Library Operating Systems (libOS) are highly efficient because the entire software stack, from the kernel to the application, is compiled, optimized, and linked together. However, in certain scenarios, such as code injection for network packet analysis or adding custom drivers, it is necessary to extend the kernel as needed. The traditional approach of modifying and recompiling the kernel source code can be time-consuming and error-prone.

The memory-safe systems programming language Rust is gaining more and more attention in the operating system development communities, as it provides memory safety without sacrificing performance or control. However, these safety guarantees only apply to the safe subset of Rust, while bare-metal programming requires some parts of the program to be written in unsafe Rust. Writing abstractions for these parts of the software that are sound, meaning that they guarantee the absence of undefined behavior and thus uphold the invariants of safe Rust, can be challenging. Producing sound code, however, is essential to avoid breakage when the code is used in new ways or the compiler behavior changes.

This work develops and benchmarks surrogate models for Dynamic Phasor (DP) simulation of electrical drives. DP simulations of complex systems may be time-consuming due to the increased number of equations. Thus, it is desirable to have a data-driven approach to compute the critical state/control variables and power losses. The surrogate models are intended to be used as a steady-state equivalent of the DP simulation model. We consider the Gaussian Process (GP), Multi Layer Perceptron, and Random Forest as surrogate models. Among other techniques, GPs are found to have good accuracy. Moreover, GPs are data-efficient and have desirable properties, such as built-in uncertainty quantification. The study shows that the GP performs better compared to other techniques in terms of the Mean Squared Error of the prediction, while still being very fast to evaluate. We illustrate the potential of these surrogate models to also predict transient behavior.

Abstract In high‐performance computing and cloud computing the introduction of heterogeneous computing resources, such as GPU accelerator have led to a dramatic increase in performance and efficiency. While the benefits of virtualization features in these environments are well researched, GPUs do not offer virtualization support that enables fine‐grained control, increased flexibility, and fault tolerance. In this article, we present Cricket: A transparent and low‐overhead solution to GPU virtualization that enables future research into other virtualization techniques, due to its open‐source nature. Cricket supports remote execution and checkpoint/restart of CUDA applications. Both features enable the distribution of GPU tasks dynamically and flexibly across computing nodes and the multitenant usage of GPU resources, thereby improving flexibility and utilization for high‐performance and cloud computing.

Abstract 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.

The current Distribution Grid Automation (DGA) Systems are being heavily dependent on the Information and Communication Technologies (ICT) infrastructure for its proper operation. The DGA architectures are predominantly centralized and usually deployed on a dedicated hardware. This increases the risk of blackouts under a coordinated cyber-physical attack. The compromise of the dedicated hardware that hosts the central coordinator of the DGA automation results in a blackout. Though many countermeasures have already been proposed for tackling different types cyber and physical attacks on the ICT infrastructure, very few measures have been proposed to ensure the availability of the grid operation functions, even when it is compromised. This study proposes an automatic, distributed approach based on Blockchain and Smart Contract that ensures the availability of the core DGA functions even if the central coordinator that operates the grid is compromised. This is done by virtualizing and migrating/re-initialising these functions from the dedicated hardware that was compromised to another. Additionally, a Multi-Attribute Decision Making based method is incorporated into the Smart Contract that helps in selection of the optimal hardware that can host the function considering its limitations (hardware and software). Finally, a proof of concept implementation of the proposed solution is presented that utilizes the Calvin IoT (Internet of Things) platform, Flow programming tool and Hyperledger fabric and its performance is evaluated.

In this paper, a robust neighbor-based distributed cooperative control strategy is proposed for dc cyber-physical microgrids, considering communication delays and slow switching topologies. The proposed robust control strategy can synchronize the voltages of a dc microgrid to the desired value while achieving the optimal load sharing for minimizing distributed energy resources' (DERs) generation cost to achieve their economic operation at the same layer via a sparse communication network considering communication delays and slow switching topologies synchronously. The continuous interaction of physical-electrical and cyber networks generally exacerbates the occurrence of communication delays. Moreover, the arbitrary switching topologies could destroy the system's transient characteristics at the switching time instants. To further quantify these impacts on the system stability, the communication delay and average switching dwell-time-dependent control conditions for the proposed control strategy are proved based on the Lyapunov-Krasovskii theory. Some sufficient conditions for the exponential stability of the cyber-physical delayed-switching system are developed, which guarantees the robustness of the proposed strategy against the communication delays and dynamically changing interaction topologies. The proposed control protocols are shown to be fully distributed and implemented through a sparse communication network. Finally, several cases on a modified IEEE 34-bus test network are investigated which demonstrate the effectiveness and performance of the results.

Selection of a suitable decoupling point for system partitioning represents one of the challenges in distributed realtime simulation (DRTS) and (distributed) power hardware in the loop (D-PHIL) setups. In case of DRTS, the decoupling point represents the point where system is partitioned between two digital real-time simulators. In D-PHIL setups, the decoupling point is a point where a device under test is connected (coupled) to the rest of the simulated system. This work proposes a methodology based on the analysis of eigenvalues and participation matrix of the monolithic continuous model of the system, which refers to naturally coupled system, to determine the suitable decoupling point for system partitioning with respect to the simulation fidelity. Sampling period and delay between subsystems are considered in the analysis, and decoupling points analysis is influenced by them. The methodology is validated based on time-domain simulations.

In this paper, a novel distributed pinning control scheme for the voltage regulation of active distributed networks through limited bandwidth communication. In actual active distributed networks, the information exchange among multiple and cooperative DGs may be subject to these constraints of communication bandwidth and storage space, DGs can only receive uniform quantized information. Towards that end, a distributed pinning control scheme will synchronize the terminal voltage of DGs to their reference value by a virtual leader through sparse communication considering communication band- width and storage space exponential convergence. By means of the Gronwall's inequality tools and algebraic graph theory, distributed pinning protocols are developed to be employed for active distributed networks, in which manner the criteria for the stability analysis to maintain the closed-loop system stable are derived. Simulation results on an active distributed network system are provided to show the effectiveness of the proposed control protocols.

This paper presents a robust distributed event-triggered control strategy that will regulate the power output of massive photovoltaic (PV) generators in a low-voltage (LV) microgird, which can achieve all of PV generators to operate at the same ratio of available power based on their status and capacity through a spare network with limited communication bandwidth and time delays. Due to employing event-triggered communication with time delays, the proposed control strategy is fully distributed and only driven at their own event time, which effectively reduces the frequency of controller updates compared with continuous-time feedback control, moreover is also robust to time delays. Furthermore, each PV only requires the local voltage and current measurement from its own and some nearest neighbors (but not all) for the distributed power control at the last event-triggered time to achieve the active and reactive power outputs to operate at the same ratio. The inequality technique is employed to devise the stability and convergence analysis of the proposed dynamic event-triggered conditions. The effectiveness of the proposed control strategy is verified under various scenarios by a modified IEEE 34-bus test network in MATLAB/SimPowerSystems.

This paper presents an agent-based power scheduling framework for interconnected local energy communities (LECs) using the Nash bargaining solution (NBS) approach to offer a fair and financial reimbursement for changing the operation objectives of such LECs. LECs are a modern form of microgrids (MGs) which consist not only of electrical energy systems but also include thermal energy. The focus is set on the LEC operator needs with different operational objectives, where in classical MGs the island aspect often dominates the operation. We assume that in the future distribution system several LECs will exist and are, therefore, interconnected with each other. It can be assumed that the operational objectives of an LEC and the distribution system operator (DSO) may differ considerably. To align these objectives and achieve system-wide cost-optimal operation, the DSO incentivizes the LECs to deviate from their previously planned power schedules by offering a financial reimbursement that is determined using the NBS mechanism. The local and global cost saving for the proposed agent-based power scheduling framework is demonstrated on a use case scenario modeled after a real pilot microgrid.

As future power systems become increasingly complex and interconnected to other energy carriers, a single research infrastructure can rarely provide the required test-beds to study a complete energy system, especially if different types of real power hardware are expected to be in-the-loop. Therefore, virtual interconnection of laboratories for large-scale systems plays an important role for geographically distributed realtime simulation. This paper presents the improvements made in simulation fidelity as well as usability for establishing future simulator and laboratory connections. A general procedure is proposed and analyzed for geographically distributed real-time simulation, which allows users easily to adapt this procedure to specific test cases. A systematic and comprehensive analysis of a dynamic phasor based co-simulation interface algorithm and its improvements are provided to demonstrate the advantages as well as limitations of this approach.

Matching power transfer between microgrids (MGs) enables maximum utilization of distributed energy resources (DERs), this paper proposes a cluster-oriented cooperative control strategy for multiple ac MG clusters, under which the power sharing among multiple MG clusters can be achieved by an intercluster scheme, whereas the frequency/voltage of all DERs within each MG cluster can also be regulated by an intracluster scheme. By pinning one or some cluster-head DERs from each MG cluster to constitute an intercluster communication network, the intercluster control layer can generate the frequency/voltage references based on the power mismatch among multiple MG clusters. In the intracluster control layer, the pinned DERs propagate these references to their neighbors in an MG cluster, and the frequency/voltage nominal set-points for each DER in the primary control process can be adjusted based on the frequency/voltage errors across the intracluster communication networks. Since the evolutions of intra- and intercluster dynamics may involve different time scales, the upper bound for the ratio of the associated intra- and intercluster time constants is finally derived to guarantee the stability of the whole multi-MG-cluster system. In special, both the intra- and intercluster controllers are designed based on their own sparse cyber networks, allowing different numbers of heterogeneous DERs in each MG cluster. The effectiveness of the control methodology is verified by the simulation of an ac multi-MG-cluster system in MATLAB/SimPowerSystems.

This paper presents a Port-Hamiltonian based modelling and control approach for MVDC Shipboard Power Systems (SPSs). The Line Regulating Converters (LRCs) consist of parallel-connected submodules based on the single-phase Dual Active Bridge (DAB) topology. The load side converters (Point of Load (POL) converters) are tightly regulated buck converters, which exhibit Constant Power Load (CPL) behavior with destabilizing effect on the MVDC bus voltage. We apply here an Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) strategy to the MVDC SPS and evaluate its performance by simulation in MATLAB Simulink. The analyzed scenarios consider large disturbances such as load step up, submodule disconnection and LRC module disconnection. Furthermore, we prove that the entire MVDC is Port-Hamiltonian and therefore the system stability is guaranteed by the IDA-PBC.

This paper investigates the applicability of a Data-Driven Control (DDC) approach for power electronic converters in DC microgrids to stabilize the bus voltage under large perturbations from connection/disconnection of converters. In DC microgrids, loads and generators are interfaced through power electronic converters. When tightly regulated, these loads exhibit a constant power load (CPL) behavior. CPLs have negative incremental impedance behavior which may lead to system instability. The DDC approach is applied to an MVDC microgrid and compared with a model-based non-linear controller; specifically, the Linearizing State Feedback (LSF), that was previously investigated by the authors and is used here as benchmark. Both control techniques act on the interface converters at the generator side. The results of the simulation cases show that the DDC approach has a good performance for bus voltage regulation under system reconfigurations. As opposed to the LSF controller, the DDC does not require accurate analytical models, or exchange of information among converters.

This paper proposes the Linear Swing Dynamics-based Virtual Synchronous Generator (LSD-VSG) for HVDC systems. The aim is to define a new role and behavior for HVDC systems in their participation to AC system frequency support. This results in enhancing the frequency stability of HVDC connected-AC grids based on the LSD concept, whereas preserving a stable operation of HVDC system. Small signal stability and parametric sensitivity analysis have been performed to support the viability of the proposed LSD-VSG and study the sensitivity of the critical eigenvalues to the closed loop system states and corresponding stability region. Additionally, various time-domain simulations are performed and the results prove the effectiveness and features of the LSD-VSG.

Transition to the next-generation power systems is resulting in the increasing deployment of High-Voltage direct current (HVdc) connections and multi-terminal HVdc grids. To support this transition, a holistic real-time simulation and testing infrastructure is beneficial. We propose realization of such infrastructure based on virtual interconnection of geographically dispersed laboratories. One of the main challenges associated with realization of this concept is compensating the effect of communication network and ensuring stability of Geographically Distributed Real-Time Simulation (GD-RTS). In this work we propose a co-simulation Interface Algorithm (IA) based on wave transformation (WT), a method widely utilized in the field of bilateral teleoperation. WT-based co-simulation IA is applied for GD-RTS of HVdc link with a partitioning point at a dc cable. While co-simulation IA based on Ideal Transformer Model (ITM) is not stable, WT-based co-simulation IA ensures simulation stability for large values of time delay.

The growing share of Distributed Generators (DG) brings with it a fundamental change in dispatchability and location of the overall power generation portfolio. A significant share of DGs are renewable-based and connected to the distribution system, replacing the conventional generators placed at high voltage levels. With this fundamental shift in the power system, more active approaches to distribution system operation are required. In this paper the authors propose a model to utilize distributed flexibilities to locally balance power generation and demand, minimizing the power exchange between low and medium voltage levels. The proposed model is applied to several low voltage networks in the German distribution system. Simulation results show that the proposed model decreases the peak load in tested LV networks by 2.6-12.5 %, and leads to an overall flattening of the load profile.

Distribution system operators in rural areas of Germany are frequently facing imminent equipment overloading caused by the feed-in of local renewable Distributed Generation (DG). A grid operator’s last resort to maintain system stability and avoid protection tripping is to temporarily curtail local feed-in until hosting capacity in the network has caught up with the demand. Due to technological limitations in today’s networks the volume of curtailed energy can be greater than what would strictly be necessary. This paper presents a case study of a 110 kV overhead line in Avacon’s network and demonstrates the limitations of today’s approach to DG curtailments, especially the relative coarse granularity of control steps. The authors develop a novel control algorithm for emergency curtailments that takes advantage of technological improvements and describes the architecture for a successful deployment at the example of Avacon’s network and SCADA. The authors compare the amount of curtailed energy under today’s best practice with the theoretical optimum and the novel approach.

In the European demonstration Project InterFLEX, five different demonstration sites are realized with a focus on flexibility services from energy generation and demand. The use of flexibility for grid and generation-supply balance management is a key factor in renewable-based electricity systems. However, the way such flexibility services are activated from an Information and Communication Technology (ICT) perspective requires harmonization across Europe. As a step on this way, this paper proposes a methodology based on the Smart Grid Architecture Model (SGAM) to study cross-demonstrator ICT interoperability and show its application in the InterFLEX context. This is a multi-player environment with Distribution System Operators (DSOs), aggregators, Electric Vehicle (EV) charging operators and other actors, providing a representative overview of contemporary flexibility use and implementation options. In particular, a SGAM clustering approach is set up in order to group devices and actors within common entities. The results of this analysis show that the use-cases and interfaces are comparable between the demos, however the chosen solutions and protocols are manifold and very demonstrator-specific. Future candidates for future selection of interfaces and protocols from an InterFLEX point of view are discussed.

In this paper we propose a sequence-to-sequence machine learning architecture for time-series forecasting based on recurrent neural networks. This architecture can be used as a general purpose forecasting method and is evaluated for the application of short-term electric load forecasting in this paper. The proposed sequence-to-sequence architecture1combines elements of auto-regressive forecasting techniques with multivariate regression by including exogenous variables for each forecasted time step as well as previous values when inferring forecasts. We assess the proposed architecture on a load data set provided by the Global Energy Forecasting Competition. The conclusion is that it outperforms other machine learning forecasting techniques as well as time-series analysis methods.1The implementation is available at: https://github.com/HenWil13/ieeeINDIN18

Summary The continuous growth of supercomputers is accompanied by increased complexity of the intra‐node level and the interconnection topology. Consequently, the whole software stack ranging from the system software to the applications has to evolve, eg, by means of fault tolerance and support for the rising intra‐node parallelism. Migration techniques are one means to address these challenges. On the one hand, they facilitate the maintenance process by enabling the evacuation of individual nodes during runtime, ie, the implementation of fault avoidance. On the other hand, they enable dynamic load balancing for an improvement of the system's efficiency. However, these prospects come along with certain challenges. On the process level, migration mechanisms have to resolve so‐called residual dependencies to the source node, eg, the communication hardware. On the job level, migrations affect the communication topology, which should be addressed by the communication stack, ie, the optimal communication path between a pair of processes might change after a migration. In this article, we explore migration mechanisms for HPC and discuss their prospects as well as the challenges. Furthermore, we present solutions enabling their efficient usage in this domain. Finally, we evaluate our prototype co‐scheduler leveraging migration for workload optimization.

As a general rule, when writing parallel applications according to the MPI standard, the programmer does not need to worry about the underlying hardware topology. This is because the MPI standard intentionally hides the actual hardware topology from the application programmer for the seizure of portability, while at the same time burdening the MPI implementation to handle hardware-related peculiarities as optimal as possible. So, for instance, with the emergence of SMP systems, locality-awareness in terms of the recognition of accelerated node-internal communication found its way into all major MPI libraries in the early 2000s. However, the actually implemented degree of such a locality-awareness can vary: From the simple usage of point-to-point communication over shared-memory, via the smart adaptation of collective communication patterns, through to the exploitation of direct accessible address spaces for one-sided communication. Until now, all these locality-related optimizations basically assume a static hierarchical topology in the course of a parallel program. In contrast, this article strives for a discussion of how dynamically changing topologies, as they may result from process migrations, can be considered during runtime for locality-awareness. In doing so, the article focuses on collective communication, but also discusses the challenges for point-to-point and one-sided communication.

This paper presents a framework for the optimal operation of data centers, leveraging their heating, ventilation, and air conditioning unit, delay-tolerant information technology workload and battery storage system for participating in demand response programs. In this context, an model predictive control based control framework has been developed that guarantees the reliable operation of the data centers core activities. We derive a modeling approach to represent the dynamics of the data centers subsystems and validate it for a data center test bed via practical experiments. Hereby, the thermal subsystem leads to deviations of less than 0.60 K in the modeled outlet temperature. The validated model is used for incremental prototyping of the proposed control via simulations under uncertainties. The results demonstrate a mean absolute error of the relative deviations between the data center consumption and the target load profile of 2.71% for an incentive-based scenario and a cost reduction of 3.86% for a price-based scenario.

This paper presents the two level optimization algorithms: a centralized day-ahead and decentralized minute-ahead algorithm for energy management in an integrated mainline railway system. All energy players, such as trains, infrastructure facilities, wayside storages, and distributed energy resources, are considered in the simulation. The algorithms are developed to demonstrate the railway energy management system architecture. This paper demonstrates the validity of the algorithms and analyzes the simulation results in offline and online real case studies. In the online case study, the developed system for minute ahead optimization and real-time operation was tested on the Malaga-Fuengirola line (Spanish railway) for a few hours. The optimization is done regarding three different objectives: cost optimization or energy consumption optimization or power demand optimization.

Most applications running on supercomputers achieve only a fraction of a system's peak performance. It has been demonstrated that the co-scheduling of applications can improve the overall system utilization. However, following this approach, applications need to fulfill certain criteria such that the mutual slowdown is kept at a minimum. In this paper, we present an HPC scheduler that applies co-scheduling and utilizes virtual machine migration for a re-orchestration of applications at runtime based on their main memory bandwidth requirements. Given a job queue consisting of main memory-bound applications and compute-bound applications, we can see a throughput increase of up to 35% while at the same time reducing energy consumption by around 30%.

In this paper, we present a methodology for the optimal sizing of the Battery Energy Storage Systems (BESS) in Data Centers (DC) to provide Frequency Containment Reserve (FCR) or Primary Control Reserve (PCR). In this context, the BESS can offer ancillary services to the grid without disrupting the normal operation of the DC. By taking into consideration the requirements in terms of backup power provision to the DC as well as the FCR, along with the investment and operating costs, the optimal sizing of the BESS is determined. The results obtained from the DC's simulation show the economic benefits of providing FCR to the grid using Li-Ion Lead-Acid BESSs, which in turn helps generate additional revenues for the DC operators and reduce the operating costs without hampering the necessary uptime requirements and the normal operation of the DC.

This paper presents a case study of different control approaches for a single phase Dual Active Bridge (DAB) based MVDC Shipboard power system (SPS). The Line Regulating Converters (LRCs) are based on DAB topology and the load side converters or Point of Load Converters (POLs) are based on buck topology. The tightly controlled POLs exhibit a constant power load (CPL) behaviour, which has a destabilising effect on the bus voltage. This paper also considers and investigates the impact of measurement uncertainties on stability and bus voltage quality. In this work, the linear control strategies LQI, H2, H∞and MPC are designed and implemented for the DAB converter. Large signal test cases such as load side, source side connections or disconnections are performed and the performance of the proposed robust control strategies are compared.

This paper proposes the application of advanced modelling and control strategies for a single phase Dual Active Bridge (DAB) based Medium Voltage DC (MVDC) Shipboard Power System (SPS). In these systems the tightly controlled point of load (POL) converters exhibit a constant power load (CPL) behaviour, which has a destabilising effect on the bus voltage. In this work, a Multiple Input Single Output (MISO) state space plant model based on switching function for the triple phase shift (TPS) modulated DAB converter is derived. For obtaining the MISO model the generalised state space averaging (GSSA) technique was used. A H∞mixed sensitivity controller is designed to tackle the negative impedance characteristics of tightly regulated Point of Load (POL) converters, presence of measurement uncertainty and high current stress. This paper also presents a detailed description of the design of weighting functions required for the mixed sensitivity formalism. The effectiveness of the proposed modelling scheme and control strategy is studied through system level simulations. A comparative study with the previously proposed TPS control strategy is performed.

In this paper, we address the challenges of Data Centers (DCs) as ancillary service providers in the Smart Grid context. A DC power management methodology has been defined for scheduling of electrical and thermal components. As a result, the power demand of HVAC (Heating, Ventilation, Air-Conditioning) system and the power produced via back-up generators adapts to sudden changes in frequency of the grid. This methodology leverages on a detailed simulation of the DC, encompassing dynamic models of the thermal and electrical components. To demonstrate the viability of the proposed power management methodology, numerical simulation experiments have been carried out oriented to future application in real pilot DCs.

The interest in the virtual integration of hardware and software assets located at geographically dispersed locations, although not new, has spiked recently. However, realizing joint real-time simulation in connected laboratories is posing new challenges. This paper discusses the generalized requirements of a framework for the virtual integration of laboratories and presents the architecture of the platform that integrates two real-time digital simulators (RTDS located at ACS, RWTH Aachen University, Germany, and OPAL-RT at Politecnico di Torino, Italy). The platform enables remote and online monitoring of the entire interconnected system which is a step towards developing Simulation as a Service concept. The application of this platform for real-time co-simulation of interconnected transmission and distribution systems is demonstrated.

Transient system-level simulations of future DC grids require modeling the behavior at the terminals of the power system's components, and thus also of power converters. The internal electrical variables of the power electronic devices are not relevant in such simulations. Based on this, this paper presents an approach for eliminating all the internal electrical nodes of a single-phase Dual-Active Bridge DC-DC converter for EMTP-type simulations. Nonetheless, the switching dynamics at the terminals of the converter are maintained, while the dynamic effects of the converter-internal circuitry like transformer and capacitors are accounted for. By reducing all internal nodes of the converter model, the computational burden is reduced, which is especially important for system-level simulations of large grids. The approach presented here is a first step towards a similar implementation for the three-phase Dual-Active Bridge DC-DC converter topology, a promising converter topology for future DC grids.

The increasing use of energy from renewable sources will entail significant changes in power systems. On the generation as well as the load side more and more power electronics devices are in operation, resulting in an increase of harmonics in the power systems. For simulating modern power systems, linear steady-state modeling approaches like single-frequency phasor analysis are not sufficient for accurate simulations or a reliable stability assessment. Different simulation techniques, taking also non-linear components and therefore the generation of harmonics into account, are already known. When the steady-state of a power system is of interest, simulations in the frequency domain are usually more efficient than simulations in the time domain. The X-Parameters behavioral modeling approach for modeling non-linear components entirely in the frequency domain is until now primarily used in microwave engineering. In this paper, this approach is applied for modeling a basic non-linear power electronics device which is common in power systems, a single-phase single diode rectifier.

This paper presents an implementation of an IEC 61850-based real-time co-simulation platform for verification of the performance of a volt-VAR optimization (VVO) engine for smart distribution networks. The proposed VVO engine is able to minimize grid loss, volt-VAR control asset operational costs, and conservation voltage reduction operational costs through its comprehensive objective functions, weighted by fuzzification using advanced metering infrastructure (AMI) data. The optimization engine receives the AMI data stream through measurement aggregators. Moreover, it sends control commands to volt-VAR control components modeled in real-time digital simulator (RTDS) through DNP.3 protocol. To check the performance and the precision of proposed VVO, a fault scenario is imposed upon the system. IEC 61850 GOOSE messages are generated and sent to change the status of specified breakers, while the VVO engine receives system reconfiguration commands via IEC61850 Manufacturing Message Specification (MMS) protocol. The results of the study on 33-node feeder showed adequate performance of proposed VVO in grid operating scenarios.

The geographically distributed simulation concept enables connecting laboratories over long distances with the goal of sharing simulation resources and integrating multiple (Power) Hardware-in-the-Loop setups. The main obstacle in applying this concept is the impact of the communication medium on fidelity and stability of the simulation. This paper presents advantages and challenges of developing a simulator-to-simulator interface based on the time-frequency representation of interface quantities. The proposed approach is first analyzed using a simple electrical circuit as case study, and then conclusions are verified on a larger system including voltage-source converters that realize a high-voltage dc point-to-point link which connects two ac systems. To assess the approach in a realistic framework, an Internet-distributed simulation platform that integrates two remote real-time digital simulators, OPAL-RT (located at University of South Carolina, USA) and OPAL-RT (located at RWTH Aachen University, Germany) is developed and both linear and nonlinear system models are simulated.

The concept of an all-electric ship, while offering unprecedented advantages from the point of view of efficiency and flexibility of operation, has introduced new challenges in terms of stability and power flow control. The advent of a full power electronics power system has raised new questions from the point of view of system dynamics, particularly when dealing with the new medium-voltage direct current distribution. The overall goal of guaranteeing a secure operation of the power system has brought researchers to consider two main approaches: reducing the dynamics of the large load to operate in a range of dynamics compatible with traditional generation systems, or making the generator set smarter through its power electronics interface. This paper compares these approaches to stable operation, focusing on the latter considered more in line with the progress of technology and in general more appealing.

Migration from today's power systems to future smart grids is a necessity as the energy demand continues to grow and an increasing amounts of renewable energies need to be accommodated in the grid. One of the key enablers of the smart grid is the integration of information and communication technology (ICT) into the grids in order to monitor and control power generation, distribution, and demand. Considering the close interdependence of future smart grids and communication networks, there is a need for numerical simulation to thoroughly understand the impact of the communication networks on the performance of power system dynamics, and vice versa. This paper provides an overview of available simulation techniques for smart grid communications with a focus on cosimulation frameworks and their enabling technologies. A decision tree comparing relative advantages of available cosimulation platforms and providing guidelines on how to select from them for a given application is presented. A case study analyzing agent-based shipboard smart grid protections with VPNET is presented.

This work introduces a multidomain simulation platform that enables a holistic analysis of city district scale energy systems. The objective for the development of the simulation platform is to provide a tool that supports the design of control and energy management algorithms for those systems. The platform allows long-term simulations of a large number of buildings, including internal energy supply or energy conversion systems, in combination with external energy supply systems like the electrical grid. The simulation of those physical systems represents the environment for sophisticated control and energy management algorithms that can be tested on the platform. The concept of this work is to combine commercial-off-the-shelf software packages, here simulators and runtime infrastructure (RTI), to a high performance multidomain cosimulation platform. The high performance of the platform regarding computation time has been achieved by exploiting the parallel computing capabilities of modern simulation servers. Especially the computation time of large numbers of instances of Modelica-based models has been reduced significantly by the development of the parallel execution framework (PEF). The implementation of the PEF, including the interface to the individual models and to the RTI, is described in detail. The partitioning of the simulated system among different simulators does not influence the simulation results, as shown on the basis of a small-scale simulation scenario. The performance regarding the computation time is demonstrated on several example simulation scenarios showing the scalability of the platform.

The operation of a microgrid becomes significantly complex with the high penetration of distributed energy resources (DERs), demand-side management, market operation, and disconnection and reconnection to the utility grid. Therefore, development of advanced tools/platforms for testing operation and control of microgrid has attracted more and more attention nowadays. The current literature reveals that the microgrid's control and management are designed to be tested either in a numerical simulation approach, or only under a specific hardware device/experiment environment; they do not deal with a comprehensive platform capable of easily executing very complex applications built by composing required functionalities in a standardized, easy-to-use, and well-defined way. To address the problem of limited testing functions of existing tools/platforms, a hardware-in-the-loop (HIL) approach, in particular combining a power-HIL (PHIL) and a signal-HIL (SHIL), is proposed in this paper. Such an approach is suitable for testing the system-level controller energy management systems (EMSs) and hardware controllers at signal level, as well as hardware devices like power converters at power level. Hence, this platform is designed for flexibility and universality. The HIL platform is presented in this work and its performance is demonstrated in a sample application.

Real-time simulation and its use in Hardware in the Loop and Power Hardware in the Loop testing may not be realized with one single hardware platform. Instead, multiple systems are needed to realistically address the analysis and testing needs of the smart grid. This is particularly the case when the target system is the future electrical distribution grid, with its multi-physic, hybrid nature, its pervasive power electronics and communication. The approach adopted at the authors' laboratory is presented to exemplify the interface and integration challenges when such platform is extended to realize relevant scenarios.

Measurements validation is a critical feature in monitoring systems required by most industry applications to achieve higher level reliability. This paper presents the use of the measurement thresholds generated from the propagation of parametric uncertainty using polynomial chaos theory (PCT) to validate the sensor measurements of an AC motor drive by means of fuzzy techniques. If measurements fail the validation check, they are replaced by reconstructed data to maintain the operation. Reconstruction is performed with a PCT observer, which also supports the evaluation of the thresholds. The algorithms proposed here have been implemented and tested both in simulation and in real time experiments on a field oriented controlled induction machine.

This short paper outlines how polynomial chaos theory (PCT) can be utilized for manipulator dynamic analysis and controller design in a 4-DOF selective compliance assembly robot-arm-type manipulator with variation in both the link masses and payload. It includes a simple linear control algorithm into the formulation to show the capability of the PCT framework.

Strong timing requirements and complex interactions with controlled elements complicate the design and validation of software controllers. Different techniques have been proposed to cope with these problems during the different development steps: for example, differential equations for modeling controlled elements, the IEC 1131-3 notations for designing the software controller, and formal models for validating the design, but no definitive solutions have been proposed yet. The paper describes PLCTOOLS, a toolbox that exploits all the aforementioned techniques to supply an integrated environment for the design, formal validation, and automatic code generation of software controllers.