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In stationary power generation units such as distributed remote site power systems and ship power systems, diesel engine generator systems are essential for supplying electricity. This paper proposes a digital twin diesel generator system for teaching and research purposes. A five-layer resilient architecture, including a web interface layer, server cluster layer, real-time data layer, controller layer, and equipment layer, is proposed in this paper. Based on the resilient architecture, users are able to build, implement and monitor the digital twin through web interfaces. Apart from MATLAB/Simulink, a modeling tool called M2PLink is developed to allow users to create mathematical models using a block diagram editor similar to Simulink. Various basic blocks for control systems are provided for users to form sophisticated models. These models are converted into executable codes which are downloaded to the simulator in the controller layer, where the real-time simulations are implemented. A web-based real-time monitoring interface with many widgets such as charts, oscilloscopes, and three-dimensional (3D) animation is also provided for users to customize their monitoring interface. All the signals can be traced and all the parameters can be tuned in the monitoring interface. The users are able to interact with the digital twin just like they do with the real system. The proposed system can not only be used for research such as digital twin-assisted real-time online monitoring but also for educational purposes, which is not only cost-effective but can also ensure the safety of the user as well as the equipment. [ABSTRACT FROM AUTHOR]

The distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time. [ABSTRACT FROM AUTHOR]

With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper. [ABSTRACT FROM AUTHOR]

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided. [ABSTRACT FROM AUTHOR]

The current development of prosumer microsources and the expected spread of electric vehicles may cause the appearance of significant current and voltage unbalance in low-voltage (LV) networks. This unbalance, which is an unfavorable phenomenon, may occur when using single-phase photovoltaic (PV) microsources and single-phase home chargers for electric vehicles. This paper presents a proposal for the symmetrization of the LV network using devices for the reconfiguration of phases in the power supply. Both the different locations of these devices and the different objective functions for device implementation are analyzed. The research was carried out on an example LV network, taking into account several variants of the development of PV microsources and home chargers for electric vehicles. The analysis indicates that the appropriate location of phase reconfiguration devices and the use of an appropriate objective function leads to a significant reduction in unfavorable unbalancing in the LV network. [ABSTRACT FROM AUTHOR]

Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method. [ABSTRACT FROM AUTHOR]

With the development of distributed generation and power electronics technology, DC microgrids have made certain progress with their advantages of simple structure, economic efficiency, and easy control. Considering factors such as the geographical location of some residential areas, microgrids with fuel cells that can operate independently are also receiving increasing attention. In this paper, an online control method named virtual hydrogen consumption is proposed based on an independent DC microgrid which solves the problem of electro‐hydrogen conversion successfully. By solving the overall consumption function, the analytical solution is further obtained to realize the online control. Based on the results of RT‐LAB platform, the proposed method can maintain the output of fuel cell system in its high efficiency range with a lower volatility. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

This paper demonstrates an enhancement of power quality for a photovoltaic (PV) system connected to the grid with a hybrid energy storage system (HESS). The proposed system utilizes a ramp-rate control (RRC) strategy to limit severe fluctuations in the PV power output. Battery storage is integrated to store surplus energy generated by the PV system and is used for continuous power application. A high-power density device, known as a supercapacitor (SC), is employed to mitigate transient fluctuations in the battery. The proposed system facilitates smooth PV power generation, stabilizes the DC bus voltage (VDC), and eliminates source current harmonics induced by non-linear loads. The Shunt Active Power Filter (SAPF) discussed in this paper serves two primary purposes. Firstly, it acts as a reactive power buffer, smoothing out fluctuations and reducing current harmonic distortions. Secondly, it enables active power injection into the grid, utilizing a specific renewable solar PV source. The efficiency of the modeled compensation system is demonstrated by the sinusoidal shape of the current and the compensation of reactive power (RPC). The targeted system showcases the effectiveness of the current setup by exhibiting low total harmonic distortion (THD). The multifunctional features of the proposed system were implemented using the MATLAB/Simulink software, and the results were validated using an OP5700 Hardware-in-the-Loop (HIL) test bench. This integration of distributed power generation capabilities not only enhances the overall power quality but also improves the efficient utilization of renewable energy resources (RESs). [ABSTRACT FROM AUTHOR]

Distributed photovoltaics (DPVs) are widely distributed and the output is random, which brings challenges to the safe operation of the distribution network, so the construction of photovoltaic aggregations can effectively participate in the flexible regulation of the power system. At present, the extraction of DPV clustering features is not sufficient, only considering the output characteristics of PVs. Certain PVs under some nodes may have a more pronounced regulation effect, but they may be ignored in the clustering process. To address the above problems, this paper proposes a DPV aggregation approach considering the distribution network topology. It combines the voltage sensitivity and the power curve and regards them as clustering features to form the DPV aggregation with the highest average voltage sensitivity participating in voltage regulation. The simulation on the IEEE 33-node system verifies that the proposed aggregation approach can select DPV aggregation more suitable for voltage regulation, and make full use of the aggregation to realize the optimal voltage regulation effect. [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers' electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers' needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system's operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system's behavior, a one-week time horizon is considered. [ABSTRACT FROM AUTHOR]

"Sharing economy" refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach. [ABSTRACT FROM AUTHOR]

The development and use of single‐phase electric energy router (SPEER) makes the household power system operate more reliably and bring economic income at the same time. This paper presents an economic energy optimization management method of SPEER with historical data of previous day and real‐time data in the grid‐connected mode of SPEER, which can be realized by DSP. First, based on the exchange power between the SPEER and the grid, the optimal charging and discharging power of the battery to make up for the energy difference between the previous day and the real time is obtained through the particle swarm intelligence algorithm, and the optimal economic model of SPEER operation is established. Then, on the hardware experimental platform, DSP is used to realize the real‐time optimization, and the optimal economic optimization algorithm proposed in this paper is implemented and verified on hardware experimental platform. The contradiction between the complexity of optimization algorithm and the limitation of hardware resources is solved in the proposed energy management system. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the "teaching" process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity. [ABSTRACT FROM AUTHOR]

The high expansion of a variable and intermittent nature of distributed generation, such as photovoltaics (PV), can cause technical issues in existing distribution networks (DN). In addition to producing electrical energy, PVs are inverter-based sources, and can help conventional control mechanisms in mitigating technical issues. This paper proposes a multi-stage optimal power flow (OPF)-based mixed-integer non-linear programming (MINLP) model for improving an operation state in LV PV-rich DN. A conventional control mechanism such as on load tap changer (OLTC) is used in the first stage to mitigate overvoltage caused by PVs. The second stage is related to reducing losses in DN using reactive power capabilities from PVs, which defines the optimization problem as a fully centralized observed from the distribution system operator's (DSO) point of view. The optimization problem is realized under the co-simulation approach in which the power system analyzer and computational intelligence (CI) optimization method interact through an interface. This approach allows keeping the original MINLP model without approximations and using any computational intelligence method. OpenDSS is used as a power system analyzer, while particle swarm optimization (PSO) is used as a CI optimization method in this paper. Detailed case studies are performed and analyzed over a single-day period. To study validation and feasibility, the proposed model is evaluated on the IEEE LV European distribution feeder. The obtained results suggest that a combination of conventional control mechanisms (OLTC) and inverter-based sources (PVs) represent a promising solution for DSO and can serve as an alternative control method in active distribution networks. [ABSTRACT FROM AUTHOR]

In recent decades, the fight against climate change and the commitment to reduce greenhouse gases have shed a light on the production of energy from renewable sources, in particular those derived from solar energy. This has required the involvement of all stakeholders (producers, but also energy operators, authorities, distributors, and final consumers) which has led to the outline of a new scenario characterized by more efficient technologies, dedicated strategies and business models, and the research of alternatives solutions. Within solar technology, great attention has been given in recent years to concentrating solar power (CSP) technologies, both from research studies and technological development sides. This paper provides a theoretical framework based on a CSP literature review to define the state of the art and to identify research gaps and future research steps related to this technology. The work is based on an innovative bibliometric study to explore technical fields related to CSP, providing both a comprehensive framework with reference to the state of the art of the technology investigated, and a detailed analysis on CSP commercial applications, making the review a very useful tool for stakeholders and decision makers The results of the analysis: (1) help to clarify the technological advances of CSP, the strengths and weaknesses of the current technologies used (parabolic and tower systems are the most widespread), and indications of the prospects for dish systems; (2) identify an alternative to the economic problem that represents an obstacle to the diffusion of CSP, for example, by identifying the ability to couple it with thermal storage as a valid method to increase the flexibility of the system and reduce costs; (3) suggested hybrids, both with renewable and non-renewable technologies, identifying strengths and weaknesses for all the proposed proposals; (4) show that it is possible to identify new ongoing research such as that related to hydrogen production. This paper represents the first part of a larger research study developed within the SOLARGRID Project, which promotes and supports the development of innovative solutions for systems and components for CSP and concentrated photovoltaics (CPV) technologies, with the aim to enhance their energetic performances and economic competitiveness in applications for the distributed generation of both electric and thermal energy frameworks. The main findings of our study highlight that, though there is an increasing number of papers on the topic of CSP, several issues remain neglected. [ABSTRACT FROM AUTHOR]

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information. [ABSTRACT FROM AUTHOR]

This paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms. [ABSTRACT FROM AUTHOR]

Temporary microgrids containing a high proportion of distributed renewable generations can offer great benefits during the recovery stage after large‐scale power system blackout, by picking up critical load and speeding up the entire grid recovery process. To solve the frequency security problem of temporary microgrids faced in the load restoration process, this paper proposes an adaptive fuzzy model predictive control (adaptive fuzzy‐MPC) strategy to obtain effective frequency regulation to suit the special characteristics of this process. First, the load restoration plan within a short time frame is regarded as the feedforward information and assists in the establishment of the MPC model. Then, considering the uncertainty of renewable distributed generations, the regulation resources are cooperated by an adaptive fuzzy controller which provides the weights of different regulation resources adopted in MPC. Specifically, the base weights of the energy storage system and renewable distributed generations are obtained from fuzzy rules, and then the weight for each regulation resource is modified by the designed adjustment rules. Simulation results illustrate that the proposed strategy can efficiently improve frequency performance while making various frequency regulation resources with different regulation capacities cooperate properly, which can further accelerate the load restoration progress and enhance system resilience. [ABSTRACT FROM AUTHOR]

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python. [ABSTRACT FROM AUTHOR]

Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed. [ABSTRACT FROM AUTHOR]

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m. [ABSTRACT FROM AUTHOR]

With the progress of communication technology, the cost of optical fiber and 5G continues to decrease, and data transmission becomes more convenient and fast, making it possible to realize differential protection of distribution network by various intelligent algorithms using signal waveforms. Aiming at the problem that the traditional relay protection device can not meet the actual demand when the single-phase ground fault occurs in the distribution network with distributed generation, this paper proposes a new differential protection scheme. The characteristic mode decomposition improved by the whale optimization algorithm is used to decompose the zero-sequence current waveform collected at both ends of the line. Based on the basic principle of current differential protection, the derivative dynamic time warping of the component with the largest fault feature can effectively solve the problem that the grounding current of the distribution network cannot meet the working requirements of the differential protection device, and ensure the safe and stable operation of the system. Finally, based on MATLAB software, the performance of this method is comprehensively evaluated by simulating different fault conditions, so as to ensure the feasibility and accuracy of this method in the case of diversified faults when the distributed generation is used as part of the power supply. [ABSTRACT FROM AUTHOR]

Virtual synchronous generator (VSG) technology has achieved some results in distributed generation networks and enhanced system stability. For problems that may occur in the system due to ignoring parameter linkages, this paper designs a synergistic controller that establishes the connection between the active frequency and the reactive voltage control loop based on synergy theory. As the output of the synergetic controller, the derived control law u is added to the power frequency control in the form of negative feedback. Two disturbance forms, a step disturbance, and a three-phase short-circuit fault, are set up. The conventional droop control and synergetic droop control results are compared using a MATLAB simulation system. Then, the system parameter variations were studied and analyzed, and the sensitivity of the control system with different values of parameters K1, K2, and K3 in the case of a three-phase short circuit was analyzed, which provides a reference guide for selecting parameters in later control optimization. Finally, the control effectiveness of a multi-machine VSG system was tested. The simulation results show that the proposed method has a pronounced effect on making the system stabilize quickly, which illustrates the effectiveness of this technique. [ABSTRACT FROM AUTHOR]

As distributed generation (DG) becomes increasingly integrated into the distribution grid, the structure of the distribution network is becoming more complex. To enhance the safety and cost-effectiveness of distribution systems, distribution network reconfiguration is gaining significant importance. Achieving optimal distribution network reconfiguration entails two key considerations: A feasible topology and economic efficiency. This paper addresses these challenges by introducing a novel approach that combines the potential island detection in undirected-graphs and the application of a whale optimization algorithm (WOA) for network reconfiguration optimization. To begin, we identified island categories based on the type of switchable-branches connected to these islands, allowing for the construction of potential island groups. Subsequently, unfeasible topologies were eliminated based on the conditions under which islands form within these potential island groups. Feasible topologies were then used to construct a model for network reconfiguration optimization. The optimal distribution network topology is determined using the WOA. In the final phase, the proposed method's effectiveness was demonstrated through a case study on the IEEE-33 node distribution network under scenarios with and without DG integration. The results showed that the proposed method exhibited better performance than traditional approaches in distribution network reconfiguration. [ABSTRACT FROM AUTHOR]

In this paper, a fault recovery strategy for a distribution network based on a pigeon-inspired optimization (PIO) algorithm is proposed to improve the recoverability of the network considering the increased proportion of distributed energy resources. First, an improved Kruskal algorithm-based island partitioning scheme is proposed considering the electrical distance and important load level during the island partitioning process. Secondly, a mathematical model of fault recovery is established with the objectives of reducing active power losses and minimizing the number of switching actions. The conventional PIO algorithm is improved using chaos, reverse strategy, and Cauchy perturbation strategy, and the improved pigeon-inspired optimization (IPIO) algorithm is applied to solve the problem of fault recovery of the distribution network. Finally, simulation analysis is carried out to verify the effectiveness of the proposed PIO algorithm considering a network restauration problem after fault. The results show that compared with traditional algorithms, the proposed PIO algorithm has stronger global search capability, effectively improving the node voltage after restauration and reducing circuit loss. [ABSTRACT FROM AUTHOR]

The marine DC power supply system is the key to a ship's power supply, which needs to convert the energy from storage batteries or distributed power generation units into stable DC voltage for electric propulsion or the ship's electronics. The LLC resonant converter can be used as the key power conversion link in the marine DC power supply system due to its ability to realize electrical isolation in a high-power environment and soft switching within a wide load range. Aiming at the problem of sudden changes in voltage gain at a high switching frequency under light load conditions and the problem of insufficient voltage gain under heavy load conditions due to the parasitic parameters of power devices (mainly referring to the junction capacitor), this paper first proposes a full operating range performance optimization design method. By adding an auxiliary circuit that can be opened according to the operating conditions and a multi-objective particle swarm parameter optimization method that considers the converter loss and voltage gain under heavy load conditions, the performance of the LLC resonant converter can be improved in a full range of operating conditions. Finally, the effectiveness of the proposed method is verified by an experimental prototype and compared with the conventional methods and existing solutions to highlight the superiority of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Driven by the demand for carbon emission reduction and environmental protection, battery swapping stations (BSS) with battery energy storage stations (BESS) and distributed generation (DG) have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BSS with photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BSS and uses the spare capacity of building special transformers and the roof PV to supply power to BSS to avoid the investment of transformers. Secondly, this paper establishes the load model of BSS and proposes the charging rules of battery swapping. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BSS operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BSS, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period; on this basis, the demand response model is further proposed. Simulation results show that the proposed strategy can improve the daily profit of BSS through shifting load. And the configuration of BESS can improve the battery swapping capacity and peak‐shaving ability. Moreover, the exponential segmented pricing mechanism can greatly reduce the number of high load periods and reduce the burden on the power supply. [ABSTRACT FROM AUTHOR]

Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of load, etc. In particular, the dissemination of distributed generation (DG), especially those based on renewable sources, has introduced new challenges to power systems, adding further randomness to the management of this segment. In this context, stochastic analysis could support planners and operators in a more appropriate manner than traditional deterministic analysis, since the former is able to properly model the power system uncertainties. The objective of this work is to present recent achievements of one of the most important techniques for stochastic analysis, the Monte Carlo Method (MCM), to study the technical and operational aspects of electric networks with DG. Besides covering the DG topic itself, this paper also addresses emerging themes related to smart grids and new technologies, such as electric vehicles, storage, demand response, and electrothermal hybrid systems. This review encompasses more than 90 recent articles, arranged according to the MCM application and the type of analysis of power systems. The majority of the papers reviewed apply the MCM within stochastic optimization, indicating a possible trend. [ABSTRACT FROM AUTHOR]

This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network. [ABSTRACT FROM AUTHOR]

The carbon factor is the core driving force behind the low-carbon transformation of the power system. The study of Emission Reduction Paths (ERPs) has become a key issue for the New-type Power System to achieve clean and low-carbon goals. In order to measure the relationship between ERP and the economy, this paper considers the retirement requirements of coal-fired power units and emission reduction requirements on a long cycle planning scale, and constructs a multi- scenario planning model that considers electric-thermal coupling, enabling the determination of installed capacity for power and heat sources each year. The effectiveness of the proposed model was verified using an improved IEEE-39 bus power system. A case study was conducted to analyze the economic performance of the system based on the selected ERP set. The results showed that under the rear acceleration's ERP, the system could achieve optimal economic performance based on the selected ERP set. [ABSTRACT FROM AUTHOR]

Increasing the penetration level of distributed generation (DG) units requires overcoming the technical challenges associated with their integration into the distribution systems, especially protection problems. Change in the current profile of the distribution system due to the presence of DG units disrupts the operation of the conventional fuse saving coordination (FSC) scheme. The first objective of this paper is to provide an overview of the state‐of‐the‐art of FSC schemes in distribution systems with distributed generators that has not been systematically presented yet. In addition to comparing the features of reliability, cost, speed, implementation, calculation burden, and requirements, the impact of presence of distributed generations on the performance of the conventional FSC scheme is investigated in details. The second objective of this paper is to propose an FSC restoration scheme for minimizing the challenges of previous works. Using a quasi‐voltage current term, the proposed scheme modifies the adjustable time coefficient of the recloser in two ways of pro and plus. The former scheme provides an approximate FSC with a simple setting while the latter scheme provides complete coordination at the expense of a more complex setting. No need for voltage measurement makes its implementation practical in available distribution systems. The effective performance of the proposed FSC scheme is verified through extensive simulation studies in the ETAP environment. [ABSTRACT FROM AUTHOR]

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With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants' benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. [ABSTRACT FROM AUTHOR]

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER. [ABSTRACT FROM AUTHOR]

A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver. [ABSTRACT FROM AUTHOR]

The conventional overcurrent protection is ineffective in isolating faults in microgrids due to the low fault current levels contributed by inverter-interfaced distributed generations (IIDGs). To extend the microgrid protection methods, the distance protection is considered as a common alternative to detect faults. However, the fault-ride-through (FRT) requirements and the high impedance fault (HIF) detection challenge the application of the conventional distance protection. To solve the two problems, a new inverse-time distance (ITD) protection for medium-voltage AC microgrids was proposed in this paper. The primary ITD protection was designed to meet the FRT requirements, and the backup protection was coordinated to isolate faults. The fault resistance and distributed generation infeed effect on the measured impedance were mitigated by a communication-assisted method proposed in this paper. Owing to the shorter communication channel time in distribution feeders and lower communication investments without a synchronous clock, the proposed method can be used to improve the ITD protection performance. An 11 kV AC microgrid was simulated with different fault types in the islanded mode and grid-connected mode. Time domain simulation verified the effectiveness of the proposed ITD protection. [ABSTRACT FROM AUTHOR]

Featured Application: Medium voltage networks that face voltage regulation issues due to high penetration of distributed generation. This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line voltage regulators. The analyzed study-case represents a real-life feeder which operates at 10 kV. It has 9 photovoltaic systems with various peak installed powers, 2 electric vehicle charging stations, and 41 secondary substations, each with an equivalent load. Measurement data of loads and irradiation data of photovoltaic systems were collected hourly for two years. Those data are used as inputs in the feeder's model in DigSilent PowerFactory where Quasi-Dynamic simulations are run. That will provide the correct tap positions as outputs. These inputs and outputs will then serve to train a Deep Neural Network which later will be used to predict the correct tap positions on input data it has not seen before. Results show that ML in general and DNN specifically show usefulness and robustness in predicting correct tap positions with very small computational requirements. [ABSTRACT FROM AUTHOR]

Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in active distribution systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of more active distribution networks and that the Internet of Energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS), which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive 26-bus microgrid (MG) test system designed to validate or propose new protection coordination schemes. The proposed MG test system comprises various components facilitating the simulation of diverse configurations and operating modes. It also presents a predefined overcurrent protection scheme which is validated with and without Distributed Generation (DG), offering detailed specifications and settings for the test network. The MG test system outlined in this paper incorporates multiple DG sources in different buses, multiple voltage levels, and two operating modes (with and without DG). Additionally, the overcurrent protection scheme considers multiple functions and devices simultaneously. The detailed features of the proposed protection coordination scheme enable the simulation of various fault types and contingencies. The paper also presents specific details of the MG test system under different single-phase and three-phase faults, reporting bus voltages in nodes, transformer and line loadability, short-circuit currents, and operating times. The MG test system is meant to serve as a valuable tool for researchers to conduct comparative studies and develop new strategies regarding the operation and planning of MGs. [ABSTRACT FROM AUTHOR]

Distributed energy resources are being progressively deployed by industry. The penetration of distributed generation in low voltage (LV) networks can position traditional consumers as market participants. With the improvements in communication networks and the introduction of new energy markets, these prosumers are incentivized to sell their excess production to other industries by participating in peer‐to‐peer (P2P) energy markets. This market paves the way for developing new technologies such as a shared battery energy storage system (SBESS). In this paper, a central storage unit rents its capacity for the prosumers to reduce the overall peak‐load of the microgrid. Each user requests the required SBESS capacity and calculates the best charging and discharging times to reduce their cost. To this end, this paper organized a P2P energy trading paradigm with the presence of SBESS. The optimization problem was simulated and solved using the alternating direction method of multipliers (ADMM) algorithm. Results demonstrate how combining features of P2P energy trading and SBESS can save up to 29% for the industrial town. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) integrate diverse energy resources using advanced communication technologies and intelligent control strategies. This integration enhances the utilization and efficiency of distributed generation. This paper explores the incorporation of VPPs into load frequency control (LFC) systems. It includes an analysis of VPP-aggregated resources' frequency regulation characteristics and a VPP-inclusive LFC model. Additionally, a decentralized automatic generation control strategy is proposed to distribute power outputs effectively, enabling swift grid frequency adjustments. This study uses MATLAB simulations to demonstrate the benefits and efficacy of VPPs in LFC, underscoring their role in advancing grid management and stability. [ABSTRACT FROM AUTHOR]

This paper proposes a reliability evaluation method of a distribution network with electric vehicles (EVs) and distributed generations (DGs) based on network equivalence and sequential Monte Carlo. Unlike traditional studies, the proposed method not only can effectively simplify the complexity structure of the distribution network with EVs and DGs, but also can establish a multidimensional reliability evaluation system for the distribution network with EVs and DGs. Firstly, the related models of DGs and EVs are established respectively in our study. Based on the minimum path method, the impact of each component fault on each load point is analyzed, and the failure impact analysis (FIA) table is constructed. Then, a simplified method for the structure of the distribution network with EVs and DGs based on network equivalence is proposed and validated. Moreover, the reliability evaluation method of the distribution network with EVs and DGs is given based on the sequential Monte Carlo. Finally, an illustrative example of the improved IEEE RBTS BUS6 F4 feeder system, as well as multiple dimensions discussions, are elaborated in detail to demonstrate the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Grid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Considering the voltage stability problem brought by large-scale distributed PV access to the distribution network, this paper proposes a generalized load model that considers the fault ride-through capability of distributed PV. Firstly, the detailed model of the distribution network is established, and the detailed model is calibrated based on the measured data, the simulation errors are below 1%. And then establish a generalized load model considering distributed PV high and low voltage traversal ability. The sensitivity analysis results are used to rank the parameters to be identified, and the parameters with higher sensitivity are identified. The parameters are obtained from the detailed model and measured data, and four sets of parameters are identified and simulated under different PV penetration rates and fault conditions. The calculated fitting errors are less than 1%. The results show that the generalized load gray box model of the distribution network with distributed PV high and low voltage ride-through capability can reflect the dynamic characteristics of the distribution network well. [ABSTRACT FROM AUTHOR]

With the large scale and high proportion of distributed generation connected to the power grid, the distributed economic dispatch system has attracted more attention because of its significant advantages. However, the distributed economic dispatch system faces more serious information security issues due to the variation of communication topology. Therefore, the purpose of this paper is to review the information security issues that may occur in the distributed economic dispatch system and the defense measures. By summarizing the existing literature on information security issues and defense measures, it can be found that the current research focuses on the cyber-side defense for information security, and lacks consideration of the cyber-physical coupling characteristics. Additionally, the separate cyber-side defense measures still have a defense blind spot and cannot respond in a timely manner to the physical-side actions caused by information security issues. Finally, the establishment of the information security issues model and the construction of the integrated security defense system are discussed from the perspective of the power cyber-physical system. This research will be helpful in the construction of the security defense system for information issues in distributed economic dispatch systems. [ABSTRACT FROM AUTHOR]

In order to cope with the failure of existing fault analysis schemes for AC/DC distribution networks with a high proportion of distributed generations, this paper proposes a fault characteristic analysis method for AC/DC distribution networks that considers the influence of distributed generation control strategies. Firstly, a transient model for the AC/DC distribution network connected to distributed generations is built. Then, the fault characteristics of the AC/DC distribution network in different stages, such as the capacitor discharge stage, inductive renewal stage, and steady state stage, is analyzed. Finally, detailed simulation analysis is conducted using PSCAD/EMTDC to validate the effectiveness of the developed scheme by the superior approximation performance between simulated curves and calculated curves. [ABSTRACT FROM AUTHOR]

The advent of distributed generation has brought with it a plethora of challenges for the nascent power systems that are being deployed on a large scale. Firstly, the majority of power electronic converters connected to the grid are in current source mode, which results in a lack of inertia and an inability to provide effective inertia or achieve damping support during fluctuations in grid frequency. Secondly, the issue of power quality, caused by the presence of harmonics, is becoming increasingly severe. This is particularly problematic in microgrids or systems with high line impedance, where harmonics can be amplified, thereby further compromising the stability of the power system. To address the deficiency in system inertia, numerous scholars are currently utilizing grid-forming (GFM) technology to achieve virtual inertia. In order to address the issue of system harmonics, it is possible to install active power filter (APF) devices at the point of common coupling (PCC), which serve to mitigate the effects of harmonics. This paper puts forth a proposal for the implementation of an APF with virtual inertia, based on PR + RC composite control. This composite control mechanism serves to enhance the harmonic suppression capabilities of the APF. The introduction of a frequency droop enables the capacitor voltage amplitude to be adjusted during fluctuations in system frequency, thereby achieving virtual inertia and providing active support for system frequency. The experimental results demonstrate that this strategy not only reduces the total harmonic distortion (THD) by 13% in comparison to PI control, indicating excellent harmonic suppression performance, but also allows the system to be inert, achieving positive results in suppressing frequency fluctuations during transients. [ABSTRACT FROM AUTHOR]

This paper presents the design of a disturbance observer-based controller that regulates the output voltage of three-phase four-leg voltage source inverters (VSIs) deployed for grid-forming operation in Renewable Energy-based Distributed Generation (REDG) Systems. The primary objective of the controller is to provide a symmetric and sinusoidal voltage at the output of the VSI when supplying highly unbalanced and nonlinear loads. The controller employs the feedback linearization (FL) technique and incorporates a disturbance observer (DO) to address a range of disturbances that include oscillations resulting from unbalanced loads, harmonics generated by nonlinear loads, and non-oscillatory disturbances. Notably, the controller adopts a direct control scheme without the need for nested current control loops and does not use any transformation frames. Simulation studies and experimental investigations were conducted to assess the controller's performance under various load conditions, including both linear and nonlinear types, as well as load transients. The findings demonstrate the controller's capability to accurately track references while complying with the IEEE power quality standards for the tested conditions. [ABSTRACT FROM AUTHOR]