To address the challenges posed by frequent source and load fluctuations in existing loop closing current calculation methods, this paper proposes an online loop closing current calculation method that considers source and load uncertainties. First, a dual-stack dynamic monitoring system is utilized to obtain real-time voltage and current variations before and after disturbances. Second, Thevenin's theorem is employed to build an equivalent model of the distribution network, simplifying the complex network into a combination of an independent voltage source and a series impedance. Then, the steady-state loop closing current is calculated based on the open-circuit voltage and equivalent impedance at both sides of the loop closing point. Next, the optimal frequency method is applied to determine the equivalent impedance and attenuation time constant at a specific frequency, achieving accurate calculation of the transient loop closing current. Finally, simulations are conducted to model the fluctuations in distributed generation and load, analyzing the steady-state and transient loop closing currents. The simulation results demonstrate that the proposed method accurately captures the effects of source and load fluctuations on the loop closing current in dynamic environments, with minimal calculation error, indicating its high practicality.

This paper presents a practical method for allocating inertia and damping in power systems integrated with renewable energy sources (RES). The frequency response model of the power system is first established, with distinct modeling of the dynamics of synchronous generators (SGs) and three types of RES units. A state-space model, which describes the relationship between frequency response and disturbances, is derived through the disturbance allocation process. A fitting method for frequency and power responses is proposed based on modal analysis. Analytical formulas for frequency stability indices (FSIs) and power indices are derived. Furthermore, applying matrix perturbation theory, an iterative optimization method for inertia and damping allocation of RES units is proposed and validated through simulation cases using the modified IEEE 39-bus model.

Various kinds of distributed generators (DGs) and loads are widely permeated in modern distribution networks (DNs). Their output characteristics and complex spatiotemporal distribution characteristics have brought serious challenges to the safe and economic operation of DNs, especially for multi-voltage levels DNs. To enhance the flexibility and controllability of DN, the soft open point integrated with the energy storage system (E-SOP) has gar-nered significant attention, as it can facilitate the flexible regula-tion of energy distribution networks across blocks. However, this also increases the difficulty of cooperative control for DNs with multi-voltage levels. To solve this problem, this paper constructs a form of flexible interconnected multi-voltage levels DNs based on E-SOP and proposes a distributed control architecture suitable for multi-voltage levels regional interconnection DN with E-SOP. Then, a target loosen-coupled self-adaptive method (TLCSAM) is developed to solve the regulation model. Finally, relevant cases are constructed in this paper to validate the effectiveness of the pro-posed dispatching strategy, the results demonstrate that the pro-posed distributed regulation strategy can significantly improve the performance of the multi-voltage level regional interconnected distribution network with E-SOP.

As the proportion of distributed energy resources (DER) continues to grow, it is imperative to enhance the economic and safe operation of the active distribution network (ADN). This paper presents an online distributed optimization model, in which loads and the active distribution network (ADN) operators transfer signals and perform local calculations. In order to significantly reduce the complexity of the model, a novel linear power flow (LPF) model that can be updated online is employed. Furthermore, the high proportion of DER access presents a time-coupled conundrum, thereby creating difficulty in finding the optimal solution. In this study, the Lyapunov drift plus penalty method (LDPPM) is employed to address this challenge by transforming the long-term optimization problem into a current-time one. Additionally, to enhance the calculation efficiency, the iterative optimization algorithm is regarded as a control process and is controlled with the PID controller to form a PID-Lagrange algorithm. The proposed PID-Lagrange algorithm has a clearly defined parameter tuning strategy that aligns with control theory, resulting in a significant reduction in operating costs by 30.27% in the case study. The results of the performance analysis demonstrate the superiority of the proposed method.

Affected by the different fault characteristics of electrochemical energy storage power stations under charging and discharging operating states, the phase comparison distance protection using positive sequence voltage as polarization voltage may operate incorrectly if faults occur in the forward direction of the energy storage side protection and the reverse direction of the system side protection. Therefore, an improved distance protection scheme that can adapt to both charging and discharging states of is studied. Firstly, the fault characteristics of the energy storage station under different charging or discharging states are analyzed, and the influence mechanism of fault characteristics on phase comparison distance protection operation performance was discussed. On this basis, an improved scheme for distance protection based on polarization voltage phase reconstruction was proposed. Finally, verification was conducted on the PSCAD/EMTDC simulation platform. The simulation results show that the proposed scheme can effectively reduce the impact of fault characteristics of energy storage power stations. The sensitivity and reliability of phase comparison distance protection are effectively improved under different charging and discharging states and transition resistance conditions.

The rapid integration of renewable energy generation has reduced the flexibility regulation capacity of power systems, necessitating the exploration of adjustable resources on the load side to establish a novel ”source-load interaction” balancing mechanism. Air conditioning (AC) load, as a critical demand response resource, has garnered increasing attention. However, existing AC load control strategies are either heavily influenced by user behavior uncertainty or overly reliant on communication and measurement infrastructure. Moreover, most approaches adopt random switching control methods, which fail to maximize user participation willingness and overlook the dynamic variations in user responsiveness, ultimately limiting their practical effectiveness. To address these challenges, this study proposes a dynamic scheduling model that comprehensively considers the aggregated response potential of AC loads and the characteristics of multiple flexible load types, thereby fully exploiting the coordinated regulation capability of load-side resources. Targeting a low-carbon community scenario (incorporating distributed wind power and residential users), the model is formulated with the dual objectives of maximizing wind power accommodation and minimizing source-load power deviation. A greedy algorithm is employed to iteratively solve the maximum available response capacity and actual dispatchable quantity of AC loads in each time slot, enabling dynamic updates of potential assessment and scheduling decisions.

This paper presents a method for simulating sector-coupled energy systems, quantifying their resilience and analyzing the cost-effectiveness of mitigation strategies for a broad range of scenarios. Using data from a German distribution grid operator, the study examines the impact of high-impact, low-probability events, such as heavy rainfall, on the power grid and evaluates the effectiveness of various mitigation measures, including Vehicle-to-Grid and Gas-to-Power systems, as well as traditional grid hardening methods. The simulation environment integrates detailed grid models to realistically assess different failure scenarios and their effects on system resilience. Results indicate that while conventional hardening methods are most effective in cost minimization, Vehicle-to-Grid mitigation measures significantly enhance resilience by mitigating initial impacts of disruptions. The study underscores the importance of sector-coupled mitigation strategies, particularly under future climate scenarios, and provides a comprehensive framework for evaluating energy system resilience.

This paper presents a novel ferromagnetically shielded fault current limiter (FS-FCL) configuration and control strategy for power systems. The proposed system introduces a controlled DC-reactor design that effectively limits fault currents without significantly affecting the system voltage during normal operation. The main novelty of this work lies in the development of a fully ferromagnetically shielded core and the series connection of the FS-FCL with the power line, which provides an immediate response to fault conditions without delay. In this configuration, the reactor’s magnetic flux density is distributed throughout the ferromagnetically shielded core, which increases the effective inductance and prevents magnetic core saturation during fault events. In addition, a control-oriented model of the FS-FCL is developed, together with an improved control algorithm based on reference voltage and current thresholds. Both qualitative analyses and quantitative simulations were performed to evaluate the system’s transient and steady-state performance. Simulation results under various fault conditions confirm that the proposed controlled FS-FCL achieves faster current limitation and lower voltage distortion compared with uncontrolled configurations, demonstrating its feasibility for practical power grid applications.

In light of the inherent unpredictability of wind power and other clean energy sources within each MG, a multi MG s electricity-carbon joint optimization operation method that considers robust market clearing is proposed. This method ensures the economic viability of the multi- MG s alliance while adhering to the principles of low-carbon operations and security. The constructed model comprises two layers. The upper layer establishes a robust clearing model of the upper-level electricity-carbon joint market with optimal accruals and benefits based on the marginal pricing theory. The lower layer considers the coupling of electricity-carbon joint trading, establishes a multi- MG s low-carbon joint optimization model based on the Nash negotiation theory, and proposes an electricity-carbon revenue allocation method based on the contribution degree of the interaction products to realize the reasonable allocation of the revenue of each MG. In conclusion, the accelerated-adaptive alternating direction multiplier method (AA-ADMM) is put forth as a means of solving the model. The results of the illustrative example serve to corroborate the efficacy of the methodology proposed in this paper.

This paper addresses issues such as power transmission interruption during rectifier-side faults and susceptibility to system overvoltage during inverter-side faults in wind power DC transmission systems. A hybrid topology comprising a rectifier-side MMC and an inverter-side LCC-MMC configuration is proposed, along with corresponding enhanced control strategies. The characteristics of the proposed hybrid HVDC under bilateral AC faults are investigated. Results indicate that during single-phase faults on the rectifier side, the proposed model and control strategy can maintain transmitted power at 0.54p.u. or higher, preventing power transmission interruptions. Compared to conventional control strategies for inverter-side hybrid DC transmission systems, the minimum power transmission value increases by 11%. Under inverter-side AC faults, wind power output can track power variations during the disturbance. During a single-phase-to-ground fault, the DC voltage of the receiving-end MMC is limited to within 1.13p.u., and during a three-phase short-circuit fault, it is restricted to within 1.29p.u., remaining below the safety threshold of 1.3p.u. Consequently, the proposed system effectively ensures the long-distance and reliable transmission of wind power, making it suitable for the dependable delivery of large-scale offshore wind power.

The short-term voltage stability (STVS) of a multi-infeed high-voltage direct current (MIHVDC) system is controlled by complex dynamic reactive power interactions that have multifactor coupling and strong time-varying characteristics. However, because of the complexity in decoupling these interactions, traditional analytical methods cannot accurately quantify their effects on stability. The STVS analysis of MIHVDC systems involves several challenges, such as high-dimensional, time-varying, and nonlinear characteristics. To address these challenges, this paper proposes an STVS evaluation method based on a multi-time-scale (MTS) interaction mechanism and time-series data mining. First, a multi-timescale interaction mechanism is proposed on the basis of dynamic responses. The short-term voltage instability process can be divided into three stages, namely, power flow transfer, dynamic reactive power regulation, and hierarchical cascade electromechanical interactions. This clarifies the accumulation and propagation mechanisms of reactive power deficiency at different timescales. Second, this paper proposes a time-series data mining method based on shapelets to solve problems in traditional STVS analysis. However, the dynamic response caused by commutation failure of the HVDC system interferes with the accuracy of time-series data mining. To solve this problem, this paper presents the optimization of the method by combining it with the dynamic mechanism and proposes the synchronous cluster of shapelets (SynCShapelet) method. In addition, the physical relationship between the shapelet and critical operating point of the dynamic load is elucidated, thus addressing the black-box problem and low confidence of machine learning methods. As demonstrated via a case study, SynCShapelet can predict the instability of the system by detecting features and incorporating the MTS interaction mechanism to preliminarily assess the path of the short-term voltage instability. In the application scenario of the STVS of MIHVDC, the proposed method provides a theoretical basis and technical support for the STVS evaluation and control strategy.

Due to long supply lines, dispersed consumers, and a high proportion of inductive motor loads, severe line loss issue may occur in rural low-voltage distribution networks (LVDNs). Passive capacitors offer a cost-effective solution for reactive power compensation. Existing literature has proposed numerous line loss compensation strategies based on passive capacitors, yet most involve complex calculations that hinder widespread adoption and large-scale implementation. In practice, distribution network operators often face limited theoretical expertise, constrained budgets, and a vast number of lines requiring compensation. Thus, a critical challenge lies in determining the placement of capacitors in a simple and effective manner. To address this gap, the paper proposes a practical capacitor placement strategy specifically for line loss reduction in LVDNs. Leveraging real-time data from consumer metering systems, it calculates active and reactive power distributions under various capacitor placement scenarios. An optimization problem is then formulated and solved under two input modes with the objective of minimizing total line loss, ultimately identifying the optimal set of capacitor installation locations. The proposed strategy is computationally efficient, low-cost, and practical for implementation. Case validation conducted on a real fish and crab farming distribution network demonstrates significant line loss reduction, confirming the strategy’s effectiveness.