For simulating cyber-physical energy systems, distinct simulation domains need to be integrated for a comprehensive analysis of the interdependent subsystems. In particular, continuous time-based power system simulation and discrete event-based simulation of information and communication technology (ICT) networks are critical for investigating future smart grids. This paper presents the novel cosimulation environment INtegrated coSimulation of Power and ICT systems for Real-time Evaluation (INSPIRE) of power and ICT systems using the high-level architecture (HLA) (IEEE Std. 1516-2010). By applying the HLA standard, the environment is extendable to other simulation domains and a variety of functionalities and models, like thermal load models and additional third-party tools. Moreover, the HLA enables computational performance by distributed execution of each simulator on its own hardware and, thus, the applicability to realistic large-scale scenarios. INSPIRE has been designed under consideration of industrial standards [IEC 61850,object linking and embedding (OLE) for process control (OPC), and the common information model (CIM) (IEC 61968/61970)] in order to enhance interoperability, and to develop and validate solutions close to practice. The simulation results underline the interdependencies of power and ICT systems, and the necessity of an integrated analysis.

Future power systems are characterized by a high degree of complexity with a large number of intelligent devices, exchanging and processing both huge amounts of data and realtime critical information. Accordingly reliable, real-time capable and secure communication networks are required for enabling autonomous monitoring, management and control to guarantee stable power system operation. In this paper, we present and analyse a flexible and dynamic network control approach based on Software-Defined Networking (SDN) for meeting the specific communication requirements of both distribution and transmission power grid. Therefore a testbed is introduced, enabling the evaluation of multiple failure scenarios such as link disturbance and congestion by analysing corresponding fast recovery and prioritization solutions. The performance and robustness of the developed strategies is shown using highly-critical monitoring and control messages on basis of IEC 61850 and considering the mutual impact with low priority background traffic. Results indicate the advantages of SDN compared to traditional routing and Quality-of-Service mechanisms, providing a more reliable communication network, which is able to handle complex failure scenarios. In particular, SDN enables the integration of diverse network management functions and thus offers the power system new options for dealing with faults even in the case of overall outages. On the basis of these results, we demonstrate challenges and derive future benefits for a SDN-enabled Smart Grid communication network, holding the potential to evolve into a self-healing infrastructure.

With the transition toward a smart grid, the power system has become strongly intertwined with the information and communication technology (ICT) infrastructure. The interdependency of both domains requires a combined analysis of physical and ICT processes, but simulating these together is a major challenge due to the fundamentally different modeling and simulation concepts. After outlining these challenges, such as time synchronization and event handling, this paper presents an overview of state-of-the-art solutions to interface power system and ICT simulators. Due to their prominence in recent research, a special focus is set on co-simulation approaches and their challenges and potentials. Further, two case studies analyzing the impact of ICT on applications in power system operation illustrate the necessity of a holistic approach and show the capabilities of state-of-the-art co-simulation platforms.

The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.

Ultra-wideband localization has become a key solution for a variety of industrial applications and recently challenged a significant amount of research. Most of the current research however, focusses primarily on the analysis of the localization accuracy of one or few mobile users in well-defined system environments. This work aims to analyze the interdependency of various system performance criteria, in particular multi-user scalability, real-time capability and energy efficiency of time of arrival based wireless localization. We provide an overview by comparing the predominant system topologies and develop analytical models validated by experiments to evaluate the individual trade-offs. Furthermore, we provide an implementation proposal and in-depth evaluation of emerging time-difference of arrival scheduled channel access to allow for reliable, highly scalable and energy efficient localization enabling a paradigm shift for many industrial applications. We could show that compared to the most commonly used schemes, the number of users and the energy consumption can be improved by orders of magnitude.