Wind turbines within a wind farm are highly coupled through wake effects，and optimizing wake distribution holds significant potential for improving overall wind energy utilization efficiency. Considering the combined impact of active yaw control and axial induction control on inter-turbine wakes，as well as differences in their regulation cycles and response times，a nested optimization method integrating both controls is proposed. Combined with an initial solution positioning strategy，the proposed approach enhances wind farm power generation while significantly reducing optimization time. To improve accuracy and reflect the effects of turbine yaw and vertical height variations within the wind farm on wake estimation，a dual-parameter Weibull distribution inflow and a three-dimensional Gaussian wake model are incorporated into the wake distribution simulation. Finally，simulation studies conducted for an offshore wind farm in the coastal waters of southeastern China demonstrate that the proposed nested optimization strategy yields superior performance in enhancing total wind farm power output.

The participation of wind farms in grid frequency regulation is a key measure to address the frequency challenges of new power systems dominated by new energy sources with low inertia. In the process of wind farms participating in grid frequency regulation，how to achieve rapid frequency support while ensuring the safe operation of wind turbine generators is of vital importance. Therefore，this paper proposes a hierarchical consistent coordinated frequency regulation control strategy for wind farms. The upper layer performs optimization allocation for frequency regulation power of wind farms based on model prediction. By analyzing the electromagnetic and mechanical characteristics of wind turbine generators participating in frequency regulation，a prediction model for the wind farm is built，and the optimization objectives of active power allocation to avoid secondary frequency drops and optimize pitch angle are formulated. The lower layer adopts consistency following control. The leading wind turbine generator receives the power allocation signal from the centralized controller，while other wind turbine generators follow the frequency regulation participation degree of the leading wind turbine generator by exchanging inertia factors with neighboring wind turbine generators. The simulation results show that the proposed strategy can effectively increase the minimum turbine speed of wind farms participating in frequency regulation，improve their stability，and achieve consistency in the frequency-regulation participation degree between non-leading and leading wind turbines. It avoids the curse of dimensionality when solving the large-scale wind-farm frequency-regulation optimization model and yields satisfactery frequency-regulation performance，which is of great significance for ensuring the safe operation of the power grid.

The structural robustness of a power grid is crucial for ensuring load power supply under fault conditions. While existing studies have revealed macro level correlations between grid structure and blackout risks，these correlations show low consistency and vary across systems of different scales，making them inadequate for guiding robust grid construction. By considering the impact of node heterogeneity on power supply areas and factors such as power path sharing on load outage probability，this paper proposes a community theory based power supply area partitioning method and an equivalent power path length metric，forming a novel grid robustness assessment framework. Validated across 62 power networks of three scales，the results demonstrate that the proposed metrics exhibit significantly improved positive correlations with blackout risks across all systems. Notably，the optimized Southern Power Grid topology designed using the proposed method reduces the average outage risk by 15.57% compared with manually designed topologies，providing effective technical support for high reliability grid construction.

The construction of large-scale renewable energy bases has promoted the low-carbon transformation of power systems，with renewable energy stations and energy storage playing increasingly important roles in power dispatch and market transactions. Meanwhile，the sharing economy model has gained favor because of its ability to improve the utilization of idle resources. In this paper，the joint optimization operation mode of clustered renewable energy stations and shared energy storage in renewable energy bases is studied. First，a joint optimization operation framework for clustered renewable energy stations and shared energy storage in renewable energy bases is established. Then，a two-stage optimization model for renewable energy stations and shared energy storage，considering green power trading，is developed. To address the uncertainty of renewable energy generation，an uncertainty set accounting for the temporal characteristics of wind and solar power errors is proposed，which can reflect the actual fluctuation range of renewable energy output. On this basis，a day-ahead and intraday two-stage robust model is built. Finally，the idle capacity of shared energy storage is utilized to participate in both energy and reserve markets，improving energy storage utilization and expanding its profit channels. Case studies verify the effectiveness of the proposed model and method，and the results show that the proposed optimization operation strategy can significantly reduce the penalty costs for renewable energy station deviations，accelerate the recovery of energy storage investment，and ultimately increase the net profit of the renewable energy base.

Conservation voltage reduction （CVR） is an effective approach for reducing system energy consumption and alleviate peak power-supply pressure. Existing CVR research mainly focuses on the coupling relationship between “voltage and power flow”，whereas the mapping relationship between “voltage and carbon flow” remains unclear，and the carbon reduction potential of CVR needs further exploration. To address this issue，with the primary objectives of reducing carbon emissions and active power consumption，an analytical relationship between “electric potential and carbon potential” is established. First，CVR models considering the temporal and spatial variations in carbon emission intensity are constructed to investigate the differential effects of load characteristics on CVR performance. Then，comprehensively considering the low-carbon and economic characteristics of the system，a coordinated “voltage-carbon emission” optimization control strategy for active distribution networks based on CVR technology is proposed，which is analyzed and solved by integrating voltage control and optimal power flow methods. Finally，the IEEE 33-node system is utilized as a typical test case to verify the effectiveness of the proposed coordinated control strategy. The results verify the inconsistency between energy-saving and carbon-reduction objectives in temporal and spatial dimensions，and demonstrate that the carbon-reduction performance can be improved by 0.65 and 3.80 percentage points respectively. The proposed coordinated optimization control method further enhances the carbon-reduction performance by optimizing control strategies from the perspective of different stakeholders.

The increasing penetration of renewable energy into the power grid has placed higher demands on the flexibility and regulation capabilities of power systems. As a flexible regulation resource，energy storage is expected to gradually replace conventional thermal power units in future power systems and undertake increasing responsibilities for frequency regulation and peak shaving. However，the high cost of energy storage limits its large-scale application. Therefore，from both technical and economic perspectives，this paper investigates the capacity planning of energy storage systems for hybrid wind-storage systems participating in peak-shaving and frequency-regulation auxiliary services. First，to address the uncertainty associated with wind power generation and load demand，based on the historical data of wind power and load，this paper uses K-means clustering method to generate their own representative scenarios. Typical joint wind-power-load scenarios and their corresponding probabilities are then generated by the Cartesian product. Then，considering the distinct requirements of peak shaving and frequency regulation，the costs and benefits associated with the participation of wind-storage systems in auxiliary services are quantitatively modeled，including the benefits of peak shaving and frequency regulation，the initial investment cost of the energy storage system，and the lifetime degradation cost of energy storage. Finally，a wind farm in a certain region is used as a case study to verify the feasibility and effectiveness of the proposed method.

To enhance the black-start capability of power systems with a high penetration of renewable energy，a black-start control strategy for hybrid wind farms considering inertial energy storage and power-handling limits of grid-forming wind turbines （GFM-WTs） is proposed. Based on the topology of hybrid wind farms，a coordinated control framework for grid-forming and grid-following wind turbines is developed. The current-speed and power-speed boundary characteristics of GFM-WTs are analytically derived，and an overload response mechanism constrained by these boundaries is proposed. A direct-current bus charging model is established to quantify the initial inertial energy demand. Then，a complete black-start control procedure is designed，including voltage and frequency establishment，power coordination，and load restoration. Simulation results demonstrate that the proposed strategy enables autonomous system restoration under the leadership of GFM-WTs，and maintains orderly recovery and stable operation under load disturbances，frequency fluctuations，and wind speed variations，thereby exhibiting strong robustness and adaptability. Moreover，the method reduces dependence on energy storage and external support，showing promising prospects for engineering applications.

Hydrogen-integrated energy systems are key components of future energy systems，and the synergistic control of multiple types of electrolyzer modules can help improve the consumption of new energy and the operational efficiency of hydrogen production modules. In this paper，the dynamic efficiency and start-stop characteristics of alkaline water electrolyzer and proton exchange membrane water electrolyzer are modelled，and the operation framework and co-optimization strategy of an electric-hydrogen-thermal integrated energy system with multiple types of electrolyzer modules are proposed to optimize the start-stop status and power of each electrolyzer module with the objective of minimizing the comprehensive cost consisting of system operation and maintenance costs，the cost of curtailed energy，the cost of starting and stopping the electrolyser，and the revenue from hydrogen production. The operation framework and optimization strategy proposed can improve the new energy consumption rate，increase the energy efficiency of the hydrogen production system by 8.13%，and increase the total cost benefit by 40.83%，as verified by the modified IEEE 39-node power-6-node thermal system.

The locational marginal pricing （LMP） mechanism cannot ensure that thermal power units recover their startup，shutdown，and no-load costs. In recent years，the large-scale integration of renewable energy has posed more severe challenges to the recovery of these costs. To address this issue，two compensation mechanisms for the startup，shutdown，and no-load costs of thermal power units have been implemented in China’s electricity market. However，no research has explored the impacts of these two mechanisms on the bidding strategies of thermal power units，as well as on the generation adequacy，electricity consumption economy，and market transparency of the power system. Based on the actual operation of China’s electricity market，this paper first introduces two mainstream cost mechanisms： one that full compensates the startup and shutdown cost，and another that compensates the startup，shutdown，and no-load cost to ensure non-negative profits. It proposes technical indicators to evaluate these two mechanisms are also proposed. Subsequently，it derives the optimal bidding strategies of thermal power units under the two mechanisms. The calculation results show that compared with the mechanism of full compensating only startup and shutdown costs，the mechanism of compensating startup，shutdown，and no-load costs to ensure non-negative profits can reduce the speculative nature of thermal power units’ bids，decrease the total user payments，and better guarantee the generation capacity adequacy and economic performance of the power system.

Efficient aggregation and regulation of demand-side flexibility resources through virtual power plants is an effective measure to improve the flexibility of new power systems. Air conditioning loads are among the most promising demand-side resources，of which the accurate modeling can lay the foundation for the design of control strategies. To overcome insufficient fitting accuracy，operating-condition variations，and low efficiency in existing air-conditioning system parameter identification，an improved dynamic parameter identification method based on Newton-Raphson-based optimizer （INRBO） for analytical solution to second-order equivalent thermal parameters （ETP） of air-conditioning systems is proposed. First，a second-order ETP model based on analytical solution is introduced to reduce the cumulative errors inherent in the traditional second-order recursive parameter identification method. Then，considering the multi-parameter dynamic characteristics caused by variable factors such as window ventilation and sudden temperature changes，a second-order ETP parameter identification model for air-conditioning systems is built with the optimization objectives of minimizing dynamic errors between the simulation system and the actual system，while taking into account the initial solid temperature in the room. Finally，to improve the solution efficiency，an INRBO algorithm based on Chebyshev chaotic mapping is proposed to solve the above model. Case study results demonstrate that the proposed dynamic parameter identification model can balance fitting accuracy and solution efficiency.

To address the limitation that the existing three-component frequency framework for short-circuit current analysis cannot fully characterize the features of frequency coupling and shifting，an analysis approach explicitly capturing the stator-rotor coupling frequency shift effect is developed to improve the calculation accuracy of doubly-fed induction generator （DFIG） short-circuit currents under large-scale wind farm fault conditions. First，the method employs the internal rotor electromotive force as a unified intermediate variable to map the rotor-side transient behaviors （e.g.，crowbar activation，rotor-side converter blocking/re-activation，and excitation limiting） under different fault ride-through strategies into a unified state space，thereby resolving the dynamic discontinuity problem caused by non-zero initial states in the phased calculation process. Then，a unified transfer function relationship between stator voltage，stator current and rotor current during the ride-through process is established，and additional eigen poles introduced by control strategy switching are further derived to reveal the intrinsic mechanism of frequency shift of slip components and power frequency components under multi-stage control actions，which form multi-frequency coupled components. After that，following the same modeling approach as that used for the rotor side，a unified transfer function relationship between the grid-side converter and stator voltage under different control strategies is established，revealing the additional harmonic components in the short-circuit current affected by the grid-side converter control from the frequency domain perspective. Finally，case studies demonstrate that the proposed approach reduces the maximum error of the existing three-component framework，which is kept within 2.2% under shallow voltage sags and within 2.8% under severe voltage sags，exhibiting excellent robustness and adaptability. Meanwhile，the relevant harmonic component decomposition results verify the existence of multi-frequency coupled components in the theoretical analysis，and based on this，effectively explain the relay maloperation mechanism caused by phasor amplification. The findings enhance the accuracy of fault analysis in large-scale wind power systems，integrating stator-rotor frequency coupling effects and multi-stage control switching into a unified short-circuit current calculation framework，thereby achieving fast and accurate solutions under various complex fault conditions.

With the continuous expansion in global natural gas consumption，the recovery and utilization of underground natural gas pressure energy is of profound significance for effectively promoting the energy structure transformation and facilitating the sustainable development of human society and living environment. To balance the operating energy efficiency and power-to-volume ratio of the natural gas pressure-differential power generation device，a dual three-phase high-speed permanent magnet synchronous generator is adopted for direct-drive grid connection. A gas-mechanical-electric integrated model based on a natural gas expander is proposed，and a vector control system for the back-to-back dual pulse width modulation converter is constructed. To address the current-coupling and harmonic issues of the high-speed generator，a harmonic current loop based on a quasi-proportional resonant regulator and a complex-vector-decoupling fundamental current loop based on active resistor are respectively designed. The simulation and experimental results demonstrate that the proposed system model and control strategy can effectively improve the disturbance rejection capability and stability of the current loop，providing a theoretical basis and technical support for the engineering application of high-speed direct-drive pressure-differential power generation systems.

Cascaded H-bridge energy storage systems can achieve energy transfer and smooth fluctuations in renewable energy generation，making them a preferred solution to improving the large-scale utilization of renewable energy. However，they face challenges in adapting to complex grid environment such as grid distortion and weak grid. To address this limitation，a model-free adaptive control method for cascaded H-bridge energy storage converters is proposed to improve their adaptability to complex power grids. First，an enhanced second-order generalized integrator is designed in the power control loop to accurately extract the fundamental component of grid voltage，thereby effectively mitigating harmonic interference. Then，a current data-driven model based on dynamic linearization theory is developed in the current control loop to reduce the dependence on model parameters and improve the robustness and dynamics performance of the converter under model mismatch. Finally，a simulation model of 10 kV/3 MW high-voltage high-capacity energy storage system and a hardware-in-the-loop experimental platform are established to validate the effectiveness and feasibility of the proposed method.

To address the limitations of conventional fault diagnosis and evaluation techniques for converter transformers under conditions of unclear fault mechanisms and insufficient fault data，an interpretable fault diagnosis method for converter transformers based on knowledge reasoning is proposed. First，grounded theory is employed to establish a fault characteristic framework，thereby reconstructing textual information from historical fault cases into structured data. Subsequently，grey relational analysis is utilized to optimize the selection of key fault characteristics of converter transformers，and a causal feature separation-based Apriori algorithm is applied to extract fault diagnosis knowledge rules. A knowledge graph is constructed to enhance the interpretability of the fault diagnosis mechanism and process of converter transformers. Finally，the effectiveness of the method is validated using actual converter transformer fault data. Validate results demonstrate that the method achieves a diagnostic accuracy of 78.26%，providing accurate，reliable，and interpretable decision support for converter transformer fault diagnosis and condition assessment. This approach holds significant practical value for guiding field testing and maintenance of converter transformers.

To meet the demand for high-precision monitoring of medium distribution in the construction of collaborative energy transmission corridors of new power systems，a complex-valued multi-frequency electrical capacitance tomography based on an end-to-end neural network is proposed as an image reconstruction method. First，a new background prediction network is built to predict the background distribution of the medium to provide prior constraints for preliminary imaging. Then，an image reconstruction enhancement network is utilized to classify the preliminary reconstructed images at the pixel level，so that each pixel corresponds to a medium type，accurately reflecting the media distribution and its characteristics in the pipeline，and realizing high-precision image reconstruction. Finally，the structural similarity index is used to quantitatively assess the imaging quality，and the proposed method is compared with typical algorithms for verification. The simulation results show that structural similarity between the reconstructed images enhanced by the neural network and the target images is all higher than 0.85，and high-precision medium distribution images can still be obtained in the presence of noise interference and frequency changes. The proposed method provides a new technical approach for high-precision imaging of medium distribution under high conductivity.

Scientific figures are essential vehicles for the visual communication of research findings in scientific papers，and their quality directly affects the readability，dissemination，and international visibility of research outcomes. Authors of Chinese scientific papers often focus primarily on algorithmic innovation and experimental research，while the scientific presentation of figures tends to receive insufficient attention. Common issues include the selection of figures，self-contained presentation，formatting standards，color schemes and axis design，as well as originality and copyright compliance. This paper，drawing on figure editing practices adopted by leading international journals，and incorporating the practical experience of pre-acceptance figure-polishing sessions for the special columns of Journal of Shanghai Jiao Tong University，summarizes the general design principles for figures in papers submitted to the column of Power Systems，reviews common figure-related issues in power engineering manuscripts，and presents a fully developed case study based on an authorized engineering manuscript. The case study systematically demonstrates problem diagnosis，optimization strategies，and a side-by-side comparison of the figures before and after revision，providing practical guidance on figure improvement for authors，reviewers，and editors of scientific journals in China.