On April 28th, 2025 Power systems in Spain and Portugal suffered stability destruction and massive blackout. The total power outage duration exceeded 18 hours, affecting the power supply for over 50 million people. The power system overview for Spain and Portugal, including the generation, power network and load profiles, are firstly introduced in this paper. Then, the entire development process of failure and blackout are presented based on the blackout investigation reports provided by the Spanish government and the Spanish power system operator, covering the system operation status before the fault, the failure progression, and the system restoration phases, and the causes of the blackout are summarized. Finally, based on the actual situation of power systems of China, suggestions are proposed for preventing large-scale power outages in the context of high penetration of renewable energy.

Electric vehicle （EV） charging stations utilize their remaining reactive power capacity to provide auxiliary services to the power grid, assisting in achieving automatic voltage control （AVC） and generating additional revenue for the charging stations. In this paper a market mechanism for the voltage regulation auxiliary services of EV charging stations is proposed, the circuit topology of charging piles within the charging station is established, and the basic principles of reactive power support by charging piles are analyzed. Based on this, using probability statistics and the Monte Carlo method, considering the charging demands of electric private cars and electric taxis, the charging behaviors of each are simulated. Combined with power constraints of the charging piles and transformer capacity constraints, the daily load distribution curve of the charging station is predicted. Furthermore, the reactive power support capability of the charging station within a day is evaluated and the revenue obtained from the participation of the charging station in voltage regulation auxiliary services within a day is calculated. Sensitivity analysis of relevant parameters of fast-charging piles within the charging station is conducted, highlighting the differences in reactive power support capabilities corresponding to different parameters of EV charging stations. This provides technical analysis tools for charging stations to declare their participation capacities in voltage regulation services and obtain relevant compensation benefits.

Aiming at the problem that key transmission sections of the power grid are frequently transferred and difficult to identify quickly and accurately in large-scale renewable energy grid connection scenarios, a key transmission section identification method based on improved normalized cut and security risk index is proposed. A weighted graph of network topology is constructed based on spectral graph theory, considering the influences of node voltage and branch power flow factors when assigning weights to branches. This aims to comprehensively reflect the electrical connectivity between nodes. The network is partitioned using an improved spectral clustering algorithm based on normalized cut, and for the transmission section formed between partitions, key transmission sections are selected using security risk index. The results from case studies on the IEEE 39-bus system and Guangdong Power Grid indicate that the proposed method can accurately and effectively identify key transmission sections within the system and assess their risk of exceeding limits. Moreover, it can identify some unrecognized key transmission sections in multi-scenario applications in the actual power grid.

In order to improve the performance of wind power probability interval prediction, a wind power interval probability prediction method is proposed which is the combination of variable bandwidth hybrid sliding Gaussian kernel density estimation (VHSKDE(Gaussian)) and normal sliding exponential iteration (NSEI) based on critical weight method and anti-entropy weight method. The combination method is called VHSKDE(Gaussian)-NSEI for short. Firstly, the error is obtained by point prediction based on variational mode decomposition and long short-term memory neural network(VMD-LSTM). Then, VHSKDE(Gaussian) and NSEI are used to estimate the probability distribution of the prediction errors, and forecast interval is obtained under corresponding confidence probability. Finally, four objective weight assignment methods are used to weight the VHSKDE(Gaussian) link and the VHSKDE(Gaussian)-NSEI combination link twice to generate the final wind power prediction interval. The research results show that the excellent performances of PICP and PIAW can be compatible by using the proposed VHSKDE(Gaussian)-NSEI prediction model under different levels of confidence. The VHSKDE(Gaussian)-NSEI prediction model has higher reliability and accuracy than NSEI and VHSKDE(Gaussian), and provides an important reference for wind power probability prediction.

Wind and photovoltaic power generation exhibit strong randomness and volatility. Improving the prediction accuracy is of great significance for constructing new power systems. Considering the certain correlation between wind and photovoltaic output in the same region, a short-term wind and photovoltaic power correlation probability interval prediction model based on similar day clustering and whale optimization algorithm-bidirectional long short-term memory neural network-Copula（WOA-BiLSTM-Copula) algorithm is proposed. Firstly, the K-means clustering algorithm is adopted to divide the numerical weather prediction （NWP） dataset, and wind and photovoltaic joint output typical similar day scenarios with correlation are extracted based on Kendall and Spearman correlation coefficients. Secondly, the non-parametric kernel density estimation method is used to establish Copula model for similar day scenes, and the optimal Copula function types of wind and photovoltaic output are determined. Then, the WOA is trained to optimize the BiLSTM and perform point predictions on wind power and photovoltaic power. Finally, the Monte Carlo method is used to sample the optimal Copula function, and the correlation probability prediction interval is generated based on wind and photovoltaic point prediction values. The simulation results show that the proposed model can effectively extract the correlation characteristics of wind and photovoltaic output, and the accuracy is higher than the existing models, which verifies the effectiveness of the model.

Wind-thermal bundled transmission is an effective way to improve the efficiency of wind power transmission channels. The ratio of wind-thermal bundled and the capacity of external transmission lines are important factors that affect the overall efficient and economic operation. A planning optimization method for transmission and consumption of wind-thermal bundled is proposed. Firstly, the planning target requirements for typical transmission and consumption of wind-thermal bundled are analyzed. Secondly, starting from meeting the overall supply and demand balance of the system, an optimization model with the objective function of minimizing the overall planning cost of wind-thermal bundled is established, which takes into account the constraints of safe and stable operation of the system. Then, a planning optimization method based on time series simulation is proposed to solve the model to compare and select the optimal solution with the smallest objective function. Finally, the effectivenesses of the proposed model and method are verified through practical cases.

Voltage source converters (VSCs)-based DC distribution networks (DCDNs) can automatically adjust the control strategy of overloaded VSC to adapt to the variation of renewable energy power, but it brings difficulties to analyze the steady-state performance of DCDNs. A piecewise linear power flow (PLPF) algorithm is proposed to quickly calculate the coupled effect of the power disturbance and VSC control strategy adjustment on steady-state power flow. Firstly, according to the VSC power balance, the critical point of VSC hitting the capacity limit is directly determined and the power variations of each node before VSC control strategy adjustment are obtained. Then, the linear power flow is revised considering the VSC control strategy adjustment. Finally, the linear power flow calculation is performed in each stage before VSC control strategy adjustment, and the steady-state power flow is obtained by using the superposition method. Simulation results show that the proposed PLPF model can directly, quickly, and accurately calculate the steady-state power flow distribution of DCDNs.

With the gradual maturity of the electricity spot market and the increase of the proportion of renewable energy participating in the electricity market, the demand and conditions for building an electricity derivative market have gradually been met. In order to provide market entities with sufficient risk management tools and to improve market efficiency, on the basis of an in-depth analysis of the practical experience of the construction of the Nordic electricity derivatives market, the key issues of China′s medium and long-term electricity market construction are studied. Firstly, on the risk management principles and construction significance of power derivatives are elaborated. Then the construction path of the Nordic electricity derivatives market is analyzed, including the operation stage of the electricity trading center and the operation stage of the securities exchange. Next, combined with the latest Nordic electricity derivatives contracts, the connection relationship between electricity derivatives and electricity spot prices is analyzed. Finally, based on the current situation of China′s medium and long-term electricity market, the inspiration of the Nordic electricity derivatives market for the construction of China′s medium and long-term electricity market is discussed from five aspects: product system, specific trading varieties, linkage mechanism, construction path, and business process.

Thoroughly studying the emission reduction responsibilities, potential, and pathways of the power industry is an important reference for promoting the green and low-carbon transformation of energy, as well as a key support for achieving the goal of peak carbon emissions and carbon neutrality for the whole society. The analysis of carbon emission reduction in the power industry needs to consider external factors such as economy, society, resources, and environment, as well as the constraints of power system operation such as electricity balance and unit output limitations. It is a complex nonlinear problem with multiple objectives, constraints, and parameters. Firstly, factors such as economic growth, industrial restructuring, and energy substitution are analyzed. The variables included electricity load, installed capacity of various power sources, energy storage capacity, carbon capture scale, and cross regional power transmission capacity. The objective function is the transformation cost of the power industry, and the constraints include electricity balance, carbon emissions, new energy utilization, hydrogen production demand, and the development of low-carbon, zero carbon, and negative carbon key technologies. A carbon reduction analysis model for the power industry is constructed, and an OpenMP multi-core parallel particle swarm optimization algorithm and a dual layer solution algorithm for multi solution tasks are proposed. Finally, based on the constructed model and algorithm, calculations and analyses are conducted on the long-term power structure, electricity balance, new energy consumption, energy storage configuration, etc., and a path for achieving carbon neutrality in China′s power industry is proposed.

Low frequency transmission system (LFTS) is a promising solution for offshore wind power integration. As a topology modified from the modular multilevel matrix converter (M3C), the hexagon converter(Hexverter) has some benefits in low frequency transmission. When Hexverter is applied to offshore wind power, the stochastic nature of renewable energy and its non-linear features make it hard for linear proportional integral (PI) control to reach desired control results. However, the Lyapunov function control strategy is better than PI control when applied to nonlinear systems. Therefore, this paper proposes a Lyapunov function control strategy based on Hexverter. Firstly, the global asymptotic stability of the Lyapunov function control strategy of Hexverter object is analyzed, and then the control law of the bilateral Lyapunov function control of Hexverter is derived based on the topology of Hexverter, and the parameter selection of Lyapunov function control is discussed. Finally, the Lyapunov function control system of Hexverter is constructed on MATLAB/Simulink and RT-LAB platforms to simulate the operation results under various working conditions, and by comparing the simulation results with PI control, the viability of the Lyapunov function control strategy proposed is confirmed.

Domestically produced high-voltage high-capacity insulated gate bipolar transistor (IGBT) devices have become one of the key technologies that constrain the economic and safety of China′s flexible and straight engineering. Focusing on the domestically produced 4 500 V/3 000 A double-sided sintered elastic pressure bonded IGBT modules, a multi⁃physical field model is established which takes into account the Anand constitutive model of the nano silver sintered layer and the mechanical properties of the disc spring, and reliability analysis is conducted. Firstly a multi⁃physical field simulation model for IGBT modules is established and its effectiveness is verified. Secondly, based on the actual service current stress of the IGBT module MMC, the losses of various power devices under rectification and inversion conditions are calculated, and the losses of IGBT devices under inversion conditions are analyzed through thermal mechanical simulation. Finally, fatigue analysis and life prediction are carried out on the collector and emitter sintered layers of domestically produced 4 500 V/3 000 A double-sided sintered elastic pressed IGBT modules based on actual service stress. The simulation results show that the sintering layer of the emitter has the lowest lifespan in the four corner areas where the chip contacts the emitter molybdenum sheet. The addition of double-sided sintering packaging and disc springs helps to improve the reliability of the module.

The transparency of low-voltage substation areas is of great significance for comprehensively improving the service quality, management level, and consumption capacity of low-voltage distribution systems, and is the only way to build a new type of low-voltage distribution network under the "dual carbon" strategy. Firstly, the core connotation and current development status of transparency in low-voltage substations are elaborated. Secondly, relevant research on the transparency of low-voltage substations at home and abroad has been reviewed. Furthermore, the key tasks of low-voltage substation transparency are proposed in five aspects: perception, measurement, communication, platform, and security, and the main contents of each key task are elaborated. Then the key technologies and future development directions of low-voltage substation transparency that need to be focused on at this stage are pointed out. Finally, transparency technology in low-voltage substation areas are prospected.

A vibration and noise control scheme based on particle damping technology is proposed to address the problem of structural noise disturbance caused by vibration of power distribution equipment. An energy consumption model for transformer particle damping is established, particle parameters based on the energy consumption value of the energy consumption model are optimized, and the prototype is processed based on the parameter scheme obtained from simulation for experimental verification.The experimental results show that when two types of iron-based alloy particles are used in combination with the particle damper, with a particle size of 3 mm and a filling rate of 80 %, the total effective acceleration values of the measurement points on the first and second floors of the power distribution room are reduced by 66.68 % and 88.33 % respectively, and the noise amplitudes are reduced by 14.53 dB and 7.02 dB respectively.The proposed scheme provides a new means for noise reduction design in the field of power distribution rooms.

The electrical and thermal characteristics of a transformer determine its load capacity and usable life, and excessive hot spot temperature accelerates the thermal aging of the insulation system, resulting in a reduction of its usable life. Taking an oil-immersed transformer as an example for research, by implanting fiber-optic temperature measurement probes inside the windings, the distribution characteristics of hot spot temperatures of high and low-voltage windings are measured and analyzed under rated operating conditions. According to the technical parameters of the transformer, the simulation calculation model is established, combined with the actual temperature rise test, to study the hot spot temperature of high and low voltage winding with time and ambient temperature change rule during different short-circuit faults within 2 Seconds. The results show that: transformer outlet short circuit fault and temperature rise test conditions of the winding hot spot temperature distribution law are different, in the outlet short circuit fault within a short period of time, high, low-voltage winding temperature with the short-circuit time of the highest temperature are a linear increase, while the top layer of the oil temperature is almost unchanged. With the rise of ambient temperature, the transformer winding hot spot temperature increases linearly, posing a serious threat to the safe operation of the transformer.

Electric vehicles (EVs) are both traffic loads of the transport network and electricity loads of power grid, and their travelling as well as charging behaviours will have an impact on the operating laws of the transport network and power grid. Aiming at the traditional EV prediction method of random sampling for a single EV unit, which fails to consider the operation state from the movement of EV groups, in this paper, a method for forecasting electric vehicle charging loads that leverages traffic equilibrium theory is proposed. Firstly, models for electric vehicles and road networks are constructed in both time and space dimensions, and an efficient path set for origin-destination (OD) pairs of electric vehicles is generated using the A* algorithm. Taking into account users' limited rationality, a charging station electricity pricing, which incorporates traffic flow constraints, is formulated by introducing time-of-use (TOU) pricing. Furthermore, a semi-dynamic traffic equilibrium model is developed by integrating stochastic utility theory with TOU pricing to address the traffic flow allocation. then based on the probabilistic numerical calculation method of combined state of charge (CSOC), the charging load of electric vehicles is solved. Finally, based on the data of new energy vehicles in Shanghai, the effectiveness of the proposed charging load prediction method is verified by analysing the improved 13 nodes road network and a regional road network in Shanghai, the results show that the proposed method can reduce the charging cost and alleviate the charging pressure of charging stations.

Gravity energy storage system (GESS) can absorb power from the power grid or the new energy station during charging process. When insufficient charging power happens due to power fluctuation of the new energy station, two energy power flow paths of GESS can be selected to respond to charging power fluctuation, which are adjusting the number of masses or absorbing energy from grid directly. However, how to select the above paths reasonably is still a problem in engineering application. To solve the above problems, an energy flow path selection method of GESS based on benefit analysis is proposed to realize the optimal charging benefit under power fluctuation of new energy station. Firstly, taking a slope GESS as an example, two energy flow paths in the energy storage process are analyzed in detail. Secondly, with the objective of the optimal benefit, the benefit analysis model is established and power shortage threshold calculation method applied in the above energy flow paths is also proposed. Finally, in the example of a 800 kW photovoltaic power station with 250 kW slope GESS, the charging cost and benefit of energy storage system with different energy flow paths are compared and analyzed to verify the proposed method under the fluctuation of renewable energy. The results show that GESS’s charging power can be adjusted in time and power shortage of new energy power generation can also be effectively solved by using the proposed method. At the same time, power charging benefit of the system benefit can be improved by 44.95 %.