The share of energy produced by small-scale renewable energy sources, including photovoltaic panels and wind turbines, will significantly increase in the near future. These systems will be integrated in microgrids to strengthen the independence of energy consumers. This work deals with energy management in microgrids, taking into account the volatile nature of renewable energy sources. In the developed approach, Multi-Agent Reinforcement Learning is applied, where agents represent microgrid components. The individual agents are trained to make good decisions with respect to adapting to the energy load in the grid. Training of agents leverages the historic energy profile data for energy consumption and renewable energy production. The implemented energy management simulation shows good performance and balances the energy flows. The quantitative performance evaluation includes comparisons with the exact solutions from a linear program. The computational results demonstrate good generalisation capabilities of the trained agents and the impact of these capabilities on the reliability and resilience of energy management in microgrids.

In this paper, we present enabling concepts for Zero Defect Manufacturing (ZDM) based on flexible human–robot interaction. We introduce the Human–Robot Multistation System (HRMS) as a novel framework for flexible, human-initiated task allocation across multiple workstations. A HRMS is defined as one or more workstations that support human–robot collaborative task execution and integrate intelligent perception and interaction systems with coordination logic, enabling alternating or collaborative task execution. These components allow human workers to interact with the system through a gesture-based modality and to receive task assignments. An agent-based task scheduler responds to human-initiated ‘Ready’ signals to pace activities ergonomically. We built a laboratory demonstrator for an Industry 5.0 ZDM final inspection/rework use case and conducted a first pilot study (n = 5, internal participants) to evaluate system usability (SUS), perception (Godspeed), mental workload (NASA-TLX), completion times, and error rates. Results indicated technical feasibility under laboratory conditions and acceptable usability, with SUS 70.5 ± 22 (‘OK’ toward ‘Good’), overall GQS 3.2 ± 0.8, RAW NASA-TLX 37 ± 16.3, mean job throughput time 232.5 ± 46.5 s, and errors in 9/10 jobs (E1–E4). In simulation, a proximity-aware shortest-path heuristic reduced walking distance by up to 70% versus FIFO without productivity loss. We conclude that HRMS is feasible with acceptable user experience under lab constraints, while recurrent task-level failures require mitigation and larger-scale validation.