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This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC. [ABSTRACT FROM AUTHOR]

The transition to Smart Grids increases the complexity of power grids by involving many more interdependent actors and integrating additional information and communications technology. To provide a common basis for Smart Grid data representation and exchange, the standardized Common Information Model (CIM) has been introduced and extended, i. a., by the Common Grid Model Exchange Specification (CGMES). An increasing acceptance by power grid operators and other actors has made CIM and CGMES more and more relevant. However, the implementation of CIM / CGMES support in software projects appears to be challenging due to the complexity of CIM / CGMES and the ongoing standardisation process with iterative adaptations. Thus, the main contribution of this paper is the presentation of a methodology for an automated generation of programming language specific code from CIM / CGMES specifications. The approach is based on the use of a template language and enables to keep software projects fully compliant with CIM / CGMES specifications. The paper outlines the process of code generation and the consecutive codebase integration for a JavaScript based CIM / CGMES web editor and for two CIM / CGMES de‐/serialiser libraries in C++ and Python. The approach is evaluated in use cases involving the visualisation and simulation of a benchmark grid. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach for multi-level parallel-in-time (PinT) electromagnetic transient (EMT) simulation. We evaluate the approach in the context of power electronics system-level simulation. While PinT approaches to power electronics simulations based on two-level algorithms have been thoroughly explored in the past, multi-level PinT approaches have not yet been investigated. We use the multigrid-reduction-in-time (MGRIT) method to parallelize a dedicated EMT simulation tool which is capable of switching between different converter models as it operates. The presented approach yields a time-parallel speed-up of up to 10 times compared to the sequential-in-time implementation. We also show that special care has to be taken to synchronize the time grids with the electronic components' switching periods, indicating that further research into the usage of different models from adequate model hierarchies is necessary. [ABSTRACT FROM AUTHOR]

In this article, an approach is proposed for the online diagnostic analysis of power electronic converters utilizing real-time, probabilistic digital twinning. Under this approach, a digital twin (DT) of a power converter is defined as a real-time, probabilistic simulation model with stochastic (random) variables, developed using generalized polynomial chaos expansion. The DT models are partitioned in perspective of control layers for power converter subsystems in the approach, with emphasis on the application and converter control layers. Real-time executed solvers of these divided probabilistic DT models are embedded into power converter controllers running on field programmable gate array (FPGA) computing devices. Using a monitoring system, the real-time probabilistic DTs at each control layer and the corresponding physical twins of the power converter are compared by the controllers to determine if the power converter is operating within probable behavior. Knowing the large computational cost of probabilistic modeling, the resource usage and timing of real-time DT solvers on modern FPGAs is reported for common power electronic converter topologies, showing the approach is feasible and able to perform probabilistic real-time simulation of smaller power converters in perspective of application and converter control layers using ≤ 2 μs time steps, with as low as 70 ns steps; while staying embeddable within FPGA-based controllers. To highlight the capabilities of the proposed approach, a case study is presented using a probabilistic DT in the application layer controller of a pair of converters to comparatively monitor their behavior and corresponding controller action under hardware-in-the-loop testing. [ABSTRACT FROM AUTHOR]

One can define an energy system as a system that converts one or more energy fluxes into other energy fluxes of a different kind. This definition may describe a relatively small system, for instance, a power plant, a chemical plant, or the heating and cooling apparatus of a single-family house, as well as one covering larger energy needs, for example, those of a city, a country, or even a continent. As energy systems are developed through the centuries, the way we structure these systems goes through changes affected by contextual conditions. Recently, concerns about the availability of traditional fossil energy sources and their environmental effects are revolutionizing the way energy systems are planned, designed, and operated. [ABSTRACT FROM AUTHOR]

This article investigates the problem of measurement selection for data-driven monitoring approaches. Several approaches to input variable selection (IVS) are analyzed, and a general procedure for finding the optimal order for the selection of candidate measurements is presented. The method is based on the extensions of partial correlation and minimal redundancy maximum relevance criteria to support IVS problems involving multiple outputs. This method can be used to find the minimal set of measurements for achieving a target estimation accuracy. The results demonstrate the advantages and limits of the introduced method in comparison to the other approaches discussed in this article. [ABSTRACT FROM AUTHOR]

In this paper we present a solution for real-time multi-FPGA simulation of energy conversion systems based on the Latency-Based Linear Multistep Compound (LB-LMC) simulation approach. The approach relies on a nodal decomposition approach derived from multiport network analysis, and fast parallel communication between Field Programmable Gate Array (FPGA) devices. The results presented in this paper –using a microgrid example– demonstrate how the developed real-time platform overcame the resource scalability limit of single FPGA solvers while achieving a time step size of 100 nanoseconds. [ABSTRACT FROM AUTHOR]

In this paper, we present a decentralized load estimation approach to support real-time volt/var optimization in distribution networks with high penetration of distributed generation. The decentralized load estimation scheme relies on local information and on a limited amount of information from neighbor areas. We defined a procedure for measurement selection and feeder partitioning so as to limit communication needs and allow prioritizing the estimation accuracy for selected loads. The measurement selection and feeder partitioning methods are based on the mutual information and minimum-redundancy-maximum-relevance concepts. We tested the proposed approach with a modified radial IEEE 34-node test feeder and a weakly meshed IEEE 123-node test feeder. The effectiveness of the method is validated using a hardware-in-the-loop simulation platform. [ABSTRACT FROM AUTHOR]

In this paper, an approach to perform model reduction of distribution grids for phasor measurement unit (PMU)-based state estimation (SE) purposes is presented. The method is based on ac load flow equations. An arbitrary number of lines and nodes can be selected and reduced to an equivalent one. The performance of the method is tested using the IEEE34 and IEEE123 test cases. An evaluation of the computational performance of the SE process is also reported. To conclude, the SE process based on the reduced model is tested against uncertainty in the parameters of the system and in the measurements so to evaluate how the performance of the SE is deteriorated by these phenomena. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate how latency insertion method (LIM) can be used for real-time simulation of high switching frequency power electronics systems and how this approach can be implemented for scalable Field Programmable Gate Array (FPGA) execution. We first present a summary of the LIM and how this method can be developed into a solver for high-performance FPGA execution. We then present how common power electronics topologies—buck and boost—can be modeled using the LIM approach, followed by how the LIM model of a three-phase dc/ac converter is created. Afterward, we demonstrate the accuracy of the developed FPGA solver through a set of power electronic system examples, with each example compared with near ideal results of same example provided by a traditional electromagnetic transient type solver. We complete the paper analyzing the scalability of the proposed approach on FPGA devices, both in terms of achievable time step as well as of resource usage. [ABSTRACT FROM AUTHOR]

We present a data-driven modeling (DDM) approach for static modeling of commercial photovoltaic (PV) microinverters. The proposed modeling approach handles all possible microinverter operating modes, including burst mode. No prior knowledge of internal components, structure, and control algorithm is assumed in developing the model. The approach is based on Artificial Neural Network (ANN) and Fast Fourier Transform (FFT). To generate the data used to train the model, a Power Hardware in the Loop (PHIL) approach is applied. Instantaneous inputs-outputs data are collected from the terminals of a commercial PV microinverter at time domain. Then, the collected data are converted to the frequency domain using Fast Fourier Transform (FFT). The ANNs that are the core of the DDM are developed in frequency domain. The outputs of the ANNs are then converted back to time domain for validation and use in system level simulation. The comparison between measured and simulated data validates the performance of the presented approach. [ABSTRACT FROM AUTHOR]

In this paper, we present a scalable approach for real-time simulation of ship power systems with high-frequency power electronics converters (100–200 kHz). The proposed approach is based on the latency-based linear multistep compound method and relies on field-programmable gate array (FPGA) execution. Several examples of increasing dimension and complexity are used to evaluate the scalability—both of in terms of computational delay and of resources usage—of the proposed approach. Real-time execution with a 50 ns time step is achieved for all the examples considered. [ABSTRACT FROM PUBLISHER]

We define here a new parallel simulation method designed for real-time execution. This method is highly parallelizable and scalable, and the simulation execution time is fully predictable, which is very important for real-time execution. The stability conditions for the method are defined. Furthermore, we show how to define appropriate flow variable injections so that this method can be extended to multiphysic applications and how to implement multirate time step in the defined method. Finally, we present simulation tests that validate the presented method. [ABSTRACT FROM AUTHOR]

This paper introduces a new data-driven bottom-up monitoring approach for distribution systems. In this approach, local estimations of the subsections into which the system is split are performed independently, thus leading to a scalable architecture. The monitoring approach is focused only on the estimation of voltage magnitude rather than the complete state of the system. This reduces the measurement requirements significantly, thus addressing economical and technical concerns for existing systems, while staying open to accommodating further incremental improvements in the available data and data quality. The estimation of each section is realized via an artificial neural network (ANN), for which a set of parameterizations is available to cope with different operating conditions. The estimation convergence is achieved even with relatively few measurements, although accuracy varies depending on the available measurements. At the Medium Voltage (MV) level, where reconfiguration is common, a configuration identification unit chooses the right ANN, the one trained for the actual network configuration. The estimation process is computationally simple and can be executed on low-cost hardware, as demonstrated in this paper by the implementation on a BeagleBone Black board. To demonstrate the concept, a prototype and a laboratory setup have been developed. The experimental test results are presented both for an Low Voltage distribution system and an MV distribution system. [ABSTRACT FROM PUBLISHER]

We define a new hybrid simulation scheme that combines latency insertion with nodal and state-space methods. The conditions for stability and multirate time stepping of the hybrid method are defined. Further, we show how to define appropriate flow variable injections so that the hybrid method can be extended to applications involving either multiple technical domains or hardware in the simulation loop. The hybrid method permits significant increases of simulation speed while contributing less than 1% to RMS simulation error. [ABSTRACT FROM AUTHOR]

This work introduces a multiphysics test bed which supports the testing of renewable energy systems in the context of home energy systems (HESs). The test bed, based on power-hardware-in-the-loop (PHIL) technology, allows for testing renewable energy systems and HESs in a holistic way. Homes, as the melting pot of different forms of energy, are one of the key enablers for the smart grid technology. The interface of different energy domains is the main propeller for innovation and development of new technologies. The test bed presented in this work comprises thermal, hydraulic, communication, and electrical interfaces, enabling a variety of testing scenarios. The modular structure of the test bed allows for a very flexible setup for analyzing different kinds of HESs. [ABSTRACT FROM PUBLISHER]

Simulation has become an indispensable support in the design process. This is particularly evident in the design of complex, multidomain, and multiphysics systems. In these cases in particular, though, the setup of the scheme to be simulated is a challenge in itself. In this paper, we propose an approach to the selection of the right level of detail of the models given the design objectives. This method is based on stochastic analysis and the results are lumped in indices that allow for a ranking of available models of each component. In detail, the main contribution is the formalization of a method to describe difference between different models of the same component. A case study is proposed to demonstrate the concept via the design of the overcurrent protection of an induction machine. [ABSTRACT FROM PUBLISHER]

Synchronized phasor measurement units (PMUs) are becoming a key element of monitoring, protection, control, and state estimation applications in power systems and are the basis for the implementation of wide area monitoring systems. This paper investigates the possibility of using a software-only precision time protocol synchronization method to synchronize different PMUs inside the same substation and how this synchronization impacts the state estimation. In this context, as a case study, the output data of the PMUs have been used to estimate the state of a portion of real power system. A laboratory experimental setup has been built to evaluate the performance achievable with the proposed synchronization solution. The results thus obtained are discussed in terms of stochastic performance. [ABSTRACT FROM AUTHOR]

This paper presents a generalized approach to the design of independent local Kalman filters (KFs) without communication to be used for state estimation in distributed generation-based power systems. The design procedure is based on an improved model of the virtual disturbance concept proposed in a previous work. The local KFs are then synthesized based only on local models of the power network and on the characteristics of the associated virtual disturbance. The proposed solution is applied to an interconnected power network. By choosing appropriate models for the virtual disturbance, the local KFs can be suited for both dc and ac distribution systems. It is shown for both cases that the local KF can infer the local states of the network, including the aggregated branch currents coming from the other buses. Simulation results show improved results with respect to the previous proposed modeling approach even when the subsystems present widely different dynamics. The herein presented approach is well suited for the agent-based decentralized control of microgrids. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the problem of system decoupling in a power-system state estimation problem. The complexity of power systems is growing, thus challenging the way measurements for state estimation are traditionally managed. Following a previous experience in defining a decentralized solution for state estimation, the authors here propose a decentralized dynamic state estimation method for a large-scale power system in combination with a procedure to automatically identify how and which state information to exchange for reconstructing the states starting from partial knowledge. In particular, the problem of selecting the variables that each observer has to estimate is partially solved within the framework of a stochastic approach, i.e., the so-called collocation method. An optimization algorithm based on dynamic programming is also developed to determine the optimal set of strongly coupled variables necessary for a sufficiently accurate estimation. The developed method is evaluated by applying to an IEEE test bus system. [ABSTRACT FROM PUBLISHER]

In the last few years, the growing complexity of the electrical power networks, mainly due to the increased use of electronic converters together with the requirements of a higher level of reliability and security, pushed the development of new techniques for the state estimation of the power systems. In this paper, the authors focus their attention on the implementation and experimental validation of a decentralized observer for the state estimation in an electric ship, whose power network is characterized by fast dynamics and by the presence of many electronic devices. The proposed solution implements a decentralized information filter (DIF). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper we present an approach for real-time simulation and Hardware-in-the-Loop (HIL) testing of Modular Multilevel Converters (MMCs) that rely on switching models while supporting system level analysis. Using the Latency Based Linear Multistep Compound (LB-LMC) approach, we achieved a 50 ns simulation time step for systems composed of several MMC converters and for converters of various complexity. To facilitate system level testing, we introduce the use of a serial communication-based (Aurora) interface for HIL testing of MMC converters and we analyzed the effect that communication latency has on the accuracy of the HIL test. The simulation and HIL results are validated against an MMC laboratory prototype. [ABSTRACT FROM AUTHOR]

This paper provides a simple low-level unidirectional global communication method for DC microgrids, and requires no hardware modifications to the microgrid and interfacing power electronic converters. The underlying premise to this communication method is injecting low-frequency low-voltage sinusoidal components into the DC microgrid power lines. This method deviates from the common bit-level communication scheme by relating parameters and commands with certain frequency components. Communication structures are included as a basis for communication protocols, and a detection method is proposed for detecting the injected frequencies. The injection method, communication structure, and detection method are implemented on a live-scale DC microgrid. [ABSTRACT FROM AUTHOR]