The large-scale integration of high proportion new energy sources has led to frequent safety and stability issues such as reduced inertia, deteriorated damping characteristics, and frequency instability in the power system. Efficient frequency support control of energy storage systems has become a key solution. In response to the shortcomings of existing frequency control methods (such as poor adaptability to weak power grids and single network control function based on phase-locked loop strategy), a multi-mode universal active frequency support control method for energy storage systems is proposed in this paper. Based on a unified control architecture, control modes such as virtual synchronous generator control (VSG), droop control, and fixed frequency control can be realized by adjusting key control parameters. 1) In virtual synchronous control mode, virtual inertia and damping support can be provided to suppress frequency transient fluctuations; 1) In droop control mode, autonomous power allocation of multiple energy storage devices can be realized under no communication conditions; 3) In fixed frequency control modes, zero-steady-state-error frequency control can be realized. Based on PSCAD/EMTDC, a simulation model was built, and the verification results under load disturbance and with different working conditions showed that this control method can adapt to different scenarios such as weak power grid, multiple energy storage parallel, high-precision frequency control, etc. The proposed control method does not require hardware modification, effectively improving the adaptability and control flexibility of grid energy storage scenarios, and providing an efficient technical solution for frequency stability of new power systems.

As flexible resources such as photovoltaic systems, energy storage systems, and electric vehicle charging stations are progressively connected to distribution grids, differences in construction scale and expansion pace, together with their gradual deployment under individual development plans, lead to insufficient coordinated planning. This makes it difficult to anticipate and mitigate power quality issues caused by multi-resource access during planning, thereby affecting safe and stable system operation. Meanwhile, coupled disturbances from different resources dynamically change mitigation requirements during operation, increasing the risk of overlooking critical mitigation nodes and periods. This study addresses these issues by proposing a method for multi-resource evolutionary access planning and mitigation requirement identification in distribution grids, considering the power quality. First, a penetration-rate combination vector for photovoltaic, storage, and charging systems is constructed. By accounting for node importance, numerous multi-resource access scenarios covering different access scales are generated. Second, power quality deviation matrices are constructed to characterize the grid state, and a deep clustering method combining an autoencoder and a Gaussian mixture model is introduced to adaptively extract latent features, classify evolution scenarios, and generate planning guidelines for multi-resource access. Then, dynamic mitigation requirement features are extracted from the voltage out-of-limit characteristic curves and harmonic over-limit characteristic curves of the nodes, and priority mitigation nodes are identified by combining the node voltage influence and harmonic propagation factors. Finally, the method is validated on an IEEE 13-node system. The study findings demonstrate the effectiveness of the method for multi-resource access planning and operational mitigation.

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches.

With the increasing deployment of EV charging infrastructure, large numbers of power electronic converters are being integrated into distribution networks, making small-signal stability a key issue for converter-intensive charging facilities. In practical EV charging stations, grid-following (GFL) and grid-forming (GFM) converters may coexist and operate in parallel. Due to their different synchronization mechanisms and control structures, the stability of such mixed systems is affected not only by grid strength but also by EV charging power levels and power sharing among converters. This paper investigates the impedance characteristics and small-signal stability of three representative parallel configurations: all-GFL, all-GFM, and mixed GFL/GFM systems. Unified dq-domain impedance models are established for both converter types, and the critical short-circuit ratio (CSCR) boundaries are determined through eigenvalue-based analysis. The effects of charging power level and power-sharing ratio on the stability boundaries are further analyzed. The results show that the all-GFL configuration becomes unstable under low-SCR conditions due to phase-locked-loop-dominated dynamics, whereas the all-GFM configuration loses stability under high-SCR conditions due to reactive-power-loop dynamics. In contrast, the mixed GFL/GFM configuration exhibits both lower and upper stability boundaries and remains stable only within an intermediate SCR range. These findings reveal the combined influence of converter control type, grid strength, and charging-power operating conditions, providing guidance for the planning and stable operation of converter-intensive EV charging facilities.

The performance disparities and operational characteristics among diverse devices within low-voltage distributed photovoltaic (PV) systems lead to variations in fault manifestations. The limited sampling frequency inherent in conventional approaches fails to capture critical fault features and temporal information in a timely manner. Moreover, the presence of periodic fluctuations in PV fault data contributes to persistently high false alarm rates. This paper proposes a high-frequency, minute-level data acquisition framework and a lightweight edge diagnosis algorithm for low-voltage distributed PV systems based on a temporal graph convolutional network. The proposed approach employs intelligent PV edge terminals to enable high-frequency data acquisition from distributed PV generation units. A graphical representation of the low-voltage distributed PV plant is constructed, from which dynamic temporal features of PV generation data are extracted using temporal convolutional layers, while topological correlations among PV devices are captured through graph convolutional layers. This enables the spatiotemporal joint modeling of fault characteristics. To accommodate the computational constraints of edge devices, a customized adaptation of the MobileNet-V3 architecture is introduced. By integrating attention mechanisms and implementing layer pruning, the model is tailored for enhanced performance in photovoltaic fault classification, thereby achieving lightweight edge diagnosis. Experimental results demonstrate that the proposed algorithm accurately diagnoses output voltage fluctuation faults in PV inverters and effectively identifies abnormal phase voltage fluctuation faults. It achieves high precision, recall, and F1 scores across various fault types. The model exhibits rapid training convergence with a low loss function value, satisfying the requirements for lightweight edge diagnosis in low-voltage distributed PV systems.

With the increase of photovoltaic (PV) penetration in the distribution network, the node voltage distribution shows different trends. This paper examines the voltage sensitivity of various nodes and evaluates the impact of power level changes in PV access nodes at different locations on the voltage at a target node, particularly when the voltage exceeds acceptable limits. Additionally, in this paper, a method for analyzing the voltage sensitivity of typical nodes within a distribution network, along with strategies for voltage management. The IEEE33-node distribution network was constructed using MATLAB for simulation verification. The simulation results indicate that varying penetration rates of distributed PV systems in the distribution network lead to three different voltage trends. The voltage sensitivity increases with the increase of impedance. For voltage violation nodes, PV nodes with higher voltage sensitivity more effectively suppress voltage violations. This paper clarifies the effects of different PV penetration rates and PV output locations on voltage distribution in the network, providing theoretical guidance for studying voltage violation control caused by PV grid integration.

With the increasing penetration of distributed energy resources (DERs), power systems are confronted with growing stochasticity and uncertainty, exposing the limitations of traditional regulation models; in response, Transmission-Distribution-Microgrid (TDM) coordinated peak regulation has emerged as a critical solution by breaking hierarchical barriers and enabling multi-resource collaboration. This survey systematically reviews the development history, core technologies, and key challenges of TDM coordinated peak regulation. The key to TDM coordination lies in integrating cross-level resources through information exchange to form a multi-level peak regulation framework. This three-level collaborative mechanism, supported by real-time data transmission and AI-driven optimization algorithms, achieves complementarity among resources at different levels, thereby enhancing the absorption capacity of renewable energy. However, there are still some challenges at present, such as issues with communication speed and latency, as well as the lack of a collaborative decision-making mechanism. This survey aims to identify future research directions. We believe that this survey will attract more and more attention, stimulate effective discussions, and also inspire new ideas for TDM research.

With the increasing penetration of distributed photovoltaics, voltage limits and reverse power transmission issues in the distribution network are becoming more severe. Additionally, since photovoltaic inverters, which can serve as reactive devices, are distributed across the network, the method of selecting reactive device locations based on fixed indicators is no longer applicable. Therefore, we propose an improved fuzzy C-scenario clustering method based on the silhouette coefficient to simplify the configuration scenario generation process and improve clustering accuracy. Furthermore, we propose a multi-voltage regulating device configuration model that considers zoning site selection, providing a foundation for zoning control in distribution networks. The model first uses a combination of zoning and indicators to determine energy storage locations. Then, SVGs and capacitors are treated as flexible resources. In coordination with photovoltaic inverters, energy storage, transformers and capacitors, the location and capacity of SVG and grid-side capacitors, the capacity of energy storage and the reactive power protocol capacity of photovoltaic inverters are centrally configured. Finally, the feasibility and practicality of the proposed model are validated using the improved IEEE 33-bus system.

With the large-scale integration of distributed photovoltaics, the morphology and characteristics of distribution networks are becoming increasingly complex. Accurate and efficient modeling of distributed photovoltaic aggregation is of significant importance for the planning and operation of new distribution networks. In this study, we first evaluated the similarity of different photovoltaic generation units by integrating the output characteristics of photovoltaic systems and the sensitivity indicators of grid connection points, proposing a distributed photovoltaic clustering method based on the K-means algorithm. Secondly, the output of the photovoltaic aggregation cluster was calculated using the capacity-weighting method. An equivalent network connecting various photovoltaic clusters was constructed based on the node elimination of the admittance matrix, and a modeling method for distributed photovoltaic aggregation was developed. Finally, the effectiveness of the method proposed in this paper was verified through the IEEE-33 test system. The results show that the distributed photovoltaics aggregation modeling method proposed in this paper can effectively simplify network topologies and improve the accuracy of the calculation results while also improving the efficiency of power flow analysis.

The droop control converter exhibits capacitive characteristics in the frequency band below 50 Hz, which leads to the problem of RLC oscillation when it interacts with the inductive power grid, thus causing harm to the power grid. Based on this, this paper firstly establishes a sequence impedance model of droop control converter using the harmonic linearization method, which reveals the mechanism of oscillation after the interaction between the droop control converter and the power grid. Then, a control strategy for active harmonic resistance based on droop control converter is proposed, which only requires modifying the voltage loop command of the converter to shape the impedance characteristics (excluding the fundamental frequency) into resistive behavior, thereby avoiding oscillations when interacting with an inductive grid. To address the possible influence of active harmonic resistance on the stability of the system, an adaptive control strategy is proposed to adjust the parameter value, thus ensuring consistent suppression of system oscillation. Finally, simulation and hardware-in-the-loop (HIL) experiments validate the effectiveness of the active harmonic resistance control strategy.

As a large number of power electronic devices and other nonlinear loads are connected to the power system, the harmonics generated by them cause great harm to the power quality of the power grid. At present, passive filter and active power filter are mainly used to deal with harmonic pollution. Active power filter is widely used, but its main control method for inverter is current mode control. However, the new energy power system shows the characteristics of inertia and insufficient damping. Therefore, the grid-forming inverter with the external characteristics of synchronous generator has attracted more and more attention, but the harmonic suppression strategy for the grid-forming inverter is rarely seen. In this paper, the author proposes a hybrid impedance reshaping strategy, which reduces the impedance of the grid-forming inverter at the harmonic frequency point through voltage feedback, and then series virtual negative impedance for the grid-forming inverter by modifying the voltage loop instruction, which equivalently increase the grid impedance, so that the grid-connected inverter can compensate the harmonics well in the normal working state. The proposed theory is proved by simulation and hardware-in-the-loop experiment.

With the increasing penetration of distributed generation (DG) in modern distribution networks, voltage regulation becomes a critical challenge. This paper proposes a voltage regulation optimization strategy based on multi distributed resource collaboration, integrating photovoltaics, energy storage systems, and reactive power compensation devices. Firstly, the regulation potential boundaries of each resource are evaluated. Secondly, a multi-resource regulation priority determination and voltage regulation model are proposed. Simulation based on the IEEE 33 system show that the proposed method significantly enhances voltage quality compared to traditional single-resource regulation methods, ensuring that voltage levels remain within acceptable limits across varying loads and PV output fluctuations.

This paper proposes a unified power quality conditioner (UPQC) based on superconducting magnetic energy storage (SMES) for protection and power smoothing in DFIG/DC microgrid hybrid power system. The DC side of the UPQC is connected in parallel with the DC bus of the DC microgrid and maintains the stability of the DC bus voltage, while on the AC side, it is connected through series transformers to the DFIG terminal to act as a dynamic voltage restorer. For a case verification, a 0.8H/718A step-shaped superconducting coil (SC) is designed by finite element simulation using H-formulation to improve the critical current and economy of SMES. Finally, a dual control for regulating positive and negative sequence components of voltage signals is used in DFIG/DC microgrid hybrid power system. The simulation results show that it can effectively smooth the power of the hybrid power generation system, the key parameters of DFIG and DC microgrid can be well suppressed under fault conditions.

Grid-forming (GFM) converters are regarded as the most promising solution for grid-connected converters of renewable energy due to their robustness against weak grids. However, attributable to their voltage source characteristics, GFM converters may experience overcurrent issues during large disturbances. The virtual impedance (VI) method is an effective method to solve this problem. Nevertheless, there exists a contradiction between the demands for VI posed by current limitations and transient stability. Firstly, the analytical equations of virtual impedance for limiting the fault steady-state current of a GFM converter with different depths of grid voltage sag are solved. On that basis, an adaptive method based on virtual impedance is proposed to limit the fault current. Moreover, the analytical equation of the output active power of the converter when using adaptive virtual impedance to limit the fault current is solved. On this basis, the effect of the virtual impedance ratio on the transient stability is investigated. Finally, an adaptive virtual-impedance-based current-limiting method with the functionality of transient stability enhancement for a grid-forming converter is innovatively proposed. The enhancement effect of the method is verified by the equal area criterion method and phase portrait method. Finally, the efficacy of the proposed method in limiting fault currents and enhancing transient stability is validated through hardware-in-the-loop (HIL) experiments.

Aiming at the problem of quantitative inertia evaluation of a new energy electric power system, the system inertia constant tracking method based on system identification is studied. The method is divided into two categories: non-recursive algorithm and recursive algorithm. The non-recursive algorithm uses a batch of data for batch processing to obtain the estimated value of the identification model parameters. The recursive algorithm is based on the estimated value of the model parameter at the previous moment and corrects the estimated value based on the new data currently obtained. From the perspective of the identification principle, the difference and internal relationship between the two in terms of calculation storage and identification speed are analyzed. The IEEE typical system is used to compare and verify the experimental examples. Theoretical analysis and experimental results show that the recursive algorithm has high identification accuracy, stable identification results and fast identification speed. It is suitable for the identification of objects with large numbers of nodes and complex structures, which is conducive to real-time monitoring and fast perception of the inertia constant of the new energy power system.

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network.

With the proposal of the dual carbon target, the distributed photovoltaic (PV) industry has rapidly developed in recent years. However, the randomness and volatility of photovoltaic energy can be transmitted to the main grid through distribution network substations, posing challenges to the stable operation of the power system. Therefore, this paper considers tapping into the regulation potential of feeder loads on the distribution network side, as well as distributed energy storage and distributed PV resources, to enhance the grid’s control methods. A power fluctuation smoothing control strategy for substations in distribution networks, accounting for multiple types of regulation resources, is proposed. In the day-ahead stage, traditional voltage regulation devices such as the OLTC (on-load tap changer) and CB (circuit breaker) are pre-dispatched based on source–load forecasts, optimizing the fluctuation range of substation power and the number of device operations. This provides optimal substation power values for day-to-day optimization. During the intraday phase, fast regulation devices such as PV (photovoltaic), SVC (static var compensator), and energy storage systems are coordinated, and an optimization model is established with the goal of reducing power curtailment while closely tracking substation trends. This model quickly calculates the active power regulation and device operations of various adjustable resources, improving the economic efficiency of the distribution network system while achieving power fluctuation smoothing at the substation level. Finally, the feasibility and effectiveness of the power fluctuation smoothing control model are verified through simulations on an improved standard distribution system.

IntroductionGrid-forming control has received increasing attention for being an effective solution to cope with low-inertia and weak damping systems. Owing to the basic characteristics of transient voltage regulation, inertia support and primary frequency regulation (PFR), virtual synchronous generator (VSG) is the most promising candidate of grid-forming control scheme. The damping characteristic plays a significant role in stabilizing when the system is disturbed. However, the traditional approaches for damping emulation pose a number of problems, such as the introduction of phase-locked loop (PLL) that may lead to stability issues, or the blurring of the functional distinction between damping characteristic and primary frequency regulation. Moreover, the grid strength affects the operational characteristics of the converters.MethodsBased on the background of these issues, firstly, an effective transient damping power strategy is proposed in this paper. In contrast to conventional damping approaches, the proposed scheme provides a positive damping during transient period that suppresses the fluctuation of active power, and has no impact on the steady-state frequency droop characteristic. Ulteriorly, based on small-signal models and classical control theory, an parameters adjustment strategy for both active and reactive power control is proposed to considerably enhance the adaptability of the converter to the variations in grid strength.Results and DiscussionThe results obtained from PSCAD/EMTDC and hardware-in-the-loop (HIL) platform verify that proposed control strategy exhibits excellent transient damping effect, the decoupling characteristic between fixed damping coefficient and PFR coefficient, and performs well across a broad spectrum of grid strengths.

An innovative control strategy for adaptive secondary frequency regulation utilizing dynamic energy storage based on primary frequency response is proposed. This strategy is inactive when the system frequency remains within a predetermined frequency deviation threshold, whereby only the primary frequency regulation is executed through a combination of virtual droop and virtual inertia. The droop coefficient is dynamically related to both the state of charge (SOC) of the energy storage and the frequency deviation, adapting in response to these parameters. If the system frequency deviation exceeds, the integration link will be activated based on the energy storage participating in the primary frequency regulation of the power grid. This activation facilitates a segmented adaptive adjustment of the integral coefficient in accordance with the dynamic characteristics of system frequency variations observed during the regulation process. Additionally, the droop coefficient is incorporated as a modifying factor into the integral coefficient to enhance the control during secondary frequency regulation until all steady-state errors are mitigated. Disengagement from the secondary frequency regulation not only accelerates the restoration of grid frequency but also ensures precise and error-free adjustment of the system frequency, thereby improving tracking and dynamic performance. The effectiveness of the proposed control strategy is demonstrated through simulation.

As a large number of load-side flexible resources are explored, more and more flexible loads are involved in the power dispatching process. However, the schedulable potential of individual flexible loads is small. It is difficult to analyze them accurately. The dispersed flexible load resources need to be aggregated to better participate in system dispatching. Therefore, how to accurately evaluate and predict the schedulable potential of flexible loads becomes a problem to be solved in demand-side management. In this paper, the behavioral characteristics of three typical flexible loads are modeled first. Then, in order to aggregate the typical flexible load models, a load schedulable potential aggregation model based on Minkowski sum is established. On this basis, an LSTM-based time series prediction method is proposed to predict the schedulable potential of flexible loads. Finally, the proposed model and prediction method are validated and analyzed by means of a case study.