research project 'AProSys' is to sustainably transform sensor-based condition monitoring into a cognitive assistance system with a focus on AI-based prognostics for application within the distribution grid in order to successfully shape the energy and mobility transition in Germany.
Duration: 01.01.2023 – 31.12.2025
Funding no.: 03EI6090B
Funding: Federal Ministry for Economic Affairs and Energy (BMWE)
Promoters: Projektträger Jülich (PtJ) – Forschungszentrum Jülich GmbH
Acknowledgment:
Funded by the Federal Ministry of Economics and Climate Protection (BMWK) under the funding code 03EI6090B.
Benefits for the target group:
In the project applied for, the transformation of the condition analysis into a cognitive assistance system is to be realized with a resource-efficient minimum use of sensors and the monitoring is to be extended to connected neighboring power engineering systems.
Project partners:
ABB AG Forschungszentrum Deutschland, Ladenburg
Institut für Elektroenergiesysteme und Hochspannungstechnik (IEH) des Karlsruher Instituts für Technologie (KIT), Karlsruhe
Institut für Technische Mechanik, Institutsteil Dynamik/Mechatronik (ITM) des Karlsruher Instituts für Technologie (KIT), Karlsruhe
Gruppe Intelligente Systeme und Maschinelles Lernen der Universität Paderborn (SICP), Paderborn
Heimann Sensor GmbH, Dresden
Lehrstuhl für Wirtschaftsinformatik, insb. Betriebliche Informationssysteme der Universität Paderborn (SICP), Paderborn
Westfalen Weser Netz GmbH, Paderborn |
| Institute / Department: | FIR e. V. an der RWTH Aachen |
| Informationsmanagement | |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften |
