To promote low-carbon development of power systems and facilitate effective accommodation of renewable energy， this paper proposes an optimal dispatch model for virtual power plants （VPPs） that accounts for wind power uncertainty and dynamic carbon quotas. First， a low-carbon cyclical system coordinating generation， load， and carbon capture is established， introducing a concentrating solar power （CSP） plant as a flexible resource. Its energy storage characteristics of solar-thermal-electric conversion provide energy consumption support for the carbon capture system. Second， to address the stochastic nature of wind power output， a scenario generation and reduction method based on Manhattan distance is used to construct representative scenarios. A dynamic carbon quota adjustment mechanism based on wind power penetration is proposed， establishing a carbon quota model that adjusts in real time according to the proportion of wind generation. Finally， an optimal dispatch model for VPPs， coordinating carbon capture plants， CSP plants， and wind farms， is developed， with a global optimization performed to minimize total system operating costs. Case studies demonstrate that， while achieving operating cost optimization， the integrated optimal dispatch model significantly enhances wind power accommodation and effectively reduces carbon emissions.

Mobile energy storage (MES) with flexible regulation and dynamic transfer capabilities can provide proactive support to facilitate the safe operation of active distribution networks (ADN). To address the challenge of emergency power supply during extreme events, a novel service restoration method for ADN is proposed, which considers the joint robust optimization of islanding power supply and MES scheduling with repair crews. First, the Davidson-WebertAkcelik traffic impedance combination function is proposed based on the mapping relationship between MES stations and roadway nodes. This function analyzes the roadway conditions of MES under an extreme event by highlighting the flexibility of MES as an emergency power source in ADN service restoration. Second, A Boolean variable-based load outage time model is established to quantify multiple switching operations of loads under power balance constraints in the service restoration. Third, a robust optimization model for islanding and MES considering roads and lines repair crews is proposed to improve ADN service restoration capability, where road repair crews (RRCs) restore road to enhance MES mobility, and line repair crews (LRCs) repair power lines to reduce MES power demand. The solution can be obtained through interactive iterative optimization using the Column and Constraint Generation (C&CG) algorithm. Finally, the coupling system including modified IEEE 33-node system and 17-node road network is simulated to demonstrate the effectiveness and feasibility of the proposed method.

Extreme ice storms cause severe icing faults on power lines and energy supply shortages, which can seriously threaten the safe operation of distribution systems. To enhance system resilience, a comprehensive resilience enhancement strategy for electric–hydrogen coupled distribution systems considering unmanned aerial vehicle (UAV) de-icing and hydrogen energy storage systems (HESS) is proposed in this paper. First, a line icing thickness model under extreme ice storms is established considering UAV de-icing effects to derive the corresponding fault rate. Second, resilience evaluation indicators are developed to quantify the disaster resistance and recovery capabilities of the electric–hydrogen coupled distribution system. Finally, a multi-dimensional disaster mitigation optimization model is formulated to strengthen the defense capability and resilience of the electric–hydrogen coupled system. Simulations are conducted on an improved IEEE 33-bus distribution system integrating UAVs and hydrogen energy storage systems, verifying that the proposed strategy significantly enhances restoration capability under extreme icing disasters while reducing economic costs.

Distributed state estimation is necessary for active distribution networks to calculate high-frequency and highdynamic random fluctuations of distributed generations and controlled loads. Given the limitations of traditional measurements and the potential problem of false data injection attacks (FDIA), it is imperative that high accuracy phasor measurement units (PMUs) with information security are implemented in active distribution networks. A novel distributed state estimation method is proposed in this paper. This method involves merging data from the PMU and supervisory control and data acquisition (SCADA) and encrypting subsequent communications. Firstly, a PMU-centric active distribution network partition topology model is proposed based on the principles of parallel computing load balancing and local communication efficiency. Secondly, to improve the observability of the system, a data fusion strategy for mixed-frequency data measured by SCADA and PMU is proposed. Thirdly, a distributed state estimation method is proposed that considers an encryption model and a distributed square root cubature Kalman-Gaussian mixture probability hypothesis density attacks (DSRCK-GMPHD) algorithm. The proposed state estimation method can improve the speed and accuracy of multi-subdomain state tracking and minimize the mismatch between the measured and actual states of the active distribution network. Finally, extensive tests were performed on the improved PG&E69 system. The test results show that the method proposed in this paper is able to accurately capture the real-time state of multiple subareas of an active distribution network and improve the security of the multi-source measurement data transmission process.

Ice storms trigger transmission line icing and complicate emergency response， severely threatening the secure operation of integrated electricity-heat energy systems. To address this， this paper proposes a resilience enhancement method for integrated electricity-heat energy systems considering UAV de-icing without power interruption during disasters and post-disaster collaborative recovery. First， a line failure rate model accounting for ice-induced line losses quantifies disaster impacts. Second， resilience metrics are established across four dimensions： defensive capacity， adaptive response， coordination capability， and recovery effectiveness. Third， a multi-objective optimization model maximizes these resilience metrics， incorporating operational constraints of UAV de-icing， building thermal inertia， organic Rankine cycle （ORC） generation， and repair crew deployment. Finally， validation using a 6-node thermal system coupled with a modified IEEE 33-node distribution network confirms the method’s efficacy.

Aiming at the problem of complex transformer structure and small amount of early winding fault sample data, in order to improve the accuracy of transformer winding fault type diagnosis, this paper proposes an early fault diagnosis model of transformer winding based on ReliefF-mRMR leakage magnetic field feature optimization and POA-LSSVM. Firstly, the consistency of the physical entity of the transformer and the simulation model is verified. The leakage magnetic field information of the early fault of the transformer winding is taken as the fault characteristic state quantity, and the fault characteristics of the leakage magnetic field are optimized and the key features are extracted by the ReliefF and mRMR feature selection algorithms. After optimization, the fault feature is input to the least squares support vector machine (LSSVM) for fault diagnosis, and the LSSVM parameters are optimized by the pelican optimization algorithm (POA). The results show that the POA-LSSVM fault diagnosis model after fault optimization by ReliefF-mRMR algorithm can effectively distinguish different early fault types of transformer windings, and compared with GA-LSSVM algorithm, PSO-LSSVM algorithm and POA-LSSVM algorithm, the fault diagnosis efficiency and classification accuracy are significantly improved. Finally, the dynamic simulation experiment of transformer is carried out to verify the effectiveness of the fault diagnosis model.

In offshore wind power systems, Virtual Inertia Control (VIC) is an effective method for providing active frequency support from offshore wind turbine generators (WTGs). Based on the principle of proportion-differential (PD) VIC for grid-following offshore WTGs and the simplified model of frequency response of offshore wind turbine generator (WTG) integrated system, the dynamic response characteristics of offshore WTGs under PD VIC are analyzed to derive the quantitative relationship between PD VIC and system frequency. Firstly, a virtual inertia frequency active support control model is established to maximize rotor kinetic energy utilization. The calculation method for the virtual inertia parameter is discussed on the basis of the principles of offshore synchronous generator inertia response and primary frequency control. Secondly, based on the simplified frequency response model of offshore WTG integrated system, the fast active power control for frequency support and the WTG’s response characteristics are clarified. The proposed VIC strategy not only fully exploits the kinetic energy of the rotor, but also responds quickly to frequency variations in the offshore wind power system. This strategy achieves the goal of active support. Simulations and experiments have demonstrated the effectiveness and feasibility of the proposed method.

As a power electronic device, the soft open point (SOP) demonstrates significant potential in improving load balancing and mitigating voltage fluctuations in active distribution networks through its flexible power regulation capability. However, the rational determination of SOP allocation and capacity remains a critical challenge due to constraints in investment and operational costs. A multi-objective planning methodology for SOP deployment in active distribution networks is proposed to improve operational efficiency and system stability by economically taking load balancing enhancement and voltage fluctuation suppression. Firstly, the operational characteristics and mathematical model of SOP in active distribution networks are systematically analyzed. Three key indicators including the annual comprehensive cost, load balancing degree and voltage fluctuation index are introduced from the perspectives of balancing the economy and reliability of the active distribution network. Subsequently, the SOP siting and capacity planning model is established with three key performance indicators to ensure economic optimization while maintaining load uniformity and voltage stability. The optimization model is solved through second-order cone programming algorithm for enhanced computational efficiency. Finally, case studies are conducted on the modified IEEE 33-node test system to validate the proposed methodology.

Improving the accuracy of offshore wind power forecasting is one of the most effective means to enhance the security and stability of offshore wind power integration. To this end， this paper proposes a review of offshore wind power forecasting studies. First， based on recent studies in offshore wind power forecasting， the review investigates three key aspects， such as data preprocessing techniques for offshore wind power， offshore wind power forecasting models， and forecasting for large-scale offshore wind farm clusters. Subsequently， the challenges currently faced in offshore wind power forecasting are analyzed. Finally， potential future research directions are explored by incorporating emerging technologies. The findings of this study can serve as a reference for the operation and maintenance of offshore wind farms as well as system dispatch after grid integration.

Photovoltaic-storage-charging integrated stations can effectively regulate photovoltaic output and balance the charging and discharging power of electric vehicles. Utilizing its flexible resource characteristics, the power station can be used as a source of power for islanded operations, which can enhance the resilience of the active distribution network (ADN). First, after the occurrence of an extreme disaster according to the distribution location of the Photovoltaic-storage-charging integrated stations (PSCISs), the active distribution network is divided into multiple independent islands in accordance with the principle of minimizing the amount of load shedding. Secondly, the PSCIS serves as a black-start power source for the regional loads. At the same time idle electric vehicles (EVs) within the islands are dispatched to the PSCIS for reverse charging, which provides additional power to ensure maximum availability for the regional load. Finally, the improved IEEE 33-node distribution network is used as a case study to validate the effectiveness of the proposed research in enhancing the resilience of the active distribution network.

With the rapid growth of offshore wind power capacity, wind turbine generators (WTGs) are required to possess low-voltage ride-through (LVRT) capability to ensure the secure and stable operation of the power grid. Existing studies have primarily focused on voltage magnitude support, while the risks induced by voltage phase angle jumps during faults have been largely overlooked. This paper investigates the phase angle disturbance of offshore WTGs under three-phase short-circuit faults. A grid-connected model incorporating long-distance alternating current submarine cables is established. Based on Thevenin equivalence and the decoupled control of the grid-side converter, an analytical expression of the point of common coupling (PCC) voltage is derived. It unveiling the mechanism of phase angle jumps. Furthermore, the impact of phase angle disturbances on the phase-locked loop (PLL) and the power output characteristics of the WTGs is analyzed. On this basis, a suppression strategy is proposed, which introduces a current compensation scheme in the grid-side converter. By dynamically injecting reactive current and incorporating a compensation term during LVRT, the abrupt phase angle variation is effectively mitigated. Simulation results demonstrate that the proposed method not only improves the recovery characteristics of the PCC voltage but also significantly reduces the magnitude of the phase angle jump and accelerates the stable convergence of the system, thereby providing a feasible technical solution for the secure integration of large-scale offshore wind power.

The low voltage ride through (LVRT) capability of offshore wind power has significant engineering value in ensuring the secure and stable operation of power grids. Long-distance submarine cable integration results in an extended electrical distance and weak grid strength, making stable fault ride-through difficult for conventional control strategies. To address this issue, an improved LVRT control strategy based on phase-locked loop (PLL) dynamic compensation is proposed. A mathematical model of the PLL is established to analyze the coupling between generator terminal voltage, phase angle, and angular velocity. Circuit theorems are applied to determine fault point voltage parameters, which are then substituted into the PLL model to derive the motion equations of the generator. The equal-area criterion is employed to define optimal transient stability conditions. A dynamic compensation mechanism is designed within the PLL to enhance the control strategy. A simulation model of an offshore direct-drive permanent magnet synchronous generator (DD-PMSG) is developed on the PSCAD platform, and comparative studies are conducted under different control strategies. Simulation results indicate that voltage oscillations are effectively suppressed under weak grid conditions. System stability is enhanced, and dynamic response characteristics are superior to those of conventional methods.

Harmonic distortion caused by phase jumps in the phase-locked loop (PLL) during asymmetric faults poses a significant threat to the secure operation of renewable energy grid-connected systems. A harmonic suppression strategy based on Vague set theory is proposed for offshore wind power AC transmission systems. By employing the three-dimensional membership framework of Vague sets—comprising true, false, and hesitation degrees—phase-locked errors are characterized, and dynamic, real-time PLL proportional-integral (PI) parameters are derived. This approach addresses the inadequacy of harmonic suppression in conventional PLL, where fixed PI parameters limit performance under asymmetric faults. The significance of this research is reflected in the improved power quality of offshore wind power grid integration, the provision of technical solutions supporting efficient clean energy utilization in alignment with “Dual Carbon” objectives, and the introduction of innovative approaches to harmonic suppression in complex grid environments. Firstly, an equivalent circuit model of the offshore wind power AC transmission system is established, and the impact of PLL phase jumps on grid harmonics during asymmetric faults is analyzed in conjunction with PLL locking mechanisms. Secondly, Vague sets are employed to model the phase-locked error interval across three dimensions, enabling adaptive PI parameter tuning to suppress harmonic content during such faults. Finally, time-domain simulations conducted in PSCAD indicate that the proposed Vague set-based control strategy reduces total harmonic distortion (THD) to 1.08%, 1.12%, and 0.97% for single-phase-to-ground, two-phase-to-ground, and two-phase short-circuit faults, respectively. These values correspond to relative reductions of 13.6%, 33.7%, and 80.87% compared to conventional control strategies, thereby confirming the efficacy of the proposed method in minimizing grid-connected harmonic distortions.

Situation awareness is a critical foundation for the safe operation of offshore wind power. To address the uncertainty risk caused by offshore wind power penetration in the extremely complex marine environment, a novel security situation awareness method for the offshore wind power networking system is proposed on the basis of the vague-CNN-LSTM model. Firstly, a partition model of offshore wind system topology is built according to the location of offshore wind farm access nodes, which can quickly capture the elements of the system situation. Secondly, a vague set is introduced to propose an interlaced offshore wind power interval division method integrating the truth-membership degree and pseudo-membership degree functions. Thirdly, the vague set interval prediction model based on vague-CNN-LSTM for offshore wind power is established to forecast the future fluctuation range of offshore wind power from different criteria, including support, opposition and hesitation uncertainty. Fourthly, early warning indexes for the security situation of the offshore wind power networking system are proposed so that the real-time and future security risks of the entire system can be perceived from the multiple layers of node-branch-area-network. Finally, the effectiveness of the proposed method is validated using a case study of an actual offshore wind farm in China.

With the increasing penetration of renewable energy, the access ratio of photovoltaic (PV) power generation is becoming much higher. To ensure the safe and reliable operation of power grids and improve their dispatching capability, it is of great significance to improve the prediction accuracy of PV generation. Thus, a PV output prediction model based on IMTGNN is proposed in this study. Firstly, the timestamp is extracted into five features which are stitched with the original data and input into the model. Then, the mutual information is incorporated into the adaptive adjacency matrix to obtain spatial relationship of nodes. Finally, selection attention is proposed and applied to the module to capture the relationship among variables. Taking the actual output samples of a PV power station in northwest China, the simulationresults show that the proposed prediction model IMTGNN has superior prediction performance.

With the implementation of the carbon peaking and carbon neutrality strategy, the proportion of new energy in the power system continues to increase, while the frequency regulation ability of the system gradually decreases. To address this problem, this paper studies the participation of distributed integrated energy microgrid group in frequency regulation auxiliary service. First, based on the concept of microgrid individuals participation in frequency regulation market after aggregation, the mode of integrated energy microgrid participating in frequency regulation auxiliary service market is described. Then, a cyber-physical-social system (CPSS) of integrated energy microgrid group is established. Based on this model, the frequency regulation cost of microgrid group is calculated. The cost model considers multiple frequency regulation modes, and classifies the social attributes of microgrid by using the grey weighted clustering method to simulate and analyze the willingness and probability value of microgrid individuals to participate in the frequency regulation market. Afterwards, based on Monte Carlo simulation, the cost calculation method for microgrid aggregators to participate in the frequency regulation auxiliary service is proposed, which provides the basis for aggregators to participate in the frequency regulation market quotation. Finally, an aggregator with five integrated energy microgrids is simulated and analyzed, and the curve of its frequency regulation cost is obtained. The simulation results show that the impact of the social attributes of the individual microgrid on the frequency regulation cost of the aggregator can reach 6.921%, so the uncertainty risk caused by the social attributes of the microgrid should be fully considered in the bidding process.

The uncertainty of wind power output and the difficulty of power storage restrict the development of new energy. As a high-quality secondary energy, hydrogen energy is green and pollution-free and has a high energy density. In order to cope with the volatility and randomness of new energy output, this paper proposes a multi-time scale probabilistic production simulation method for wind-solar hydrogen integrated energy system considering hydrogen storage. First, thermal energy recovery is considered in the hydrogen storage system model, and the wind-solar hydrogen integrated energy system model including electrothermal hydrogen multiple energy storage is constructed. Then, multi-time scale probabilistic production simulation is conducted for the wind-solar hydrogen integrated energy system, and the system maintenance arrangement and hydrogen storage seasonal distribution scheme are obtained through medium and long-term production simulation. The simulation results are taken as the boundary for short-term production simulation to achieve the cooperation of electrothermal hydrogen multiple energy storage and to smooth the random fluctuation of wind and solar power. Finally, an IEEE-RTS79 node example is given to verify that the proposed method can improve the reliability, flexibility and low-carbon feature of system operation.

Abstract Several resonant overvoltage accidents caused by machine-grid interaction in offshore wind power generation systems have led to significant economic losses over the years. To solve this problem, based on the equivalent distributed parameter circuit model of AC submarine cable delivery system, a method combining impedance simulation-time domain simulation and spectrum analysis is proposed to reveal the mechanism of resonant overvoltage and its influencing factors. First of all, considering the phase locked loop (PLL) model and the cable distribution parameter model, an impedance model of AC submarine cable delivery system for offshore wind power is established, which takes into account the dynamic characteristics of PLL and current loop, and provides a model basis for the theoretical analysis. Then, equations for current and frequency at the point of common coupling (PCC) are derived to analyze the mechanism of resonant overvoltage generation and the influence of submarine cable length and power grid strength on resonance. Finally, PSCAD is used to verify the resonance mechanism of AC submarine cable delivery system and its influencing factors.

The complex and multiple uncertainties of offshore wind power pose great challenges to the safety and robustness of transmission grid structures. In order to improve the adaptability of grid structure to offshore wind power, a robust expansion planning method based on Vague soft set is proposed. First, Monte Carlo simulation is employed to construct the offshore wind Vague scenarios, which transform multiple comprehensive uncertainties of offshore wind power into uncertain parameter sets from true membership function, pseudo-membership function, and unknown information measure based on the Vague soft set theory. Then, a two-stage robust expansion planning model based on Vague scenario set is established for transmission network with offshore wind power penetration. The minimum total investment cost of offshore and onshore line and network loss is taken as the objective function in the first stage, while the minimum objectives of wind abandonment and cutting load for offshore wind power are proposed with the alternating current power flow constraint based on second-order cone relaxation in the second stage. Based on the expected values of wind abandonment and cutting load returned by the second stage model, the operation variables of the first stage model are modified to ultimately obtain the iterative transmission network robust planning scheme. Finally, the Gurobi mathematical optimization engine is used to analyze the Garver 6-node system and IEEE 39-node system to verify the effectiveness and feasibility of the proposed robust expansion planning method.