This paper presents the prototype implementation of an advanced automation architecture for electrical railway systems, designed to operate them as cyber-physical systems, such as smart grids. This REM-S architecture, from the literature, is a distributed system coordinating rolling stock, electrical substations, sideway resources within the constraints, and objectives (overall energy demand, power consumption, and cost optimization) dictated by the control center. The prototype software suites presented here are the REM-S Offline Suite and the REM-S Online Suite. Among others, they consist of an application for day-ahead optimization, which is performed by the control center for the next day, and another application for minute-ahead optimization, which is continuously performed during rail operation on so-called intelligent substations at time intervals of, e.g. 15 min. The REM-S Offline Suite can be used for dry runs of test scenarios before running the aforementioned applications in combination with others on the participating trains, substations, and so forth, as part of the REM-S Online Suite within a railway system in real time. The REM-S Online Suite, which is an implementation of a distributed optimization, was validated in a field test, performed in a suburban 3-kV dc railway line in Málaga, Spain during real rail operation where the objectives within the constraints given by the control center have been successfully reached. Besides, the software architecture of both software suites also selected algorithms that are presented for a better understanding of the overall communication and distributed real-time optimization processes.

Multi-agent systems (MAS) represent a distributed computing paradigm well suited to tackle today’s challenges in the field of the Internet of Things (IoT). Both share many similarities such as the interconnection of distributed devices and their cooperation. The combination of MAS and IoT would allow the transfer of the experience gained in MAS research to the broader range of IoT applications. The key enabler for utilizing MAS in the IoT is the ability to build large-scale and fault-tolerant MASs since IoT concepts comprise possibly thousands or even millions of devices. However, well known multi-agent platforms (MAP), e. g., Java Agent DE-velopment Framework (JADE), are not able to deal with these challenges. To this aim, we present a cloud-native Multi-Agent Platform (cloneMAP) as a modern MAP based on cloud-computing techniques to enable scalability and fault-tolerance. A microservice architecture is used to implement it in a distributed way utilizing the open-source container orchestration system Kubernetes. Thereby, bottlenecks and single-points of failure are conceptually avoided. A comparison with JADE via relevant performance metrics indicates the massively improved scalability. Furthermore, the implementation of a large-scale use case verifies cloneMAP’s suitability for IoT applications. This leads to the conclusion that cloneMAP extends the range of possible MAS applications and enables the integration with IoT concepts.

Smart grids evolve rapidly towards a system that includes components from different domains, which makes interdisciplinary modelling and analysis indispensable. In this paper, we present a cosimulation architecture for smart grids together with a comprehensive data model for the holistic representation of the power system, the communication network, and the energy market. Cosimulation is preferred over a monolithic approach since it allows leveraging the capabilities of existing, well‐established domain‐specific software. The challenges that arise in a multidomain smart grid cosimulation are identified for typical use cases through a discussion of the recent literature. Based on the identified requirements and use cases, a joint representation of the smart grid ecosystem is facilitated by a comprehensive data model. The proposed data model is then integrated in a software architecture, where the domain‐specific simulators for the power grid, the communication network, and the market mechanisms are combined in a cosimulation framework. The details of the software architecture and its implementation are presented. Finally, the implemented framework is used for the cosimulation of a virtual power plant, where battery storages are controlled by a novel peak‐shaving algorithm, and the battery storages and the market entity are interfaced through a communication network.

The integration of renewable energy sources into modern power systems requires simulations with smaller step sizes, larger network models and the incorporation of complex nonlinear component models. These features make it more difficult to meet computation time requirements in real-time simulations and have motivated the development of high-performance LU decomposition methods. Since nonlinear component models cause numerical variations in the system matrix between simulation steps, this paper places a particular focus on the recomputation of LU decomposition, i.e., on the refactorisation step. The main contribution is the adoption of a factorisation path algorithm for partial refactorisation, which takes into account that only a subset of matrix entries change their values. The approach is integrated into the modern LU decomposition method NICSLU and benchmarked against the methods SuperLU and KLU. A performance analysis was carried out considering benchmark as well as real power systems. The results show the significant speedup of refactorisation computation times in use cases involving system matrices of different sizes, a variety of sparsity patterns and different ratios of numerically varying matrix entries. Consequently, the presented high-performance LU decomposition method can assist in meeting computation time requirements in real-time simulations of modern power systems.

In this paper we introduce an approach to accelerate many-scenario (i.e., hundreds to thousands) power system simulations which is based on a highly scalable and flexible open-source software environment. In this approach, the parallel execution of simulations follows the single program, multiple data (SPMD) paradigm, where the dynamic simulation program is executed in parallel and takes different inputs to generate different scenarios. The power system is modeled using an existing Modelica library and compiled to a simulation executable using the OpenModelica Compiler. Furthermore, the parallel simulation is performed with the aid of a message-passing interface (MPI) and the approach includes dynamic workload balancing. Finally, benchmarks with the simulation environment are performed on high-performance computing (HPC) clusters with four test cases. The results show high scalability and a considerable parallel speedup of the proposed approach in the simulation of all scenarios.