Table of Content

28 June 2025, Volume 58 Issue 6

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A Novel Low-Carbon and High-Performance Distribution System Powered by Artificial Intelligence

Coordinated Optimization Method for Virtual Power Plants and Distribution Networks Considering Distributed Energy Storage and Photovoltaics

LIU Junhui, GONG Jian, TONG Bingshen, LI Songjie, ZHANG Yihan, CUI Shichang, TIAN Chunzheng, ZHANG Yongbin

2025, 58(6):  1-9.  DOI: 10.11930/j.issn.1004-9649.202407022

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With the continuous increase in photovoltaic penetration and user electricity consumption, serious upper and lower voltage limit violations often occur at partial nodes of distribution networks due to significant power imbalances between the source side and the user side, which not only increases the power and energy loss of the grid, but also affects the normal electricity consumption of users. This study developed a coordinated dispatch models between virtual power plants (VPPs) and distribution networks from the perspective of supply-demand balance in power distribution systems. By leveraging the capability of VPPs to aggregate diverse distributed energy resources (DERs), the models enable VPPs to actively participate in distribution market operations through the provision of active and reactive power support. This approach effectively mitigates both the upper and lower voltage limit violations, thereby enhancing power quality and the security/reliability of the distribution systems, while simultaneously reducing the total operational costs of the grid. Simulation results demonstrate that the coordinated dispatch models developed in this paper effectively alleviate the upper and lower voltage limit violations in distribution networks by utilizing the flexibility of VPPs, reduce the total operating cost and enhance the overall safety and stability performance of the distribution networks.

Short-Term Power Load Forecasting Based on MSCNN-BiGRU-Attention

LI Ke, PAN Tinglong, XU Dezhi

2025, 58(6):  10-18.  DOI: 10.11930/j.issn.1004-9649.202406098

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To address the problem of difficult extraction of key features in power load, a multi-scale convolutional neural network-bi-directional gated recurrent unit-Attention (MSCNN-BiGRU-Attention) hybrid model is proposed for short-term power load forecasting. Firstly, the Spearman correlation coefficient was used to analyze the correlation of power load data set, and the features with high correlation were screened out to build the power load data set. Secondly, the data was input into the multi-scale convolutional neural network (MSCNN) to extract the multi-scale time sequence of power load data. Then, the extracted temporal features were input into the bidirectional gated recurrent unit (BiGRU) neural network for temporal prediction, and the temporal features were filtered and screened by attention mechanism. Finally, the outputs are integrated through a fully connected layer to generate the predicted values. With the 3 years of multidimensional power load data from a region in Australia as a data set, five control models were established. Meanwhile, we selected two years of multidimensional power load data from a region in southern China as the validation dataset for the models. The results show that MSCNN-BiGRU-Attention hybrid model can achieve better prediction effects than other models, thus effectively solving the problem of difficult extraction of the key features of regional power load.

Short-term Load Forecasting Based on a Combined ICEEMDAN-PE and IDBO-Informer Model

YU Duo, CAO Yi, WANG Hairong, ZHAO Aodong, CAO Qian

2025, 58(6):  19-32.  DOI: 10.11930/j.issn.1004-9649.202410086

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To address the problems of insufficient noise processing, limited feature extraction ability and complex model training when using traditional methods to deal with complex load data, an innovative forecasting model based on a combined ICEEMDAN-PE and IDBO-Informer is proposed. Firstly, the raw load data were preprocessed using wavelet soft-threshold denoising algorithm to reduce noise interference. Secondly, ICEEMDAN was used for multi-scale decomposition of load data to precisely characterize load features, and the permutation entropy was used to evaluate the component complexity. Finally, an improved Dung Beetle Optimizer (IDBO) was proposed by synergistically integrating chaotic and opposition-based learning strategies for population initialization, incorporating adaptive step size, convex lens opposition imaging, and stochastic differential mutation strategies. This approach optimizes hyperparameters of the Informer forecasting model, significantly enhancing computational efficiency and prediction accuracy. The experimental results show that the model performs well in short-term load forecasting, with MAE of 81.3 MW (the original load data range is about 500 MW to 1500 MW), RMSE of 109.2 MW and R² score of 0.991, which is much better than the traditional method, and fully verifies the innovation and superiority of the model.

Probability Evaluation Method of Distributed Photovoltaic Carrying Capacity under Risk Suppression in New Power System

WANG Fangmin, XU Jiayu, SU Ning, NIU Huanna, YUAN Jiaxing, MEN Panlong

2025, 58(6):  33-44.  DOI: 10.11930/j.issn.1004-9649.202407042

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In order to suppress the safety and stable operation risks brought by the disorderly integration of distributed photovoltaics in the context of the new power system, a hierarchical probability evaluation method based on historical scene statistics is proposed. This method establishes a grading probability evaluation model of PV bearing capacity based on reverse load ratio and a safety check probability evaluation model. The grading probability evaluation process of PV bearing capacity weaknesses based on historical scene statistics is given. By constructing a PV bearing capacity evaluation model based on percentile statistics, a hierarchical probability evaluation method for the capacity of distributed PV access distribution network is finally formed. The effectiveness and universality of the proposed method are verified by the improved guideline example and the actual distribution network case. Experiments show that the method can scientifically show the new PV capacity of each power supply area under different percentiles, and the identified PV capacity weakness is more in line with statistical significance.

Data-Driven Analysis and Control of Power System Security and Stability

A Demand Side Adjustment Capacity Sharing Model Based on Cooperative Game

ZHANG Jie, HUA Yufei, WANG Chen

2025, 58(6):  45-55.  DOI: 10.11930/j.issn.1004-9649.202406076

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Under the background of demand response (DR) program, if load aggregators (LAs) are allowed to share the demand side adjustment capability, specifically, those with higher unit adjustment cost can purchase the adjustment services from those with lower unit cost, then those with lower costs can increase the revenue from sharing services, and those with higher costs can reduce the adjustment costs, thus achieving a win-win situation. Therefore, a demand side adjustment capability sharing mode is proposed. Firstly, the demand side adjustment cost of all LAs are evaluated, including the load adjustment cost and the energy storage system (ESS) adjustment cost. Then, a non-cooperative DR optimization model is established to estimate the optimal response, DR reward revenue and potential adjustment capability. Since the essence of sharing is to redistribute the adjustment cost, a demand side adjustment cost optimization model based on cooperative game is established to solve the demand side adjustment costs and responses of each LA after sharing, and calculate the settlement costs among LAs. The case study shows that the profits of LAs from DR program have been improved after participating in the sharing market.

An Electricity Market Trading Model for Distributed Resource Aggregators Considering Risk Management

ZHAI Zhe, CHEN Ziyu, LIU Qixing, LIANG Yanjie, LI Zhiyong

2025, 58(6):  56-66, 155.  DOI: 10.11930/j.issn.1004-9649.202411001

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In the context of new power system, the large-scale integration of distributed energy resources has led to the emergence of distributed energy resource aggregators as new entities in the electricity market. However, market transactions are subject to various uncertainties, such as clearing prices and the output of wind and solar power sources. Therefore, it is necessary to propose an electricity market trading model for distributed resource aggregators that considers risk management, providing trading strategies that balance risk and return for aggregators. Firstly, the market organizational structure was analyzed. Secondly, the risk losses caused by uncertainties were quantified using Conditional Value at Risk (CVaR). A bidding model that considers the uncertainty of clearing prices and a scheduling decision-making model that accounts for the uncertainty of wind and solar power output were proposed, forming a trading strategy for distributed energy resource aggregators to participate in the energy-reserve auxiliary services market. Thirdly, a joint clearing model for the energy-reserve auxiliary services market was constructed. Finally, using actual operational data from the energy and reserve auxiliary services market in a certain region as an example, the proposed electricity market trading model for distributed energy resource aggregators was applied to the bidding and clearing processes of that market. The results show that the proposed method can guide distributed energy resource aggregators in making rational quantity bids and offers, thereby increasing their market participation profits.

Output Power Optimization of Photovoltaic and Energy Storage Hybrid System Based on Fuzzy Control Algorithm

XIAO Xiangqi, ZOU Sheng, HE Xing, XIAO Jianhong, MA Bin

2025, 58(6):  67-75.  DOI: 10.11930/j.issn.1004-9649.202406062

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Aiming at the power allocation problem of multiple energy storage converters in existing photovoltaic and energy storage hybrid systems, an optimized output power strategy for photovoltaic and energy storage hybrid systems based on a fuzzy control algorithm is proposed. This strategy formulates corresponding fuzzy sets and membership functions based on the relationship between the transmission power of energy storage converters and the health characteristics of batteries. Using fuzzy control algorithm, when the temperature of the energy storage unit is lower than or higher than the preset warning temperature, the strategy combines the aging law and the health factor coefficient of the energy storage unit to optimize power distribution. This effectively reduces energy losses in the energy storage units and extends battery life. Simulation results show that, compared with traditional control strategies, the proposed strategy can slow down the aging rate of batteries by 19.1% under the same energy storage capacity consumption, and effectively mitigate the rapid temperature rise and energy loss of energy storage units. Overall, it enhances the safety and economy of the photovoltaic and energy storage hybrid system.

FMM Testing and Improved SVM Diagnostic Verification Technology for Intelligent Substation Protection

LV Pengfei, QIU Yutao, JIN Sheng, WANG Zhihua

2025, 58(6):  76-82.  DOI: 10.11930/j.issn.1004-9649.202408049

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To address the problems of complex testing, long construction period, and insufficient diagnostic testing results for numerous secondary devices in smart substations, this paper introduces the forward maximum matching (FMM) algorithm to achieve automatic protection testing, and improves the support vector machines (SVM) model based on the frog leaping algorithm (FLA) to complete the verification and diagnosis of test results. For the protection configuration and information relationship of intelligent stations, propose an architecture for protecting automatic testing and diagnosis systems; By analyzing different protection principles and the relationship between applied voltage/current and setpoints, establish protection testing requirements, construct a set of testing templates, compare similarity using hash and edit distance algorithms, and FMM algorithm was used to achieve automatic testing of the device under test. The effectiveness of the proposed technology was verified through case analysis and engineering examples.

The Communication Mode of The Master Station Based on COA Algorithm to Optimize PDU and TPDU Parameters of Large Data Transmission

YANG Bin, ZHEN Jialin, LIU Wei, XIONG Wei

2025, 58(6):  83-89.  DOI: 10.11930/j.issn.1004-9649.202408057

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Aiming at the important parameters of protocol data unit (PDU) and transport protocol data unit (TPDU) in the process of fault and big data transmission, this paper proposes a communication mode of the main substation with guaranteed confidence based on the optimization technique of coati optimization algorithm (COA). Based on network architecture of the guarantee system and its PDU and TPDU parameters, this paper establishes a maximum flow communication model considering the constraints of recording communication duration and instantaneous flow, and COA algorithm is introduced to optimize the parameters, so as to realize the optimal bandwidth configuration under this background. The test of multi-category substation communication services is carried out and the stress test of communication network under extreme conditions is considered. The proposed method can effectively improve the information transmission capability and reliability of the substation.

A New Generation Efficient Testing Method for Substation Protection Based on Cloud-based DDPG Optimization and Collaboration

YE Yuanbo, WANG Jiwen, SUN Zhenxing, ZHOU Kun, MAO Yurong

2025, 58(6):  90-96.  DOI: 10.11930/j.issn.1004-9649.202408072

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Aiming at the new generation of substation, a protection efficiency testing method is proposed. Based on the new generation of substation system, the architecture of protection system based on cloud data optimization and coordination is designed, the self-adaptive test instrument is designed, and its information flow transmission relationship with cloud coordination is analyzed. Further the method of minimum unit decomposition based on sub-module test is proposed, the conversion time of switching process is analyzed, the sub-module test sequence optimization problem is characterized as a decision-making process, the mathematical model of protection test is established, and the optimal sub-module test sequence is solved based on deep deterministic policy gradient (DDPG) algorithm The proposed method helps to improve the intelligence level of relay protection test.

A Data-driven Approach to Automatic Generation and Autonomous Verification of Virtual Circuits in Intelligent Substation

CAO Haiou, HU Xiaoli, DAI Wei, HU Jiatong, TANG Changfeng

2025, 58(6):  97-104.  DOI: 10.11930/j.issn.1004-9649.202408089

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A data-driven intelligent substation virtual loop automatic generation and independent verification method is proposed. The virtual connection information of the historical substation configuration description (CD) file is rapidly extracted by using the document object model (DOM) method, and the historical SCD database is constructed. The precise matching of the information of the intelligent electronic (IED) to be designed and the historical IED information is realized, and then the automatic generation scheme of the virtual connection is proposed. In view of the problems ofconnection and missed connection in practical application, the standard template library is constructed to verify the virtual connection generated automatically, and the visualization technology is used to realize the alarm, reminding the to add and modify the virtual connection. The correctness of the proposed method is verified based on an actual intelligent substation case.

Consistency Economic Dispatch Method for Power Systems Considering Virtual Power Plants

LIU Gang, TIAN Zhihao, HUANG Yingfeng, BAI Shibin, ZHANG Yue, LI Tong, DING Zhenhuan

2025, 58(6):  105-111.  DOI: 10.11930/j.issn.1004-9649.202410087

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Distributed algorithms have shown great potential in solving control problems for large-scale systems. This paper extends the leader-follower incremental cost consensus algorithms (ICC) to a leaderless distributed scheduling optimization algorithm. The proposed leaderless incremental cost consensus (LICC) algorithm can continuously track the system's load difference and schedule generators without a leader. Furthermore, this paper establishes a distributed economic dispatch model for virtual power plants and combines it with consensus algorithms. A dynamic model based on the IEEE 14 node system is implemented in Matlab/Simulink and RT-LAB to verify the feasibility of the algorithm. The system includes generators and virtual power plants, equipped with frequency control and voltage regulation functions. The effectiveness of the proposed LICC algorithm is demonstrated on a real-time power system simulation emulator.

Optimal Scheduling Strategy of Park-level Virtual Power Plant for Demand Response

WEI Chunhui, SHAN Linsen, HU Dadong, GAO Qianheng, ZHANG Xinsong, XUE Xiaocen

2025, 58(6):  112-121.  DOI: 10.11930/j.issn.1004-9649.202410059

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The park-level virtual power plant (PVPP) can aggregate diversified and flexible resources with the industrial parks to fully explore demand response potential, thus improving the profits of the PVPP. In order to optimize the demand response strategy of the PVPP in the electricity carbon joint market, a two-stage optimal scheduling model of the PVPP is developed here, in which, carbon emission costs are included in the optimization objects. The developed model includes two stages, i.e., a day-ahead stage and a real-time stage, and can be solved by the GAMS software. In the day-ahead stage, photovoltaic (PV) generation power is predicted by the improved radial basis function neural network, and the demand response bidding capacities are determined aiming to maximize the net benefits of the PVPP resulted from the demand response. In the real-time stage, the day-ahead schedules are revised according to real PV generation power, thus minimizing the negative effects of the PV power generation forecasting errors on the net benefits of the PVPP resulted from the demand response. Finally, the simulation analysis based on a PVPP is carried out. The results demonstrate that the strategy developed in this paper can maximize the net benefits of the PVPP resulted from the demand response in the context of considering the carbon emission costs.

New Energy and Energy Storage

Integrated Assessment of Transient Angle Stability and Voltage Stability Considering Renewable Energy Sources

BU Yuluo, WU Junyong, SHI Fashun, JI Jiashen

2025, 58(6):  122-136.  DOI: 10.11930/j.issn.1004-9649.202410057

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Transient angle instability and transient voltage instability often occur simultaneously and interact with each other, which increases the difficulty of stability assessment and emergency control. To achieve comprehensive guidance for emergency control through stability assessment, an instability mode recongnition method is proposed. This method describes the fault severity using the critical fault clearing time, characterizes the dominance of angle instability and voltage instability based on their occurrence order, and quantifies the coupling degree with the time difference between them. A four-quadrant instability mode recongnition diagram is constructed. To achieve the integrated online assessment, an improved convolutional neural network (CNN) model based on the convolutional block attention module (CBAM) is developed, and a two-stage integrated stability assessment scheme is proposed based on this model. Finally, the New England 10-machine 39-bus system is used for simulation verification, and the results show that the proposed method can achieve comprehensiveness, effectiveness and accuracy. Further, the applicability of the proposed method in systems with renewable energy is demonstrated using a modified 10-machine 39-bus system incorporating renewable energy.

Analysis of High Proportion of New Energy Consumption Abroad and Suggestions for Sustainable Development of New Energy in China

YE Xiaoning, WANG Caixia, SHI Zhiyong, BU Yuluo, YANG Chao, WU Si

2025, 58(6):  137-144.  DOI: 10.11930/j.issn.1004-9649.202411062

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Driven by the global energy transformation, the proportion of new energy such as wind power and photovoltaics in the power systems continues to rise in many countries. However, with the increase of new energy installed capacities, how to effectively improve the absorption level and utilization rate of new energy has become an urgent problem needing to be solved by all countries. To meet this challenge, this paper firstly selected two regions, California in US and Germany, to analyze and sort out the general rules of market-oriented absorption of new energy. Then, the history of new energy absorption in China was traced and the new situation of new energy absorption during the "14th Five-Year Plan" period was analyzed. Thirdly, the impact of utilization rate on the profitability of new energy under the market-oriented absorption situation was quantitatively analyzed. Finally, combined with the actual situation of new energy development in China, the strategic suggestions for improving the absorption level of new energy was proposed, so as to help China to achieve more efficient development and utilization of new energy in the future and promote the green and low-carbon transformation of energy.

Battery Optimization Operation Strategy for the "SOP-Storage-Charger" Devices Based on Adjustable Virtual Impedance

QIN Kun, QU Zhijiang, HAN Jianwei, XU Tao, GAO Feng, CHI Xiaoli

2025, 58(6):  145-155.  DOI: 10.11930/j.issn.1004-9649.202405139

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The soft open points-storage-charger (SSC) device integrates flexible interconnection, energy storage, and charging functions, which can mitigate energy fluctuations in the distribution network. However, during its operation, the frequent utilization of energy storage resources leads to high-power charging and discharging of batteries, thereby affecting their lifespan. To address this issue, a battery optimization operation strategy for the SSC device based on adjustable virtual impedance is proposed. Firstly, the functions of flexible interconnection, energy storage, and charging are all equivalently modeled as virtual impedances. The various energy pathways during the energy fluctuation process in the distribution network are analyzed. By adjusting the virtual impedance, the fluctuating energy can be effectively managed. Based on this, considering the operational characteristics of the SSC device, control methods for the adjustable virtual impedance of each part are proposed, and the physical significance and design principles of key parameters are analyzed. Finally, an experimental platform is built based on a hardware-in-the-loop (HIL) system to verify the theoretical results. The research demonstrates that the proposed method can mitigate voltage fluctuations in the distribution network while reducing the magnitude of charging and discharging currents in the energy storage system, thereby improving battery lifespan.

Research Overview of Hydropower Participation in Electricity Market

LIU Jiangwei, CHEN Yiping, XIAO Yunpeng, CHEN Kai, GUO Jincheng, WANG Jianxue

2025, 58(6):  156-171.  DOI: 10.11930/j.issn.1004-9649.202407097

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Hydropower has the advantages of convenient start and stop, fast climbing speed and strong adjustment ability. At the same time, China has the largest installed capacity of hydropower in the world with it being the second largest conventional energy in the country. Therefore, it is of great significance to study the participation of hydropower in the electricity market mechanism to promote the construction of China's electricity market and realize the clean transformation of energy. However, the existing reviews mostly focus on the research of new energy participating in the market, and lacks the analysis of researches on hydropower participating in the electricity market. Based on the current situation of hydropower development, this paper compares the differences between hydropower and new energy power in participating in the electricity market, analyzes the special problems faced by hydropower in participating in the electricity market, and summarizes the market structures of hydropower-rich regions at home and abroad and the relevant mechanisms of hydropower participating in the electricity market. On this basis, the existing research results such as clearing models and bidding strategies of hydropower participating in electricity market are summarized, and the further research directions of hydropower participating in the electricity market are put forward.

Low Voltage Substation Photovoltaic Ultra Short Term Power Prediction Method Based on FCM-SENet-TCN

WEI Wei, YU He, YE Li, WANG Yingchun

2025, 58(6):  172-179.  DOI: 10.11930/j.issn.1004-9649.202409031

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The existing methods for predicting photovoltaic power face problems such as excessive initial data redundancy and difficulty in extracting predictive features when facing distributed photovoltaics in low-voltage substations, resulting in insufficient prediction accuracy. Therefore, this article proposes a low-voltage photovoltaic ultra short term power prediction method based on FCM-SENet TCN. Firstly, the fuzzy C-means clustering algorithm (FCM) is used to fully explore multi-source meteorological environment data, clustering the initial dataset with different weather conditions to reduce initial data redundancy; Secondly, the Squeeze and Excitation Networks (SENet) will be integrated into the Temporal Convolutional Network (TCN) to efficiently extract complex features and improve prediction accuracy; Finally, the average absolute percentage error and root mean square error are used as evaluation indicators to assess the prediction results. The simulation results show that the proposed prediction method can fully utilize the initial meteorological data and make more accurate ultra short term power predictions based on the output characteristics of distributed photovoltaic generators in low-voltage substations.

New-Type Power Grid

Comprehensive Cost Analysis of Multiple Entities under the Coupling Mechanism of Electricity and Carbon

WANG Yanyang, LI Jianzhao, LIU Jingqing, TANG Cheng, WANG Jiantao, LU Jinling, REN Hui

2025, 58(6):  180-189.  DOI: 10.11930/j.issn.1004-9649.202404029

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Under the dual carbon vision, the electricity-carbon coupling trading is a development trend in the future. Constructing a generation cost calculation model considering the electricity-carbon coupling of different market entities is beneficial for the decision-making of power generation entities and for the reliable operation of the market under the supervision of power trading institutions. By analyzing the cost structure of multiple power market entities under the electricity-carbon coupling with the life cycle method, a comprehensive cost accounting method for the traditional energy units represented by thermal power and the new energy units represented by photovoltaic under the electricity-carbon coupling mechanism is proposed. Based on the southern power grid of Hebei Province, the comprehensive cost of the market entities is calculated and sensitivity analysis is carried out, and the impacts of the electricity-carbon coupling mechanism on the comprehensive costs of different power units are quantified. The rusults show that the comprehensive cost of the thermal power units is highly sensitive to changes in carbon emission factors, while the comprehensive cost of photovoltaic units is mainly affected by operating costs and power generation utilization hours. This study can provide a references for further improving the electricity price mechanism and market-oriented system, accelerating unit transformation, and promoting energy structure transformation.

Reserve Fee Mechanism Design for Distributed Generation Users Adapting to New Power Systems

SUN Qixing, ZHANG Chao, ZHANG Mengge, YOU Peipei, LI Junlong

2025, 58(6):  190-197.  DOI: 10.11930/j.issn.1004-9649.202406046

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With the advancement of power supply technology and the development of diverse emerging business models, the number of users with distributed generation (DG) integrated into the main grid is steadily increasing and the scale of self-consumption from distributed generation is also steadily expanding. However, the current transmission and distribution (T&D) pricing policy has not established an equitable pricing mechanism for such users, resulting in their underpayment of system reserve charges and social responsibility fees. This leads to an unfair allocation of power system costs, which undermines the sustainable development of the new power systems. Based on the classic transmission and distribution electricity pricing theories both at home and abroad, this study designs a reserve fee mechanism that adapts to the DG development through analyzing the T&D costs that should be borne by DG users. In the short term, it rationally assigns network charges and social responsibility fees to DG beneficiary users; in the long term, it explores to design a T&D pricing mechanism based on Peak Contribution method to ensure equitable allocation of T&D costs associated with DG to the benefiting users. Finally, case studies are carried out with typical provinces.

Criterion and Delay Optimization of Circuit Breaker Failure Protection Based on Tail Current Recognition

CHEN Xiangwen, LIU Yang, ZHAO Qingchun, ZHANG Jin, JIN Mingliang, XU Xiaochun, WANG Yulong, XIE Hua

2025, 58(6):  198-205.  DOI: 10.11930/j.issn.1004-9649.202409096

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When the circuit breaker acts, the current on the primary side of the current transformer suddenly decreases, and the inductive effect will form a tail current on secondary side, which will cause the time of return of the failure protection to be too long, resulting in the failure protection maloperation. A comprehensive criterion for the identification of circuiter failure protection based on tail current is proposed to improve the reliability of identification. Firstly, the working principle of the current transformer is analyzed, and the mechanism of the influence the tail current on the failure protection of the circuit breaker is explored. Secondly, the minimum algorithm and Fréchet distance algorithm are combined to construct a fast and reliable of the tail current comprehensive judgment criteria; Finally, according to the action delay and requirements of the circuit breaker failure protection, the delay of the circuit breaker failure protection optimized. The proposed method can effectively avoid the maloperation of the circuit breaker failure protection.

Secondary Measurement Loop Error Assessment in Substations with Application of Deep Learning Networks

WU Jiangxiong, LIU Qiankuan, YANG Guoyan, JIANG Liandian

2025, 58(6):  206-212.  DOI: 10.11930/j.issn.1004-9649.202406003

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Measuring and protection devices in substations are susceptible to errors in the monitored current variations due to environmental and wear and tear, which leads to the risk of inadvertent refusal of the measuring circuits, and it is difficult to detect such amplitude variations by the conventional monitoring methods, based on which, this paper proposes a conditional generative adversarial network (CGAN) and an improved long and short-term memory network (STMN) for the error assessment method. Firstly, the current data of the measurement loop under normal operation is obtained, and the CGAN method is introduced to enhance the generation of error data; secondly, the EMD decomposition of the generated data is performed to form samples and the optimal set of features is selected; in order to further evaluate the error state, the improved LSTM algorithm is used to train the model; finally, a PSCAD/EMTDC simulation model is constructed to verify the reliability and accuracy of the methodology presented in this paper. Finally, a PSCAD/EMTDC simulation model is built to verify the reliability and accuracy of the proposed method. Finally, a PSCAD/EMTDC simulation model is constructed to verify the reliability and accuracy of the proposed method. The test results show that the new method adopted in this paper can reliably evaluate the error state of 2% of the measurement loop of the secondary system.