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The advancing climate change and the resulting need to restructure the energy supply industry mark the beginning of an era of significant upheavals, profoundly impacting energy distribution. The ongoing integration of renewable energy sources introduces new strains on power grids, as they are now confronted with bidirectional energy flows as well as significant fluctuations in energy generation and demand. These developments pose unprecedented challenges to the physical integrity and operational efficiency of distribution networks. Therefore, electrical distribution grid operators (DGOs) must balance these new challenges while ensuring supply security and sustainability, as well as considering economic efficiency. To meet these new challenges, the development of innovative maintenance strategies is essential. In this context, the digitalisation of power grids emerges as a key technology, offering solutions through the introduction of holistic assistance and prognosis systems (APS). This paper focuses on the collection and analysis of requirements leading to the development of an assistance system for maintenance workers in distribution networks based on Large Language Models (LLM). The LLM aims to support workforce management by providing maintenance workers with targeted information and real-time recommendations. The methodology involves conducting expert interviews to gather essential requirements, followed by a thorough analysis of the collected data. Based on this, the foundation for developing the LLM-based assistance system is established. The results demonstrate how LLMs can enhance the efficiency and effectiveness of maintenance management by optimising workflows and supporting personnel in decision-making processes.
Das EU-Projekt PROSPECTS 5.0 dient dem Übergang der Industrie 4.0 zur Industrie 5.0. Der Fokus von Industrie 5.0 liegt auf Menschenzentriertheit, Nachhaltigkeit und Resilienz. Großunternehmen, KMU und Start-ups sollen durch die angestrebten Projektergebnisse von PROSPECTS 5.0 bei der digitalen und ökologischen Transformation in Richtung Industrie 5.0 unterstützt werden. Gleichzeitig soll die Projektarbeit dazu dienen, politische Entscheidungsprozesse zu erleichtern und die Forschung zu stärken. Besonderes Augenmerk liegt hierbei auf produzierenden Unternehmen, da diese eine zentrale Rolle in der industriellen Wertschöpfung spielen. Durch die Implementierung menschenzentrierter Produktionsmethoden
können produzierende Unternehmen ihre Effizienz steigern, nachhaltiger wirtschaften und flexibler auf Marktveränderungen reagieren. Dies fördert nicht nur die Arbeitszufriedenheit und Kreativität der Beschäftigten, sondern steigert letztlich auch die Wettbewerbsfähigkeit der Unternehmen. Durch die Einbindung zahlreicher Stakeholder entstehen praxisnahe und breit anwendbare Lösungen, die auf die spezifischen Bedürfnisse der verschiedenen Zielgruppen abgestimmt sind.
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.