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The impact of mobility decisions not only shapes urban traffic patterns and planning, but also its associated effects, such as greenhouse gas (GHG) emissions. Although e-bike sharing is not a new concept, it has shown significant strides in technological progress in recent years due to the ongoing process of digitalization, specifically towards decarbonization effects. Past studies have shown that e-bike sharing shows a potential as a fast, mobile, and environmentally friendly alternative to cars and public transport. Although e-bikes represent a viable alternative to traditional means of transportation, there is a lack of quantification in understanding the impact and acceptance of e-bikes towards social contexts as well as its adoption as a type of sharing concept. In this paper, we employ the Unified Theory of Acceptance and Use of Technology (UTAUT) model as an analytical framework to discern the use and acceptance of e-bike sharing as an emerging technological concept across different cities and social contexts. Our findings reveal that the e-bike sharing system's utilization is skewed towards a small percentage of "frequent users", and overall usage is biased towards younger, more-educated, and higher-income populations who live in bike-friendly areas. Our work contributes to the feasibility of embedding the e-bike sharing concept in the scope of the energy transition. 

The design and analysis of multi-agent human cyber-physical systems in safety-critical or industry-critical domains calls for an adequate semantic foundation capable of exhaustively and rigorously describing all emergent effects in the joint dynamic behavior of the agents that are relevant to their safety and well-behavior. We present such a semantic foundation. This framework extends beyond previous approaches by extending the agent-local dynamic state beyond state components under direct control of the agent and belief about other agents (as previously suggested for understanding cooperative as well as rational behavior) to agent-local evidence and belief about the overall cooperative, competitive, or coopetitive game structure. We argue that this extension is necessary for rigorously analyzing systems of human cyber-physical systems because humans are known to employ cognitive replacement models of system dynamics that are both non-stationary and potentially incongruent. These replacement models induce visible and potentially harmful effects on their joint emergent behavior and the interaction with cyber-physical system components. 

As automation increases qualitatively and quantitatively in safety-critical human cyber-physical systems, it is becoming more and more challenging to increase the probability or ensure that human operators still perceive key artifacts and comprehend their roles in the system. In the companion paper, we proposed an abstract reference architecture capable of expressing all classes of system-level interactions in human cyber-physical systems. Here we demonstrate how this reference architecture supports the analysis of levels of communication between agents and helps to identify the potential for misunderstandings and misconceptions. We then develop a metamodel for safe human machine interaction. Therefore, we ask what type of information exchange must be supported on what level so that humans and systems can cooperate as a team, what is the criticality of exchanged information, what are timing requirements for such interactions, and how can we communicate highly critical information in a limited time frame in spite of the many sources of a distorted perception. We highlight shared stumbling blocks and illustrate shared design principles, which rest on established ontologies specific to particular application classes. In order to overcome the partial opacity of internal states of agents, we anticipate a key role of virtual twins of both human and technical cooperation partners for designing a suitable communication. 

This study develops a comparative, sociotechnical design perspective for interdisciplinary teams of social scientists and computer scientists. Sociotechnical design refers to identifying both technical and governance challenges and to understanding the ways in which the two types of problems affect and define each other. Approaching design as an open-ended, iterative process, the study develops a triple comparative perspective to problem finding and solutions: across two types of technological systems (the smart grid and connected and automated vehicles), three areas of societal implication and values (safety, equity, and privacy), and two continents (North America and Europe with a focus on the U.S. and Germany). The study then describes the implementation in an international collaboration of research and teaching. The collaborative experience and comparative research provide insights into the salience of the values across technological systems, portability of solutions across technological systems, and potential for policy harmonization across countries. 

Simulation-based analysis is essential in the model-based design process of Cyber-Physical Systems (CPS). Since heterogeneity is inherent to CPS, virtual prototyping of CPS designs and the simulation of their behavior in various environments typically involve a number of physical and computation/communication domains interacting with each other. Affordability of the model-based design process makes the use of existing domain-specific modeling and simulation tools all but mandatory. However, this pressure establishes the requirement for integrating the domain-specific models and simulators into a semantically consistent and efficient system-of-system simulation. The focus of the paper is the interoperability of popular integration platforms supporting heterogeneous multi-model simulations. We examine the relationship among three existing platforms: the High-Level Architecture (HLA)-based CPS Wind Tunnel (CPSWT), MOSAIK, and the Functional Mockup Unit (FMU). We discuss approaches to establish interoperability and present results of ongoing work in the context of an example. 

The recent attention towards research and development in cyber-physical energy systems has introduced the necessity of emerging multi-domain co-simulation tools. Different educational, research and industrial efforts have been set to tackle the co-simulation topic from several perspectives. The majority of previous works has addressed the standardization of models and interfaces for data exchange, automation of simulation, as well as improving performance and accuracy of co-simulation setups. Furthermore, the domains of interest so far have involved communication, control, markets and the environment in addition to physical energy systems. However, the current characteristics and state of co-simulation testbeds need to be re-evaluated for future research demands. These demands vary from new domains of interest, such as human and social behavior models, to new applications of co-simulation, such as holistic prognosis and system planning. This paper aims to formulate these research demands that can then be used as a road map and guideline for future development of co-simulation in cyber-physical energy systems. 

We propose a reference architecture of safety-critical or industry-critical human cyber-physical systems (CPSs) capable of expressing essential classes of system-level interactions between CPS and humans relevant for the societal acceptance of such systems. To reach this quality gate, the expressivity of the model must go beyond classical viewpoints such as operational, functional, and architectural views and views used for safety and security analysis. The model does so by incorporating elements of such systems for mutual introspections in situational awareness, capabilities, and intentions to enable a synergetic, trusted relation in the interaction of humans and CPSs, which we see as a prerequisite for their societal acceptance. The reference architecture is represented as a metamodel incorporating conceptual and behavioral semantic aspects. We illustrate the key concepts of the metamodel with examples from cooperative autonomous driving, the operating room of the future, cockpit-tower interaction, and crisis management. 

To reshape energy systems towards renewable energy resources, decision makers need to decide today on how to make the transition. Energy scenarios are widely used to guide decision making in this context. While considerable effort has been put into developing energy scenarios, researchers have pointed out three requirements for energy scenarios that are not fulfilled satisfactorily yet: The development and evaluation of energy scenarios should (1) incorporate the concept of sustainability, (2) provide decision support in a transparent way and (3) be replicable for other researchers. To meet these requirements, we combine different methodological approaches: story-and-simulation (SAS) scenarios, multi-criteria decision-making (MCDM), information modeling and co-simulation. We show in this paper how the combination of these methods can lead to an integrated approach for sustainability evaluation of energy scenarios with automated information exchange. Our approach consists of a sustainability evaluation process (SEP) and an information model for modeling dependencies. The objectives are to guide decisions towards sustainable development of the energy sector and to make the scenario and decision support processes more transparent for both decision makers and researchers. 

The complex and often safety-critical nature of cyber-physical energy systems makes validation a key challenge in facilitating the energy transition, especially when it comes to the testing on system level. Reliable and reproducible validation experiments can be guided by the concept of design of experiments, which is, however, so far not fully adopted by researchers. This paper suggests a structured guideline for design of experiments application within the holistic testing procedure suggested by the European ERIGrid project. In this paper, a general workflow as well as a practical example are provided with the aim to give domain experts a basic understanding of design of experiments compliant testing. 

Traditional power systems education and training is flanked by the demand for coping with the rising complexity of energy systems, like the integration of renewable and distributed generation, communication, control and information technology. A broad understanding of these topics by the current/future researchers and engineers is becoming more and more necessary. This paper identifies educational and training needs addressing the higher complexity of intelligent energy systems. Education needs and requirements are discussed, such as the development of systems-oriented skills and cross-disciplinary learning. Education and training possibilities and necessary tools are described focusing on classroom but also on laboratory-based learning methods. In this context, experiences of using notebooks, co-simulation approaches, hardware-in-the-loop methods and remote labs experiments are discussed.