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This paper presents the design and validation of a novel adaptive islanding detection method (AIDM) for a hybrid AC/DC microgrid network using a combination of Artificial Intelligence (AI) and Signal Processing (SP) approaches. The proposed AIDM is aimed to detect and discriminate between the different fault/disturbance conditions that result in islanding and/or non-islanding conditions in a hybrid microgrid. For the islanding and non-islanding conditions detection by the AIDM, firstly, fault/disturbance signals are obtained from a test microgrid. Secondly, these signals are decomposed using Dual-Tree Complex Wavelet Transform. Thirdly, a Synthetic Minority Oversampling Technique (SMOTE) is applied for data preprocessing to increase the accuracy of the classifier. Finally, an artificial neural network (ANN) is used as the classifier for training and testing the proposed AIDM for different microgrid configurations and event scenarios. The proposed method is tested with different data categories from three different microgrid test systems with different scenarios. All modeling and simulations are executed in MATLAB Simulink Version 2023a. Results indicate that the proposed scheme could detect and discriminate between islanding and non-islanding conditions accurately in terms of dependability, precision, and accuracy. An average accuracy of 99–100% could be achieved when tested and validated with microgrid networks adapted from IEEE 13-bus systems.

The hybrid AC/DC microgrid is an independent and controllable energy system that connects various types of distributed power sources, energy storage, and loads. It offers advantages such as a high power quality, flexibility, and cost effectiveness. The operation states of the microgrid primarily include grid-connected and islanded modes. The smooth switching between these two states is a key technology for ensuring the flexible and efficient operation of the microgrid. In this paper, the typical structure of an AC–DC hybrid microgrid and its coordination control strategy are introduced, and an improved microgrid model is proposed. Secondly, an adaptive current–voltage–frequency integrated control method based on signal compensation is proposed to solve the impulse current and voltage generated during the switching between a grid-connected state and an off-grid state. Finally, in response to unplanned grid-connected scenarios, an improved pre-synchronization control strategy based on BP neural networks is introduced to rapidly restore stable operation. The proposed control strategies enhanced the steady-state and transient stability of the hybrid wind–solar–energy storage AC/DC microgrid, achieving seamless grid-connected and islanded transitions without disturbances. The simulation and experimental results validated the correctness and effectiveness of the proposed theories.

Recently, the establishment of technical standards for grid connection has gained interest in academia and industry. These standards have focused on the reactive power control function of the grid-connected inverter and maintenance of grid operation, and include detailed information about the grid support function. However, remote control communication and control devices for grid support functions, and other distributed sources, such as wind power and energy storage systems, other than inverters have not been addressed. In this paper, the control of the interlinking converter (ILC) in a hybrid microgrid considering low voltage ride-through (LVRT) among grid support functions is investigated. The proposed method consists of an energy management system considering LVRT and a cooperative control scheme. In the energy management system, an algorithm capable of mode selection was constructed by applying the LVRT curve. Then, considering the LVRT situation, the allowable reactive power range of the ILC was mathematically analyzed through the cooperative control of the energy storage device and the ILC. The proposed method enables us to perform active and reactive power control of the ILC in a hybrid distribution network, considering the power factor under various conditions. This functionality, such as supplying reactive power, significantly contributes to the enhanced grid resilience with distributed power sources, including renewable energy. The proposed strategies were verified through experiments after configuring an experimental set of distributed power sources.

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration.

A fundamental strategy for utilizing green energy from renewable sources to tackle global warming is the microgrid (MG). Due to the predominance of AC microgrids in the existing power system and the substantial increase in DC power generation and DC load demand, the development of AC/DC hybrid microgrids (HMG) is inevitable. Despite increased theoretical efficiency and minimized AC/DC/AC conversion losses, uncertain loading, grid outages, and intermittent complexion of renewables have increased the complexity, which poses a significant threat toward system stability in an HMG. As a result, the amount of research on the stability, management, and control of HMG is growing exponentially, which makes it imperative to recognize existing problems and emerging trends. In this survey, several strategies from the most recent literature developed to address the challenges of HMG are reviewed. Power flow analysis, power sharing (energy management), local and global control of DGs, and a brief examination of the complexity of HMG’s protection plans make up the four elements of the review technique in this article. During critical analysis, the test system employed for validation is also taken into consideration. A comprehensive review of the literature demonstrates that MILP is a frequently employed technique for the supervisory control of HMG, whereas tweaking bidirectional converter control is the most common approach in the literature to achieve efficient power sharing. Finally, this review identified the limitations, undiscovered challenges, and major hurdles that need to be addressed in order to develop a sustainable control and management scheme for stable multimode HMG operation.

Due to the voltage drop of the line impedance, power-transfer deviation occurs in the interlinking converter (IC) of hybrid microgrids. In this paper, an IC control strategy in hybrid microgrids is proposed, which can estimate the line resistance precisely. Moreover, the proposed method can easily realize accurate power transmission between sub-grids and maintain the power balance of hybrid microgrid. Firstly, one specified small signal is injected into the IC at the AC bus of the distributed generation so as to estimate the line impedance. The frequency of the injected small signal is highly relevant to the AC bus voltage. As a result, the IC can accurately detect its line impedance by extracting the injected small signal frequency with a filter and phase-lock-loop (PLL) without communication. Secondly, with the line impedance estimation, the voltage drop on the line can be compensated with local information of the IC, and consequently, accurate power transfer can be realized. Finally, the validity of the proposed method is verified by simulation and experiment results.

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

This study proposes an advanced control scheme for the interlinking inverters of the hybrid AC/DC microgrids, which facilitates a seamless transition between grid-connected and stand-alone/islanding modes for the microgrid. Due to a nonlinear relationship between the terminal voltages of the voltage-source inverter (VSI) interfacing through inductor–capacitor (LC) filters with the grid voltages and currents, a feedback linearization technique (FLT) is employed to control the interlinking VSI under both grid-connected and islanding operations. The FLT-based current controllers are applied in the grid-connected mode, in which they adjust the power exchange between the DC and AC subgrids and mitigate the distortion of the grid currents produced by nonlinear loads. Under the stand-alone operation, the AC bus voltages are directly regulated by the FLT-voltage controllers of the interlinking VSI. In order to reduce the inrush currents and voltage overshot at the instant of mode switching, the FLT-based controllers are performed all the time regardless of the operating modes, where the voltage references for the VSI are not changed abruptly. The control performance of the VSI is highly satisfactory with low-transient overshoot values of the voltages and currents and low total harmonic distortion (THD) values of the grid currents and AC bus voltages are about 3.5% and 2.7%, respectively, under the nonlinear load condition. The validity of the new control strategy is verified by the simulation work, which investigates the operation of a hybrid AC/DC microgrid.

In the last several years, the coordination control of hybrid AC/DC microgrids (HMGs) has been gaining increasingly more attention. However, most of these discussions are focused on single-bus HMGs whose AC or DC bus is not sectionalized by AC or DC breakers. Compared with these single-bus HMGs, the bus-sectionalized HMG has more flexible topologies, more diverse operation modes, and consequently higher service reliability. However, meanwhile, these benefits also bring challenges to the stable operation of bus-sectionalized HMGs, particularly for mode switching. Relying on the national HMG demonstrative project in Shaoxing, China, this paper makes efforts to present the hierarchical control paradigm of a typical bus-sectionalized HMG toward standardization. The test results demonstrate that the proposed system provides seamless switching and uninterrupted power supply without controller reconfiguration among different operation modes. The operational data are also brought forth and analyzed to provide significant and useful experiences for designing and developing similar HMGs in the future.

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels.

With the significant increase in DC loads (such as data and telecommunication centers) at the power distribution level, an additional set of power electronic converters are required to connect these DC loads to the AC-dominant power network. Notably, hybrid AC/DC microgrids (MGs) serve as promising solutions to satisfying both the AC and DC loads with a reduced number of installed converters. Since DC loads may be randomly distributed in the MG, how to place DC feeders to simultaneously fulfill the economic and security requirements of MG operations remains a challenging problem. To address this issue, this paper proposes a hybrid AC/DC MG planning model to determine the optimal placement of DC feeders with the objective of minimizing the total cost of the investment of distributed energy resources (DERs), converters, and AC/DC distribution lines, as well as the operation of DERs. In particular, the power flow of the hybrid AC/DC MG is derived in a unified manner and then incorporated in the planning model. Eventually, the proposed model suffices to find the optimal number and siting for both DERs and DC feeders while ensuring the continuality of the DC feeders. The proposed model is tested in two MG-based distribution systems, and its effectiveness is validated by the results of numerical experiments.

This paper proposes a distributed coordination control for multiple bidirectional power converters (BPCs) in a hybrid AC/DC microgrid with consideration of state-of-charge (SOC) of storages. The researched hybrid AC/DC microgrid is composed of both AC and DC subgrids connected by multiple parallel BPCs. In the literature, the storages of a hybrid microgrid are considered to allocate in only the AC subgrid or DC subgrid, which reduces the reliability of the whole system, especially during the islanded mode. Besides, the SOC management has not been considered in BPCs’ operating strategy. This paper considers a hybrid microgrid topology which has energy storages in both AC side and DC side. This ensures the reliability while increasing the complexity of the control strategy at the same time. Further, a distributed coordination control method for multiple BPCs based on SOC was proposed to enhance the reliability of hybrid microgrid. Finally, the performance of the proposed control methods was verified by real-time hardware-in-loop (HIL) tests.

A hybrid alternate current/direct current (AC/DC) microgrid consists of an AC subgrid and a DC subgrid, and the subgrids are connected through the interlink bidirectional AC/DC converter. In the stand-alone operation mode, it is desirable that the interlink bidirectional AC/DC converter manages proportional power sharing between the subgrids by transferring power from the under-loaded subgrid to the over-loaded one. In terms of system security, the interlink bidirectional AC/DC converter takes an important role, so proper control strategies need to be established. In addition, it is assumed that a battery energy storage system is installed in one subgrid, and the coordinated control of interlink bidirectional AC/DC converter and battery energy storage system converter is required so that the power sharing scheme between subgrids becomes more efficient. For the purpose of designing a tracking controller for the power sharing by interlink bidirectional AC/DC converter in a hybrid AC/DC microgrid, a droop control method generates a power reference for interlink bidirectional AC/DC converter based on the deviation of the system frequency and voltages first and then interlink bidirectional AC/DC converter needs to transfer the power reference to the over-loaded subgrid. For efficiency of this power transferring, a linear quadratic regulator with exponential weighting for the current regulation of interlink bidirectional AC/DC converter is designed in such a way that the resulting microgrid can operate robustly against various uncertainties and the power sharing is carried out quickly. Simulation results show that the proposed interlink bidirectional AC/DC converter control strategy provides robust and efficient power sharing scheme between the subgrids without deteriorating the secure system operation.

Interest in DC microgrids is rapidly increasing along with the improvement of DC power technology because of its advantages. To support the integration process of DC microgrids with the existing AC utility grids, the form of hybrid AC/DC microgrids is considered for higher power conversion efficiency, lower component cost and better power quality. In the system, AC and DC portions are connected through interlink bidirectional AC/DC converters (IC) with a proper control system and power management. In the stand-alone operation mode of AC/DC hybrid microgrids, the control of power injection through the IC is crucial in order to maintain the system security. This paper mainly deals with a coordination control strategy of IC and a battery energy storage system (BESS) converter under stand-alone operation. A coordinated control strategy for the IC, which considers the state of charge (SOC) level of BESS and the load shedding scheme as the last resort, is proposed to obtain better power sharing between AC and DC subgrids. The scheme will be tested with a hybrid AC/DC microgrid, using the tool of the PSCAD/EMTDC software.