The rapid fluctuations in renewable energy output cause frequent voltage oscillations in power grids, severely threatening the safe and economical operation of power grids. To address this issue, a real-time reactive power optimization method based on graph convolutional networks （GCN） is proposed. Firstly, a multi-objective reactive power optimization model considering the importance of nodes is constructed. Based on this, the graph representation of the reactive power optimization model is established, and the adjacency matrix is restructured in conjunction with the optimization problem. Then, the optimal solution set is mapped using the GCN algorithm, and the improved CRITIC-AHP-TOPSIS combined weighting algorithm is employed to select the optimal solution, forming a real-time optimization strategy. Finally, using the modified IEEE 39-node system and an actual power grid as examples, the proposed method is verified. The results show that the method not only has the advantages of fast solving speed and avoiding local optima but also achieves more favorable voltage deviation and active power loss, ensuring the safety and economy of new power system operation.

Driven by the "dual carbon" goals, the user side of the new power system faces multidimensional security risks due to the large-scale integration of new grid-connected entities such as distributed photovoltaic systems, electric vehicle charging stations, and energy storage equipment.Firstly, the challenges to equipment safety, grid security, and societal safety posed by the massive integration of these new entities are systematically analyzed, core bottlenecks at the technical and managerial levels, such as uneven equipment quality, difficulties in cross-modal data fusion, and insufficient safety protections, are highlighted. To address the heterogeneity, semantic differences, and correlation characteristics of multimodal data, a multimodal large model construction technology is proposed, incorporating data augmentation generation, cross-modal semantic alignment, and parameter-efficient fine-tuning methods to enhance risk perception accuracy. Furthermore, integrated security prevention and control strategies are introduced from three perspectives: grid optimization, intelligent operation and maintenance, and resilience enhancement. These measures provide theoretical support and practical pathways for the security governance of the new power system, facilitating the coordinated development of energy transition and security under the "dual carbon" objectives.

With the increasing complexity of "high-proportion renewable and high-electrification" power systems, traditional model-driven stability analysis methods face growing limitations. Artificial intelligence （AI） techniques, offering advantages in both speed and accuracy, have emerged as a key research focus. This paper focuses on the development path of power system stability analysis technology from model driven to data-driven, and further to hybrid intelligence integrating domain knowledge and data. Firstly, the technical requirements of typical stability analysis tasks are outlined, and traditional model driven methods are reviewed.Subsequently, the application results of artificial intelligence methods in different scenarios are summarized, and their advantages and disadvantages are analyzed. Further the information driven methods and research progress of integrating power system knowledge and mechanisms are explored. Finally, based on the power system characteristics under the background of "double high", the current challenges faced are analyzed, and future research directions are discussed.

With the growing determination of the international community to strengthen climate and environmental governance, and under the multiple drivers of China's ‘Double Carbon’ policy and marine power strategy, renewable energy is gradually replacing fossil fuels as a new type of power generation resource. Among these, wave energy generation has been extensively studied due to its abundant resources and environmental benefits. Compared to the low energy utilization efficiency of single wave energy generation systems, the hybrid wind-solar-wave system （HWSWS） can maximize multi-energy complementarity. Therefore, improving the efficiency and output of HWSWS is of profound significance. To fully leverage the advantages of wind, solar, and wave energy, this paper proposes an array layout optimization strategy of HWSWS based on the improved rime optimization algorithm （IRIME）. By integrating logistic chaotic mapping, golden sine strategy, and Lévy flight strategy, the original rime optimization algorithm （RIME） is improved. To validate the effectiveness of IRIME in optimizing HWSWS arrays, this paper performs optimizations on arrays at the five HWSWS and nine HWSWS scales. The simulation results show that the HWSWS optimized by IRIME achieves the maximum power output. Compared to RIME, particle swarm optimization （PSO）, grey wolf optimizer （GWO）, and aquila optimizer （AO）, the output power of the five HWSWS array increases by 36.2 kW, 83.3 kW, 27.6 kW, and 38.0 kW, respectively. Besides, the output power of the nine HWSWS array increases by 45.5 kW, 191.2 kW, 168.5 kW, and 66.5 kW, respectively, demonstrating the effectiveness and superiority of IRIME.

The rapid integration of Internet of Things （IoT） technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring, two-way communication, and advanced metering infrastructure services. However, this digital transformation also exposes power system to evolving threats, ranging from cyber intrusions and electricity theft to device malfunctions, and the unpredictable nature of these anomalies, coupled with the scarcity of labeled fault data, makes real-time detection exceptionally challenging. To address these difficulties, a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules. The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies, while the second captures intrinsic temporal patterns for enhanced contextual discrimination. The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids. Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework, achieving average recall and F1 score of more than 0.85.

Along with the growing integration of renewable energy resources, the new power systems, which are dominated by inverter-based resources （IBRs）, are facing critical challenges in both planning and operation stages. The conventionally used system strength metric, short-circuit ratio （SCR）, exhibits limitations in assessing connections of new IBRs due to their unique dynamic behaviour and control interactions. In this paper, the definition of system strength is reviewed. The underlying principles of conventional SCR and its variants are then discussed, with their constraints explained. To describe the system strength in a more comprehensive way, this paper further classifies system strength into three categories: quasi-static, small-signal, and large-signal. For each category, relevant metrics are introduced and their relative merits are discussed. Electromagnetic transient simulations are presented to illustrate key insights.

An innovative converter bridge arm protection method is proposed for the modular multilevel converter - high voltage direct current （MMC-HVDC） system. Firstly, the current flow paths are analyzed when the single bridge arm of the converter is grounded and when the single-phase bridge arm is indirectly grounded. An equivalent circuit is constructed, and the expressions of the bridge arm current before and after the converter is locked are derived. Based on this, the unique characteristics of the sum of the three-phase bridge arm currents after a fault are analyzed, and a protection strategy is proposed to distinguish internal faults from external faults of the bridge arm by detecting whether the fundamental frequency components of the three-phase bridge arm and the current are zero. Given that the existing bridge arm protection methods cannot effectively deal with the short-circuit problem between single-phase bridge arms of the converter, the characteristics under such short-circuit conditions are further studied. It is found that the currents of the upper and lower bridge arms of the faulty phase only contain the fundamental frequency component and don’t contain the DC component, and thus the corresponding action criteria are formulated. By outputting the short-circuit protection criterion between the three-phase bridge arm and current protection and the single-phase bridge arm through or gate, various faults occurring on the converter bridge arm can be comprehensively identified, enriching and improving the protection principle of the converter bridge arm. Finally, by building the simulation model of the bipolar MMC-HVDC system in MATLAB/Simulink, the effectiveness and reliability of the proposed protection method are verified.

To address the issues of uneven output current distribution and voltage drop in energy storage unit converters under traditional droop control, an improved scheme is proposed. Firstly, an AC small-signal disturbance is introduced on the basis of traditional droop control to formulate a frequency-current composite droop control strategy, achieving precise proportional current distribution among distributed power converters in DC microgrids. Secondly, the large-signal stability criterion for microgrids under the improved droop control is derived based on mixed potential function theory. Finally, MATLAB/Simulink simulations demonstrate that the proposed frequency droop control strategy effectively achieves accurate current distribution in converters while validating the correctness and effectiveness of the stability criterion. Additionally, the critical range for sudden increases in constant power loads and the feasible domain for voltage controller parameters are determined for specific DC microgrid structural parameters.

The low-voltage output analog signals of direct circuit voltage transformers （DCVTs） are transmitted through cables without electrical isolation and are susceptible to electromagnetic interference. A scheme for transmitting analog signals with high linearity using optical fibers is designed. Using dual light sources and photodetectors to form a transmission channel and a closed-loop negative feedback channel, the photodetector in the negative feedback channel converts the current and feeds it back to the input end to suppress the nonlinearity of the electro-optical transmission coefficient. The nonlinear suppression control strategy and effect are analyzed through a control system model, and the key elements of the circuit design of the analog signal fiber optic transmission system are analyzed. A comparative test is conducted on the nonlinear characteristics of open-loop transmission and closed-loop feedback transmission of analog signal fiber optic transmission systems. The results show that the nonlinear error of analog signal fiber optic transmission is reduced from a few percent to within ± 0.05 % through the closed-loop negative feedback transmission scheme. Finally, the analog signal fiber optic transmission system is applied to DCVT for linearity testing. The experimental results show that the analog signal fiber optic transmission system can be well applied to the application requirements of electrical isolation and elimination of electromagnetic interference of analog signals in DC distribution networks.

The widespread integration of flexible resources such as distributed photovoltaics, energy storage, and electric vehicles into low-voltage substations poses new challenges to the assessment of open capacity in substations. Therefore, an open capacity elastic interval evaluation method for low-voltage substation considering flexible resource regulation is proposed. Firstly, a mathematical model is established for the flexibility resources of the low-voltage substation and its adjustable potential is analyzed. Secondly, based on flexible resource optimal dispatch and considering uncertainty factors, an open capacity elastic interval evaluation model for low-voltage substation is constructed using information gap decision theory (IGDT). Finally, through simulations of different station areas, the open capacity elastic interval under various flexible resource combinations is analyzed from the perspectives of power supply and load access, and the effectiveness of the model is verified.

The capacity-to-load ratio is an important indicator to guide the power grid planning, and has a decisive influence on the selection of substation capacities and the construction of power lines. A capacity-to-load ratio optimization method considering collaborative planning of multi-voltage level power grids is proposed in this paper, which aims to minimize the construction cost and takes into account the synergetic planning of substations and power lines of multi-voltage levels. By combining the Bayesian optimization algorithm (BOA) and genetic algorithms (GA), a BOA-GA solving algorithm for the planning model is proposed. This algorithm automatically generates planning schemes for multi-voltage level power grids, and obtains the optimal capacity-to-load ratio, as well as the optimal investment of the power grid construction. Additionally, the study reveals that the relationship between capacity-to-load ratio and the overall investment of power grid construction is a quadratic function over a long time scale, and the curve is a parabola going upwards. Case studies demonstrate that the proposed model can achieve optimal configuration of multi-voltage level capacity-to-load ratio and automatically plan the network structure. The construction cost is significantly reduced while various technical indicators are satisfied. The proposed method can provide effective theoretical and methodological guidance for multi-voltage level power grid synergetic planning.

As a carrier of power transmission, high-voltage overhead transmission lines are an important guarantee for the stable operation of the power system. Therefore, it is particularly important to detect abnormal phenomena for transmission lines as early as possible (such as foreign objects, broken strands, etc.) and implement maintenance in a timely manner. In this paper, the vigorous promotion of intelligent operation robots will help ensure the safe, efficient, and economical operation of the power grid system. A pose estimation method of unmanned aerial vehicles is proposed based on point-line feature fusion to address the weak pattern recognition and automatic tracking capabilities in existing power operation drones, as well as the poor robustness of visual odometry. A complete target detection and tracking system of unmanned aerial vehiclesis developed. Compared to other similar unmanned aerial vehicles environment perception schemes, the proposed target tracking algorithm can efficiently extract point and line features from infrared images, and achieve target detection and automatic tracking functions of small multi rotor drones in transmission line scenes with extremely low power consumption.

The massive integration of distributed photovoltaics into power grid has caused rapid growth of data collection volume, which has put enormous pressure on services data communication and processing. This paper proposes a data compression access control method for distributed photovoltaic information aggregation. On the basis of constructing a distributed photovoltaic data compression and information aggregation framework, the characteristics of distributed photovoltaic information aggregation services are considered and the data compression access control problem is converted to maximize the information aggregation delay gain. In view of the contradiction between the reduction of transmission delay and the increase of compression delay, as well as the challenges such as the coupling between data compression ratio selection and gateway selection, a distributed photovoltaic information aggregation algorithm is proposed based on adaptive differential genetic game. The proposed algorithm considers the bilateral game relationship between distributed photovoltaic information aggregation delay and reliability, to ensure the maximization of delay gain and long-term reliability constraint guarantee requirements. Simulation results show that compared to the comparison algorithm, the proposed algorithm improves delay gain performance by 33.81% and 16.05, and transmission reliability by 23.61% and 11.25% respectively.

Aiming at the urgent need for orderly control of large-scale rooftop photovoltaic access to the distribution network, a comprehensive sorting method of rooftop photovoltaic grid-connected considering source-load matching characteristics is proposed. Firstly, considering the demand of load and photovoltaic coordination characteristics, a comprehensive evaluation index system is designed, which takes into account the demand of photovoltaic owner side, power grid side and power user side. Secondly, an evaluation method based on improved analytic hierarchy process-improved anti-entropy weight method-game combination weighting method- improved technique for order preference by similarity to ideal solution is proposed. Then, the subjective weighting is carried out according to the improved analytic hierarchy process, and the correlation and volatility between the indexes are taken into account. The proposed improved anti-entropy weight method is used to determine the objective weight of each index, and then the comprehensive weight is obtained based on the game theory to ensure the rationality of the weight. Moreover, in order to improve the overall discrimination of each scheme, the improved technique for order preference by similarity to ideal solution is used to sort the rooftop photovoltaic access scheme. Finally, taking the IEEE 33-node system as an example, the effectiveness of the proposed index system and ranking method is verified on the MATLAB platform.

Aiming at the power battery under complex and variable working conditions, offline parameter identification cannot reflect the dynamic characteristics of the battery in real time, resulting in low parameter identification accuracy, and the untraceable Kalman filter (UKF) is very limited to deal with the noise when estimating the state of charge (SOC) of the battery, and at the same time, the non-positivity problem in the processing of the covariance matrix leads to algorithm fluctuations and estimation failures. In this paper, based on the dual polarization (DP) circuit model, the forgotten factor recursive least squares (FFRLS) and singular value decomposition-adaptive untraceable Kalman filter (SVD-AUKF) methods for online estimation of battery SOC are proposed. The simulation results show that the mean absolute error (MAE) and root mean square error (RMSE) of SVD-AUKF for simulation validation are 0.528 6 % and 0.544 7 %, respectively, when comparing with the real SOC values under the complex operating conditions (U.S.Federal Urban Operating Conditions (FUDS)), and the MAE and RMSE are improved on the basis of traditional UKF algorithm, respectively, by 57.96 % and 63.3 %, further indicating that SVD-AUKF is more accurate and stable.

Aiming at the problems of environmental pollution and the conflict of interests of multiple energy suppliers in the integrated energy system, an economic analysis model of the integrated energy system based on cooperative game under the carbon trading mechanism is proposed. Firstly, in order to control CO₂ emissions, a cooperative game model with three energy supplying entities, including renewable energy power plant, carbon capture power plant and gas-fired thermal power plant, is established by introducing the carbon trading mechanism, and the optimization objective of the model is to minimize the sum of operation and maintenance cost, penalty cost, carbon related cost and environmental cost. In order to rationally allocate the cooperative costs of multiple energy supply subjects, the Shapley value method is firstly used for the initial allocation. Then, a correction factor that integrates the cost contribution coefficient and environmental contribution coefficient is introduced to further optimize the initial allocation results, and an improved Shapley value method apportionment mechanism is proposed. The simulation results show that the cooperative operation of the energy entities can effectively enhance the new energy consumption capacity, promote the complementary utilization of multiple energy sources, and significantly improve the system economy and reduce carbon emissions, and the improved Shapley value method can make the cost sharing among the energy entities more fair and reasonable.