Islanded Mode Research Articles

Topology and control strategy optimization of an auxiliary resonant commutated pole-based, soft-switching grid-connected inverter

With the development of new energy industries such as photovoltaics, microgrids, or distributed energy sources require many DC-AC grid-connected interfaces. Reducing the switching loss of the inverter is important to improve the transmission efficiency of the inverter, reduce the heat generation of the inverter, promote the high frequency and miniaturization of the inverter, and efficiently use the distributed energy. Therefore, considering the wide application of DC-AC power electronic interfaces in microgrid and distributed energy, and to make up for existing deficiencies in traditional hard-switching inverters, an optimal control strategy and topology for an optimal-auxiliary resonant commutated pole (O-ARCP) inverter is proposed in this article. Firstly, this paper introduces the proposed inverter topology and analyzes the operation mode of the circuit with the control strategy. Then simulation experiments in islanding mode are carried out to verify the rationality of the content, and finally simplified experimental verification is carried out based on the simulation results. Simulation and experimental testing reveal that all switches of the proposed topology are in soft-switching mode, which proves the effectiveness of the proposed control strategy and analysis. The analysis and validation of this paper provide assistance in the development of control strategies and structures for soft-switching inverters.

Control techniques for operation of roof-top solar photovoltaics based microgrid in islanded mode

This paper presents the control techniques for the operation of roof-top solar photovoltaics based single-phase ac microgrid in islanded mode. The proposed microgrid consists of four roof-top photovoltaics on various buildings, battery energy storage system in each building, and loads. In each building, the roof-top photovoltaic system is connected to the single-phase ac loads and proposed single-phase ac microgrid through the two-stage converter, which consists of dc-dc boost converter and bidirectional single-phase voltage source converter. A bidirectional dc-dc converter is used to connect the battery energy storage system to the photovoltaic system. The control technique of the bidirectional single-phase voltage source converter, using the feed-forward and feed-back control signals, has been suggested into a rotating d-q synchronous reference frame, with the dual controllers, for controlling the power mismatch and rated sinusoidal ac voltage of the microgrid. The control technique of the bidirectional dc-dc converter, used for the battery energy storage system, is implemented with two voltage controllers and one current controller for managing of the power mismatch in the proposed microgrid system. In this work, the power control algorithms have also been developed, which are aimed to manage the power balance in the single roof-top PV based building as well as in the proposed microgrid, under islanded mode, for various operating scenarios including fault condition.

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

Vulnerable Area Identification of Islanded Combined Electrical and Heat Networks Based on Static Sensitivity Analysis

In combined electrical and heat networks (CEHNs) under the Islanded mode, the district heating network (DHN) is more vulnerable to fluctuations in the electrical load, resulting in the transgression of the CHEN power flow. Identifying vulnerable areas in islanded CEHNs is necessary. In this paper, we introduce a static sensitivity analysis method into islanded CHENs, which can identify vulnerable areas susceptible to these impacts, and explore the energy interaction mechanisms between the electrical network (EN) and DHN. We established a power flow model of the islanded CEHN, and developed the sensitivity matrix. Then, the decomposition model is solved, based on which the static sensitivity matrices can be calculated. The case study shows that the sensitivity can effectively represent the impact of EN load changes on the mass flow rate of the DHN, thus we can locate the weak areas of the CEHN. It can provide auxiliary information for the safe and stable operation of an islanded CEHN, with fewer calculations compared to the power flow calculation method. In addition, the results present the enhancement of islanded CHEN stability by using a kind of combination of CHP units.

Power transition cycles of reversible solid oxide cells and its impacts on microgrids

Currently, reversible solid oxide cells (rSOC) are the only devices that allows a bidirectional conversion of H2O and H2, being able to operate as fuel cell and as electrolyzer. Thanks to the high-temperature operation, rSOC present a higher efficiency and additionally, provide a feasible solution for long-term energy storage in electrical systems. Experimental testing of rSOC have been mainly focused on cells characterization, thermal or degradation analysis, but the study of transition cycles has not been widely studied. The transitions between the operation as a solid oxide fuel cell (SOFC) and as a solid oxide electrolysis cell (SOEC) might have a significant impact on the rest of the electrical system in which the rSOC is integrated. This article analyzes experimentally the power responses of a rSOC stack, during each operating mode (SOEC-SOFC) and during transition between both modes. The results suggest that transition cycles can be achieved in less than 8 min and the total transition from SOEC rated power to SOFC rated power in less than 10 min, having a significant impact on microgrid operations, especially in islanded mode. The obtained results indicate that the most suitable role for rSOC in a microgrid is as grid-following. The grid-forming role is only possible if the rSOC operates along with a fast-response power source.

Distributed Power Management and Adaptive Coordinated Control for SST-Based Power Systems

For better utilization of DC-inherent renewable energy with high penetration and fluctuation, the solid-state transformer (SST)-based power systems have received great attention and arose increasing demand for grid supports and power management. Given the plug-and-play function of distributed renewable energy and loads, the economical and balanced power allocation as well as the flexible seamless transition among different operation modes are not considered comprehensively in existing studies. Therefore, distributed power management and adaptive coordinated control strategy are proposed for SST-interfaced power systems. 1) The application of SST as an alternative to multiple converters to interface the high-voltage grid (HG) and hybrid AC/DC microgrids (MGs) can reduce system communication. Compared with traditional transformers, the controllability and intelligence of SST-enabled power systems are improved. 2) The flexible seamless transition and adaptive coordinated control among HG-connected mode, MG interconnected mode and islanding mode are realized to adapt to different fault conditions. 3) Considering system operation limits, the economical and balanced power allocation is realized accurately, and the voltage quality is effectively ensured in various operation modes. Finally, the feasibility and effectiveness of the proposed system and control strategy are tested on the OPAL-RT-based platform.

An Adaptive Virtual Oscillator Control Structure for Grid-Forming Inverters

The electrical grid is facing unprecedented challenges due to the increasing penetration of inverter-based resources. Grid forming inverters (GFMIs) are a promising technology to address these challenges. Recently, the virtual oscillator based GFMI control is attracting more attention due to its superior performance over other control strategies. In this article, an adaptive control strategy is proposed to provide flexible operation and transition between grid-connected and islanded modes. The controller adapts the virtual oscillator's parameter values depending on the operation mode. It also provides a feedback signal using a measured frequency to account for any steady-state errors and to allow a seamless transition from grid-connected to islanded mode. To show the feasibility of the proposed controller, this article discusses the simulation results from the implementation of the controller on a single inverter system and on a group of inverters on a large practical utility feeder, the IEEE 13 node feeder, using the DIgSILENT simulation environment.

Frequency-Locked Loop Based New Automatic Phase-Shift Method for Active Islanding Detection of Three-Phase MicroGrid

This paper proposes an active Islanding Detection Method (IDM) integrating Frequency Locked-Loop (FLL) for the detection of voltage phase and frequency at the point of common coupling (PCC) and a modified Auto Phase Shift (APS) method for three-phase Distributed Energy Resource (DER) serving a local RLC load. The FLL is composed of a double second-order generalized integrator (DSOGI) to attenuate the harmonics and pass the fundamental positive sequence components in a frequency adaptive manner. Different from traditional APS methods, a new variable frequency term with an exponential factor is injected into the phase shift algorithm to reduce the region of Non-Detection Zone (NDZ). This modified approach enables the decentralized power generation system to detect the islanding phenomenon more quickly. Simulations and experimental tests conducted on a DG system on OPALRT (OP5600) and hardware test-bed demonstrate the behaviour of the proposed model within IEEE Standards. Additionally, a comparative evaluation was performed on the seamless transition of a microgrid (MG) from Grid-connected mode to Islanded mode and vice versa, using two different MGs, each having a single voltage source converter (VSC) and a local load but with control circuits incorporating DSOGI-FLL and ROGI-FLL respectively. Hardware testing results indicated that the control circuit based on ROGI-FLL demonstrated superior performance.

Data-Mining Model-Based Enhanced Differential Relaying Scheme for Microgrids

The article presents a data-mining model-based enhanced differential relaying scheme for fault detection and classification task in the microgrids. The proposed research work aims to provide a fixed setting, fast, and reliable scheme for accurately detecting and classifying faults in the microgrid. The presented scheme fetches the three-phase current signals from both ends of the distribution lines and computes the differential current phasor, including the distributed generation (DG) in the fault model. The deep neural network (DNN)-based data-mining models for fault detection and classification are built using differential current phasor datasets generated by wide variations in fault and operating conditions of the microgrid, including different types of faults with the change in fault location, fault resistance, DG penetration in grid-connected and islanding mode of operation with types of DG units. The developed DNN models are validated for their ability to detect the faults in a low-voltage CERTS microgrid and subsequently for a medium-voltage IEEE-34 bus microgrid on a MATLAB/SIMULINK platform. Furthermore, the proposed intelligent scheme is also tested on the Typhoon hardware-in-loop platform for real-time performance evaluation. The accuracy and speed in detecting and classifying faults indicate that the proposed DNN-based intelligent differential relaying scheme can be a potential candidate for providing reliable protection measures for the microgrid.

Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks

This paper proposes a statistical feature-based deep neural network (S-DNN) islanding detection technique and classification of non-islanding disturbances for hybrid systems with both synchronous and inverter-based distributed generators. The proposed technique extracts five statistical features from the point of common coupling (PCC)'s three-phase voltage. Several islanding and non-islanding scenarios for the test system were developed in the MATLAB environment. The S-DNN model is employed to effectively detect islanding and classify non-islanding disturbances using extracted features. In the detection phase of this study, the model is designed to identify events that occur in both islanded and non-islanding modes, providing the capability to accurately detect islanding conditions. In the classification phase, the model focuses specifically on non-islanding disturbances, such as line faults, load switching, and capacitor switching disturbances, enabling precise categorization of these distinct types of events. By leveraging the S-DNN model's capabilities, this study aims to enhance both islanding detection and non-islanding disturbance classification. By introducing white Gaussian noise with varying signal-to-noise ratios (30 dB and 40 dB) to the data, we aimed to evaluate the method's ability to handle and mitigate the effects of noisy and uncertain input signals. The inclusion of noise was intended to simulate realistic conditions where measurement inaccuracies or disturbances can be present. The decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM) are put up against the proposed technique, and it performs better than them in terms of accuracy, precision, recall, and F1 score.

Linear Mode Decomposition in Magnetohydrodynamics Revisited

Small-amplitude fluctuations in the magnetized solar wind are measured typically by a single spacecraft. In the magnetohydrodynamics (MHD) description, fluctuations are typically expressed in terms of the fundamental modes admitted by the system. An important question is how to resolve an observed set of fluctuations, typically plasma moments such as the density, velocity, pressure, and magnetic field fluctuations, into their constituent fundamental MHD modal components. Despite its importance in understanding the basic elements of waves and turbulence in the solar wind, this problem has not yet been fully resolved. Here, we introduce a new method that identifies between wave modes and advected structures such as magnetic islands or entropy modes and computes the phase information associated with the eligible MHD modes. The mode-decomposition method developed here identifies the admissible modes in an MHD plasma from a set of plasma and magnetic field fluctuations measured by a single spacecraft at a specific frequency and an inferred wavenumber k m . We present data from three typical intervals measured by the Wind and Solar Orbiter spacecraft at ∼1 au and show how the new method identifies both propagating (wave) and nonpropagating (structures) modes, including entropy and magnetic island modes. This allows us to identify and characterize the separate MHD modes in an observed plasma parcel and to derive wavenumber spectra of entropic density, fast and slow magnetosonic, Alfvénic, and magnetic island fluctuations for the first time. These results help identify the fundamental building blocks of turbulence in the magnetized solar wind.

Microgrid Frequency Regulation and Optimal Sizing of Emergency Generator Considering VSG Coupled Electric Vehicles

Recently, the transition from conventional to renewable energy sources (RESs), from internal combustion engine vehicles to electric vehicles (EVs), and from the main grid to microgrids (MGs) are essential goals to both reduce greenhouse gas emissions and ensure the stability of power systems. However, the transitions cause new concerns in the grid including technical challenges and financial viability. This study examines the RES-based MG under realistic conditions considering the uncertainty in fleet size of EVs, emergency generator capacity, solar irradiation, and wind speed in island mode. The work aims to provide effective solutions including conventional methods alongside today’s trend namely doubly fed electrical generators (DFIG), vehicle-to-grid mechanism (V2G), maximum power point tracking controller (MPPT), voltage source inverter (VSI) with high switching frequency for technical challenges, and virtual synchronous generator (VSG) mechanism for financial viability. The paper provides a guide for resizing the emergency generators capacity depending on system instability. The observations verify that the control mechanisms reinforce the system to remain stable by decreasing the range of frequency fluctuation from 3.1 to <0.05 Hz, the peak point of frequency from 51.8 to 50.05 Hz, and the emergency generator capacity from 0.7605 to 0.3420 MVAr at MATLAB and Simulink.

Optimal generation planning in a micro-grid for supplying electrical and thermal loads in order to reduce pollutant emissions

The depletion of fossil fuels and growing environmental concerns have propelled the need for renewable energy sources. Micro-grids (MGs) have emerged as a viable solution for utilizing these resources effectively. This paper addresses the unit sizing problem for a two MG system using an intelligent method that considers air pollution emissions while catering to electrical and thermal loads. The MG incorporates photovoltaic (PV) panels, wind turbines (WTs), and a storage energy system comprising an electrolyzer, hydrogen tank, and fuel cell (FC) for combined heat and power (CHP) generation. In cases of excess power generation, an electric heater and backup boiler are employed to meet thermal loads. To account for air pollutant emissions, it is considered as a cost constraint in the objective function. Reliability is ensured by incorporating load curtailment costs as a limiting constraint. The MG is simulated in both islanding and grid-connected modes, with a generation planning program considering load growth. The IEEE RTS data is used for a peak 500 kW electrical load, and seasonal climate changes are factored in when deriving the thermal load. The modified particle swarm optimization (MPSO) algorithm is employed to find the global optimal solution. This research addresses key motivations, problems, and solutions in the field of renewable energy-based MGs. It introduces hydrogen tanks and batteries as storage systems to enhance system reliability. The integration of complementary energy sources, such as wind and solar, is proposed to improve system reliability and affordability. The use of batteries, solar, wind, and boilers for electrical and thermal load provision is more economically viable than hydrogen storage due to fuel prices. The proposed model significantly reduces pollution, enhances efficiency, decreases MG costs, and reduces dependence on fossil fuels. Integration with the grid reduces boiler usage, minimizes additional wasted power, generates income from power sales, and further reduces costs. Optimal size determination reveals the affordability challenges of wind and solar sources compared to microturbines, necessitating government intervention through subsidies or penalties for polluting resources. The results demonstrate that increasing subsidies expands the use of wind and solar resources, while the growth of fuel prices impacts resource size and necessitates storage use. Fine-tuning the penalty factor reduces pollution, but exceeding the limit reduces the adoption of renewables. Lastly, interest rate increases influence resource installations in different stages of the development plan. The outcomes of this study highlight the feasibility and effectiveness of the proposed model in addressing the challenges of MG design, operation, and integration of renewable energy sources. The findings contribute to the advancement of sustainable energy systems and provide valuable insights for policymakers and researchers in the field.

Power management of grid-connected PV wind hybrid system incorporated with energy storage system

Renewable Energy Sources (RES) such as solar photovoltaic (PV) and wind-based generation cannot meet dynamic load demand all the time because of their intermittent nature. Battery Energy Storage System (BESS) is integrated with RES to meet dynamic load demands. Proper power management is needed for the efficient and reliable performance of the system. This paper presents power management of PV-Wind- Battery hybrid system for both grid-connected and islanded modes of operation as well. The power balance is maintained by a power management system (PMS) under different environmental conditions and load conditions, and modes of operation. The control method for state-of-charge (SOC) and battery charge/discharge is applied to ensure efficient performance and safe operation of BESS. Different cases, such as surplus/deficit power available from RES with respect to load demand and battery capacity, have been considered and carried out to verify the performance of PMS using the MATLAB/Simulink platform.

Risk-based preventive energy management for resilient microgrids

A microgrid's energy management system (EMS) is typically formulated as a deterministic optimization problem. However, more risks and uncertainties are emerging due to the increasing complexity of the power system structure with intelligent distributed energy integration. As a result, different types of risks must be considered while implementing the EMS to improve system resiliency. In this paper, a risk-based EMS is introduced for islanded microgrids. A scenario-based optimization method is formulated for the EMS, where each system component's probability of failure (PoF) is considered in each scenario. In addition, the loads are classified into critical, semi-critical, and non-critical loads to prioritize serving the essential loads during a shortage of generation resources in microgrids. The nonlinear power flow equation of the new problem formulation is relaxed into a convex form to reduce the computational burden. In detail, the system's resiliency is improved by employing three functionalities in the EMS as a preventive measure: a) prioritizing to serve the critical loads, b) lowering the total amount of load served to mitigate the impact of the failure of any system component, and c) reducing the dependency on the generation resources with high PoF. The proposed preventive and risk-based EMS is validated against IEEE-14, 30, and 118 bus systems. The results demonstrate that the proposed EMS sheds more loads than the base EMS, based on the PoF of each system component, due to the knowledge that risks of failure of any component may induce a cascading failure and even blackout if only base EMS is provided. Moreover, the proposed EMS precisely considers the criticality of loads for curtailment to match the available system resources. The curtailment indirectly improves the system's resiliency by increasing the chance of survivability in the islanded mode with the probability of future component failure.

Enhanced energy management system for isolated microgrid with diesel generators, renewable generation, and energy storages

This paper presents an enhanced control and energy management strategy for the remote isolated power system. The presented control method includes the hybrid energy storage system control with a supercapacitor voltage restoration loop. This method improves the transient response and extends the battery lifespan while maintaining the SC voltage at a defined range of operation. To maximize the usage of renewable energy and reduce the diesel generator’s frequent turn-on and off operation, the novel battery state of charge stop control is included in the energy management system. This improves the lifetime of diesel generators and reduces maintenance costs. In the proposed control method, a stabilizing control method is also introduced which helps in a seamless transition from grid-connected mode to islanded operating mode. The effectiveness of the proposed EMS and control method is validated by real-time simulation studies.

A Technique to Optimally Prevent the Voltage and Frequency Violation in Renewable Energy Integrated Microgrids

A microgrid (MG) is always prone to the uncertainties of its demand variation and the generation of its non-dispatchable renewable sources when operating in islanded mode. Such variation events can lead to the voltage or frequency (VF) violation in the MG. However, there are some techniques available in the literature that can predict these events a few minutes ahead. Using such techniques, the VF violation can also be predicted and prevented with the introduction of a suitable preventive controller. Hence, this paper proposes a look-ahead controller that uses the short-horizon prediction data of demand and renewable generation to determine any prospective VF violation. If a violation is predicted, the proposed technique will aim to define the most optimal generation level of dispatchable sources, the MG’s best network configuration and the engagement level of the supportive actions, such as exchanging power with neighboring microgrids, utilizing energy storages, a demand response and renewable energy curtailment (if and when available). The technical, reliability and environmental aspects of the MG are considered within the proposed technique along with the operational cost. The determined optimal control variables are then sent to the local controllers to apply the proper arrangements in the system to retain the VF within the desired range. The performance of the developed technique is validated through extensive numerical analyses in MATLAB.

Three Time-Scale Singular Perturbation Hybrid Control and Large-Signal Analysis Stability in AC-Microgrids

This work is devoted to propose a distributed control hybrid algorithm for the primary and secondary control loops of AC-bus microgrids in islanded mode and to provide a large-signal stability. A primary control loop based on Hybrid Dynamical System (HDS) theory manages the DC/AC inverters considering structural non-linearities in the model (affine terms and hybrid dynamics of their signals) and implementation constraints (dwell-time) and ensures the robustness of the output voltage with respect to any model variation. Moreover, the secondary control loop is designed based on the droop concept, which ensures that SOCs, reactive and active powers converge to their reference values, and on Multi-Agent System (MAS) theory to get a consensus among the reactive power and the State Of Charge (SOC) of the batteries in discharging mode. Moreover, it guarantees reliability and robustness with respect to any disconnection. The tuned system parameters are chosen in order to fit a three-time scale model (a time scale for each control objective: power converter control, droop control and consensus controls), which allows providing a large-signal stability analysis for the complete hybrid system based on HDS, MAS and singular perturbation theory.

Transient Synchronization Stability of an Islanded AC Microgrid Considering Interactions Between Grid-Forming and Grid-Following Converters

Microgrid has been rapidly developed for the integration of distributed renewable energy, owing to its superiority of flexible operation and high reliability. Without frequency support from the main grid, the microgrid under islanded operating mode is more likely to lose stability in the presence of large-signal disturbances. However, previous studies mainly focus on weak grid conditions and ignore the interaction effects between different-type converters. Therefore, this paper investigates the transient synchronization stability of an islanded AC microgrid considering dynamic interactions between grid-forming and grid-following converters. Firstly, a simplified nonlinear model of the islanded microgrid is established, which reveals the nonlinear interaction and damping impact on system stability. Based on the passivity principle and LaSalle’s theorem, an explicit stability criterion is formulated to reveal the instability mechanism. With the proposed stability criterion, the impacts of different system parameters are analyzed, which points out the direction of parameters optimization to enhance stability. To validate the effectiveness of the proposed method, simulations and experiments are carried out.

A novel SVM based adaptive scheme for accurate fault identification in microgrid

Microgrids, which include distributed energy resources, provide a more reliable power supply. However, despite the fast advancements in microgrid protection, the microgrid’s dynamic behaviour challenges the protection. The large-scale integration of renewable energy sources (RES) and the addition of electric vehicle (EV) charging loads cause additional protection issues on the grid due to its volatile nature. Conventional fault prediction schemes do not provide reliable protection with the dynamic nature of microgrids. This article proposes an adaptive scheme that accurately detects the volatile changes in the microgrid under normal and fault conditions. The novelty of the proposed approach is that the general-purpose adaptive scheme is based on a two-level SVM classifier model to ensure accuracy and speed of operation. The SVM-1 model identifies the mode of operation, the variations in RES generation, the random addition of EV charging load, and the occurrence of a fault in the microgrid. SVM-2 identifies the exact type and location of fault that occurred. The SVM model uses RMS values of three-phase voltage and current measurements as data inputs. The efficacy of the proposed scheme is tested on an IEEE 9-bus microgrid test bed by simulation experiments using MATLAB/SIMULINK. The sensitivity analysis results prove the robustness of the proposed scheme. Moreover, the proposed method is validated using real-time experiments on Hardware-In-Loop (HIL) real-time simulator, OPAL-RT, and Raspberry Pi microcontroller. Accurate online detection of faulted lines and fault types is obtained within a cycle of AC in grid-connected and islanded modes of operation.