Decentralized renewable energy generation and consumption through microgrids, coupled with short- and long-term storage systems and enhanced demand flexibility, represent a promising strategy for mitigating grid stress and reducing emissions in the industrial sector. However, transitioning into a sustainable industry often poses challenges in terms of economic feasibility. This review surveys current optimization approaches and simulation functionalities to enhance feasibility. It follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, covering 1066 studies from 2016 to 2023 across three research areas: optimal system sizing of microgrids (OSS), optimization of electrical energy distribution to storage systems and consumers (EED), and energy flexibilization of factories (EF). As a result, 24 filtered sources from these areas were analyzed. Quantitative analysis indicated that evolutionary and swarm-inspired metaheuristics are predominantly applied in OSS, whereas exact linear problem solvers are favored for EED and EF optimization. A range of functionalities is available, and approaches often prioritize individual functionalities, such as load forecasting, dynamic electricity pricing, and statistical representation of energy generation, rather than comprehensively integrating them. Furthermore, no current approach simultaneously integrates optimization and simulation models across all three research areas.

Occupational exoskeletons for industrial workplaces hold significant promise for improving worker ergonomics and safety. However, the successful selection of an exoskeleton depends on informed decision-making processes that consider various factors ranging from biomechanical performance to usability and compatibility with work tasks. This paper presents a methodology that aims to develop a co-simulation-based selection tool for selecting an exoskeleton for specific industrial work tasks. It integrates multidisciplinary knowledge from biomechanics, human factors engineering, and industrial ergonomics for assessing the suitability of exoskeletons across diverse industrial applications. The methodology is designed as a stage-gate process with five main stages corresponding to the product development process. It describes the main tasks in each phase, their results, and the gates between the stages. The tasks and results are derived and detailed from the current literature and preliminary work. The gates include the specification of the simulation and decision-relevant input and output parameters, the design of the co-simulation model consisting of task and biomechanical simulation, the weighting of the individual decision criteria, and the subsequent implementation of the multi-criteria decision analysis to create a ranking of suitable exoskeletons. This work concludes by elaborating on the impact of the novel co-simulation methodology on research and industry. Research implications include advanced simulation methods for exoskeleton evaluation, the systematic comparison of different exoskeletons, and the development of decision analysis models. Benefits to the industry include improved compatibility, informed selection processes, reduced investment risks, and increased technology adoption.

Die erfolgreiche Auswahl eines industriellen Exoskeletts erfordert für den Selektionsprozess eine fundierte Kenntnis über die betrachteten Arbeitsaufgaben. Dieser Beitrag beleuchtet zuerst den Status Quo bei Vorgehensweisen zur Auswahl eines Exoskelettes. Folgend wird ein Ansatz zur Überbrückung der Informationslücke zwischen der Aufnahme von Bewegungsdaten und der arbeitswissenschaftlichen Bewertung von Tätigkeiten vorgestellt, der als Start eines wissensbasierten Auswahlprozesses dient.