Table of Content

28 November 2024, Volume 57 Issue 11

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Key Safety Technology of Lithium-Ion Battery Body for Energy Storage

Key Technology and Development Prospect of Ontology Safety for Lithium-Ion Battery Storage Power Stations

Xiangyang XIA, Xinxin TAN, Zhouping SHAN, Hui LI, Zhiqiang XU, Jinbo WU, Jiahui YUE, Guiquan CHEN

2024, 57(11):  1-17.  DOI: 10.11930/j.issn.1004-9649.202405062

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The introduction of the "dual carbon" targets and the ongoing advancement of low-carbon transitions in energy and electricity have posed significant challenges to the new-type power system, which primarily relies on renewable energy sources, particularly in terms of large-scale, safe, and efficient energy storage. In this context, energy storage stations, as a crucial component of the energy system, are of utmost importance in terms of safety management, directly influencing the stable operation and sustainable development of the entire power system. This essay delves into the current research status of lithium-ion battery safety management. Firstly, it systematically reviews the various battery health assessment methods widely used today and comprehensively summarizes the selection of health indicators in data-driven approaches. Secondly, it discusses the latest research hotspots in existing battery state assessment technologies from three perspectives: battery state evaluation based on data fragmentation, the construction of battery edge platforms, and intelligent inspection of energy storage stations. The essay also points out the future direction and key challenges of energy storage safety assessment. Lastly, it presents insights into the safety control technologies for energy storage stations, addressing the system stability considering battery parameter variations and the multi-objective control of energy storage systems.

Temperature Prediction of Lithium-Ion Batteries Based on Physical Information and Deep Neural Network

Laien CHEN, Xiaoyong ZENG, Zihao ZENG, Caichen CHENG, Yaoke SUN

2024, 57(11):  18-25.  DOI: 10.11930/j.issn.1004-9649.202403004

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Accurately predicting the temperature of lithium-ion batteries is a key technology for battery management systems. A deep neural network is constructed for temperature prediction of lithium-ion batteries based on their dynamic as well as time-dependent characteristics. The model can extract the potential high-dimension features of the data and appropriately reduce their dimensionality to reduce the model complexity while capturing the long-term dependence of temperature through the layer of long short-term memory cells. In addition, the heat generation rate is calculated in real-time through the open circuit voltage, terminal voltage and current of the lithium-ion battery, thus providing additional physical information input to the deep neural network. The results show that the method has better temperature prediction performance compared to other methods.

Power System

Differential Protection of Distribution Network Based on VMD and Duffing Oscillator

Jianbao ZHANG, Lei WANG, Weijian JIANG

2024, 57(11):  26-35.  DOI: 10.11930/j.issn.1004-9649.202307005

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The distribution network is grounded through arc suppression coil. When a single-phase grounding fault occurs, the faulting characteristics are often weak and unobvious, which makes it difficult to accurately locate the fault line. In recent years, with the development of communication and other related technologies, the cost of differential protection has decreased continuously, which makes it possible to realize the differential protection of distribution network. Therefore, a line selection method based on variational mode decomposition (VMD) and Duffing oscillator system is proposed. Firstly, the high-frequency component of transient zero-sequence current is obtained with VMD, and the phase difference of high-frequency zero-sequence current on both sides of the line is added to the Duffing system. And then, by analyzing the relationship between the phase difference of high-frequency components on both sides of the line and the state of the Duffing system, a fault criterion based on dynamic time warping (DTW) is constructed to realize the accurate location of the fault. Finally, numerical simulation proves that the proposed line selection method can quickly and accurately locate the single-phase ground fault sections for the lines that have different transition resistances and distributed power sources. The proposed fault section localization algorithm can effectively extract fault characteristics, thereby improving the reliability of protection and the accuracy of line selection.

Annual Daily Average Load Curve Prediction Considering Dynamic Time Anchors and Typical Feature Constraints

Dan LI, Shuai HE, Wei YAN, Yue HU, Zeren FANG, Yunyan LIANG

2024, 57(11):  36-47.  DOI: 10.11930/j.issn.1004-9649.202308114

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Based on the trends, periodicity and calendar features of power load, it is realized to accurately predict the annual daily average load curves considering the dynamic time anchors and typical feature constraints, Firstly, a dynamic time anchor matrix is built based on the calendar association between the historical year and the target year. Then, based on the historical annual daily average load shape-factor curves obtained after normalization and periodic smoothing treatment, it is proposed to use the DTA-Soft-DBA to predict the target year's daily average load shape-factor curve. After inverse normalization and inverse periodic smoothing treatment, the annual daily average load prediction curves are obtained by correcting the typical feature constraints with the predicted value of power and electricity features. The case study results of an area in China show that the proposed method has higher prediction accuracy, and the results are consistent with the predicted values of typical features and the temporal variations within the target year. The proposed method can effectively integrate the common rules of historical sample time series with different calendar characteristics, which is reasonable and interpretable.

Application of Electromagnetic and Electromechanical Transient Simulation to Dynamic Modeling of Multi-energy System

Zhanbo WANG, Sirui ZHANG, Mingyu JIANG, Yue XIA, Shengqiang GAO, Shuaiyu BU

2024, 57(11):  48-61.  DOI: 10.11930/j.issn.1004-9649.202305091

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Thermal network is an important component of multi-energy system. There are significant differences in modeling methods between electric network and heating network. It is difficult to directly apply traditional power system transients simulation program to modeling of multi-energy system. With the help of electrical-thermal analogy, the dynamic models of thermal circuit and hydraulic circuit of pipe represented using circuit equivalents. At the same time, the equivalent circuit model of the air source heat pump considering the operating characteristics of the equipment is constructed based on the table lookup method. In order to improve the simulation efficiency, a new method for modeling and simulation of electric and heating multi-energy systems are proposed based on electromagnetic and electromechanical transient simulation technique. The electric network is simulated in electromagnetic transients program. the heating network which is modeled with basic circuit elements is simulated in electromechanical transients program. The air source heat pump which connects the electric and heating networks is used as interface model. The multi-energy system model is capable of using a large time step of size to maintain high simulation efficiency. Diverse tests are carried out to validate the proposed models.

State Estimation and Bad Data Detection in Hybrid AC/DC Systems with LCC/MMC

Huashi ZHAO, Yaohui HUANG, Zhiqiang SONG, Jianzhong XU, Kexin ZHENG, Kangkang LIANG

2024, 57(11):  62-69.  DOI: 10.11930/j.issn.1004-9649.202307020

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Based on the CIM/XML and CIM/E documents exported from the regional dispatching system, this paper focuses on data generation and starts by converting the exported documents into raw input data for state estimation. Considering the interactions between the AC system and LCC, MMC, and between LCC and MMC, the unified iterative method is used to model the AC/DC state estimation of the 500kV subnetwork. Subsequently, the Gaussian noise is added to the original measurement data, and the maximum residual test method is employed for detecting and identifying bad data. Finally, the effectiveness of the proposed models for AC/DC state estimation and the detection and identification of bad data are validated through simulation data.

Research on Control Strategy of Power Conversion System Based on Virtual Oscillator Control

Xiangyang XIA, Daiyu JIANG, Xiaoyong ZENG, Fen GONG, Xiaozhong WU, Xia HUA, Yanpeng LUO, Chao SHI

2024, 57(11):  70-77.  DOI: 10.11930/j.issn.1004-9649.202308073

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In response to the problems of slow synchronization speed and poor active power tracking effect in traditional control methods of energy storage converters, this paper proposes a control strategy for energy storage converters based on virtual oscillator control. This strategy is based on virtual oscillator control, using an improved particle swarm optimization algorithm that optimizes inertia weights and learning factors to tune controller parameters. Avoiding the measurement of active and reactive power, it has better dynamic response than traditional droop control. Finally, corresponding control measures were taken for the two parallel energy converters in the energy storage system under sudden load changes. The simulation results show that the new control method proposed in this paper has faster synchronization speed and better active power tracking performance.

The Design of Standard Digital Application Framework and its Field Practice on Device Side

Xinyi HE, Ming DONG, Xin SUN, Yong YAN, Ying YAO

2024, 57(11):  78-87.  DOI: 10.11930/j.issn.1004-9649.202406047

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In order to meet the demand for deep mining and knowledge rule construction of electric power standard literature, this paper proposed a novel electric power standard digitization technology, which is based on conditional event graph and provided a new solution for the efficient and intelligent development of the electric power industry. Firstly, in order to realize the refined knowledge extraction of electric power standard documents, this paper combined the rule and semantic role annotation techniques to extract the key knowledge triad, and in the knowledge fusion stage, the word similarity method was used to integrate the knowledge and improve the accuracy of the atlas. Secondly, this paper established an improved standard knowledge graph for the electric power domain, which expresses the knowledge and rules in electric power standards more comprehensively. Finally, this paper combined Cypher query language and generative rules to realize the auxiliary knowledge quiz and condition assessment tasks of electric power equipment, which greatly enhances the utility of the graph.

Development Achievements and Policy Suggestions of China's West to East Power Transmission for 40 Years

Shuang LIANG, She WANG, hui XU

2024, 57(11):  88-93.  DOI: 10.11930/j.issn.1004-9649.202406048

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The West to East Power Transmission is a landmark project of national Western Development Strategy, which is an important measure to promote regional coordinated development and achieve optimal allocation of power resources. After 40 years of development, China's West to East power transmission has formed a three channel transmission pattern of "North, Central, and South", with continuous improvement in transmission capacity and significant development results. This article comprehensively summarizes the outstanding achievements of the West to East Power Transmission in ensuring power supply, promoting low-carbon transformation, driving industrial upgrading, and promoting economic development. It systematically analyzes the multiple challenges faced in the continuous promotion of the West to East Power Transmission, and proposes countermeasures and suggestions from channel planning, power coordination, operational safety, market mechanisms, and other aspects to promote the high-quality development of the West to East Power Transmission.

New Energy

A Spot Price Limit Adaptive Model Considering the Impact Layer Under the Risk of New Energy Entering the Market

Yi QI, Jing ZHANG, Jing LIU, Shengnan WEI, Yanling WANG, Zhaohao DING

2024, 57(11):  94-101.  DOI: 10.11930/j.issn.1004-9649.202312065

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In the initial stage of the spot market, flat price limit standards are commonly used to prevent extreme price risks. However, as the construction of new power systems accelerates and new energy enters the market on a large scale, the impact of intermittent output on spot prices is expanding, and the risk of new energy entering the market increases. The price limit design needs to further consider issues such as conventional unit revenue, new energy installation ratio, and load demand. Therefore, considering the factors that have a strong linkage with spot prices, the main factors are selected to form the price limit influence layer. On the basis of improving the traditional price control model, the influence factors and the adaptive coefficients of the series of models are introduced to propose a spot market price limit adaptive model that takes into account the multi factor influence layer under the risk of new energy entering the market. The simulation results show that, The model can achieve dynamic tracking of spot prices in different demand scenarios to ensure system security and stability.

Capacity Configuration for "PEDF" System Driven by Safe and Stable Operation

Huihong YUAN, Shile WENG, Liangjin CHEN, Yitao ZHU, Lijun ZHANG, Bei QI

2024, 57(11):  102-107.  DOI: 10.11930/j.issn.1004-9649.202405013

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PEDF (photovoltaic, energy storage, direct current, and flexibility) is an important support for the new power system to achieve zero carbon electricity in buildings. To mitigate the high initial investment costs of crucial equipment in the early stage of system design, and to enhance power supply reliability, an improved energy valley optimization (EVO) algorithm for designing the capacity allocation of crucial equipment in PEDF systems for civil buildings is proposed, from the comprehensive perspectives of system economics, low carbon operation, and safety control. Simulation results are compared with those obtained from the Grey Wolf and traditional EVO algorithms. The results demonstrate that the improved EVO algorithm yields superior configuration results and faster convergence when applied to optimizing equipment capacity allocation in PEDF systems. These findings validate the effectiveness and feasibility of the proposed method, offering valuable insights for selecting crucial equipment capacities in PEDF systems for civil buildings.

Virtual Synchronous Control Frequency Regulation Strategy for Adjustable Self-standby Rate in Photovoltaic Plants

Jiangfeng ZHANG, Song KE, Wenjin CHEN, Tianyu WANG, Keke ZHENG, Jun YANG

2024, 57(11):  108-118.  DOI: 10.11930/j.issn.1004-9649.202305054

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The photovoltaic plants controlled by self-standby can provide frequency regulation capability for the power grid. But the existing self-standby control is difficult to evaluate the maximum power and and the economy is required to be quantified. Additionally, it can not provide virtual inertia. The effect of frequency regulation response of photovoltaic plants needs to be improved. To address this problem, this paper proposes a virtual synchronous FR control strategy for PV power plants with adjustable self-standby. Firstly, based on the P-V operation characteristics of PV, a PV maximum power estimation strategy is proposed, as well as a variable step voltage control strategy to achieve PV self-standby control. Second, based on the virtual synchronous generator control strategy and the photovoltaic plant architecture, the frequency feedback control loop is introduced. And the photovoltaic self-standby adjustable control strategy is proposed, supporting the frequency stability of the power grid in the form of virtual inertia. In addition, energy storages connected in parallel on the DC side of the photovoltaic ensure the stability of the DC bus voltage during the dynamic adjustment of the photovoltaic operating point with the self-backup rate. The influence of the error of the maximum power estimation algorithm on the virtual synchronous frequency control strategy is further analyzed. And the influence of the change of virtual synchronous control parameters on the frequency modulation effect is discussed. Finally, Simulink simulation results verify the effectiveness of the proposed control strategy and provide theoretical support for the grid-connected regulation of PV.

Hybrid Energy Storage Power Allocation Strategy Based on NGO-VMD

Haiyan WANG, Linyu QIAN

2024, 57(11):  119-128.  DOI: 10.11930/j.issn.1004-9649.202308010

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Power fluctuations during wind power grid connection affect the stability of the power grid. To address this issue, a hybrid energy storage power allocation strategy based on the northern goshawk optimization (NGO) algorithm was proposed to optimize the parameters of variational mode decomposition (VMD). Firstly, the generation power of wind power was filtered in accordance with the regulations of wind power grid connection technology using the adaptive averaging filtering method, and the fluctuation power was calculated from the filtered power. Then, the optimal combination of K value (number of decomposition modes) and α value (quadratic penalty factor) in the NGO-VMD algorithm was used to realize power allocation between lithium batteries and supercapacitors after the fluctuation power signal was decomposed by VMD. Finally, the state of charge (SOC) of the hybrid energy storage system (HESS) was optimized using dual fuzzy control to achieve the secondary power allocation of the HESS. Simulation outcomes demonstrate that employing this control strategy achieves not only compliance with the maximum power fluctuation requirements of wind power grid connection but also the maintenance of SOC within a reasonable range, ensuring the long-term secure operation of HESS.

Risk Analysis of Insufficient Flexibility from Regulation Resources in High Proportion Renewable Energy Power Systems

Jing XU, Tiejun ZHAO, Xiaogang GAO, Ju YE, Lingling SUN

2024, 57(11):  129-138.  DOI: 10.11930/j.issn.1004-9649.202401117

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High-penetration renewable energy power systems introduce significant volatility and uncertainty, exposing the power system to operational risks associated with inadequate flexibility. Assessing the risk of insufficient flexibility under uncertain conditions is crucial for controlling the operational risk levels of power systems and evaluating the merits of planning scenarios. This study explores quantitative assessment methods for the risk of inadequate flexibility in renewable energy power system regulation resources and proposes a risk evaluation index system for this risk. Firstly, a data-driven modeling approach for source-load uncertainty is introduced based on kernel density estimation and order optimization theory. To enhance the adequacy of source-load sample data in power systems, a reconstruction method for low-probability risk sample sets in power systems based on cloud modeling is proposed, enabling cost-free and flexible acquisition of training samples. Secondly, a quantitative assessment method for the risk of inadequate flexibility in renewable energy power system regulation resources is developed from two aspects: ramping capability and regulation depth. Finally, case studies validate the effectiveness and feasibility of the proposed methods.

Pilot Protection of New Energy Transmission Line in Active Distribution Network Based on 5G Communication

Tiecheng LI, Hui FAN, Weiming ZHANG, Xianzhi WANG, Yihong ZHANG, Zhihui DAI

2024, 57(11):  139-150.  DOI: 10.11930/j.issn.1004-9649.202307031

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Traditional pilot differential protection will have the problem of reliability reduction or even failure to operate after the access of new energy stations. To this end, this paper first proposes a rank difference accumulation waveform similarity algorithm and analyzes its anti-interference characteristics. The algorithm measures the similarity of two waveforms by calculating the maximum mismatch degree of two sets of data, which significantly reduces the influence of abnormal data on waveform similarity judgment. On this basis, using the difference of transient current waveform characteristics on both sides of the transmission line, the paper proposes a new pilot protection principle based on 5G communication technology and constructs a complete protection scheme. Finally, the performance of the proposed protection scheme is verified with PSCAD/EMTDC. The simulation results show that the scheme is not limited by the types of new energy and is suitable for all kinds of new energy stations. It has a good ability to resist delay, misoperation, noise, and abnormal data. It also has good operation performance in the cases of weak output of new energy and circuit breaker reclosing on permanent fault.

Technology and Economics

Operational Decision Model for Demand Response Considering Carbon Reduction Value of Adjustable Loads

Xiaoxuan ZHANG, Song XUE, Ye XU, Yi XU, Zeyu DING, Qingkai SUN

2024, 57(11):  151-160.  DOI: 10.11930/j.issn.1004-9649.202406091

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Demand response is one of the important adjustable load resources, which plays an important role in promoting carbon reduction and new energy consumption in new power systems. This article integrated carbon flow theory, intelligent optimization algorithm, and multi-attribute evaluation methods into adjustable load operation strategy decision-making for demand response. It constructed a demand response strategy optimization model with the objective function of minimizing user electricity costs and constraints covering power supply and demand balance and unit output limitations. A genetic algorithm was used to determine the various chromosomes of the advantageous population that are conducive to achieving optimization goals. By combining the economic benefits of each chromosome, the consumption of new energy, and the calculation results of user carbon emission intensity, the improved entropy weight method and weight sum method were combined to comprehensively evaluate and rank all chromosomes. As a result, the demand response strategy that ensures the global best economic benefits, consumption of new energy, and carbon reduction effects of the system was obtained, which maximized the value of adjustable load resources. The feasibility and effectiveness of the method were finally verified through examples.

Bidding Strategy for Thermal Power Generation Companies Based on Multi-agent Deep Deterministic Policy Gradient Algorithm

Xingping ZHANG, Teng WANG, Xinyue ZHANG, Haonan ZHANG

2024, 57(11):  161-172.  DOI: 10.11930/j.issn.1004-9649.202309119

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Thermal power is an important support for the new power system. It is of great significance to study the bidding strategy for thermal power generation companies and the influence of different clearing mechanisms to ensure their low-carbon and efficient operation. A bidding strategy model is constructed based on the multi-agent deep deterministic policy gradient algorithm to analyze the differential bidding strategies for different combinations of thermal power generation companies. The multi-agent price and quantity bidding strategy is optimized, and the market impact of different market clearing mechanisms is explored. The simulation results indicate that the proposed bidding strategy model can guide the thermal power generation companies to optimize their bidding methods and improve the market efficiency. When the penetration rate of new energy is low, the applicability of different clearing mechanisms varies for various types of units; with the increase of the penetration rate of new energy, the pay as bid mechanism can be used to enhance the economic and environmental efficiency of the electricity market; when the penetration rate of new energy reaches a high level, the random matching clearing mechanism can effectively address market volatility risks.

Analysis on the Effect of Time-of-Use Electricity Price on Electricity Cost Based on Difference-in-Differences Model

Junlong LI, Peipei YOU, Chao ZHANG, Lurui FANG, Wenzhe ZHANG

2024, 57(11):  173-182.  DOI: 10.11930/j.issn.1004-9649.202311104

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An effective time-of-use (TOU) electricity pricing mechanism is essential to stimulate customers to provide load flexibility by price signals, and can directly determine users' electricity cost. TOU electricity price has been adjusted in many provinces in China recently. This paper introduces and analyzes the new round of adjustment. Based on the difference-in-differences (DiD) method, the TOU effect on customers' electricity cost is calculated. Furthermore, enabled by a multiple regression model, the effect of TOU mechanism on electricity cost is revealed by fitting the decoupled coefficient obtained by DiD. Based on the real electricity cost of TOU and non-TOU customers of specific provinces, types, and voltage levels in July 2022 and July 2023, the TOU effects and general trends are obtained. Then, the accuracy of the regression model is verified. Finally, the TOU effect on the cost per kilowatt hour of commercial customers under different price ratios is analyzed. It is found that there is an optimal critical peak and peak ratio to minimize the electricity cost for commercial customers.

Information and Communication

Routing Algorithm for Power Communication Networks Based on Serivce Differentiated Transmission Requirements

Songping XUE, Dequan GAO, Ziyan ZHAO, Yuqian LIN, Zejing GUANG, Dawei ZHANG

2024, 57(11):  183-190.  DOI: 10.11930/j.issn.1004-9649.202405020

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The electric power communication network, pivotal in ensuring the stable operation of the power grid, is tasked with transmitting control instructions and collecting status data. Addressing the intelligent routing challenge within the electric power communication network under multiple constraints, we propose an innovative routing algorithm that seamlessly integrates Message Passing Neural Network (MPNN) with deep reinforcement learning algorithms. Implemented through the TensorFlow framework, this algorithm has been rigorously validated in a simulation environment constructed using OpenAI Gym. After undergoing over 8,000 training iterations, the algorithm demonstrates remarkable performance enhancements, outperforming traditional shortest path and load balancing algorithms in terms of routing selection capabilities. Furthermore, it has exhibited robust adaptability and resilience in generalization tests on new topology maps and link failure simulation experiments.

Low-Power Data Return Method for Strong Interference Power IoT

Zanhong WU

2024, 57(11):  191-198.  DOI: 10.11930/j.issn.1004-9649.202307015

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In the power Internet of Tings (IoT) scenarios, numerous devices and complex electromagnetic environment cause time-varying broadband strong interference to wireless data transmission of the sensor nodes. To address this issue, a low-power data return method is proposed. Through synchronous scheduling, distributed real-time narrowband interference monitoring, optimal channel data uploading etc., the probability of data collision under strong interference environment is greatly reduced and the communication capacity is improved, and the overall low-power characteristics of the system are guaranteed at the same time. The simulation results indicate that the proposed method shows a performance improvement of over two times in throughput compared to traditional methods. Additionally, the power consumption has also decreased by over 50%. In the whole, the proposed method greatly improves the communication capacity and reduces the power consumption.