In response to the challenges where large-scale renewable energy integration leads to intricate source-network-load-storage elements and surging complexity in new power systems, rendering traditional balancing architectures and hierarchical analysis methods inadequate, theoretical achievements and research technologies regarding hierarchical and partitioned balance architectures are comprehensively reviewed. The adaptability requirements of new power systems for such architectures are elucidated, followed by a summary and comparative analysis of existing hierarchical control and partitioning strategies. Furthermore, layer-zone fusion mechanisms are explored, and existing technical limitations are analyzed from data and modeling perspectives. The results indicate that while renewable energy control pressures can be alleviated by existing strategies, deficiencies remain in handling massive heterogeneous data fusion and precise modeling of complex systems; moreover, high dynamic balance demands are difficult to be met by current layer-zone coordination mechanisms. Future hierarchical and partitioned balance architectures are identified as a critical direction for supporting the operation of new power systems. Notably, a novel technical pathway for achieving safe and efficient operation under the “carbon neutralization and carbon peaking” goals is offered by the introduction of large models and artificial intelligence technologies.
To effectively identify and eliminate abnormal data in the measured data of wind turbines, an anomaly detection algorithm based on manifold learning is proposed through the analysis of high-dimensional measured data from wind turbines. Firstly, the k-nearest neighbor mutual information algorithm is employed to select feature variables for the wind turbine. Subsequently, an optimized t-distributed stochastic neighbor embedding (t-SNE) algorithm is utilized. This optimized algorithm replaces the sample distance metric with a weighted sum of the Euclidean distance and the local principal component analysis (LPCA) difference, enabling the extraction of low-dimensional features with inherent patterns from the high-dimensional manifold data. This facilitates the distinct separation of data with different distribution characteristics in a visualized two-dimensional space. Furthermore, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is applied to cluster the data within this two-dimensional space. The results demonstrate that, compared to the principal component analysis (PCA) algorithm, locally linear embedding (LLE) algorithm, and the original t-SNE algorithm, the proposed method can effectively achieve visual separation and clustering for data under various complex operating conditions, successfully identifying and eliminating abnormal data.
The development of wind power generation is a critical measure for achieving the “dual carbon” goals. The safe and reliable operation of wind turbines is the foundation for ensuring their sustainable operation. As key fastening components of wind turbines, connection bolts are subjected to alternating loads and environmental corrosion over long periods, making them prone to loosening, fatigue fractures, and other safety risks that threaten the operational safety of the turbines. To address this issue, this study proposes a stress state detection sensor device for tower and blade root bolts based on magnetic field signals. On this basis, a dedicated experimental testing system is established to quantitatively investigate the correlation between magnetic memory signals and bolt stress. Experimental results demonstrate that stress-induced magnetic signals can be effectively detected on the bolt surface. Under tensile loading, the magnetic memory signals exhibit a clear linear response to stress variations, enabling reliable stress state monitoring through magnetic measurements. Furthermore, the influence of bolt material on the relationship between stress and magnetic signal variation is relatively minor. In contrast, significant differences are observed in the slopes of the magnetic signal–stress curves for bolts of different strength grades, indicating the necessity of prior calibration. The test analysis in this paper can provide the necessary experimental foundation and reference data for engineering applications such as remote monitoring and early diagnosis of wind turbine bolt conditions.
In response to the challenge of early warning for abnormal oil sump temperatures in wind turbine gearboxes, a fault warning method based on supervisory control and data acquisition (SCADA) data is proposed to enhance the operational reliability of the turbines. Firstly, by integrating wind speed-power distribution characteristics, an outlier detection approach utilizing the interquartile range and longitudinal filtering based on data dispersion is employed to eliminate anomalous power points. Subsequently, key input features influencing oil sump temperature are identified using a random forest algorithm, leading to the development of a temperature prediction model based on categorical boosting (CatBoost). The hyperparameters of this model are optimized using tree-structured parzen estimator (TPE). Finally, dynamic warning thresholds are established through statistical process control based on residual distributions. In a practical case study from a specific wind farm, this model issued effective warnings approximately 5 hours prior to gearbox failure; notably, the time points at which residuals exceeded control limits closely aligned with the progression of faults.The proposed method demonstrates significant efficacy in identifying abnormal conditions related to oil sump temperatures and possesses strong early warning capabilities along with substantial engineering application value.
Renewable energy sources such as wind and solar power exhibit intermittency and volatility due to weather conditions, which can compromise the reliable operation of multi-energy complementary systems. Hydrogen energy, as a high-quality secondary energy source, offers advantages of being green, pollution-free, and possessing high energy density. To address the uncertainty in new energy output, this paper constructs a multi-energy complementary cogeneration system model. This system integrates a thermal power unit, wind turbines, photovoltaic generators, an electric boiler, and a hydrogen storage system, incorporating waste heat recovery to enhance system flexibility and energy utilization efficiency. Based on this, an optimization scheduling model is established with the dual objectives of minimizing total operating costs and reducing carbon emissions. For this model, an improved multi-objective simulated annealing particle swarm optimization algorithm is proposed, effectively accelerating convergence and preventing local optima. Simulation analysis using a case study from a region in Shandong province demonstrates that the proposed method reduces the system’s total operating costs by an average of 12.51% and carbon emissions by 5.53%, validating the feasibility and superiority of the developed model and algorithm.
The construction of new power system requires hybrid microgrids (HMG) to have inertia support capability, the application of virtual synchronous generator (VSG) technology makes the sub-grids present different inertia characteristics, and the interlinking converter (ILC) makes the electrical characteristics of AC and DC buses coupled with each other during load fluctuation, resulting in ILC transmission power oscillation, which affects the dynamic stability of the system, therefore, the ILC transient power compensation is proposed to take into account the difference in inertia of sub-grids. During load fluctuation, the ILC couples the electrical characteristics of the AC and DC buses, which leads to the oscillation of the ILC transmission power and affects the dynamic stability of the system, therefore, the transient power compensation control of the ILC that takes into account the inertia difference of the sub-networks is proposed. A normalized equivalent VSG model of the AC-DC subnetwork is established to evaluate the equivalent inertia level of the subnetwork. The subnetwork equivalent inertia is further introduced into the transient power compensation controller to inject transient compensation power into the ILC to suppress the oscillation of the transmitted power and reduce the risk of overrun of the bus electrical quantity change rate. A simulation model is built based on the Matlab/Simulink platform, and the effectiveness of the proposed control strategy is verified under various operating conditions. Compared with the existing control strategy, the proposed control strategy can realize the overall power coordination and load sharing of the system, the drop of the AC/DC bus electric quantity change rate is mitigated, the ILC transmission power is smoother, and the dynamic performance of the system is improved.
Extracting the latent value embedded in electricity load data constitutes one of the key challenges in the power industry. To address the difficulty faced by conventional clustering approaches in capturing the intrinsic features of high-dimensional load data, this paper proposes an optimized clustering method based on a one-dimensional convolutional autoencoder (1D-CAE). First, a 1D-CAE is employed to extract temporal features from daily customer load profiles through nonlinear dimensionality reduction, with the objective of minimizing reconstruction loss. Second, we introduce an improved Cayley orthogonal constraint to enhance the structural information of the clustering space, thereby optimizing the mapping of latent features and improving clustering stability. Third, a generative adversarial network (GAN) is integrated with K-means clustering to refine the cluster centers and fine-tune the encoder. Finally, the effectiveness of the proposed method is evaluated on real-world load datasets using three widely accepted internal validation metrics: the Davies–Bouldin index (DBI), the Calinski–Harabasz index (CHI), and the silhouette coefficient (SC). Experimental results demonstrate that the proposed approach significantly enhances both inter-cluster separability and intra-cluster compactness. The study confirms that the method can effectively identify and extract morphological characteristics of diverse load profiles, offering robust support for demand response and optimal dispatch in virtual power plants.
Against the risk of frequency instability arising from reduced system inertia due to the integration of high-proportion new energy into the power grid, this paper proposes a power system safety forewarning and auxiliary decision-making method considering minimum inertia constraints. Firstly, a dual-constraint critical inertia evaluation model is established, which calculates the critical inertia by integrating the rate of change of frequency constraint and the minimum frequency constraint, thereby improving the accuracy of the inertia safety boundary. Secondly, an equivalent inertia calculation framework for the source-grid-load-storage system is constructed to accurately calculate the total system inertia level and quantify the specific contributions of virtual inertia from conventional units and new energy sources, load inertia, and dynamic inertia from energy storage. Once the actual system inertia falls below the critical inertia threshold, a forewarning is activated and the inertia deficit is quantified; meanwhile, to minimize the system operating cost, a multi-resource optimal dispatch model incorporating inertia security constraints is developed. By coordinately adjusting the output of conventional units, the frequency regulation strategies of new energy sources, and the charging-discharging strategies of energy storage, the proposed method achieves inertia safety forewarning and auxiliary decision-making for power systems. Simulation results demonstrate that: the proposed dual-constraint inertia safety model effectively avoids the risk of missed judgment inherent in traditional single-constraint models; the proposed forewarning mechanism enables advance identification of inertia shortages and quantifies the deficit; the proposed auxiliary decision-making scheme significantly reduces system operating costs while ensuring frequency security.
The increasing penetration of renewable energy and growing electricity demand in islanded microgrids have intensified the uncertainties on both the generation and load sides, posing severe challenges to their secure, stable, and economic operation. Traditional robust optimization methods, which over-emphasize extreme system conditions, often compromise operational economy. This paper employs fuzzy theory to generate stochastic optimization scenarios for the system. Based on the probability of scenario occurrence and the minimum hybrid energy storage system capacity required for each scenario, a scenario reduction process is conducted. A stochastic optimization-based dispatch method for islanded microgrids is proposed. The method involves establishing uncertainty models for renewable energy and load to generate stochastic scenarios, formulating a mathematical model, performing demand response dispatch under each scenario, and finally filtering out extreme scenarios. Based on the proposed method, experimental verification is carried out in an island microgrid case. The proposed method reduces the system operating cost by 20.17% compared to the traditional robust optimization approach. The results verify the effectiveness and superiority of the proposed method.
With the gradual complexity of the active power-frequency coupling characteristics of the new power system, the traditional single energy storage grid-connected frequency regulation strategy has brought huge pressure to the primary frequency regulation of the power system. The participation mode of multi-type energy storage in primary frequency regulation and the frequency characteristics of the power grid under the coordinated control of energy storage need to be studied urgently. This paper studies the frequency response mechanism of electrochemical energy storage based on droop control and flywheel energy storage based on virtual synchronous machine control respectively. When multi-type energy storage participates in the primary frequency regulation of the power system, the low-pass filter link is used to process the frequency change rate signal to achieve the coordinated control effect of energy storage. Then, the coordinated control model of multi-type energy storage is combined with the power system containing new energy and traditional thermal power, and the frequency response model of the power system is established. The model is used to quantitatively analyze the influence of energy storage-related parameters on the system frequency change rate and steady-state frequency deviation, and the parameter sensitivity analysis is carried out. Finally, the model is built on Matlab/ Simulink to verify the influence of multi-type energy storage-related frequency regulation parameters on the system frequency characteristics. The research proves that considering the coordinated control of multi-type energy storage in the frequency regulation unit of the new power system can improve the frequency stability characteristics of the power system.
Aiming at the problem of sustainable supply of energy resources in traditional islands, a multi-time scale optimal operation method of independent island zero-carbon microgrid is proposed to meet the requirements of stability, flexibility and economy of independent island microgrid. Firstly, according to the actual situation of island energy consumption, the wind-solar-storage-hydrogen-water system model of independent island zero-carbon microgrid is constructed. Secondly, considering that hydrogen energy storage has the ability of long-term energy storage, a multi-time scale optimization model spanning week-ahead, day-ahead, and intra-day is established, and a multi-time scale operation scheduling strategy considering wind and solar uncertainty is proposed. In the week-ahead stage, the trend of wind and solar resources is predicted based on historical data, and the start-stop plan of hydrogen energy storage is formulated. In the day-ahead stage, the stochastic optimization method based on multi-scenario technology is used to deal with the uncertainty of wind and solar energy and formulate the start-stop plan of seawater desalination unit. In the intra-day stage, the operation status of the hybrid energy storage and seawater desalination system is dynamically adjusted in combination with real-time wind and solar data. Finally, the simulation results show that the proposed method can better utilize the characteristics of hydrogen energy storage for a long time to store energy and promote the system’s wind and solar consumption. When the wind and solar resources are insufficient, it can effectively reduce the load loss and improve the reliability of the independent island zero-carbon microgrid system.
Against the backdrop of global energy transition and the rapid development of the new energy vehicle industry, as critical refueling infrastructure, the optimal layout of battery swap stations is essential for enhancing both service efficiency and power infrastructure effectiveness. This study focuses on the operational passenger vehicle battery swap market in City B. Operational data of battery-swap taxis are obtained through market research. A hybrid queueing model is introduced to establish a saturation prediction model based on dynamic dilution effects. Additionally, a fusion algorithm coupling the Voronoi diagram with the particle swarm optimization algorithm is proposed. Based on the aforementioned methods, a “prediction-optimization-layout” collaborative planning framework is constructed, quantifying policy sensitivity and supply-demand interactions. The reliability of the prediction of 23 theoretically new battery swap stations by 2025 is further verified through Monte Carlo simulation. Through the coordinated allocation of battery swap stations and charging guns (65 stations + 4 charging guns), the average user waiting time is controlled within 10 min, and the actual station construction demand is optimized to 13 stations. By integrating dynamic spatial partitioning with global optimization, the challenge of site optimization in high-density urban areas is addressed. The research findings provide an implementable solution for battery swap network planning that balances service efficiency and investment costs and also offer valuable insights for optimizing distributed power infrastructure.
TSINGHUA JOURNALS HOME
