Table of Content

25 October 2026, Volume 46 Issue 5

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Source?Grid Coordination and Energy Conversion and Utilization

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Overview of Quantification and Aggregation Methods for Demand-Side Resource Adjustable Capacity in New-Type Power Systems

TONG Yuxuan, GUO Zhengyang, SONG Changhao

2026, 46(5):  1-9.  doi:10.3969/j.issn.1008-0198.2026.05.001

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The new power system imposes higher requirements on grid flexibility, and the large-scale aggregation of distributed resources on the demand side has become an important way to enhance regulation capabilities. However, the small capacity, dispersion, and heterogeneity of individual resources present challenges such as the curse of dimensionality, strong heterogeneity, computational complexity, and multiple uncertainties in aggregation. This paper systematically reviews methods for quantifying and aggregating the adjustable capacity of demand-side resources. First, the concept of flexibility feasible region and the mathematical modeling characteristics of typical resources are introduced. Second, it analyzes the mathematical essence of aggregation and its NP-hard computational bottleneck, followed by a classification and comparison of outer and inner approximation methods, examining their trade-offs among accuracy, efficiency, and feasibility. Then, the key issues including uncertainty, network constraints, and dimensionality reduction redundancy handling are discussed. Finally, this paper provides an outlook on research directions such as high-precision inner approximation methods, data-physics hybrid-driven frameworks, robust aggregation machanisms, and customized solutions for emerging resource types.

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Research on the Influence of Large-scale Offshore Wind Farm Grid Integration on System Power Quality

PAN Meifeng, CHENG Tianle

2026, 46(5):  10-15.  doi:10.3969/j.issn.1008-0198.2026.05.002

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The grid integration of large-scale offshore wind farms exerts significant adverse impacts on power systems in terms of voltage deviation, voltage fluctuation, flicker, and harmonics. Taking Hainan D Offshore Wind Farm as the research object, this paper analyzes the power compensation capacity of static var generator (SVG), as well as the corresponding limit values of relevant power quality indicators at the 220 kV point of common coupling. It also examines the voltage deviation and fluctuation and flicker values caused by switching operations in the 0% to 100% output range under four typical operating conditions of the wind farm. The impact of 2nd to 25th harmonic current and voltage distortion rates on the system is simulated and analyzed. The results indicate that all indicators of D Offshore Wind Farm comply with national standard limits, and the grid connection scheme is technically feasible, and the obtained results can provide reliable engineering references for the power quality assessment of similar large-scale offshore wind power grid integration projects.

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Analysis of Voltage and Frequency Characteristics of Small Hydropower Islanded Grid Considering Governor and Excitation Regulation Characteristics

TAN Liming, OUYANG Fan, LI Zhenwen, LIU Yonggang, XIAO Fan, OUYANG Zongshuai

2026, 46(5):  16-23.  doi:10.3969/j.issn.1008-0198.2026.05.003

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Existing studies mostly treat voltage and frequency as decoupled independent variables, neglecting the voltage-frequency coupling effect caused by constant-impedance loads and frequency-dependent synchronous reactance. This paper establishes a coupled voltage-frequency steady-state analytical model considering governor droop characteristics and excitation saturation constraints, derives simultaneous voltage-frequency equations for the islanded grid, and reveals the impact mechanism of excitation saturation on steady-state operating points. A simulation model of a 10 kV neutral ungrounded distribution network incorporating small hydroelectric generating units is established, and the correctness of the coupled model has been verified under load levels ranging from 50% to 150%. Simulation results show that the excitation system rapidly saturates at its ceiling after islanding, fully exposing the voltage-frequency coupling effect. The voltage-dependent characteristic of constant-impedance loads cannot compensate for the power deficit exceeding unit capacity under heavy-load operating conditions.

Power Grid Operation and Control

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Estimation Method of Inertial Time Constant in Power System Based on Improved Long Short-Term Memory Network Model

ZHENG Jin, ZHANG Dengji, LI Yibo, LIAN Chenxi, YANG Ling

2026, 46(5):  24-32.  doi:10.3969/j.issn.1008-0198.2026.05.004

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With the continuous integration of renewable energy, the inertia time constant of the power system has decreased, and accurate estimation of the inertia time constant is becoming increasingly important for the stable and safe operation of the power grid. This article proposes a method for estimating the inertia time constant of power systems by combining the carnivorous plant algorithm (CPA) and long short-term memory network (LSTM) model. The CPA is used to improve the LSTM model and achieve automatic tuning of model hyperparameters, resulting in higher optimization accuracy and stronger global search capability. Finally, the standard IEEE 10-machine 39-bus test system is estabished for verification. Compared with four conventional LSTM-based model inertia time constant estimation methods, the method proposed in this paper has higher estimation accuracy and faster convergence speed.

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Application Research on C₄F₇Nbased Ternary Environmentally Friendly Insulating Gas under the Dual Carbon Goals

ZHANG Yuzhao, DING Weidong, WU Yuewei, WU Wei

2026, 46(5):  33-39.  doi:10.3969/j.issn.1008-0198.2026.05.005

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To address the limitations of perfluoroisobutyronitrile (C₄F₇N)/carbon dioxide (CO₂) binary gas mixtures in terms of low-temperature adaptability and global warming potential (GWP) optimization, 2,3,3,3-tetrafluoropropene (HFO-1234yf)-which features low GWP and low liquefaction temperature-is introduced as a third component to construct a C₄F₇N/HFO-1234yf/CO₂ ternary gas mixture. The liquefaction temperature of the mixture is calculated by combining the Antoine equation with Dalton’s law of partial pressures, and the relative global warming potential (R-GWP) index is introduced to comprehensively evaluate the greenhouse effect of the mixture. Experiments are conducted on the power-frequency insulation performance of the ternary gas mixture. The results show that the GWP of the ternary mixture decreases with increasing HFO-1234yf content; the GWP of 10% C₄F₇N/20% HFO-1234yf/70% CO₂ is approximately 0.8 times that of 10% C₄F₇N/90% CO₂, and its R-GWP at 0.5 MPa is only 1.2% that of SF₆. Under the constraints of a total filling pressure of 0.6 MPa and a minimum liquefaction temperature of -30 ℃, the maximum allowable molar fractions of C₄F₇N and HFO-1234yf are 6.61% and 20.79%, respectively, indicating that the addition of HFO-1234yf has little effect on the liquefaction temperature. At 0.6 MPa, the power-frequency breakdown voltage of 5% C₄F₇N/9% HFO-1234yf/86% CO₂ is 1.36 times that of the binary mixture with the same C₄F₇N proportion, and at 0.8 MPa it reaches the level of SF₆ at 0.6 MPa. These findings demonstrate that the proposed C₄F₇N/HFO-1234yf/CO₂ ternary gas mixture can effectively reduce GWP and improve power-frequency breakdown characteristics while maintaining liquefaction properties comparable to those of C₄F₇N/CO₂, thereby providing a theoretical basis and data support for the engineering application of environmentally friendly insulating gases in power equipment.

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A Distributed Collaborative Operation for Distribution Networks Considering Data Lightweighting and Privacy Protection

GUO Ziran, XIAO Xiaolong, LU Xiaoxing, LÜ Shukang, XIE Wenqiang, ZHENG Xian

2026, 46(5):  40-49.  doi:10.3969/j.issn.1008-0198.2026.05.006

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To address limited communication bandwidth and information eavesdropping risks in distribution network coordinated operation, this paper proposes a lightweight secure quantization-based transmission method with dynamic step sizes. Based on the cyber-physical coupled distribution network architecture, a multi-resource coordinated optimization model is established. A dynamic stochastic quantizer with error feedback is designed to reduce data volume via quantization and ensure transmission privacy through its inherent stochasticity. To mitigate steady-state oscillations induced by quantization deviation, a truncation error compensation mechanism is introduced, and a distributed coordinated algorithm directly processing quantized information is developed. Case studies conducted on a practical regional distribution network in Jiangsu Province demonstrate that the algorithm reduces bandwidth consumption and enhances privacy while guaranteeing convergence to optimal results, enabling reliable distributed coordination under bandwidth constraints, eavesdropping attacks, and intermittent communication packet loss scenarios.

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Interval Assessment Method for Voltage Sag in Industrial Parks Considering Tolerance Uncertainty

ZHAO Peng, HE Zhiqiang, LI Yanping

2026, 46(5):  50-57.  doi:10.3969/j.issn.1008-0198.2026.05.007

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To address the issue that existing assessment methods fail to consider the differences and uncertainties in the tolerance capabilities of sensitive equipment within industrial parks, this paper proposes a novel interval assessment method for voltage sag severity by integrating affine arithmetic and game theory-combined weighting. The affine algorithm is introduced to handle the uncertainty of equipment tolerance parameters, aiming to overcome the “interval explosion” problem inherent in traditional interval operations, thereby quantifying the impact of voltage sags more precisely. First, the propagation mechanism of voltage sags is analyzed, and a sag tolerance factor model based on equipment tolerance characteristics is constructed. Second, interval number theory is introduced to characterize the uncertainty in equipment tolerance parameters. Affine arithmetic is employed to handle this uncertainty, mitigate the excessive conservatism of conventional interval operations, thereby ultimately establishing an interval assessment model for the comprehensive severity of voltage sags on the user side. Finally, the IEEE 30-bus system is used as a case study to simulate voltage sag propagation and equipment responses under different fault scenarios. Simulation results indicate that compared to traditional interval algorithms, the average width of the assessment intervals obtained by the proposed method is reduced by over 80%. This effectively suppresses the "interval explosion" phenomenon, significantly lowers the conservatism of the assessment results, and thus more accurately characterizes the uncertainty of equipment tolerance characteristics. This provides a reliable scientific basis for power quality management and sensitive equipment selection in industrial parks.

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Active-Reactive Power Bi-Level Coordinated Optimization of Active Distribution Networks Based on Improved BES and SOCP

ZHAO Difan, CHEN Xiuchun

2026, 46(5):  58-65.  doi:10.3969/j.issn.1008-0198.2026.05.008

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For the nonlinear optimization problem of coordinated scheduling of discrete devices and continuous variables in multi-source distribution networks, this paper develops a double-layer coordinated optimization scheme of active and reactive power for distribution networks, which combines the improved bald eagle search(BES) algorithm with second-order cone programming (SOCP). First, a multi-objective active and reactive power coordinated optimization model is established by comprehensively considering the costs of electricity purchasing, operation and maintenance, grid loss, and wind curtailment. Second, by introducing the Halton sequence and refractive opposition-based learning to improve the population initialization, and embedding the golden sine operator and dynamic spiral search mechanism in the dive stage, the global exploration ability and local convergence accuracy of the BES algorithm are enhanced. Finally, to address the difficulty in solving the coupling of discrete and continuous variables, a double-layer optimization architecture is established. The upper-layer improved BES conducts heuristic search for the discrete device states, and the lower-layer SOCP is nested to achieve the exact solution for the convex relaxation of continuous variables. Simulation results comprehensively verify the effectiveness and superiority of the proposed strategy.

Development and Application of New Energy

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Research on PI+MPC Control Strategy for Quadratic Boost Converter

YANG Enquan, GONG Bingkuan, ZANG Kun, CUI Chongyu, YANG Fan

2026, 46(5):  66-75.  doi:10.3969/j.issn.1008-0198.2026.05.009

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To address the issues of complex dynamic processes and insufficient robustness of the quadratic Boost converter under high-gain operating conditions, a composite control strategy combining an outer-loop PI controller with an inner-loop model predictive controller is proposed. Under the same main-circuit model and parameter settings, three control schemes, namely single-loop PI, dual-loop PI, and PI+MPC, are simulated and compared under different disturbance conditions. The results show that all three methods can achieve basic voltage regulation under no-disturbance conditions. Under input-voltage sag disturbance, the PI+MPC control can still recover to the reference value, whereas the minimum output voltage of the single-loop PI scheme drops to merely about 1.88 V and fails to re-establish stable output after recovery. Under strong-disturbance conditions, compared with dual-loop PI control, the proposed PI+MPC control shortens the recovery time by approximately 12.7%, reduces the voltage sag magnitude by 54.3%, and decreases the peak-to-peak value of local steady-state fluctuations by 11.3%. The results verify the effectiveness of the proposed PI+MPC composite control strategy in improving the dynamic response, disturbance rejection capability, and overall regulation performance of the quadratic Boost converter.

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Structural Optimization of Multi-Heat-Source Cold Plates Using Neural Network and Genetic Algorithm

ZHANG Yifei, ZHUANG Yubin, CUI Pan, LIU Zhichun

2026, 46(5):  76-84.  doi:10.3969/j.issn.1008-0198.2026.05.010

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For the structural optimization of multi-heat-source liquid-cooled plates, a multi-zone independent regulation and optimization method for straight rib cold plate flow channels is proposed, combining neural networks and genetic algorithms. Using the maximum temperature of heat sources and the inlet-outlet pressure difference as performance indicators, the limitations of conventional overall channel control are highlighted. Two optimization objectives are formulated and solved by combining via neural networks and genetic algorithms. The results show that minimizing the pressure difference reduces flow resistance by 47.4%, which is 34.9% lower than the result obtained via the conventional full-domain optimization. Under the comprehensive performance objective, flow resistance is reduced by 28% (12.2% lower than the overall optimization), while the temperature in critical regions decreases by 0.54 ℃, with all heat source temperatures maintained within reasonable ranges. This method provides a scalable framework for efficient design of multi-heat-source liquid-cooled cold plates, enabling flexible strategy formulation based on the weighting of thermal and hydraulic objectives, thereby effectively improving design efficiency and overall performance.

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Siting and Sizing Modeling and Optimization of Centralized Charging Facilities in Novel Rural Distribution Networks with Distributed Photovoltaics

ZHAO Miao, DUAN Xujin, XIANG Hong, XIE Jin’an, PENG Simin, LI Jinliang

2026, 46(5):  85-94.  doi:10.3969/j.issn.1008-0198.2026.05.011

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Facing the dual challenges of high penetration of distributed photovoltaic (PV) generation and surging electric vehicle (EV) charging demand in Hunan’s new-type rural distribution networks, this paper proposes a phased collaborative optimization model integrating siting, sizing, and operational verification, which explicitly accounts for voltage quality, network loss, PV accommodation, and reactive power optimization. The proposed model decouples planning decisions from operational simulations: the upper-level layer minimizes the total annual social cost, including operating costs, user time costs, and PV curtailment penalties; the lower-level layer performs reactive power verification for the planning schemes by minimizing network losses and voltage deviations. To achieve efficient solution under rural dispersed load scenarios, an integrated strategy combining dynamic partitioning with multi-objective normalization is adopted, and an improved particle swarm optimization algorithm is introduced for iterative optimization. A case study is conducted on a newly planned rural area in a county of Hunan Province. The optimization results show that when five charging stations are constructed, the total annual social cost reaches its minimum of 5.2945 million CNY. After the lower-level reactive power optimization, the system active power loss is reduced by 36.28%, the maximum voltage deviation decreases by 58.8%, and the local PV accommodation capacity is significantly enhanced.

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Virtual Inertia Model Predictive Control Method for Multi-port Energy Routing System

LIANG Weichen, LI Xuan, WANG Yajuan, YUAN Dengjia, WANG Jie, HUANG Wentao

2026, 46(5):  95-102.  doi:10.3969/j.issn.1008-0198.2026.05.012

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The multi-port energy routing system with a common DC bus is an effective approach for aggregating and regulating heterogeneous sources and loads. The stability of the DC bus voltage is critical for enabling the system to withstand renewable energy fluctuations and sudden load changes. To improve the DC voltage stability of the energy routing system under highly fluctuating source-load conditions, a virtual inertia optimization control strategy based on model predictive control (MPC) is proposed. A state-space model for the virtual inertia control system is established, incorporating dynamic voltage constraints and system evolution trends within the optimization horizon. Furthermore, a variable virtual inertia coefficient based on dynamic feedback correction of power and DC voltage deviations is proposed, enabling predictive regulation that suppresses DC voltage deviations while accelerating voltage recovery. A simulation model of the multi-port energy routing system is built in MATLAB/Simulink. Compared with traditional droop control and fixed virtual inertia control, the simulation results verify that the proposed method can effectively suppress sudden DC bus voltage variations, accelerate voltage recovery, and improve the dynamic response performance of the DC bus voltage under random port source-load power fluctuations. The proposed method can provides a valuable theoretical reference and technical support for flexible regulation of distribution networks and stable operation of substation DC systems under high-penetration renewable energy integration.

Artifical Intelligence and Digitization

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An Improved LSB Watermarking Method for Data Leakage Traceability in Power Grid Data Middle Platforms

ZHAO Wenxiao, ZHANG Shigeng, LUO Haokai, YU Qi, SUN Yizhen

2026, 46(5):  103-113.  doi:10.3969/j.issn.1008-0198.2026.05.013

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Aiming at the demand of data leakage traceability in the process of high-precision measurement data sharing in the power grid data middle platform, the traditional LSB database watermarking method is difficult to maintain data accuracy and watermark robustness at the same time. To solve this problem, this paper proposes a collaborative optimization LSB watermarking method with precision constraints for power grid data leakage traceability. Firstly, the embeddable bit layer is dynamically determined according to the allowable error of the service, and the watermark embedding distortion is controlled under the premise of meeting the service accuracy constraint. Secondly, in order to solve the problem of reduction of embedding capacity caused by accuracy constraints, the pseudo-random decentralized embedding and BCH error correction coding mechanism are combined to improve the watermark anti-deletion, anti-rearrangement and anti-format conversion capabilities. Finally, an auxiliary traceability mechanism based on virtual primary key is introduced to improve the positioning stability in complex data flow environment. Experiments conducted in the real environment of the power grid data middle platform show that, compared with the conventional LSB watermarking method, the proposed approach reduces the root mean square error change rate to 0.15%, maintains strong robustness across various attack scenarios, and achieves a minimum watermark bit error rate of 5.9%. The traceability accuracy reaches 100%, demonstrating good engineering applicability for power grid data sharing scenarios.

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Safety Assessment Method for Metal Structure of Power Construction Crawler Cranes Based on Combined Weighting and Variable Fuzzy Sets

ZOU Yongxing, YAO Zhenyu, JIANG Lei, XIE Zixuan, TIAN Chen

2026, 46(5):  114-120.  doi:10.3969/j.issn.1008-0198.2026.05.014

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To address the problem that traditional safety assessment methods for the metal structures of power construction crawler crane mostly rely on single weighting or simple weighting and are struggle to integrate both inspection data and expert experience, this paper proposes a metal structure safety assessment method based on combined weighting and variable fuzzy sets. Firstly, a multi-level evaluation index system including the crawler chassis, frame, boom, jib, hook, and slewing ring is established. Secondly, an interpolation scoring method is used to determine the indicator scores for strength, stiffness, cracks, deformation, corrosion, etc., and a membership degree matrix is constructed. Finally, the structural safety grade is determined by integrating subjective and objective weights and combining with variable fuzzy set theory. Taking a crawler crane as an example, the traditional single weighting method yields a grade characteristic value of approximately 2.24 and the proposed method yields a grade characteristic value of 1.61, which is about 28.1% lower than that obtained by the traditional method and more consistent with the on-site inspection findings. The results show that the proposed method can improve the accuracy of the metal structure safety assessment of crawler cranes, and can provide a reference for equipment inspection, maintenance, and risk management.

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AI Status Warning Technology for Transformer Oil Chromatography in Substation Centralized Monitoring System

LI Jingcheng, WANG Lijian, ZHANG Xia, LI Aowei

2026, 46(5):  121-126.  doi:10.3969/j.issn.1008-0198.2026.05.015

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This paper proposes a condition early warning and auxiliary decision-making method based on the substation centralized monitoring system. First, an AI early warning architecture for oil chromatography under the substation centralized monitoring system is designed. Second, the Isomap algorithm is applied to perform nonlinear dimensionality reduction on the collected oil chromatography fault sample dataset, and additive Gaussian noise is introduced for fault sample data augmentation to enhance the robustness of the AI early warning model. Then, the processed data are fed into the XGBoost algorithm for model training: the XGBoost algorithm identifies fault types, while the Guangming Electric Power large model provides semantic interpretation, cause analysis and corresponding operation and maintenance (O&M) suggestions. Finally, the proposed method is launched for pilot operation on a new generation of centralized control system of a certain power supply company. Experimental results show that the method achieves reliable fault identification performance, with a precision of 93.9%, a recall of 93.2%, and an F₁ score of 0.935. The F₁ score is 2.9, 17.2, 14.6, and 4.7 percentage points higher than that of XGBoost, SVM, KNN, and RF methods, respectively. Moreover, the semantic interpretation of Guangming Power's large model is clear, and the operation maintenance suggestions are feasible, which can be further promoted and applied in related fields.

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Protection Setting Verification Technology for Distribution Terminals Based on Knowledge Graph Semantic Matching and DQN Continual Learning

YANG Han, WEI Zihao, ZOU Minhao, CHEN Fang, LIU Chang

2026, 46(5):  127-134.  doi:10.3969/j.issn.1008-0198.2026.05.016

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To address the difficulty in automatic verification caused by inconsistent field naming in protection setting verification of distribution terminals, an intelligent verification method based on knowledge graph semantic matching and deep Q-network (DQN) continual learning is proposed. A protection setting knowledge graph is first constructed, in which setting sheet fields and terminal data fields are defined as standard entities and alias entities, respectively. Semantic similarity between fields is computed using a feature lexicon and the term frequency-inverse document frequency (TF-IDF) method combined with cosine similarity, enabling intelligent field matching. A DQN-based continual learning mechanism is further introduced, allowing the system to learn intrinsic associations among field features from manual review records and to provide reasonable recommendations for unseen yet feature-similar fields, while dynamically updating the knowledge graph. A graphical software tool is developed based on the proposed method. Finally, an experiment is conducted on-site for the verification of protection setting values of distribution terminals in a certain power supply company. The results demonstrate that the proposed technology achieves accurate semantic matching under both standard and variant data conditions, and the average verification time of the intelligent tool is significantly reduced compared to manual verification, greatly improving the efficiency of protection setting verification for distribution terminals.

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Fault Diagnosis of Axial-Flow Pump Acoustic Signals Based on DWT and Multi-Scale Convolutional Temporal Fusion Network

XIE Jiemin, HUANG Bo, TANG Chuanqi, ZHANG Hongqiang

2026, 46(5):  135-141.  doi:10.3969/j.issn.1008-0198.2026.05.017

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When dealing with low signal-to-noise ratio and non-stationary axial flow pump sound signals, traditional models exhibit limitations in terms of single receptive fields and poor time tracking ability. To address these issues, this paper proposes a fault diagnosis method combining discrete wavelet transform and a multi-scale convolutional temporal fusion network (DWT-MSFANet). First, the model introduces the Discrete Wavelet Transform to preprocess the original acoustic signals, effectively filtering out intense environmental background noise. Second, a parallel convolutional architecture with four scales of convolutional kernels is constructed to simultaneously capture high-frequency, microscopic transient mechanical impacts and low-frequency, macroscopic hydraulic pulsation characteristics within the fault signals. Subsequently, the cross-scale spatial features are fed into a Gated Recurrent Unit to further excavate the long-range temporal dependencies of fault evolution under continuous hydraulic excitation. Finally, a self-attention mechanism is employed to execute a weighted focus on critical fault frequency bands. Case analysis demonstrates that the proposed method consistently outperforms baseline models across all evaluation metrics.

Electric Power Prevention and Reduction

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Prediction of Aerodynamic Parameters of Iced Conductors Based on Wind Tunnel Test

ZHU Xuetong, QIU Gang, ZHANG Nailong, ZHOU Wenxuan, YIN Fanghui

2026, 46(5):  142-150.  doi:10.3969/j.issn.1008-0198.2026.05.018

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Aiming at the problems of high acquisition cost, insufficient coverage of working conditions, and difficulty in rapid prediction of aerodynamic parameters of iced quad-bundled conductors, a machine-learning-based prediction method using wind tunnel test data is proposed. Based on wind tunnel test data of iced quad-bundled conductors, bundle spacing, wind attack angle, wind speed, ice thickness, conductor diameter are selected as the original input parameters, while Reynolds number and relative ice thickness are introduced as physics-enhanced features. Prediction models for the drag coefficient and lift coefficient are established separately. Random forest, extremely randomized trees, gradient-boosted decision trees, and Gaussian process regression models are compared, and their performance is evaluated using the coefficient of determination, mean absolute error, root mean square error, and mean square error. The results show that the Gaussian process regression model achieves the best prediction performance, with testing-set coefficients of determination of 0.992 7 for the drag coefficient and 0.977 9 for the lift coefficient, outperforming the other models. The aerodynamic curve reconstruction results under typical working conditions indicate that the proposed model can effectively capture the overall trends and local fluctuations of the drag and lift coefficients as a function of wind attack angle. The proposed method can serve as a reference for rapid prediction of aerodynamic parameters, galloping analysis, and engineering risk assessment of iced quad-bundled conductors.

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Post-Disaster Resource Dispatch and Repair Optimization of Distribution Networks Considering Dynamic Traffic

DENG Qiangbin, LI Jiashun, CHEN Chun

2026, 46(5):  151-161.  doi:10.3969/j.issn.1008-0198.2026.05.019

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To address the difficulty in accurately characterizing emergency resource travel times caused by dynamically evolving traffic conditions under extreme disasters, which may adversely affect post-disaster distribution network restoration decisions, a coordinated optimization method for emergency resource dispatch and repair considering dynamic traffic is proposed. A cell transmission model is employed to characterize the spatiotemporal evolution of post-disaster traffic flow, while a traffic delay model is used to estimate time-varying travel times. The resulting travel-time information is further embedded into the coordinated scheduling model of mobile emergency power supplies and repair crews, thereby enabling joint optimization of traffic accessibility and the distribution network restoration process. Case studies demonstrate that the proposed method can effectively coordinate mobile emergency power supply and line repair while prioritizing the restoration of critical loads. Compared with the nearby repair plan, the time required to achieve 90% of the cumulative weighted recovery contribution decreases from 190.46 minutes to 109.29 minutes. During the recovery period, the node voltage can remain between 0.96 and 1.03 p.u., effectively improving the post-disaster recovery efficiency and the executability of the dispatch plan.

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Equivalent Parameters Calculation and Regulation Method for Harvesting Energy from Overhead Ground Wires with Tuning Considering Conductor De-icing Process

XU Jiahao, HUANG Xuan, WU Qihang, CHEN Kai, WU Sisheng, SHU Shengwen

2026, 46(5):  162-170.  doi:10.3969/j.issn.1008-0198.2026.05.020

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Conductor de-icing changes the spatial coupling between phase conductors and overhead ground wires, causing fluctuations in induced voltage and equivalent capacitance and potentially producing primary-side overvoltage in the energy-harvesting reactor. This paper proposes an equivalent-parameter calculation and active regulation method that accounts for the dynamic response of conductor de-icing. A fast calculation model is developed by representing conductor motion with its time-varying average height above ground. Finite-element simulations are then used to evaluate the effects of ice thickness and de-icing rate, and a self-excited magnetic-state regulation strategy is introduced. The results show that both factors significantly affect the equivalent parameters; under heavy icing, the time histories exhibit a distinct “double-peak-dip” pattern. By dynamically adjusting reactor inductance, the proposed strategy suppresses primary-side overvoltage while maintaining high harvested power. The transient average and steady-state powers are 801.02 W and 749.61 W, respectively. The method supports parameter calculation and safe operation of tuned overhead-ground-wire energy-harvesting systems under de-icing disturbances.