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Table of Content

    28 May 2024, Volume 57 Issue 5
    Flexible Resource Operation and Key Technologies of New Power System Source Network Load Storage
    Coordinated Optimal Scheduling of Source and Load in Integrated Energy System Considering Flexible Resources
    Funian HU, Pengcheng ZHANG, Xiaobo ZHOU, Jun CHEN
    2024, 57(5):  2-13.  DOI: 10.11930/j.issn.1004-9649.202306096
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    The uncertainty and volatility of renewable energy and loads pose enormous challenges to the safe and flexible operation of integrated energy systems (IES). In order to improve the flexible adjustment ability of IES and the consumption level of renewable energy, this paper proposes an IES source-load coordination optimization scheduling method considering flexible resources. According to the operational flexibility requirements of the system, the flexibility capabilities of various resources are finely described. A joint operating model of combined heating and power (CHP) unit and hydrogen fuel cell (HFC) is established at the source-side according to the operating characteristics of the electric-hydrogen coupling unit, and a comprehensive flexible supply model of system is constructed at the load-side with consideration of the flexible supply capacity of comprehensive demand response. Two scheduling modes is divided according to the vacancy of operation flexibility at different times, and an optimal scheduling model based on IES flexibility constraints is constructed. A simulation analysis is carried out, and the simulation results show that the proposed optimal scheduling method can effectively improve the flexible adjustment ability of IES and the consumption level of renewable energy.

    Optimal Scheduling of Integrated Energy Systems Considering Flexible Demand Response and Carbon Emission-Green Certificate Joint Trading
    Cailing ZHANG, Shuang WANG, Shuna GE, Deng PAN, Yan ZHANG, Wei HAN, Wenyan DUAN
    2024, 57(5):  14-25.  DOI: 10.11930/j.issn.1004-9649.202305134
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    Under the background of "carbon peaking and carbon neutrality", tapping the demand side resources with effective combination of carbon emission trading and green certificate trading mechanism will help to achieve the development of low-carbon economy in the integrated energy system. For this reason, this paper proposes a low-carbon optimal scheduling strategy for the integrated energy system (IES) considering flexible demand response and laddered carbon emission and green certificate joint trading. Firstly, a waste heat power generation link containing the organic Rankine cycle is introduced at the source side to decouple the constraint of "determining electricity by heat" for CHP, and an integrated demand response model is introduced at the load side, thus forming an flexible demand response model for source and load coordination. Secondly, the principles of carbon emission trading and green certificate trading mechanisms are studied and the correlation between the them is analyzed, and a ladder-type carbon emission and green certificate joint trading mechanism is constructed. Finally, taking into account both the economy and low-carbon performance of the system, a day-ahead low-carbon economic optimization model is constructed with the goal of minimizing the total cost. The simulation results show that the total cost and carbon emissions of the system have decreased by 13.37% and 11.44% respectively after considering the laddered carbon emission and green certificate joint trading mechanism. Compared with the traditional demand response models, the proposed flexible demand response model has reduced the total cost and carbon emissions by 3.87% and 2.85% respectively, effectively achieving economic, flexible, and low-carbon operation of the system.

    Optimal Scheduling Strategy for Wind-Solar-Hydro Alliance Considering Compensation of Regulation by Hydropower
    Xianshan LI, Shengbiao DING, Fei LI, Xin LI
    2024, 57(5):  26-38.  DOI: 10.11930/j.issn.1004-9649.202308001
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    Aiming at the key problems in cross-region consumption scheduling of the wind-solar-hydro integrated energy, such as the cascade energy storage, cascade hydropower joint regulation and scheduling methods, and fair allocation of hydropower regulation costs, a four-stage optimization scheduling model of wind-solar-hydro alliance is proposed with consideration of the compensation of hydropower flexible regulation costs. Firstly, a wind-solar power fluctuation suppression model based on cascade energy storage regulation is established to minimize the suppression error and cost. A fair allocation method for regulating cost and power of cascade energy storage based on wind-solar fluctuation coefficient is proposed. Secondly, based on the tracking of the power grid load by the joint output of the wind-solar-hydro alliance of the cascade energy storage and cascade hydropower joint regulation, a master-slave game model of wind-solar-hydro alliance and regional power grid is constructed to optimize the power grid price and alliance power sales plan, thus achieving the maximum benefit of both parties. Thirdly, a robust optimization model of the first two-stage scheduling model is constructed with consideration of the uncertainty of wind-solar-hydro, and a energy scheduling strategy is obtained for each entity of the alliance considering both economy and robustness. Finally, an asymmetric Nash negotiation model based on the partner's marginal contribution index is constructed to realize the fair distribution of cooperation surplus and the fair allocation of hydropower regulation costs. The case study results show that the proposed method can fairly improve the net profit of all stakeholders after cooperation, and is conducive to maintaining the stability of alliance cooperation and promoting the cross-regional consumption of wind-solar-hydro integrated energy.

    Load Scheduling Optimization of Home Electric Heating Integrated Energy System with Electric Vehicle
    Yunlong WANG, Lu HAN, Shulin LUO, Tao WU
    2024, 57(5):  39-49.  DOI: 10.11930/j.issn.1004-9649.202305056
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    With the introduction of such policies as "carbon neutrality" and "carbon peak", the dispatch optimization of new energy and heterogeneous energy has become the main measure to reduce carbon emissions. As the home integrated energy system accounts for a large proportion on the energy demand side, it is urgently needed to construct a reasonable home energy system to realize the energy management of home load optimization, In order to realize the flexible dispatch of load in the home electric-thermal integrated energy system, this paper proposes a comprehensive optimization dispatch scheme of domestic fuel cell-based combined heat and power (DFCCHP) system integrating heat pump and electric vehicle, with full consideration of the output influence of electric vehicles and heterogeneous energy equipment. Firstly, the electric and thermal loads are classified in detail according to the home electricity and heat characteristics, and the predicted mean vote (PMV) is introduced to control the indoor temperature, and a load model is established. Secondly, the heat pump and electric vehicle are introduced, and a home integrated energy system optimization dispatch model is established with the minimum energy purchase cost as the objective under time-of-use electricity price and time-of-use gas price, and the Cplex solver is used to solve the model. Finally, the rationality, feasibility and environmental protection of the proposed dispatch model, as well as the impact of heat pump and electric vehicle on the economic performance of the system are verified through simulation. The results show that under different weather conditions, the introduction of heat pump and electric vehicle can effectively reduce the system's energy purchase cost and carbon emissions, and the reached conclusions can provide a certain theoretical analysis basis for further improving the home integrated energy system topology and load optimization dispatch.

    Adequacy Evaluation of Power System Ramping Capability Based on Net Load Forecast Error Statistics
    Zhongfei CHEN, Yue ZHAO, Qiuna CAI, Qiaoyu ZHANG, Zelin WANG, Xiaojuan DAI, Yuguo CHEN
    2024, 57(5):  50-60.  DOI: 10.11930/j.issn.1004-9649.202309084
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    In the context of rapid development of renewable energy, the power system is required to keep sufficient ramping capability for coping with the renewable generation's fluctuation and intermittence. Analyzing the supply-demand situation of the power system's ramping capability can discover the risk of insufficient ramping capability and improve the security and stability of the power system operation. This paper firstly proposes the logic and procedure for power system ramping capability adequacy evaluation, and clarifies the definitions and concepts of the netload ramping capacity, uncertainty ramping capacity, surplus ramping capability and its demand-supply ratio. And the theories and specific calculation methods for estimating the netload forecast error based on the confidence statistics and quantile regression, as well as the specific calculation methods for the above parameters, are subsequently proposed. Finally, an example analysis is carried out based on the data of forty historical operating days and four typical days in Guangdong to validate the effectiveness of the proposed adequacy evaluation method. This research indicates that the surplus ramping capability demand-supply ratio can effectively identify the ramping characteristics in different typical days and special periods, reflecting the adequacy of ramping capability in the day and minute scale; the indicators such as coverage, excess, and estimation quantity can be used to evaluate the applicability of error estimation models in different regions and scenarios, serving as a reference for model selection.

    Load Forecast of Electric Trucks Aggregation Based on Higher-order Markov Chains
    Hang LIU, Hao SHEN, Yong YANG, Ling JI, Yang YU
    2024, 57(5):  61-69.  DOI: 10.11930/j.issn.1004-9649.202306066
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    Compared with ordinary electric vehicles, electric trucks have higher charging power, larger battery capacity and more considerable dispatching potential, while their charging load presents greater randomness due to many factors such as cargo weight, logistics characteristics and driving path. To this end, this paper proposes a electric trucks aggregation load prediction method based on higher-order Markov chain considering logistics characteristics. Firstly, on the basis of considering the soft time window constraint to realize the path planning of the electric trucks, the charging time of the electric trucks is predicted by analyzing their driving characteristics to obtain the charging quantity of the electric trucks at each moment. Secondly, the charge state interval of the electric trucks is partitioned with fuzzy two-level discretization, and each large interval is further subdivided into n small intervals so as to improve the prediction accuracy. And then, after obtaining the charge state multi-step transfer probability of the electric trucks, a aggregation load prediction model is established by using high-order Markov chain to achieve more accurate load prediction. Finally, the actual electric truck data of a logistics park is used for simulation verification, and the results show that the proposed load prediction model accurately predicts the aggregation power of electric trucks and reduces the prediction error of the ordinary Markov chain method.

    Orderly Charging Strategy and Application Effect of PV-DC Intelligent EV Chargers
    Yifeng DING, Shuang ZENG, Baoqun ZHANG, Liyong WANG, Chang LIU, Zhi FU, Ji ZHANG
    2024, 57(5):  70-77.  DOI: 10.11930/j.issn.1004-9649.202305101
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    To achieve the goal of carbon peaking and carbon neutrality , building rooftops can be an important scenario for distributed photovoltaics, and electrification of vehicles is a key measure for carbon reduction in the transportation sector. However, the large amount of photovoltaic output and electric vehicle charging demand are mismatched in time, which puts great pressure on the stability of the power grid. This paper proposes an orderly charging strategy for photovoltaic-direct current (PV-DC) intelligent charging station, which can reduce external power supply while meeting charging demand, thus effectively improving the photovoltaic self-consumption capacity and load satisfaction rate. Taking an office building in Beijing as an example, the system performance and technical advantages under different working conditions were analyzed through actual testing and simulation calculations. The results show that the proposed strategy can fully utilize the building photovoltaics to meet the electric vehicle charging demand without the need for external power supply. The load satisfaction rate can reach 100%. Compared with the traditional constant power charging method, the photovoltaic self-consumption rate increased by 42%, while the maximum photovoltaic grid-connected power decreased by 54%. The proposed strategy can provide a reference and guidance for the efficient utilization of building photovoltaics and the electrification of the transportation industry.

    Mechanism and Optimized Operation of the Electricity Carbon Synergy Market Under the New Energy System
    National Carbon Price Prediction Considering Carbon Emissions from the Power Industry
    Yirong WANG, Haolin CHEN, Lishen LIN, Jin TANG
    2024, 57(5):  79-87.  DOI: 10.11930/j.issn.1004-9649.202308084
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    In order to better predict the trend of national carbon prices, a national carbon price prediction model is constructed based on the autoregressive integrated moving average with exogenous variable model (ARIMAX), using different exogenous variables during the fulfillment and non-fulfillment period. Firstly, based on research on the institutional rules of the national carbon market and analysis of trading characteristics, it is found that the national carbon price is mainly influenced by the expectations of participants during the non-fulfillment period, and is mainly driven by the fulfillment demand of enterprises during the fulfillment period. Secondly, in terms of model training, an autoregressive differential moving average model is adopted to introduce different exogenous variables at different stages to improve the effectiveness of carbon price prediction. Finally, the real price data of the first compliance period in the national carbon market are used for verification, and the results show that the proposed national carbon price prediction model in this article is superior to the benchmark model in terms of accuracy.

    Carbon Emission Factors and Decoupling Effects of China's Power Industry under the Background of Carbon Peak
    Xudong LI, Qingbo TAN, Haochen ZHAO, Ning QIAO, Liwei LIU, Caixia TAN, Zhongfu TAN
    2024, 57(5):  88-98.  DOI: 10.11930/j.issn.1004-9649.202306019
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    It is of great practical significance to explore the driving factors and decoupling effects of CO2 emissions in electric power industry, which can not only promote the realization of "dual carbon" goal, but also improve the overall environmental quality in China. In this paper, the CO2 emissions of China's electric power industry from 2004 to 2020 are estimated, and the driving factors and decoupling status of CO2 emissions of the electric power industry are studied with the LMDI model and Tapio decoupling model. On this basis, the CO2 emissions and the decoupling status of the electric power industry during 2021-2030 were analyzed based on the SSA-LSSVM prediction model. The results show that: (1) the economic growth is the main factor of CO2 emission growth in the electric power industry, and the effects of power production structure and power production intensity have an obvious inhibition effects on CO2 emissions; (2) during the whole study period, the CO2 emissions from the power industry was in a weak decoupling status from economic growth; (3) from the predicted value of CO2 emissions from the electric power industry, the CO2 emissions from the electric power industry show an upward trend under the baseline scenario, low-carbon scenario and strong low-carbon scenario, and the CO2 emissions from the electric power industry is in a weak decoupling status from economic growth during 2022-2030. Based on the research results, in order to reduce the CO2 emissions of China's electric power industry, it is proposed to change the economic growth mode to achieve the green and low-carbon economic growth, to develop clean energy to build a new power system, to promote low-carbon technology innovation to realize the decoupling of carbon emissions of electric power industry.

    Carbon Allocation Throughout the Entire Process of Electric Energy Trading Considering Spot Trading
    Wenlong LI, Yun ZHOU, Ling LUO, Tiantian CHEN, Donghan FENG
    2024, 57(5):  99-112.  DOI: 10.11930/j.issn.1004-9649.202401120
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    In the context of carbon peaking and carbon neutrality goals, power consumers are increasingly paying attention to the environmental attributes associated with traded energy in power transactions. Therefore, the development of a trading-based method for allocating carbon responsibility to power consumers is imperative. This paper fully considers the trading characteristics across the entire process of electric energy trading, proposes a new carbon emission flow model and new solution method for spot trading, designs a connection mechanism for the carbon emission flow of each process of electric energy trading, and establishes a carbon allocation method throughout the entire process of electric energy trading considering spot trading. Finally, this paper conducted numerical analysis using the PJM 5-bus system, IEEE 39-bus system and IEEE 118-bus system. The results demonstrate that the proposed method can stably and reasonably achieve the allocation of spot trading carbon responsibility, assist market participants in managing carbon responsibility risks, efficiently model the impact of the entire process of electric energy trading on carbon allocation, and demonstrate efficient computational performance.

    Analysis Model to Study the Influence of Electrocarbon Coupling on Settlement Price of Coal Power Units in Spot Market
    Xiangguang LI, Qingbo TAN, Fanqi LI, Xudong LI, Zhongfu TAN
    2024, 57(5):  113-125.  DOI: 10.11930/j.issn.1004-9649.202301056
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    The coal power industry has generated the largest proportion of carbon dioxide emissions, which makes itself the first entity to be introduced into the national carbon market, while carbon emission costs will also have certain impacts on the settlement of electricity prices in the coal power spot market. Hence, this paper constructs a bidding scheduling model for coal-fired power units in the spot market and conducts simulation analysis with and without considering the cost of carbon emissions respectively. Finally by taking the power market of Guangdong Province as the study case, the changes in the spot market bidding and clearance of coal power units are simulated under the scenarios of so-called "with vs. without wind power output" and "different carbon markets". The results show that with the gradual improvement of the carbon market, the price and total quota amount of carbon are further tightened, and the bid price of coal-fired power units increases gradually, so does the settlement electricity price in the spot market. The average electricity clearing prices in the light, moderate, and heavy carbon markets in the summer with wind and light are 0.1607 CNY/(kW·h), 0.1863 CNY/(kW·h), and 0.2461 CNY/(kW·h), respectively, up by 0.18%, 16.14%, and 53.41% compared to the scenarios when the carbon market is not introduced.

    Price Decision Optimization for Electricity Retailers Based on Dual Game under Carbon Constraints
    Fangshu LI, Kun YU, Xingying CHEN, Haochen HUA
    2024, 57(5):  126-136.  DOI: 10.11930/j.issn.1004-9649.202309065
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    Under carbon constraints, multiple retailers competing for sale of electricity and multiple users purchasing electricity in the market bring challenges to electricity retailers in setting their selling price to increase their own profits. Thereby, a dual game price decision optimization model for multiple electricity retailers competing for sale of electricity and multiple users purchasing electricity is proposed to address the issue of retail electricity pricing under carbon constraints. Firstly, a decision-making model for each market participant is established. Secondly, a dual game framework is established for electricity retailers, in which non-cooperative games are formed among electricity retailers, and master-slave games are formed between retailers and users. And then, the loop nested solver IPOPT is used to solve the dual game problem of retailers. Finally, the proposed model is validated and analyzed through simulation examples. The simulation shows that the retailers can realize their profit maximization through the proposed model to optimize reasonable electricity selling price, and at the same time the users can obtain the optimal electricity purchase strategy through the game model to improve their comprehensive satisfaction of electricity consumption.

    Low-Carbon-Economic Collaborative Optimal Dispatching of Microgrid Considering Electricity-Hydrogen Integration
    Lingling TAN, Wei TANG, Dongqing CHU, Zihan YU, Xingquan JI, Yumin ZHANG
    2024, 57(5):  137-148.  DOI: 10.11930/j.issn.1004-9649.202312018
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    In order to solve the problem that microgrids lack accurate characterization of carbon emission flow, which leads to the increasing contradiction between the low-carbon operation mode and the economic operation mode in operation level and the poor balance between low-carbon and economy, this paper constructs an electricity-hydrogen integrated operation framework considering power to hydrogen (P2H) and and multi-energy coupling, and proposes a low-carbon - economic collaborative double-layer optimal dispatching model for microgrid considering carbon emission flow and low-carbon demand response. In the upper optimization dispatching model, with the integrated wind, solar and hydrogen storage industrial park as a typical microgrid scenario and the coordinated optimization of low-carbon and economy as the objective, the source-side carbon trading mechanism is integrated into the dispatching decision-making objective to fully explore the low-carbon - economy coordinated operation mode of microgrid, with which the optimal low-carbon economic dispatching strategy of microgrid is formulated. In the low-carbon demand response model, with the saved carbon emission costs by reduced carbon emissions of microgrid users as an incentive signal, a mapping relationship between energy flow and carbon emission flow of microgrid is established to realize the transfer and allocation of carbon emission responsibilities and fine evaluation of carbon emission characteristics of microgrid operations, so as to establish a low-carbon DR model which guides users to participate in carbon emission reduction strategies according to the spatial and temporal differences of load carbon emission intensity, and to deeply explore the synergy between low-carbon and economy of source-load of microgrid. Finally, a case study of an industrial park microgrid is conducted to verify the effectiveness of the proposed model.

    Power System
    Stochastic Optimal of Integrated Energy System in Low-Carbon Parks Considering Carbon Capture Storage and Power to Hydrogen
    Jiangnan LI, Renli CHENG, Baorong ZHOU, Jiajin LIU, Tian MAO, Wenmeng ZHAO, Tao WANG, Guanglei HUANG, Yinliang XU
    2024, 57(5):  149-156.  DOI: 10.11930/j.issn.1004-9649.202303109
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    In response to the needs of zero-carbon transformation in industrial parks, a multi-energy complementary technology combination of "new energy power generation + hydrogen energy storage" and "thermal power + carbon capture" has been established. Considering add the corresponding emission reduction of hydrogen vehicles to carbon trading and the direct sale of hydrogen energy to hydrogen vehicles, the system aims to achieve optimal economy and low-carbon performance. Besides, the system quantifies low-carbon benefits through the consumption rate of renewable energy, new energy abandonment rate and emission reduction of hydrogen vehicles. The results show that the system can not only increase the consumption of wind and solar new energy, reduce carbon emissions, but also reduce operational costs. The system can verify the feasibility of the transformation of the zero-carbon park.

    Transient Stability Assessment of Graph Attention Networks Considering Data Missing
    Shengcun ZHOU, Yi LUO, Xuancheng YI, Yaning WU, Ding LI, Yi XIONG
    2024, 57(5):  157-167.  DOI: 10.11930/j.issn.1004-9649.202307019
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    The performance of the transient stability assessment model based on artificial intelligence is highly dependent on the observability of the system, while the factors such as communication delays and PMU faults can easily lead to data missing, which degrades the model's assessment performance. To address this problem, this paper proposes a transient stability assessment model based on graph attention network (GAT). Firstly, a mask matrix representing the system observability is obtained based on the original network topology and PMU configuration scheme, and the mask matrix is used to train the model under the condition of any PMU missing. Secondly, the spatio-temporal information of the input node is extracted through the multi-head attention mechanism of the GAT network, and different weights are used to aggregate the neighborhood characteristics of the target node to make full use of observable data. Finally, the focus loss function is used to enhance the model's learning ability for unstable samples. The simulation results show that the proposed method can maximize the use of observable data with high precision and strong robustness, and is not limited by the network topology and easy to migrate.

    Low-Voltage Substation Area Topology Recognition Method Based on AKNN Anomaly Detection and ADPC Clustering
    Ziyi SHI, Xiangyang XIA, Jiabin LIU, Yangyang GU, Yulong WANG, Jiayao HONG
    2024, 57(5):  168-177.  DOI: 10.11930/j.issn.1004-9649.202307030
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    The accurate record of topology information of the low-voltage station area is the basis for line loss analysis and three-phase imbalance control. Aiming at the problem of high cost and low efficiency of topology file investigation at present, a low-voltage substation area topology recognition method is proposed based on adaptive k nearest neighbor (AKNN) anomaly detection and adaptive density peaks clustering (ADPC). The similarity of voltage series between users in the low-voltage substation area is measured using dynamic time warping (DTW), and the abnormal relationship between users and transformer is checked and corrected with the AKNN anomaly detection algorithm. After getting the right relationship, the ADPC algorithm is used to identify the phase for users in the substation area. Finally, the case study of the actual substation area proves that the proposed method can effectively realize the topology identification of the low-voltage substation area without human parameter setting, and has high applicability and accuracy.

    Surrogate Model-based Fast Calculation of Power Cable Temperature Field: Method and Application
    Li HUANG, Yun LIANG, Hui HUANG, Xiaoyan SUN, Shan WANG, Yuqiang YANG
    2024, 57(5):  178-187.  DOI: 10.11930/j.issn.1004-9649.202306070
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    Accurately predicting the temperature inside cables is crucial for effective cable management. However, this process can be time-consuming through numerical calculations and experimental measurements. To address this issue, a new method for constructing surrogate model based on multi-physics field simulation data is proposed to calculate the cable's internal temperature field quickly and accurately. A single-core 110 kV high-voltage cable is taken as a study subject. Firstly, the Latin hypercube design and the Latin hypercube method optimized by the maximum-minimum idea are compared in constructing the sample space. The optimal sampling scheme is selected to construct the surrogate mode through RBF neural network, and a temperature data set is created for testing using ambient temperature and load capacity. Additionally, the surrogate model is optimized by the particle swarm algorithm, and the temperature field distribution is visualized with the grid node data. The calculation result of the internal temperature of the 110 kV single-core high-voltage cable shows that the proposed surrogate model-based fast calculation method for power cable temperature field has high accuracy and efficiency.

    Fault Location Method for Distribution Network with Photovoltaic Power Based on Negative Sequence Component
    Manlin HU, Nan LI, Yiming LI, Zhiyong PI, Yi ZHU, Zhenxing LI
    2024, 57(5):  188-199.  DOI: 10.11930/j.issn.1004-9649.202307027
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    In order to solve the problem that the fault current can not correctly indicate the fault section due to the connection of photovoltaic power to the distribution network, the three-sequence component of the faulty distribution network with photovoltaic power generation is analyzed, and the negative sequence component is selected as the fault characteristic quantity. A negative sequence reconstruction scheme is proposed for the asymmetric fault scenario. The MK trend detection is used to search the negative-sequence reconstruction sequence with the most obvious fault characteristics. The negative sequence reconstruction voltage amplitude is used to determine the suspicious fault section, and the fault section is finally determined by the phase direction of negative sequence reconstruction current. A distribution network model with photovoltaic power generation is established with the PSCAD/EMTDC simulation platform. The results show that the proposed method can accurately locate the fault section of the distribution network with photovoltaic power generation under different fault types and fault locations, and can provide a theoretical basis for the rapid fault location of the distribution network with photovoltaic power generation.

    SF6 Gas Temperature Hysteresis Model Based Density Monitoring Failure Judgement Criterion
    Bangchao ZHU, Qiongling SHANG, Zhuxian HUANG
    2024, 57(5):  200-210.  DOI: 10.11930/j.issn.1004-9649.202311004
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    The malfunctioning of SF6 gas density monitoring in GIS equipment could erroneously indicate a leakage, potentially compromising the electrical grid's safe operation. In response, a thermodynamics-based temperature hysteresis model for SF6 gas was developed and refined, tailored for digital gas density monitoring apparatus. The model's unknown parameters were deduced from temperature hysteresis experiments. The simulations highlighted a temperature compensation deviation in the SF6 gas temperature hysteresis model of ±0.6 ℃, with a pressure calculation discrepancy, corrected for temperature, showing only ±0.002 MPa against sensor-detected pressures. A strategic approach for identifying density monitoring failures was devised, which relies on the corroboration between calculated and sensor-detected pressures. This approach was put to the test using data from a pilot device in a 220 kV substation. After applying temperature adjustments through the SF6 gas temperature hysteresis model and recalibrating the pressure to standard conditions at 20 ℃, the results remained within a tight margin of 0.002 MPa, attesting to the model's precision and the practicality of the proposed SF6 gas density monitoring failure detection strategy in operational environments.

    New Energy
    Adaptive VSG Control Strategy of Sending End for Large-Scale Renewable Energy Connected to Weakly-Synchronized Support VSC-HVDC System
    Zimin ZHU, Jinfang ZHANG, Qing CHANG, Zhuan ZHOU, Xiaolin ZHANG
    2024, 57(5):  211-221.  DOI: 10.11930/j.issn.1004-9649.202304066
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    Renewable energy collection and output through voltage source converter based high voltage direct current (VSC-HVDC) technology is an effective way to promote renewable energy consumption. However, the continuous increase in the penetration rate of renewable energy has led to a continuous decline in the strength of the power grid, and the traditional grid-following (GFL) converter technology can no longer meet the needs of stable system operation. In order to improve the adaptability of the weak grid of the system and meet the application scenarios of large-scale renewable energy connected to weakly-synchronized support VSC-HVDC system, this paper proposes to adopt VSG control strategy in the converter station of the sending end of the VSC-HVDC system. Firstly, a small signal mathematical model of rectifier-side control is established, and the influence of virtual impedance on system stability is studied by the root locus method. Secondly, an improved virtual synchronous generator (VSG) control algorithm for constructing virtual reactance adaptive adjustment items using electrical quantities such as alternating voltage (AC) change rate and voltage difference is proposed, which can improve the equivalent short-circuit ratio of the AC system at the sending end while ensuring that the equivalent impedance of the system is inductive, so as to improve the overall performance of the system. Finally, the effectiveness of the proposed control strategy is verified by PSCAD/EMTDC electromagnetic transient simulation.

    Anomaly Detection for Distributed Photovoltaic Systems Based on STL-Bayesian Spatio-Temporal Model
    Yunyi LIU, Yuan TANG, Sheng SU, Yuzhou WU, Xiaoqian WANG
    2024, 57(5):  222-231.  DOI: 10.11930/j.issn.1004-9649.202305120
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    Distributed photovoltaic (PV) power generation systems generally do not come equipped with a variety of sensors and monitoring devices, limiting the data available for reflecting equipment operation and conducting anomaly detection. This article proposes a PV anomaly detection method based on the STL-Bayesian spatio-temporal model, which utilizes the spatio-temporal transferability of meteorological data to uncover the correlation of PV power output and perform anomaly detection. Firstly, the seasonal and trend decomposition using Loess (STL) is employed to decompose the PV active power time series data into three components. Then, the influence of different lengths of input data on the decomposition results and the spatio-temporal distribution characteristics of the components within the region are investigated. Subsequently, Bayesian models are constructed to perform short-term and ultra-short-term spatial interpolation on the trend component and the residual component, respectively, so as to obtain the PV output within the region. Finally, the earth move's distance (EMD) between the actual values and regression values is calculated to detect abnormal states. The analysis of the algorithm shows that the model has a high accuracy in the detection of both reversible and irreversible anomalies under distributed PV scenarios.

    Study on Vertical Extrapolation Model of Wind Speed in Inland Complex Wind Farms
    Rui HU, Shuzhou WU, Yonghua LI, Jiafei QIAO
    2024, 57(5):  232-239.  DOI: 10.11930/j.issn.1004-9649.202305049
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    According to the evaluation of onshore wind energy resources, the hub height of the wind turbine is greater than the height of the wind tower. The influences of atmospheric stability and surface roughness changes are ignored, and the wind shear index is affected by ground topography and geomorphic factors. Moreover, the wind speed is difficult to predict accurately when increased to the hub height. Therefore, based on Monin Obukhov's similarity theory and the least squares calculation model of dynamic roughness, a fitting method based on atmospheric thermal stability under neutral conditions was established. Firstly the spatial distribution of wind shear index at different locations and its correlation with changes in atmospheric stability were evaluated. Secondly, based on the measured data of onshore wind farms with two different landforms, the proposed method was compared with the commonly used calculation scheme of wind speed at hub height using comprehensive shear extrapolation. The results show that the daily variation of wind shear index is correlated with that of atmospheric stability. The new calculation method of the extrapolated wind speed model can evaluate the vertical distribution of wind speed at target height more accurately.

    Research on Development Scenario of Renewable Energy in Receiving-End Power Grid Based on Production Simulation
    Shuai WANG, Yuehui HUANG, Yuanhong NIE, Siyang LIU
    2024, 57(5):  240-250.  DOI: 10.11930/j.issn.1004-9649.202307086
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    Driven by the goals of "carbon peaking and carbon neutrality", the large-scale integration of renewable energy into the receiving-end power grid, coupled with the increasing proportion of external electricity, has made it more difficult to balance electricity and achieve clean substitution in the receiving-end power grid. Based on actual operating data, the low output characteristics of renewable energy and its matching characteristics with load were analyzed, and the key issues faced by high-proportion renewable energy power systems were summarized. On this basis, a time series production simulation model for the power system was established, and the scenario for the 2060 case was designed based on the standardization of the 2030 forecast data. Quantitative analysis was conducted on the allocation of renewable energy, coal-fired power, and flexible resources in typical provincial receiving-end power grids, as well as the implementation of clean substitution solutions in different renewable energy development scenarios, and relevant measures and suggestions were proposed.

    Technology and Economics
    Prediction of Transmission Line Cost Based on Embedding Method and Ensemble Learning
    Yuming YE, Qiqi QIAN, Zhengdong WAN, Jigang ZHANG
    2024, 57(5):  251-260.  DOI: 10.11930/j.issn.1004-9649.202303125
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    Accurate prediction of transmission line project cost is of great significance to construction quality and cost control. Since the feature dimension in the traditional transmission line project cost prediction is too high and a single prediction model is difficult to fit the complex cost data, a transmission line project cost prediction method is proposed based on embedding dimensionality reduction and ensemble learning. Firstly, the features are sorted with the embedding method and the XGBoost model to screen out the features that have a significant impact on the cost, achieving the data dimensionality reduction. Then the XGBoost, random forest, SVM and other models are integrated to form a two-layer ensemble learning model. Finally, a case study is carried out based on the data of real transmission line projects, and the proposed method is compared with the XGBoost, random forest, SVM, ELM, and BP neural network models. The rusults show that the mean absolute percentage error of the proposed method is within 4%, which is superior to other single model, and is of great value to the research of transmission line project cost control.