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DC microgrids require advanced protection techniques for fault detection and isolation (FDI). In this work, an FDI method able to respond to different types of component faults is developed based on system modeling. First, the state-space representation of a multiterminal dc microgrid with component faults is derived. Then, an FDI function based on ${\mathcal {H}}_{-}/{\mathcal {H}}_{\infty }$ observers is designed. To achieve the desired selectivity in fault isolation, the linear matrix inequality (LMI) optimization approach is adopted in the observer design. The performance of the proposed FDI method is verified under the real-time (RT) simulation of a three-terminal low-voltage dc microgrid and with a small-scale laboratory dc grid. The proposed FDI method is proved to be effective to detect and isolate different faults in dc microgrids with a response time of 1 ms.

DC microgrids are becoming an attractive technology for future power grids. However, the fault detection and isolation (FDI) schemes for dc microgrids are still in the nascent state. This article presents an FDI method for dc microgrids using the regularity information of the second derivative of current (SDOC). As the major contribution of this article, the coherence between the singular feature in the SDOC and the short-circuit fault in dc lines is first proved and applied to FDI. Furthermore, a singularity detection approach using stationary wavelet transform (SWT) is introduced. With this FDI method, not only the fault isolation but also the fault-type classification can be achieved based on only local current measurements. Moreover, compared with other local current-based methods, this method has a distinct advantage in the robustness against the nonfault disturbances. The effectiveness of this FDI method was verified through hardware tests under the real-time (RT) simulation of various fault scenarios in a 5-kV three-terminal dc microgrid model based on dual-active-bridge (DAB) converters. This FDI method can be generalized to dc microgrids with different topologies and converters.

This brief develops a stochastic distributed pinning control scheme for the voltage regulation of co-multi-inverter (CMI) networks subject to additive noise and symmetric time-delay disturbances. In actual CMI networks, the information exchange among multiple and cooperative inverters may be disturbed by communication noises and affected by communication time delays. Towards that end, the proposed distributed pinning control scheme can synchronize the terminal voltages of inverters to their reference value by a virtual leader through sparse communication networks considering time delays and noise disturbances in mean square convergence. By means of the stochastic analysis tools and algebraic graph theory, distributed pinning protocols are developed to be employed for CMIs, in which manner the criteria for the delay boundedness considering noise disturbances to maintain the system stability are derived. Simulation results on a CMI system are provided to show the effectiveness of the proposed control protocols.

Phasor Measurement Units (PMUs) are measurement devices long used in transmission systems and today even more essential for a proper monitoring of distribution grids. The expected massive penetration of distributed energy resources (DERs) is slowly taking place, carrying along a new set of challenges that put to test traditional instruments and requiring more performance and flexibility to adapt to this evolving scenario. Cheap devices based on single board computer (SBC) are proving to be a valid alternative to traditional PMU architectures, able to combine together high-performance, great versatility and low-cost. However, such devices lack a proper modeling of their measurement errors that conversely would be extremely useful for improving their design and evaluate their performance in accordance with the relevant standards. The paper intends to fill this gap by discussing the error sources and their effects on the observed signals. An analysis of error statistics is presented, in order to give a more complete metrological characterization., 10 pages, 6 figures, 4 tables

Distribution system state estimation (DSSE) is one of the key functions used by distribution system operators (DSOs) for the management and control of the distribution grids. In general, distribution networks are featured by a very large number of nodes. Thus, performing the state estimation (SE) of the whole network is computationally demanding. From the system observability point of view, the scarcity of the installed measurement units in the distribution systems is a huge barrier to perform DSSE. Moreover, the unbalanced nature of distribution systems makes estimators based on the positive-sequence model unfit. To tackle these issues, this article presents a new data-driven three-phase state estimator based on an artificial neural network (ANN) and sparse measurements from phasor measurement units (PMUs), together with its observability assessment. Unlike existing estimators performing weakly under non-Gaussian noise, the proposed estimator provides accurate estimates. The proposed SE technique is executed extremely fast (in a few milliseconds). This feature facilitates the real-time monitoring of the distribution grids, as the estimators are coupled with the PMUs. In this context, PMUs play a crucial role as they can provide synchronized phasor measurements with a high reporting rate ( ${f}_{{\text {PMU}}} {> 1}~{\text {Hz}}$ ). To reduce the computational cost of the SE problem with distributed computation, multiple ANN three-phase estimators are executed in parallel and integrated in a multiarea SE architecture. The simulation results on the benchmark IEEE 123-bus test system support the feasibility of the solution.

This article presents a new Kalman filter (KF) approach to power system state estimation (SE) based on phasor measurement units (PMUs), in which the knowledge of the system frequency is exploited to ensure the accuracy of the estimated quantities even under off-nominal conditions. In the proposed solution, the frequency is added as a new state variable to be estimated so that its value can be known with lower uncertainty, thus leading to more accurate estimates also for node voltages and branch currents. All the frequency measurements available from PMUs can be exploited through the presented method to improve the estimation. In order to assess the benefits given by the integration of the frequency knowledge, the performance of the new approach is compared to different SE methodologies, by means of simulations carried out on the New England IEEE 39-bus system under different realistic operating conditions and measurement configurations. Performed tests take into account, in particular, the possible occurrence of off-nominal frequency conditions, highlighting the issues associated with traditional PMU-based KF approaches and proving the effectiveness of the proposed solution.

This study proposes a novel method that combines sliding mode control (SMC) and virtual oscillator control (VOC) to eliminate voltage harmonics and unbalance caused by non-linear and unbalanced loads in a three-phase low-voltage microgrid. The conventional VOC is modified to fit the three-phase four-leg inverter systems and a novel SMC control is designed using the high-pass filter technique and linearised super twist algorithm. The proposed control method can keep the voltage harmonics and unbalance at the point of common coupling within the IEEE standard limits, under heavily non-linear and unbalanced load conditions. The validity of the proposed method is verified by both offline simulations in Matlab/Simulink and Control Hardware-in-the Loop simulations using an OPAL-RT platform.

This paper proposes a stochastic distributed secondary control scheme for both frequency/voltage restoration and optimal active power sharing (e.g., minimize the total generation cost) of ac microgrids by employing event-triggered communication mechanism in noisy environments. Compared with existing ideal and periodic communication among distributed generations (DG), the proposed stochastic distributed secondary control scheme can achieve mean-square synchronization for frequency and voltage restoration of DGs and the optimal active power sharing for their economic operation through a sparse communication network, even though the communication channels are susceptible to noise interferences and limited bandwidth constraints. The stochastic distributed control protocols are designed to be employed into the secondary control stage for microgrids, which is a fully distributed control paradigm. With the proposed control protocols, control deviations of frequency and voltage produced during the primary control stage can be well remedied and the optimal active power sharing for their economic operation can be well achieved simultaneously. Furthermore, the graph theory, stochastic theory and Lyapunov functional approach are employed to derive the stability and convergence analysis of the proposed dynamic event-triggered conditions considering noise interferences. Simulation results on an islanded microgrid test system are presented to demonstrate the effectiveness of the proposed control protocols.

This article presents a novel distributed event-driven control strategy that will dispatch and share the active and reactive power outputs of massive current-controlled voltage source inverter (CCVSI)-based distributed generators (DGs) in an ac microgird. The proposed distributed power sharing 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, each CCVSI-based DG only requires the local voltage and current measurement from its own and some nearest neighbors (but not all) for the distributed power sharing control at the last event-driven time by using low-bandwidth communication links and then, updates the control inputs to restore the active and reactive powers to desired values for further reducing the consumption of computing and communication resources to some extent. The Lyapunov technique is employed to derive the stability and convergence analysis of the proposed dynamic event-driven conditions. The effectiveness of the proposed control strategy is verified under various scenarios by a modified IEEE 34-bus test network in MATLAB/SimPowerSystems.

An increasing number of offshore wind farms are connected to the public grid with large-scale HVDC links. The electrical transients caused by the energization of the export transformers appear more significant in the converter-driven offshore AC grid with low short-circuit power, than in direct AC grid connections. This may lead to a violation of the 2% voltage dip criterion, thus seriously affecting the production of neighboring wind farms. Additionally, the transformer and the entire insulation system are exposed to mechanical and dielectric stress. This paper investigates the influence of three factors, namely excitation, switching angle, and remnant core magnetization, onto the inrush current. Electromagnetic transient simulations and field measurements show that with a smart use of the on load tap changer, point on wave switching, and a demagnetization of the core by a cable-shunt swing circuit, the three factors above can be used as degrees-of-freedom to effectively mitigate the inrush current phenomenon.

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.

21st IFAC World Congress, online, 11 Jul 2020 - 17 Jul 2020; IFAC-PapersOnLine 53(2), 13056-13063 (2020). doi:10.1016/j.ifacol.2020.12.2253 special issue: "21th IFAC World Congress / Edited by Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann", Published by Elsevier, Frankfurt

This paper proposes a method to control the power-sharing between parallel-connected photovoltaics generation in an islanded microgrid. The two-stage converter-based PV generation is used combined with modified virtual oscillator control and cascade sliding mode control. With this proposed control configuration, the power-sharing in proportion to the inverter power rating and maximum power point tracking is guaranteed autonomously without the need of energy storage systems, while maintaining the primary advantages of the virtual oscillator control method. The effectiveness of the proposed control scheme is validated through simulations in MATLAB/Simulink.

Energies 14(21), 7128 (2021). doi:10.3390/en14217128 special issue: "Special Issue "Big Data and Advanced Analytics in Energy Systems and Applications" / Special Issue Editors: Prof. Dr. Constantinos S. Psomopoulos, Guest Editor; Dr. Helen C. Leligou, Guest Editor; Prof. Dr. Ferdinanda Ponci, Guest Editor; Prof. Dr. Josep M. Guerrero, Guest Editor; Prof. Dr. Elisa Pe��alvo-L��pez, Guest Editor", Published by MDPI, Basel

IEEE transactions on industry applications : IA (2022). doi:10.1109/TIA.2022.3142661, Published by IEEE, New York, NY

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.

Power electronics-based medium-voltage direct current (MVdc) microgrids consist of several interconnected feedback-controlled switching converters. Such systems experience bus voltage stability challenges owing to the negative incremental resistance of constant power loads and converter control loop interactions. To tackle the stability challenges, this paper presents the application of the interconnection and damping assignment passivity-based control (IDA-PBC) approach to the port-controlled Hamiltonian model of dual active bridge (DAB) source-side converters in an MVdc microgrid. For the DABs, a fundamental average model approach considering phase shift modulation is provided and used for deriving the corresponding IDA-PBC control law. We analyze the effectiveness of the controller on large signal scenarios considering disturbances such as load step up, and DAB disconnection. Hardware-in-the-Loop experiments using Opal-RT and Labview field programmable gate arrays (FPGAs), as well as, low power prototype tests are carried out to demonstrate the validity and feasibility of the proposed approach.

SoftwareX 16, 100839 (2021). doi:10.1016/j.softx.2021.100839, Published by Elsevier, Amsterdam [u.a.]

The increasing and fast deployment of distributed generation is posing challenges to the operation and control of power systems due to the resulting reduction in the overall system rotational inertia and damping. Therefore, it becomes quite crucial for the transmission system operator to monitor the varying system inertia and damping in order to take proper actions to maintain the system stability. This paper presents an inertia estimation algorithm for low-inertia systems to estimate the inertia (both mechanical and virtual) and damping of systems with mixed generation resources and/or the resource itself. Moreover, the effect of high penetration of distributed energy resources and the resulting heterogeneous distribution of inertia on the overall system inertia estimation is investigated. A comprehensive set of case studies and scenarios of the IEEE 39-bus system provides results to demonstrate the performance of the proposed estimator.

In this paper, a simple low order robust current controller with capacitor-current-feedback active damping is developed to reject grid impedance variations. Two control parameters among five are identified as the parameters that affect the system’s stability significantly. Thus, the goal of the robust controller design is to fix the three control parameters, varying only the other two, and to determine the robust stability region in the two-parameter plane by using D-decomposition method under the specified uncertainty region. The D-decomposition method maps the roots of closed-loop characteristic polynomials onto the graphic control parameter plane since certain relationship exists between the value of the control parameters and the value of the characteristic roots. By constraining all the closed-loop poles inside the unity circle in z-plane, the two control parameters are then the solutions to the characteristic polynomials which mathematically determines the robust stability region. Any parameters selected from this region can reject the specified grid impedance uncertainty. A robust performance region (settling time and damping ratio) is further determined inside the robust stability region, thus achieving robust stability, fast response and high quality current injection. All the analytical studies and simulation results verified the control design. The developed simple low order robust controller has been proven to be able to guarantee robust stability and achieve desired robust performance with the specified grid impedance uncertainty.

This work discusses three promising strategies for the compensation of deviations within the online phase of a cooperative energy management concept. Whereas in the planning phase, hourly schedules for the active electro‐thermal devices are negotiated on a daily basis, the second phase tracks the schedule, including forecast updates, and compensates the deviations. The latter online compensation is realized with a distributed and hierarchical‐distributed model predictive control strategies, which are compared with continuous rescheduling with receding horizon and evaluated in terms of computational and optimization performance and scalability. The optimization is implemented and deployed using an open‐source optimization framework, which automatically manages optimization control, integration of renewable generation and load forecasts, multi‐instantiation and communication over an internet of things communication protocol. We address the case of significant deviations from the day‐ahead forecast and the tracking of an obsolete schedule and suggest a combined method, which combines the advantages of rescheduling and a distributed MPC strategy. The method applies a dynamic threshold to evaluate the deviation trend over time and to decide if compensation is still feasible or if a rescheduling should be triggered. The methods are evaluated based on several performance indicators such as residual deviation, number of switching events and computation and communication requirements and scalability, and offer a methodology for the control design of systems of different dimensions.

This paper presents a new control method for power electronic interfaced distributed energy resources in DC grids. The proposed control method relies on a data-driven identification approach that is used in an adaptive control framework and does not require accurate models of the converters or offline training for control synthesis. In this paper, the proposed control method is able to guarantee a good performance and bus voltage stability without prior knowledge of the uncertainties. These uncertainties include measurement noise and network-induced phenomena from interactions with neighboring nodes, such as system reconfigurations resulting from converters connection/disconnection.

This study addresses the secondary voltage restoration problem of ac autonomous microgrids with additive measurement noises and time delays. Existing distributed control methods commonly assume ideal communication among distributed generators (DG), however, the channels are prone to additive noises (i.e. caused by environmental causes, such as rain etc.) and time delays, then each DG obtains noisy and delayed measurements of the states of its neighbouring DGs. The proposed distributed cooperative control strategy will achieve voltage restoration of DGs through a sparse communication network subject to noisy and delayed measurements. The theoretical concepts and necessary conditions for stability and robust performance of the proposed distributed secondary voltage control strategy are outlined by the graph theory, stochastic theory, and Lyapunov functional approach. Furthermore, the proposed control strategy is fully distributed and therefore the central controller is no longer required bringing about several advantages, e.g. plug-and-play property. Simulation results on an autonomous ac microgrid test system are obtained to evaluate the performance of the proposed control method to achieve voltage restoration in the MATLAB/SimPowerSystems Toolbox.

This paper proposes a novel method to determine and control active and reactive power output of the virtual ocillator control-based inverter in grid-connected mode. An analytical expression is introduced to describe the quasi-steady-state relationship between the parameters of the inverter including its controllers, and the power injection and voltage at the inverter terminals. We introduce a new, complex valued parameter ( K ) to the conventional virtual oscillator control (VOC) scheme in order to control the inverter output power. We compare analytic and numeric solutions of the above analytical expression with time-domain simulation results, using different K values and line impedances to validate the analytical expression. In the second part of the paper, we design a controller: we use the above expression to derive K for given reference powers and use adaptive integrators to compensate for the remaining errors. We compare this control scheme to a method that uses PI controllers, and also to a simple decoupling method for independent control of active and reactive power. The controller based on the analytical method exhibits very good dynamic and decoupling performance. A single VOC-controlled inverter connected to a nonstiff grid is simulated in MATLAB/Simulink for demonstration, and is also validated using real-time simulations.

This paper presents a tool for multi-objective cross-domain optimization of heterogenous energy and non-energy domains for urban areas in a comprehensive optimization approach. The multi-objective optimization concept facilitates the definition of any number of objective functions, versatile in their origin and properties, flexibly adaptable to area specifics or regional and communal priorities. The applied optimization for several domains at the lowest system level yields a comprehensive holistic solution optimal for the particular subsystem, as it is fitted for this subsystem according to its characteristics. The main advantage is the exploitation of cross-domain interactions and synergies and, therefore, the improved overall performance of the system. However, the cross-domain approach requires high-level modeling in order to overcome compatibility issues, which decreases the accuracy of domain-specific simulation and optimization compared to methods which apply detailed models. Based on the mathematical concept of multi-objective Pareto optimization, its adaptation, implementation and application in the context of Smart Cities are presented in detail. Several optimization formulations and methods are applied to the test scenario to demonstrate the concept, compare the results and evaluate the computational performance of the application. The e-constraint method appears to be better suitable for heterogeneous domains, as the weighted sum method is highly sensitive to weight setting for heterogeneous objectives. The computational performance is very good in terms of computation time but meets numerical limitations for samples of around 500 buildings, leading to numerical infeasibility for samples of 1300 buildings. This is solved with the presented method for model aggregation. The tool is available on GitHub.

This paper analyzes and compares one central and one distributed optimization method applied to complex city districts embedded in a hierarchical architecture. We define a complex city district by the coexistence of residential, nonresidential, and industry. The proposed methods are for offline, day-ahead optimization for operation planning purposes of, e.g., electrothermal heating systems. This paper analyzes how this way city districts may provide flexibility for demand response purposes. The optimization objective is the matching of supply and demand on system level by maximizing the usage of renewable energy generation. We provide quantitative and qualitative results to compare the performance of the algorithms and their potential for providing flexibility.

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.

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 a hierarchical robust distributed optimization for day-ahead and intra-day scheduling of the operation of flexible devices (electro-thermal heating units) within a city district. An aggregation service provider, which acts as an aggregator, performs this distributed optimization to maximize the flexibility potential of its customers to provide services to other actors, such as to a balance responsible party or the distribution system operator. Our optimization algorithm is based upon the alternating direction method of multipliers and prioritizes each individual customer and its own private objective. A model predictive control and robust design guarantee that uncertainty, e.g., electrical or thermal demand, is managed within the optimization process. The work includes a customer versus system level objective (aggregator) analysis under uncertainty.

Published by MDPI, Basel, Energies 14(7), 1965 (2021). doi:10.3390/en14071965 special issue: "Special Issue "Monitoring and Distributed Control for Power Systems" / Special Issue Editor: Prof. Dr. Ferdinanda Ponci, Guest Editor"

Energy informatics 4(8), (2021). doi:10.1186/s42162-021-00143-x, Published by Springer International Publishing, Cham

Energies : open-access journal of related scientific research, technology development and studies in policy and management 14(7), 1967 (2021). doi:10.3390/en14071967 special issue: "Special Issue "Applications of Medium Voltage Direct Current in Electric Systems" / Special Issue Editors: Prof. Dr. Luis M. Fernández-Ramírez, Guest Editor; Prof. Dr. Dmitri Vinnikov, Guest Editor; Prof. Dr. Carlos Andrés García Vázquez, Guest Editor", Published by MDPI, Basel

IEEE access 9, 11543-11562 (2021). doi:10.1109/ACCESS.2020.3047986, Published by IEEE, New York, NY

Conference for Wind Power Drives, CWD 2021, online, 9 Mar 2021 - 11 Mar 2021 Antriebstechnisches Kolloquium = Drive Train Technology Conference, ATK 2021, online, 9 Mar 2021 - 11 Mar 2021; Forschung im Ingenieurwesen = Engineering research (2021). doi:10.1007/s10010-021-00446-1, Published by Springer Nature, Berlin ; Heidelberg

Energies : open-access journal of related scientific research, technology development and studies in policy and management 14(12), 3533 (2021). doi:10.3390/en14123533 special issue: "Special Issue "Monitoring and Distributed Control for Power Systems" / Special Issue Editor: Prof. Dr. Ferdinanda Ponci, Guest Editor", Published by MDPI, Basel

GPUs have achieved widespread adoption for High-Performance Computing and Cloud applications. However, the closed-source nature of CUDA has hindered the development of otherwise commonly used virtualization techniques. In this paper, we evaluate the feasibility of building a GPU virtualization layer that isolates the GPU and CPU parts of CUDA applications to achieve better control of the interactions between applications and the CUDA libraries. We present our open-source tool that transparently intercepts CUDA library calls and executes them in a separate process using remote procedure calls. This allows the execution of CUDA applications on machines without a GPU and provides a basis for the development of tools that require fine-grained control of the GPU resources, such as checkpoint/restore and job schedulers.

Autonomous agents and multi-agent systems 35(1), 10 (2021). doi:10.1007/s10458-020-09489-0, Published by Springer Science + Business Media B.V, Dordrecht [u.a.]

In this paper we demonstrate theoretically the stability of MTDC systems with multiple ADRC-controlled converters. In particular, the ADRC model, consisting of a linear quadratic Gaussian (LQG) structure with reference trajectory generator, is applied for the voltage regulation of the DC/DC source-side converters in MTDC systems. Small-signal stability analysis demonstrates the ability of the local ADRC converter controllers to estimate and track the system dynamics, cancel internal and external disturbances and thus stabilise the system. The stability analysis shows theoretically the robustness of the ADRC model to uncertain load and converter models, and the ability to mitigate different interactions in the system. Time domain simulations show the ability of the linear ADRC model to deal with the non-linearities of the MTDC systems. Finally, experimental results demonstrate the easy hardware implementation of the ADRC model for the voltage regulation of DC/DC converters, and its ability to track the system dynamics fast and accurately for the tight DC voltage regulation.

Nowadays, by the integration of the internet into many computational areas, smart energy automation and the reliable controlling system can be achieved by introducing the Internet of Things (IoT). As IoT becomes an active research area, different methods, and strategies from different points of view have been explored to promote the development and popularity of IoT. One trend is viewing IoT as Web of Things (WoT) where the open Web standards are supported for information sharing and device interpretation. This WoT vision enables a new way of narrowing the barrier between the virtual and physical worlds. This paper presents the result of a European project in demonstrating different ICT architectures for context information management. It uses some key enabling technologies of WoT to share the data from the smart energy system and end up with a hybrid ICT architecture. The demonstrated architectures can be further powered with any intelligent algorithms and applications to provide a fully digitalized environment.

The increasing number of natural calamities and unprecedented terrorist attacks may significantly impact the supply security of the distribution grids. These High Impact Low Probability (HILP) events cause major blackouts in the electrical networks that may impair operation of other critical infrastructures (in health, transportation, gas networks, emergency services, communication, etc.). Therefore, a fast service restoration scheme is required, which ensures supply security to the system components. To achieve this goal, this work presents a rule-based Service Restoration (SR) algorithm, which can act as first responder to an emergency outage in active distribution grids. This algorithm accounts for the load criticality, to tackle single and multiple faults in the grid. The optimality of the SR schemes is assessed via Multiple-Criteria Decision Analysis (MCDA), considering the power loss and/or the line utilization. A software platform for the testing and deployment of the SR algorithm for real time application has been developed and is presented here.

The fraction of generation from power electronics driven Distributed Energy resources (DERs) is increasing. Therefore, DERs must also take up functionalities, such as inertia provision and oscillation damping, traditionally offered by synchronous generators (SG). Numerous control schemes for virtual inertia provision by emulation of SGs are presented in literature. However, a drawback of SG emulation is undesired low-frequency oscillations. In this paper, we propose a virtual inertia control based on a state-feedback Linear Quadratic Integral controller combined with pole placement, where the optimal feedback gain is designed to achieve improved dynamic performance resulting in better frequency response and improved oscillations damping with minimum control effort. The proposed control scheme is integrated into the state-of-the-art VSM controller and verified through simulations in MATLAB/Simulink.

Novel concepts enabling a resilient future power system and their subsequent experimental evaluation are experiencing a steadily growing challenge: large scale complexity and questionable scalability. The requirements on a research infrastructure (RI) to cope with the trends of such a dynamic system therefore grow in size, diversity and costs, making the feasibility of rigorous advancements questionable by a single RI. Analysis of large scale system complexity has been made possible by the real-time coupling of geographically separated RIs undertaking geographically distributed simulations (GDS), the concept of which brings the equipment, models and expertise of independent RIs, in combination, to optimally address the challenge. This article presents the outputs of IEEE PES Task Force on Interfacing Techniques for Simulation Tools towards standardization of GDS as a concept. First, the taxonomy for setups utilized for GDS is established followed by a comprehensive overview of the advancements in real-time couplings reported in literature. The overview encompasses fundamental technological design considerations for GDS. The article further presents four application oriented case studies (real-world implementations) where GDS setups have been utilized, demonstrating their practicality and potential in enabling the analysis of future complex power systems.

During emergencies LTE/5G-based public mobile land networks (PLMNs) restrict network access by normal users, which means a lack of service reliability which limits application of LTE/5G for machine-type communication (MTC) in critical applications, such as power systems This paper shows how existing LTE/5G features can be used to differentiate MTC of devices in a microgrid from other MTC or human-to-human (H2H) communication and ensure that these microgrid devices have service during emergencies, which enables use of LTE/5G communication to co-ordinate the use of distributed energy resources (DER) in microgrids, so that they can autarkically perform blackout recovery of an islanded microgrid. It is shown that this method allows the blackout recovery 100 times faster than with a conventional black start. The microgrid blackout recovery is demonstrated using the LTE/5G PLMN Access Barring feature. The disadvantage of using PLMN-based Access Barring is the need to define two separate PLMNs in one radio cell, which is an inefficient use of radio spectrum. However, this can be avoided by using the Extended Access Class Barring (EAB) override or application-specific congestion control (ACDC) features of the CAT-M1 low-power wide-area MTC technology, which are included in LTE and 5G standards.

The high penetration of power-electronics-driven power generation and the transition towards low-inertia power systems is affecting the fundamentals of grid operation. The loss of Synchronous Generators (SGs) together with their mechanical inertia and their frequency control mechanisms brings new challenges to power system stability. Numerous decentralized control schemes for virtual inertia provision by emulation of SGs are presented in literature. However, the SG emulation has the drawback of nonlinear swing dynamics. As an alternative, the Linear Swing Dynamics (LSD) concept was proposed to exploit the smartness of power-electronic converters to not only secure the operation of future power systems but also enhance their dynamic behavior. In this work, we propose a feasible decentralized LSD control scheme for multi-machine power system with CPLs. The proposed control scheme is integrated into the state-of-the-art VSM controller and verified through simulations in MATLAB Simulink.

The penetration of electric vehicles is expected to boost the peak power demand which often leads to voltage limit violations in distribution grids. Hence, the Distribution System Operator (DSO) implements services that control the power exchange in the grid to avoid voltage violations. The service is implemented by the DSO by acquiring measurements and information from the grid and gathering them in Energy Management System (EMS) through automation architecture. However, the performance of the EMS is affected by the automation architecture and communication technologies. Hence in this paper, the authors study the sources of latency from the automation architecture and its impact on the performance of an energy management strategy for voltage limit violations.

Energies 13(15), 3879 (2020). doi:10.3390/en13153879, Published by MDPI, Basel

More and more power electronics based components such as photovoltaic inverters, wind energy systems, energy storage systems or electric car chargers are integrated into modern grids. During their development, the components’ behavior in a grid is validated by using grid emulators. Existing grid emulators act as an ideal voltage source or a voltage source behind a resistive-inductive impedance. But measurements reported in literature show that grid impedances of grids containing power electronics based components are much more complex than first order resistive-inductive impedances. Therefore, in this paper a novel AC and DC grid emulator concept is proposed. It enables, different from existing grid emulators, the emulation of higher order grid impedances. Requirements, which provide guidelines for the implementation of the grid emulator, are identified based on the proposed grid emulator concept. Based on those guidelines, a simulation-based single-phase grid emulator prototype, combining a real-time simulation with a power amplifier into a single device, is developed. Two PHiL interface algorithms for linking the real-time simulation and the power amplifier are compared with respect to their influence on the emulation bandwidth and stability of the grid emulator setup. Transient simulations illustrate the grid emulator’s performance and verify the theoretical results, thus validating the proposed concept.

Energies 13(7), 1648 (2020). doi:10.3390/en13071648, Published by MDPI, Basel

Utilization of the modular multilevel converter (MMC) topology can enable transformer-less interfacing between electric vehicle (EV) charging infrastructure and the power distribution grid. Such configurations are claimed to significantly reduce the system costs, space requirements and complexity for high-power charging facilities. On the other hand, ensuring the correct operation of such system is challenging under unevenly distributed loads in the MMC arms. Proper selection of the charging points to allocate the EVs can limit the loading unbalances between the arms and phases of the MMC system. This paper presents two load allocation strategies. The first strategy takes only the present loading into account, while the second one optimizes the decision according to the individual energy demands of the EVs over time. Simulations demonstrated that the optimized strategy minimizes loading unbalances between the MMC phases and arms during the charging operations. Furthermore, it can contribute to larger demand fulfillment. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Over the last decade, the Common Information Model (CIM), as specified by IEC 61970 / 61968, has become an important document format for the storage of power grid data. Its object-oriented design makes it easily maintainable and extensible for many use cases in the energy sector. As a result, more and more power grid analysis and simulation tools allow the import and export of CIM based power grid data. Unfortunately, many of them are proprietary and therefore not convenient in the research area since their component models and numerical back-ends often cannot be modified by the user. Thus, open-source alternatives are in demand, such as simulation environments based on the popular modeling language Modelica. Therefore, this paper presents our approach of a template based CIM to Modelica converter. The usage of templates makes it easily adaptable for the generation of Modelica system models targeting arbitrary Modelica libraries. The presented approach is implemented in an open-source project called CIMverter, evaluated on a real-world case with two Modelica power system libraries, and validated against a proprietary simulation tool.