To address the complexity of individually controlling high-penetration distributed photovoltaics (PV) and the impact of correlation between random variables on distributed PV planning, a dual-layer planning method for high-penetration distributed PV considering operational partitioning and correlation is proposed. Firstly, load nodes in the distribution network are clustered based on modularity and active power balance indicators. Secondly, to analyze the correlation between PV and loads, a 3PEM stochastic power flow model based on the Nataf inverse transform is established. Then, a dual-layer planning method for high-penetration distributed PV is developed. The outer layer minimizes the annual comprehensive cost by optimizing the allocation capacities of distributed PV and energy storage for each cluster, utilizing an improved genetic algorithm. The inner layer, based on chance constraints, minimizes the deviation rate of comprehensive node voltage expectation and the operational risk value as multi-objective functions, optimizing the output of distributed PV and the placement of energy storage at each node, employing non-dominated sorting genetic algorithm 2(NSGA2) and entropy-based technique for order preference by similarity to ideal solution(TOPSIS) to find the optimal configuration. Finally, the feasibility of the proposed model is demonstrated through a case study of IEEE 33-node system.

With the large-scale integration of renewable energy sources such as wind and solar power into the power system, there is an increased demand for precise characterization of their operational characteristics. Due to the randomness and volatility of wind energy, higher performance requirements are imposed on wind turbine control systems. By rapidly and accurately identifying the parameters of wind turbine control systems and precisely describing the characteristics of wind turbines, it contributes to power system analysis. After analyzing intelligent identification methods such as genetic algorithms, a new approach combining genetic algorithms with tabu search algorithms is proposed to improve the accuracy and speed of wind turbine parameter identification. Through parameter identification of the inner and outer loop parameters of the turbine-side converter and grid-side converter in the wind power generation system, and by comparing the convergence errors and iteration counts of different control algorithms, experimental results demonstrate that the proposed genetic-tabu search algorithm exhibits significant advantages in global optimization and rapid search capabilities compared to parameter identification methods such as the simplex method, genetic algorithm, and tabu search.

In response to the phenomenon of the negative effects of disorderly charging and discharging of electric vehicles and batteries in photovoltaic battery charging and swapping station(BCSS), a multi-party collaborative optimization strategy integrating multi-mode power replenishment and battery regulation is proposed to ensure user power replenishment optimality, BCSS economy, and regional power grid stability, guiding users′ replenishment decisions and battery scheduling within the station. Firstly, based on the principles of power anxiety and time redundancy, a price perception incentive model is established based on power replenishment incentives and time anxiety, and user demand for power replenishment is constructed considering demand price elasticity and price perception incentives. Furthermore, a power replenishment decision model is established based on the regret theorem. Secondly, based on the load level of the power grid and the guidance of battery capacity, a battery regulation model is established considering the charging and discharging status of the battery. Finally, a multi-party demand optimization scheduling model is established to maximize the economic benefits of charging and swapping stations and minimize regional power grid load fluctuations. Simulation results demonstrate the effectiveness of the proposed strategy, with reducing user expenses by 8.3 %, increasing economic benefits of BCSS by 16.5 %, and reducing regional grid load fluctuations by 41.7 %.

The doubly-fed induction generator(DFIG) unit can obtain active power reserve by overspeed load shedding to participate in frequency modulation, but due to the minimum speed limit of DFIG, it may cause serious secondary frequency drop when speed is recovered. To solve this problem, a frequency modulation strategy is proposed based on bidirectional DC/DC converter and supercapacitor energy storage. Firstly, based on the traditional overspeed load shedding and frequency modulation principle of DFIG, the causes and consequences of secondary frequency drop are analyzed. Then, combined with the operation characteristics of the supercapacitor and the DFIG grid-side converter, the supercapacitor is connected in parallel to the DC bus of the DFIG back-to-back converter through the DC/DC converter interface, and the DFIG grid-side converter collaborates with the energy storage characteristics of the supercapacitor to participate in frequency modulation, effectively avoiding the secondary frequency drop and achieving good frequency modulation effect. Finally, the effectiveness and superiority of the proposed method are verified by simulation.

To improve the operational efficiency of wind farms and ensure the stable operation of the power system, an ultra-short-term wind power forecasting method based on information recombination and TCN-LSTM-MHSA is proposed. Using variational mode decomposition to split the power data, the subsequences are reorganized into high entropy sequences, medium entropy sequences, and low entropy sequences with different information types based on the evaluation results of sample entropy; Extracting feature representations of data through temporal convolutional network (TCN), further processing data features using the long short-term memory network (LSTM), and parallel learning of different attention representations using multi-head self-attention mechanism (MHSA) to construct a wind power prediction model. Using two datasets with different output capacity and fan composition as benchmarks to validate the model, the results show that the model in this paper has better prediction ability.

The participation of loads with adjustment potential in spot market will bring huge economic and social benefits. The multiple dimension evaluation research of adjustable load participation in spot market is beneficial for promoting adjustable load participation in the spot market. Firstly, a value evaluation based on scenario simulation and clearing comparison is proposed. Secondly, a market clearing model for adjustable load participation in spot trading is given. Thirdly, the value evaluation indicators of security, flexibility, low carbon and economy are constructed from the perspectives of power grid safe operation, adjustment capability, low carbon emission reduction and the revenue of market entities. Finally, two scenarios before and after the participation of adjustable load participation in the spot market are simulated using an improved IEEE 30-node system, and the results verified the effectiveness of the proposed evaluation indicators and methods.

At present, the voltage support capability of receiving-end power grid is decreasing, so it is urgent to optimize the dynamic reactive power configuration for the security of the transient voltage of the system. Firstly, the generation method for the scene and fault set of the receiving-end power grid for STATCOM configuration is provided. Secondly, a sensitivity index based on the transient voltage severity is constructed to optimize the location of the STATCOM. Thirdly, a capacity optimization model with configuration cost as the objective function and considering transient voltage stability constraints is constructed. Then, the optimal configuration scheme is obtained by using particle swarm optimization algorithm. Finally, through the simulation calculation of STATCOM optimization configuration of Guangdong power grid, the results show that the proposed method is effective.

A two-stage robust optimization model for the planning capacity of the new power system "source-storage" of new power system is established, taking into account the uncertainty of power output and load demand. Using even theory and Karush Kuhn Tucker conditions (KKT), the robust optimization problem with min-max-min structure is decomposed into two stages: the main problem and sub problems for solution. The main problem is the minimization optimization problem structure, which adopts a relaxed form and considers the carbon emission requirements of new power system and the constraints of wind and solar power curtailment. The optimal values of the installed capacity of various power sources are determined, and the upper bound of the optimal solution of the original problem is obtained. The sub-problem is a maximum-minimum optimization problem structure containing nonlinear variables. It is modeled using dual theory and KKT conditions, and the Big-M method is used to handle nonlinear constraints. A box type uncertain set is used to describe the uncertain scenarios of wind and solar power output. Based on the optimization results of the first stage, various worst-case scenarios are determined. The simulation results of three scenarios in the example validate the feasibility and adaptability of the proposed model and method.

With the rapid development of information and communication technology, 5G base stations (5G BS), as high-density and high-energy-consuming load nodes, present both opportunities and challenges in the expansion planning of active distribution network (ADN). An ADN expansion planning method considering the dispatchable potential of 5G BS is proposed. Firstly, based on the communication load rate of 5G BS, the backup battery of the 5G BS is dynamically divided into minimum backup capacity and dispatchable capacity. Secondly, to address the uncertainty of distributed generation (DG) output and load demand, a clustering algorithm based on variational Bayesian Gaussian mixture model is used to construct typical daily scenarios. On this basis, with the minimum annual comprehensive cost as the objective function, an ADN expansion planning model considering 5G BS dispatchable potential is established. To improve the efficiency of model solving, the second-order cone relaxation method and the Big-M method are used to transform the model into a mixed-integer second-order cone programming problem. Finally, based on the Portugal 54-node simulation analysis, the feasibility and effectiveness of the proposed method are verified.

In large-scale distribution networks, load transfer can effectively alleviate power supply shortages in individual partitions. However, due to the excessive network size, traditional mathematical optimization methods struggle to address this issue. First, a quantitative assessment model for partition transfer capability based on network simplification is proposed to select the scale of partitions for inter-sub-district operation. Second, an optimization method for inter-sub-district operation between distribution network partitions using deep reinforcement learning is introduced to quickly identify inter-sub-district operation support schemes. This approach improves the traditional reward function to enhance the agent's adaptability and generalization chracteristic across different load scenarios. Then, the dueling double deep Q-network(D3QN)algorithm is employed for policy learning to tackle the complexity of large-scale systems. Finally, simulations on the IEEE 33-node system and a practical 445-node system are conducted to validate the effectiveness of the proposed method.

Low-voltage flexible interconnection forms interconnection and mutual power supply between low-voltage substation areas in the same regional distribution network, which can improve the total supply capacity (TSC) of the distribution system. Moreover, the distributed energy connected to the low-voltage substation area has temporal and uncertain characteristics, which affect the variation of TSC value. Therefore, a short-term TSC evaluation method for low-voltage flexible interconnected distribution networks based on temporal convolutional network (TCN) is proposed, which combines speed and accuracy. Firstly, short-term TSC analysis of low-voltage flexible interconnected distribution networks is conducted and establish a data-driven short-term TSC evaluation architecture is established. Then, a model-driven short-term TSC evaluation method is adopted to obtain short-term TSC values to obtain training samples. The TCN model is then trained offline to obtain a nonlinear mapping between short-term TSC values and influencing factors with certain temporal characteristics. This enables the online evaluation of short-term TSC by the TCN system. Finally, the effectiveness of the proposed method is verified through system testing of low-voltage flexible interconnected distribution networks.

Currently, the Z-source network is applied in matrix converters (MCs) to address the low voltage transfer ratio (VTR) issue of MCs. A novel structure of the Z-source ultra-sparse matrix converter (ZSUSMC)is proposed by integrating the Z-source network with the ultra-sparse matrix converter. This structure inserts the Z-source network between the rectification stage and the inversion stage of the ultra-sparse matrix converter. Additionally, two space vector modulation (SVM) methods which is improved zero-vector and non-zero-vector are analyzed and compared in the rectification stage, along with their respective advantages and disadvantages. Furthermore, to reduce output harmonic content and the number of switching state transitions, a new switching transition mode is proposed to minimize switching state changes throughout the entire switching cycle. Finally, the ZSUSMC modulation method is analyzed through software simulation and hardware experiments. The results demonstrate that the ZSUSMC can improve the voltage transfer ratio, with each modulation method having its own characteristics. Moreover, the new switching transition mode enhances the output power quality.

Analytical calculation based on transmission line model is a classical method to study the circulation of submarine cables, but this method considers the submarine cable armour layer is a uniform whole layer structure, while the actual armour is made of metal wires (steel wires) stranded together, and it will lead to errors and reduce the accuracy of submarine cable circulation analysis. For this reason, an optimization method for the transmission line model of metal wire armoured submarine cable is proposed based on the improved whale optimization algorithm (IWOA). Firstly, a test platform based on the simplified substitution model of submarine cable is built, and the validity of the 2D finite element simulation model for calculating the circulation flow of submarine cable with metal wire armouring structure is verified based on the measured data, and then the IWOA algorithm is used to carry out the joint analysis of finite element-MATLAB, to obtain the electrical resistivity and magnetic permeability of the whole layer of the armouring after equivalence, and the analytical model of submarine cable transmission line reflecting the structure of the metal wire armouring is established based on the equivalence results. Taking the 220 kV submarine cable as an object for example analysis, referring to the finite element results, compared with the traditional analytical calculation method which directly reduces the wire armouring to the whole layer structure, the accuracy of induced voltage and ground loop current obtained by the method proposed is improved by 290 % and 40 %, respectively, and the absolute error is less than 4.9% and 1.3 %, respectively, and the computational efficiency is significantly higher than that of the finite element model.

In order to study the DC breakdown characteristics of the conductor-plane air gap under the crown fire conditions, a 5 m air gap conductor-plane breakdown test platform is set up. The combustion characteristics and the DC breakdown characteristics of the air gap of pine crib fire and crown fire at different combustion phases are studied. The relationship between the flame height and the breakdown voltage of the gap is analyzed and fitting equations are given through regression analysis. The results show that: at different combustion phases, the crown fire has different impacts on the insulation strength of the air gap between the conductor and the plane; at the maximum combustion phase, the average breakdown voltage gradient of the pine crib fire is 104.5 kV/m, and the average breakdown voltage gradient of the pine crown fire is 84.7 kV/m, which is 38.9 % and 31.6 % of the average breakdown voltage gradient of the air gap at the same altitude, respectively. The average breakdown voltage gradient of the gap has a good linear relationship with the flame height. Comparing the DC breakdown voltage between the pine crib fire and crown fire, the influence of the difference in flame characteristics between the temperature and height of the flame zone on the insulation strength of the gap is weaker than the influence of the difference in the characteristics of the smoke particles in the smoke zone on the insulation strength of the gap. The results could provide guidance for risk assessment of short-circuit breakdowns on transmission lines under forest fire conditions.

In order to investigate the failure mechanisms of compression type tension clamp steel core and steel anchor in transmission lines that frequently fracture, the compression and tensile tests of NY-400/35 compression type tension clamp steel anchor are designed to obtain the ultimate bearing capacity of the compression area of the tension clamp steel anchor, and the simulation model of tension clamp steel anchor side is established by using Solidworks. The finite element analysis method is used to simulate the crimping and tensile process of the steel core crimping area of the tension clamp steel anchor, and the dangerous section and failure mechanism of the force transmission path of the tension clamp steel anchor are obtained by comparing with the experimental results. The results show that the failure form of the tensioning clamp of the steel anchor only is the fracture of the steel core, and the grip force value is only 50% of the calculated breaking force of the wire. There are three dangerous cross sections of the steel anchor at the exit of the steel anchor and the last die pressing joint and the steel anchor in the transition zone of the groove. Failure occurs when the clamp carries more than three sections of failure load or the maximum contact force between steel core and steel anchor. The failure load of dangerous section is positively correlated with the strength limit and cross-sectional area, and inversely correlated with the stress concentration level.

Local tube-through cables are the bottleneck locations of urban power grid current carrying capacity, the temperature monitoring of which is crucial. A real-time temperature inversion model based on LassoNet embedding and improved BP neural network is proposed to address the problems of low accuracy of current temperature measurement methods, inability to determine the optimal combination of temperature measurement points for different situations, and unsuitable input selection methods for "black box" networks. Firstly, the LassoNet network is used to autonomously quantify and select the optimal combination of temperature measurement points suitable for local tube-through cables neural networks; Subsequently, iCircle mapping, inertia weight concept, and Levy flight hybrid strategy are introduced to improve the initial distribution, search strategy, and iterative method of sparrow search algorithm (SSA) to enhance global optimization performance. The improved SSA is used to optimize the parameters of BP neural network, achieving fast and high-precision inversion of hot spot temperature under multiple operating conditions. A finite element simulation model of YJLW03-64/110 kV cable local conduit is established, and the accuracy of the model is verified by comparing it with IEC standards. Then, a hot spot temperature sample dataset is constructed under different load types. Based on this dataset, the proposed method is compared and analyzed with five typical inverse algorithms. At the same time, in order to verify the transferability of the algorithm, temperature inversion of 220 kV local tube-through cables and 110 kV cable joints is tested. The results show that the proposed inversion method can control the error within 1.5 ℃, has a fast convergence speed, can systematically select temperature measurement points, and has higher accuracy and robustness.