• Adaptive Linear Quadratic Gaussian Primary Frequency Regulation Control Strategy for Doubly-fed Wind Turbines Considering Fatigue Loads

  LIU Jun, QUAN Zhechen, ZHU Shixiang, WU Peidong

  Available online:March 14, 2025  DOI: 10.7500/AEPS20240207003

  Abstract(0) HTML(0) PDF(3.02 M)( 0)

  Abstract:In view of the problem that the fatigue loads of doubly-fed wind turbines will significantly increase when participating in power system frequency regulation, an adaptive linear quadratic Gaussian (LQG) primary frequency regulation control strategy for doubly-fed wind turbines considering fatigue loads is proposed. Two linearized doubly-fed wind turbine frequency regulation models are constructed using the small-signal method, one below the rated wind speed and the other above the rated wind speed. The expected output power of the wind turbines is determined by the inertial response and static droop characteristics of the equivalent synchronous generator. A performance index function with the goal of minimizing the difference between the expected output power and the actual output power of the wind turbine, as well as the fatigue load, is constructed. The optimal frequency regulation power of the wind turbine is obtained in real-time by quickly solving the Riccati equation of the performance index function, thereby achieving primary frequency regulation control while reducing the fatigue load of the wind turbine. Furthermore, a method of adaptively adjusting the weight of the performance index function according to the remaining rotor kinetic energy and frequency deviation of the wind turbine is proposed to further optimize the frequency regulation capability of the unit. The simulation results show that the proposed strategy can effectively improve the frequency regulation capability of wind turbines while reducing the fatigue load of the units.

• Bi-level Energy Scheduling Strategy for Hydrogen-powered Ships Combined with Multi-step Load Forecasting

  JIANG Jundao, ZOU Liang, LIU Xingdou, HAN Zhiyun, WANG Rui, PANG Shuo

  Available online:March 14, 2025  DOI: 10.7500/AEPS20241030006

  Abstract(2) HTML(0) PDF(2.40 M)( 3)

  Abstract:Hydrogen-powered ships using hydrogen fuel cells and lithium batteries as hybrid power system are gradually becoming one of the development directions for green shipping. Existing energy scheduling strategies struggle to respond in real-time to complex operating conditions and frequent load fluctuations, leading to increased energy consumption and accelerated degradation of system performance. To reduce operation costs, a bi-level energy scheduling strategy combined with multi-step load forecasting is proposed. First, to address the high dynamics and randomness of ship loads, a second-level ship load forecasting model is developed. This model integrates robust local mean decomposition (RLMD) and improved vector autoregressive integrated moving average (VARIMA) based on the It? process (IP), which performs real-time multi-step forecasting for ship loads. Then, the proposed bi-level model predictive control (MPC) strategy is used for energy optimization scheduling. Combined with the future ship load sequence provided by the load forecasting model, the upper-level MPC optimizes the efficiency of hydrogen fuel cells and maintains the state of charge of the lithium batteries in a healthy range, while the lower-level MPC minimizes the fuel costs and degradation costs of both types of energy supply equipment. Finally, hydrogen fuel cells and lithium batteries perform energy scheduling based on the command signals output by the lower-level MPC. Experimental results demonstrate that the proposed strategy significantly improves the operation economy and durability of hydrogen-powered ships compared to other methods.

• Optimal Planning of Power System Considering N-1 Safety Criteria and Low-carbon Economic Contribution

  ZHANG Yumin, YU Zihan, YE Pingfeng, JI Xingquan, YANG Ming, WANG Chengfu

  Available online:March 14, 2025  DOI: 10.7500/AEPS20240717003

  Abstract(4) HTML(4) PDF(2.02 M)( 10)

  Abstract:To fully explore the potential value of different components in the economy and low-carbon operation of the power system and reduce the safety risk of system operation, a bi-level planning model for the power system is proposed, which takes into account the N-1 safety criteria and the low-carbon economic contribution (LCEC) of components. At the planning level, the contribution of each unit and line to be built to the low-carbon and economy of the system is characterized through LCEC, and the contribution results are combined with N-1 safety criteria in the form of membership function to realize the optimal matching of low-carbon and economy in space. At the operation level, aiming at analyzing the synergy ability between low-carbon operation and economic operation, the LCEC of each unit and line to be built in the current planning scheme is calculated, and then the contribution weight of each unit and line to the low-carbon and economy synergy operation of the power grid is obtained. On this basis, the gradual expansion method is used to calculate the effectiveness indicators of each line one by one, and it is used as one of the influencing factors for evaluating the construction sequence of the line, reducing the potential safety risks in the low-carbon and economy synergy operation. The practicality of the proposed model is validated through improved PJM-5 and IEEE RTS-24 node systems.

• Reinforcement Learning Based Dynamic Aggregation and Multi-timescale Bidding Strategy for Virtual Power Plants

  GAO Yuanhao, ZHANG Jihang, ZHAO Yincheng, GUO Mingxing, WANG Su, JIANG Chuanwen, WANG Lingling

  Available online:March 14, 2025  DOI: 10.7500/AEPS20240430012

  Abstract(4) HTML(2) PDF(1.88 M)( 11)

  Abstract:With the acceleration of the construction of new power systems, virtual power plants have become an important means to aggregate flexible resources and participate in market transactions. In order to improve the market competitiveness of virtual power plants, a dynamic aggregation and multi-timescale bidding strategy for virtual power plants is proposed. Firstly, considering the adjustable capability of flexibility resources, a “cloud-edge-end” hierarchical and partitioned scheduling architecture of virtual power plants is constructed. Secondly, on this basis, considering the internal communication cost of virtual power plants and the degree of market fit, a hierarchical and partitioned aggregation strategy is obtained and the external characteristic identification is carried out. Then, a multi-timescale bidding model of virtual power plants is constructed and solved by using a constraint-aware deep reinforcement learning method to obtain the bidding strategy. Finally, the proposed dynamic aggregation method and bidding model are verified through case analysis to reduce the cost and operation risk of virtual power plants and improve the overall revenue.

• Gridding Charging Demand Forecasting for Private Electric Vehicles Considering Regional Differences

  WEN Xin, HUANG Xueliang, GAO Shan, LIU Yu, GU Yaru

  Available online:March 13, 2025  DOI: 10.7500/AEPS20231207002

  Abstract(12) HTML(2) PDF(4.73 M)( 15)

  Abstract:To a certain extent, the difference in the location of geographical regions and the difference in functional attributes of regional grids result in the great difference in electric vehicle charging load within each grid. Because of the insufficient consideration of the difference in dynamic charging load distribution of electric vehicles in current research on electric vehicle charging demand forecasting, this paper proposes a data-driven gridding charging demand forecasting method for private electric vehicles considering the differences of geographical regions and the diversity of user trips. Firstly, data mining on the travel tracks of private electric vehicle users, the urban traffic network, and other data types is conducted. Mathematical models are constructed to obtain the origin-destination information of multi-stage trips and the basic travel patterns of private electric vehicle users. Secondly, the latitude and longitude coordinates of each point of interest (POI) are mapped to the geographic network based on the geographic information system platform. Various POI quantity sets combined with users’ daily travel purposes in the geographic area grid are classified. The natural classification method is adopted to implement accurate grid division of the studied geographic area. The functional area grid includes five categories: the work area, the business area, the living area, the residential area, and the mixed area. An origin-destination information probability matrix for each functional area is established in multiple periods. Combined with the obtained distribution results of private electric vehicles in each grid, this paper establishes an electric vehicle charging load forecasting model based on the Monte Carlo method to capture the continuous changes of electric vehicle electricity amount transferred between grids. Based on the actual historical data of electric vehicles in Suzhou, China, and taking a region of Suzhou as the application environment, the simulation of charging demand forecasting for private electric vehicles in each functional region is completed. The simulation results verify the rationality of regional grid division and the accuracy of charging demand forecasting.

• Optimization Strategy for Rotor-side Converter of Grid-forming DFIG-based Wind Turbines Under Unbalanced Weak Grid

  XU Shang, XIE Zhen, YANG Shuxin, LI Mengjie, YANG Shuying, ZHANG Xing

  Available online:March 13, 2025  DOI: 10.7500/AEPS20240513004

  Abstract(9) HTML(0) PDF(4.38 M)( 14)

  Abstract:When doubly-fed wind turbine units operate under unbalanced power grid, the system has positive and negative sequence paths. With the weakening of power grid strength, the coupling between the turbine and the grid intensifies, and disturbances in unbalanced stator voltage will affect the normal operation of the system. In order to study the unbalanced operation and stability mechanism of doubly-fed wind turbine units under weak grid, firstly, a small-signal state-space model of doubly-fed wind turbine unit is constructed, and the stability and unbalance degree of positive and negative sequence rotor current control are analyzed based on the system model. Then, the optimal control strategy based on adaptive virtual impedance is proposed to realize the cooperative compensation of the unbalanced stator voltage and current under the weak grid. The theoretical analysis proves that the proposed strategy effectively improves the stability of the doubly-fed wind turbine units under the unbalanced weak grid. Finally, through the hardware-in-the-loop experiment platform, the effectiveness of the proposed optimization strategy is verified.

• Multi-time-scale Collaborative Optimization Strategy for Shared Storage Capacity Considering Coupling of Hydro-Electric Risk

  ZHOU Sheng, PENG Lizhuo, REN Jiaorong, GAO Qiang, YANG Li, LIN Zhenzhi

  Available online:March 13, 2025  DOI: 10.7500/AEPS20240717008

  Abstract(6) HTML(0) PDF(2.75 M)( 12)

  Abstract:Frequent extreme weather events in recent years have increased the risk of water level control and capability operation in the hydropower enriched virtual power plant (HEVPP). First, to enhance the risk response ability of HEVPP on multiple time scales, an optimal operation model of HEVPP shared storage capacity considering the coupling of hydro-electric risk is proposed. Among them, the upper-layer model constructs a cross-month rolling optimization model considering the uncertainty of incoming water volume, decomposing the annual operation plan to obtain the end-of-month water level guidance boundary and monthly electricity plan. The lower-layer model proposes an overall risk description method for HEVPP based on the joint opportunity constraints of reserve capacity, constructs a monthly output and reserve model considering the deviation of hydro-electric operation, and forms a two-layer optimal model of HEVPP considering the end-of-month water level connection of shared storage capacity to deal with the potential hydro-electric risk during the rolling cycle. Then, based on the principle of risk sharing, a joint opportunity constraint transformation method for the monthly shared storage capacity in the lower-layer model is proposed, achieving the effective decoupling of HEVPP hydro-electric operation constraints. Finally, taking an HEVPP in Lishui City, Zhejiang Province, China as an example for numerical analysis. The results show that through rolling optimization of HEVPP operation plan, the multi-time-scale coupling of hydro-electric risk can be effectively dealt with, and the proposed reserve capacity allocation method can improve the water level control capability and economic benefits of each internal hydropower station.

• Review on Research of Power-Transportation Fusion: Models, Algorithms and Key Problems

  YANG Meng, CHEN Yue, XU Xiaoyuan, WANG Han, WEI Wei

  Available online:March 13, 2025  DOI: 10.7500/AEPS20240305007

  Abstract(11) HTML(2) PDF(2.20 M)( 15)

  Abstract:The substantial growth in the number of electric vehicles has significantly deepened the integration of power systems and urban transportation systems, bringing both opportunities and challenges. Research on power-transportation fusion is burgeoning. First, common traffic assignment models according to time scales and application conditions are categorized. On this basis, coupling architectures and modeling methods for power-transportation systems are summarized. Furthermore, effective algorithms for addressing the solving difficulties in power-transportation systems, including the model nonconvexity and nonlinearity, multi-agent participation, and uncertainty management, are introduced. Then, recent advances in key problems of power-transportation system such as expansion planning, economic operation, low-carbon operation, peer-to-peer trading, and emergency restoration are elaborated in detail. Finally, the key technologies of future power-transportation coupling systems in research objects such as shared electric vehicles, hydrogen fuel cell vehicles, and mobile charging stations, as well as in research methods such as data-driven and online optimization are analyzed and prospected

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Volume 49,2025 Issue 4

>Risk Assessment of Electricity Power Balance and Improvement of Power Protection Capability of New Power System Under Extreme Weather Conditions

• Electric Power and Energy Balancing System for Power System with Renewable Energy in Extreme Weather

  YE Lin, PEI Ming, YANG Jianbin, SONG Xuri, LUO Yadi, ZHANG Zhenyu, YU Yijun, TANG Yong

  2025,49(4):2-18, DOI: 10.7500/AEPS20240506001

  Abstract(152) HTML(367) PDF(5.06 M)( 381)

  Abstract:Frequent extreme weather events pose significant challenges to the balance of electric power and energy in the renewable energy power system. The system security risks caused by extreme weather are gradually increasing. Starting from the impact of extreme weather on the source-grid-load aspects of the power system with renewable energy, this paper analyzes and discusses the electric power and energy balancing system in extreme weather. It covers multi-source data integration of resources, meteorology, environment, and power grids, renewable energy resource assessment, and power prediction of renewable energy, electric power balance, energy balance, and optimal scheduling in extreme weather. First, this paper reveals the interaction influence mechanisms of data of resource, meteorology, environment and power grid in time and space, proposes a method for expanding small samples in extreme weather, and establishes a model for assessing renewable energy resources in extreme weather. Based on this, this paper establishes a theoretical system of multi-timescale source-load combined prediction in extreme weather and proposes a vector-based evaluation method of prediction errors, to provide decision support for the optimization of electric power and energy balance of the power system with renewable energy. Based on prediction, taking frequency security as the main factor, this paper proposes methods and strategies for electric power balance in extreme weather and analyzes the influences of extreme weather events on the energy balance at different timescales, to ensure energy balance from multiple perspectives, including the generation side, power grid side and load side. The optimal dispatching framework for the renewable energy power system in extreme weather is proposed. Finally, this paper prospects the research topics for the power system with renewable energy in ensuring the balance of electric power and energy in extreme weather.

• Evaluation of Compound-disaster Failure Rate for Transmission Lines from Perspective of Whole Reductionism Thinking —Part One Framework Design

  XUE Yusheng, CHANG Kang, YU Chen, XUE Feng

  2025,49(4):19-31, DOI: 10.7500/AEPS20240520002

  Abstract(89) HTML(81) PDF(1.21 M)( 307)

  Abstract:The global warming trend has increased the occurring probability of compound disasters. Accurate evaluation of the failure rate for transmission lines under compound disasters is the quantitative basis for the power grid risk warning and emergency decision-making. The method of cumulative failure rate based on independent events of disaster types cannot reflect the time-varying nature of disaster evolution or the coupling characteristics of mutual influence between disaster types. It is difficult to objectively reflect the fault risk faced by transmission lines in the compound disaster scenarios. Whole reductionism thinking (WRT) integrates the global view of holism with the mechanistic view of reductionism, and it is a new paradigm for studying complexity problems. The complexity root of compound disasters is analyzed from the perspective of time-varying and coupling characteristics. From the perspective of disaster-type independent analysis and compound-disaster analysis, the current status and problems faced by evaluation technologies of the failure rate for transmission lines under natural disasters are reviewed. A research approach for the WRT-based comprehensive disaster analysis method is then proposed, and an evaluation framework of “offline optimization based on classification features, and online execution based on actual disaster situations” is established. Several key technologies of comprehensive disaster analysis method are finally discussed.

• Evaluation of Compound-disaster Failure Rate for Transmission Lines from Perspective of Whole Reductionism Thinking —Part Two Algorithm and Application

  CHANG Kang, XUE Yusheng, YU Chen, XUE Feng

  2025,49(4):32-41, DOI: 10.7500/AEPS20240520003

  Abstract(74) HTML(61) PDF(1.96 M)( 274)

  Abstract:In response to the problem that the disaster-type independent analysis is difficult to adapt to the time-varying and coupling characteristics of the complex external environment of the power grid, the first paper in the series established a comprehensive disaster analysis framework based on whole reductionism thinking (WRT). As a sequel, an online evaluation algorithm of failure rate for transmission lines based on comprehensive disaster analysis is proposed. Firstly, according to the coarse-grained characteristics of the time series of the meteorological disaster status and trends, the compound-disaster status and trends are classified and encoded. Secondly, by dividing compound disaster areas, compound geographic feature areas, and overlapping areas of compound disasters and geographic features, a granularity adaptive division method for line segments is proposed. Then, according to the mechanism of “offline optimization based on classification features, and online execution based on actual disaster situations”, the failure rate of each line segment under each type of compound-disaster status and trends is evaluated. Taking the disaster causing process of compound disasters as a whole, the time series of failure rate of each transmission line is obtained by aggregating the failure rates of line segments. Finally, the effectiveness of the proposed method is verified by taking the actual power grid operation data of a certain province in China as a case.

• Risk Assessment and Control of Power System Under Extreme Steady Weather Events

  JIANG Haiyang, JIANG Minghua, DING Kun, DU Ershun, LAN Xinyao, FANG Yuchen, ZHANG Ning, KANG Chongqing

  2025,49(4):42-52, DOI: 10.7500/AEPS20240513006

  Abstract(97) HTML(76) PDF(2.67 M)( 294)

  Abstract:With the high proportion of renewable energy, extreme weather events bring great risks and challenges to power production. Among these extreme weather events, extreme steady weather, as a special atmospheric feature, usually tends to result in continuous low renewable energy output for days, which greatly impacts the long-term supply-demand balance of new power systems. Aiming at the risk of long-term supply-demand imbalance caused by extreme steady weather, a quantitative risk assessment and control method is proposed from the perspective of optimal planning. Firstly, the extreme steady weather for power security is defined, and extreme scenarios are identified and extracted. Then, based on the extracted scenarios, the power production simulation is carried out, and the evaluation index system of long-term power supply-demand imbalance is constructed. On this basis, a risk assessment model of long-term supply-demand imbalance in extreme steady weather based on chance constraints is proposed. Finally, the above-mentioned model is embedded into the traditional power planning model to realize the optimal allocation of long-term flexible resources for the risk management of long-term supply-demand imbalance. Based on the IEEE RTS-79 case analysis, the effectiveness of the proposed method is demonstrated, and the role of long-term energy storage in the risk management of long-term supply-demand imbalance in extreme steady weather is explored.

• Analytical Method for Defense Capability of Large-scale Cascade Hydropower Stations Against Extreme Fluctuations of Renewable Energy in Power Grid

  ZHANG Juntao, CHENG Chuntian, CAO Hui, CAI Huaxiang

  2025,49(4):53-64, DOI: 10.7500/AEPS20240521006

  Abstract(66) HTML(50) PDF(2.76 M)( 236)

  Abstract:Due to the differences in operation conditions during dry and wet seasons, complex relations between water power and electric power, and comprehensive utilization demands such as flood control, ecology, navigation and so on, it is difficult to solve how to evaluate the defense capability of 10 GW-level large-scale cascade hydropower stations against long-period extreme fluctuations of renewable energy. First, the analytical characterization method of extreme fluctuations of renewable energy is proposed, and the extreme fluctuation events are mined from the historical data so that the characteristics of wind power-photovoltaic extreme fluctuations can be revealed. On this basis, a perturbation optimization evaluation model of defense capability of cascade hydropower plants against extreme fluctuations of renewable energy is established. The 0-1 mixed-integer linearization modeling technique is adopted to efficiently solve the mathematical model. Taking a river basin cascade hydropower stations station of a Southwest provincial power grid in China as an application case, the defense capability of cascade hydropower stations against extreme fluctuations of renewable energy and its sensitivity to the renewable energy installed capacity are analyzed, and the results can provide guidance for electric power and energy balance scheduling under extreme weather in power grid.

• Full-scenario Risk Assessment of New Power System Planning Scheme for Multiple Types of Extreme Weather

  LIU Wenxia, LIU Jiayi, WAN Haiyang, WANG Yashu, ZHANG Shuai, FENG Wei, YANG Tianmeng

  2025,49(4):65-78, DOI: 10.7500/AEPS20240430015

  Abstract(89) HTML(68) PDF(1.70 M)( 269)

  Abstract:In the context of frequent occurrence of multiple types of extreme weather and the continuous increase in the proportion of renewable energy, to ensure the safe and stable operation of the future power system under extreme weather conditions, a full-scenario risk assessment method of new power system planning scheme for multiple types of extreme weather is proposed. Firstly, potential extreme weather that causes actual regional power grid risks is screened from historical information, and the mapping relationship between meteorological factors and power grid status is analyzed to propose a power grid risk scenario recognition method based on extreme meteorological conditions. Secondly, a multi-uncertainty model for risk scenarios is established for factors such as source and load power, equipment failures under extreme weather conditions, risk scenarios and their probabilities are generated. At the same time, considering the characteristics of the future power grid and the impact of extreme weather, a targeted risk consequence index system for planning schemes has been proposed from the perspectives of adequacy, flexibility and safety stability. In response to the problem of inaccurate and efficient simulation of the operation status of the actual power grid risk assessment in the future, a practical power grid consequence calculation method based on the state grid planning simulation analysis and calculation platform has been proposed. Finally, the planned power grid in Northeast China in 2025 has been evaluated, and its high-risk areas and risk categories under high-temperature and windless, and blizzard weather conditions have been analyzed.

• Exploration of Temporal-Spatial Distribution Characteristics of Long-period Low-wind-speed Events and Their Impacts on Power Supply

  LYU Xunyan, XIAO Jinyu, HOU Jinming, JIN Chen, JIANG Haiyang, DU Ershun

  2025,49(4):79-89, DOI: 10.7500/AEPS20240419001

  Abstract(70) HTML(47) PDF(2.55 M)( 276)

  Abstract:Energy storage technology provides an important technical means for the new power system to deal with the problem of insufficient system supply adequacy caused by the randomness and volatility of high proportion of renewable energy generation. However, low-probability long-period meteorological events of low wind-speed and low irradiation bring a high-risk impact on the reliable power supply. Considering the spatial complementary characteristics of the event occurrence law, the overall optimization of the renewable energy base location in the planning stage is one of the important technical means to improve the adequacy of renewable energy generation in the receiving area. Based on the high-resolution long-period historical meteorological analysis data set, focusing on the temporal-spatial distribution characteristics of long-period low-wind-speed steady weather events, the definition index of the long-period low-wind-speed steady weather events is proposed, the steady weather screening is completed, and the temporal distribution characteristics of a single geographical grid point are quantitatively analyzed. On this basis, the calculation results of the electric power meteorological region in China based on the spatial morphological characteristics of the event are proposed, and the influence of long-period low-wind-speed steady weather events on the power supply adequacy and the location and layout of renewable energy is quantitatively evaluated .

• Marketization Mechanism Design for Risk Management and Resilience Enhancement of Power System Under Extreme Weather Conditions

  XU Mingqian, LI Gengfeng, BIE Zhaohong, ZOU Wenqiu, LI Minghao, BIAN Yiheng

  2025,49(4):90-102, DOI: 10.7500/AEPS20240428006

  Abstract(73) HTML(385) PDF(1.54 M)( 270)

  Abstract:The resilience enhancement of power systems under natural disaster conditions is of great significance to ensure the safe and stable operation of power systems. Designing the corresponding marketization mechanisms is an important way to enhance the power system resilience and incentivize the investment of electric power resources. Therefore, a marketization mechanism framework for the resilience enhancement of power systems is proposed, and the detailed definitions and modes of resilience insurance and pre-disaster electricity futures markets are given. Subsequently, insurance theory is introduced to establish the resilience insurance model, and the model of the pre-disaster electricity futures market is also proposed. Furthermore, a case analysis is conducted on the resilience insurance model, and the analysis results show that the proposed marketization mechanism can achieve the goal of maximizing the net profit of the insurance company and enhancing the power system resilience at the same time. Finally, the preliminary laws of balancing the resilience and economy of power systems are presented.

• Power Source Planning of New Power System for Guaranteeing Supply-Demand Security Under Extreme Weather

  RUAN Qiantu, YE Rong

  2025,49(4):103-115, DOI: 10.7500/AEPS20231227005

  Abstract(68) HTML(77) PDF(1.85 M)( 341)

  Abstract:In recent years, the increasing extreme weather such as the continuous high-temperature draught and low-temperature ice-coating has caused the power supply-demand imbalance in regional power systems, resulting in the occurrence of large-scale power outages or load curtailment. The main reason is that in the new power system, extreme weather has higher impact on supply-demand balance. Aiming at the long-term climate change, a framework of resilience assessment and enhancement of the power system is illustrated. Firstly, a compact operation model of power systems considering the climate parameters is established to characterize the impact of different types of extreme weather on various power sources, power grids, and loads. Secondly, an extreme scenario generation method based on the modular denoising variational autoencoder (MDVAE) algorithm is proposed. Finally, a multi-type power source expansion planning method of new power systems is proposed for power supply-demand guarantees under the continuous extreme weather. Based on the typical source-load scenario set generated from the extreme weather data in a province in South China, the proposed collaborative planning method is verified by the simulation with IEEE standard cases.

• Optimal Scheduling Method for Power Grid Considering Participation of Electric Vehicles in Flexible Regulation During Cold Wave Weather

  JIAO Zhijie, XU Yin, LIU Zhao, WANG Xiaojun, HE Jinghan, SI Fangyuan

  2025,49(4):116-127, DOI: 10.7500/AEPS20240519001

  Abstract(79) HTML(61) PDF(2.50 M)( 386)

  Abstract:When the arrival of a cold wave triggers a sudden drop in weather temperature, the energy consumption of the load increases, the output of renewable energy sharply decreases and the system backup and the power supply capacity of the higher-level power grid are insufficient, resulting in a significant power supply shortage problem within the power grid in a short time. With the increasing number of electric vehicles on the load side and the improvement in the responsiveness of flexible resources, the load-side flexibility resources are adjusted to compensate for the power shortage caused by the cold wave. This paper first clarifies that the flexibility resources varies with changing scenarios, explores and constructs models of flexible resources such as electric vehicles during cold wave. Secondly, considering that electric vehicles as flexibility resources need to participate in power grid scheduling through aggregation, a method for aggregating electric vehicles with uncertain connection times is developed. Subsequently, based on the flexibility resources during cold wave weather, non-residential loads and residential loads, a rolling optimal scheduling method for the power grid during cold wave weather is proposed with the objective of minimizing social losses. The electricity adjustment of various resources in the grid during cold wave weather is determined. Finally, through case studies, the proposed method is shown to effectively address power supply shortages in the power grid during cold wave weather.

• Multi-timescale Balancing Method for Power Systems Considering Renewable Energy Accommodation and Power Supply Support

  FENG Songjie, WEI Wei

  2025,49(4):128-140, DOI: 10.7500/AEPS20231228005

  Abstract(95) HTML(93) PDF(2.86 M)( 340)

  Abstract:As the proportion of the renewable energy access continues to increase, traditional thermal power is gradually replaced. Ensuring daily peak regulation under conventional conditions and power supply support under adverse weather conditions with limited flexibility is the basis for the safe operation of new power systems. On this basis, a multi-timescale balancing method for power systems considering renewable energy accommodation and power supply support is proposed. Firstly, the optimal scheduling curve of long-term energy storage in several year-round net load scenarios is calculated and taken as the operation experience. Then, in the week-ahead stage, according to the renewable energy power prediction to determine whether it is adverse weather conditions, the power supply support strategy is designed for adverse weather conditions, and a flexibility-based scheduling strategy collaborating day-ahead, intra-day and real-time stages is designed for conventional scenarios. The proposed method does not require the accurate output curve prediction of the renewable energy, and can realize the organic coordination of multi-time scale scheduling from year-round to real-time scheduling, as well as the multi-source coordination of long-term and short-term energy storage and conventional units. Finally, a numerical case is given with reference to a regional power grid data and compared with the traditional rolling optimization method to verify the advantages of the proposed method and the effectiveness of the power supply support strategy.

• Simulation and Analysis of Medium- and Long-term Changes and Confidence Intervals in Power Sources and Loads Under Heat Waves and Cold Waves

  BAI Jie, QIN Xiaohui, DING Baodi, ZHAO Mingxin, LIU Yang

  2025,49(4):141-151, DOI: 10.7500/AEPS20240430008

  Abstract(67) HTML(63) PDF(1.78 M)( 313)

  Abstract:The climate change is intensifying, while the daily variation characteristics and relationships of wind, photovoltaic, and other renewable energy outputs and loads under regional-scale extreme temperature events remain unclear. Therefore, this paper uses independent models to simulate the impact of weather on load and wind, photovoltaic, and hydro power outputs. The joint probability and confidence intervals of outputs are calculated using Copula functions. Based on climate model data, the daily variation characteristics of power output and load as well as the power supply-demand relationship are estimated under heat waves and cold waves in typical provinces of North China and Southwest China by 2030, the target year for China’s carbon emission peak. A novel boosting ensemble learning model is proposed to predict the impact of weather on photovoltaic output, and calibrated with historical measured data. The validation shows that the model achieves a mean daily output error of 1.27% under actual extreme weather conditions, and the mean absolute error is significantly lower than other ensemble learning methods. The medium- and long-term forecast indicates that in 2030, typical provinces in North China and Southwest China are prone to experience power supply shortages during the evening hours of days under heat waves and cold waves, and indicators such as peak values and time of power supply and demand under extreme temperature events in the relevant regions in the future are given.

>Basic Research

• Value-oriented Photovoltaic Power Forecasting Method for Operation Revenue Improvement of Photovoltaic-Energy Storage Plants

  XU Duo, XU Xiaoyuan, QIN Fang, WANG Mengyuan, YAN Zheng, LU Jianyu, YAO Hongchun

  2025,49(4):152-164, DOI: 10.7500/AEPS20240529001

  Abstract(48) HTML(43) PDF(2.59 M)( 235)

  Abstract:The operation of photovoltaic-energy storage plants includes two parts: photovoltaic power forecasting and resource optimal scheduling. The photovoltaic power forecasting is usually carried out first with the goal of the highest forecasting accuracy, and then the optimal scheduling of the photovoltaic-energy storage plant is carried out based on the forecasting curve. However, there is a nonlinear and asymmetric relationship between the objective function value of optimal scheduling problem and the photovoltaic power forecasting error. At the approximate forecasting error level, the photovoltaic power forecasting result aiming at the highest accuracy does not necessarily lead to the maximum operation revenue of the photovoltaic-energy storage plant. In this regard, a value-oriented photovoltaic power forecasting method for improving the operation revenue of photovoltaic-energy storage plants is proposed. Firstly, a bi-level optimization problem is constructed, which includes photovoltaic power forecasting and power plant operation. The upper layer is the training problem of the day-ahead photovoltaic power forecasting model, and the lower layer is the two-stage optimal scheduling problem of day-ahead bidding and intra-day operation for the photovoltaic-energy storage plant under the conditions of given photovoltaic power forecasting model. Then, the upper forecasting problem is transformed into a combined forecasting form, and the forecasting model parameters are set as weight coefficients. A parameter solving method for the forecasting model based on iterative optimization is designed. Finally, using actual photovoltaic power plant data and electricity price data for case analysis, and compared with the photovoltaic power forecasting method that aims to achieve the highest forecasting accuracy, the effectiveness of the proposed method in improving the operation income of power plants is verified.

• Distributed Algorithm for Robust Economic Dispatch of Integrated Transmission and Distribution System Considering Wind Power Uncertainty

  HUANG Xinyi, LIU Mingbo, LIN Shunjiang, ZHU Jianquan

  2025,49(4):165-177, DOI: 10.7500/AEPS20240711004

  Abstract(71) HTML(49) PDF(1.84 M)( 269)

  Abstract:As wind power is widely integrated into transmission systems and distribution systems in centralized and distributed forms, it is a new challenge how to implement the coordinated economic dispatch of integrated transmission and distribution systems to ensure the economy and security of the whole system considering the uncertainty of wind power outputs. In this paper, firstly, a robust economic dispatch model of integrated transmission and distribution systems considering the wind power uncertainties is proposed, and a polyhedral uncertainty set is adopted to describe the uncertainty of wind power outputs. Secondly, under the framework of the column and constraint generation (C&CG) algorithm, a distributed algorithm based on Lipschitz dynamic programming and analytical target cascading is proposed for the C&CG master problem and C&CG sub-problems, respectively, to ensure the relative independence of information between the transmission system and distribution system. Finally, the simulation is carried out on a small integrated transmission and distribution system and an actual integrated transmission and distribution system. The results verify the correctness and effectiveness of the proposed algorithm.

• Medium- and Long-term Variable-resolution Scheduling of Micro-energy Networks with Hydrogen Based on Adaptive Time Division

  LIU Hong, HUI Zhizhou, ZHANG Peng, LI Junkai, ZHANG Shida, YANG Baijie

  2025,49(4):178-187, DOI: 10.7500/AEPS20240326005

  Abstract(44) HTML(42) PDF(1.64 M)( 224)

  Abstract:The medium- and long-term scheduling of traditional microgrids is difficult to take into account the energy cycle process of energy storage during the daily energy balance, which may not only lead to the inability of the daily energy balancing scheme to support the charging and discharging strategy of energy storage during the initial period of the day, but also cannot adapt to the high loss of the electricity-hydrogen conversion process. Therefore, a two-stage stochastic optimal scheduling method combining adaptive period division and variable-resolution is proposed. Firstly, for the “near small and far big” problem of uncertainty, a model of source-load output characteristics based on the modified martingale model is established. Secondly, a two-stage variable-resolution stochastic optimization architecture for the medium- and long-term scheduling of micro-energy networks with hydrogen is constructed. At stage one, an adaptive time division method based on deep neural network is proposed. At stage two, with the goal of minimizing the system operation costs and combining with time-segment chance constraints, the stochastic optimal scheduling models with coarse and fine resolutions are established, respectively. The latter arranges the hourly equipment output plans based on the state of charge of hydrogen storage equipment decided by the former, and a solution scheme based on sampling method is proposed. Finally, the effectiveness of the proposed model and method is verified through numerical simulations.

• Construction and Comprehensive Evaluation Method for Trustworthiness Indicator System of Intelligent Assessment Model for Power Grid Stability

  LIU Huiyu, WANG Yuhong, SHI Fang, ZHOU Xu, LI Baoluo, JI Kaixuan

  2025,49(4):188-202, DOI: 10.7500/AEPS20240508001

  Abstract(48) HTML(58) PDF(1.75 M)( 284)

  Abstract:The untrustworthiness issues of artificial intelligence (AI) algorithm hinder its practical application in the scenarios such as power grid stability analysis and control. At present, there are no specific trustworthiness evaluation indicators applicable to the intelligent assessment model for power grid stability. Regarding the characteristics of the electric power industry, the trustworthiness evaluation for AI stability assessment models is carried out, and the five sub-indicators of correctness, complexity, robustness, transferability and interpretability are chosen to construct the trustworthiness evaluation indicator system of the intelligent assessment model for power grid stability, and the specific calculation methods for each sub-indicator are summarized. Meanwhile, the fuzzy analytic hierarchy process is introduced and combined with subjective and objective evaluation to determine the weights of each sub-indicator and calculate the comprehensive trustworthiness indicator. Finally, by taking the voltage support strength, broadband oscillation, and frequency stability assessment models as a case, the trustworthiness evaluation and analysis are carried out, and the results verify the effectiveness of the proposed method.

• Optimal Control Strategy for Droop-controlled Inverter Considering Source-side Voltage Drop and Fault Reclosing

  PENG Zihao, XIAO Fan, TU Chunming, GUO Qi, XIE Weijie, WANG Qing

  2025,49(4):203-213, DOI: 10.7500/AEPS20240417008

  Abstract(46) HTML(52) PDF(3.88 M)( 220)

  Abstract:The voltage drop on the source side in the distribution network will lead to the transient instability of droop-controlled grid-connected inverters. Subsequent traditional unchecked reclosing will bring overcurrent problems, posing significant challenges to the safe operation of the inverter. To address the problems of transient power angle instability and overcurrent in the droop-controlled inverters due to the source-side voltage drops in the distribution network, as well as the impulse current generated by traditional unchecked automatic reclosing, this paper proposes an optimal control method for droop-controlled inverters during the full cycle of the fault transient. This method effectively improves the grid-connected operation stability of inverters and reduces the impulse current during reclosing to achieve a smooth grid connection. Firstly, the paper analyzes the transient characteristics of the inverter during the fault, and the relationship between the instantaneous overvoltage of the reclosing and the voltage amplitude, phase angle and frequency deviation on both sides of the high-voltage-side circuit breaker. Secondly, during the voltage drop phase, the reference value of the inverter power is dynamically adjusted based on the relationship between the active power of the droop-controlled inverter and the voltage output characteristics of the point of common coupling, as well as the influence of reactive power on the line current amplitude. This adjustment is made according to the actual working conditions, and the transient power angle stability before and after the fault is controlled to limit the output current of the inverter below the safety threshold. During the reclosing transition phase, the comprehensive adjustment mechanism of the frequency-phase angle is introduced to reduce the voltage frequency and phase angle difference. This mechanism reduces the number of proportional-integral controllers in the command calculation link and greatly shortens the time required for quasi-synchronization compared to the existing grid-connected control mechanism. Finally, the correctness of the theoretical analysis and proposed method is validated by combining simulation and experimental results.

• Super-resolution Reconstruction Technology for Power Data Based on Improved Diffusion Model

  XUE Tongdan, WANG Hong, QI Linhai, YAN Jiangyu, JIANG Meijing, TAO Shun

  2025,49(4):214-223, DOI: 10.7500/AEPS20231017002

  Abstract(49) HTML(58) PDF(1.77 M)( 283)

  Abstract:Super-resolution reconstruction for low-frequency power data is helpful to realize the accurate situation awareness and decision analysis of power systems. The existing super-resolution reconstruction algorithms have problems such as imprecise reconstruction result and weak generality. Therefore, a super-resolution reconstruction technology for power data based on improved diffusion model is proposed. The diffusion model can capture subtle and complex features in the data, and can gradually generate high-frequency power data under the guidance of low-frequency data. By combining the long short-term memory network with the diffusion model, the ability of the model to mine time series data is further enhanced, and the ability of super-resolution reconstruction is improved. The power data in load and harmonic scenarios are used for case verification, and the experiments show that the proposed method can accurately reconstruct high-frequency data. At the same time, the model has good generalization and flexibility, which can be applied to untrained electrical parameters and buildings’ data, and can also reconstruct data with different precision.

• Anomaly Detection in Distribution Main Station Logs Based on Large Language Model

  Wang Shen, WEI Xingshen, ZHU Weiping, ZHU Daohua, GUAN Zhitao

  Available online:February 27, 2025  DOI: 10.7500/AEPS20240523001

  Abstract(247) HTML(254) PDF(1016.64 K)( 261)

  Abstract:Log anomaly detection is one of the key technologies to monitor the operation of distribution master station system and identify abnormal behavior. Existing log anomaly detection methods based on deep learning rely on a large amount of in-domain training data, and the scarcity of training data will lead to a significant decline in performance. Aiming at the above problems, based on the contextual reasoning characteristics of large language models, an adaptive hint strategy is designed and a training-free anomaly detection scheme for distribution master logs is implemented. Firstly, a demonstration example filtering algorithm is designed to dynamically select several high-quality demonstration examples from a small number of labeled local logs for different online logs. Then, combined with the task description and human experience knowledge, a text hint is automatically constructed to guide the large language model to complete the anomaly detection task of distribution master station logs. The experimental results on the general data set and the self-built distribution master station data set show that the proposed scheme has better performance than the existing methods, showing higher flexibility and generalization.

• Emergency Frequency Control Strategy of Microgrid Clusters for Extreme Survival of Critical Urban Loads

  WANG Ziyuan, XU Yin, WU Xiangyu, LI Jiaxu

  Available online:February 26, 2025  DOI: 10.7500/AEPS20241024004

  Abstract(99) HTML(50) PDF(1.97 M)( 245)

  Abstract:The proportion of electricity received outside the urban power grid is high, and extreme events leading to connectivity failures in the power transmission channels of the urban superior power grid may cause major power outages. In this extreme scenario, by flexible self-configuration operation and supply guarantee of microgrid clusters, the critical load survival can be achieved. However, the large transient frequency fluctuation of microgrids during sudden power shortages directly affects the success or failure of self-configuration operation and supply guarantee. Firstly, a framework for solving the extreme survival problem of microgrid clusters is proposed, and dynamic frequency constraints for microgrids considering multi-source collaboration are constructed based on the system frequency response model. Secondly, the frequency response model of microgrid containing nonlinear constraints such as ordinary differential equations and limiting links is differentially discretized based on the forward Euler method. Then, according to the generator tripping and load shedding during the transient frequency response of microgrid clusters, a microgrid cluster model for extreme survival is constructed considering inertia equivalent constraints, control action delay constraints and dynamic segmentation constraints. By solving a mixed-integer linear programming model, the emergency frequency control strategy and the segmentation state of microgrid clusters are obtained to coordinate multiple frequency regulation resources and minimize the load shedding volume while ensuring frequency safety. This model can be efficiently solved using mature commercial solvers. Finally, the effectiveness and superiority of the proposed emergency frequency control strategy of microgrid clusters are verified through numerical simulation analysis.

• Optimal Scheduling Method for Power Grid Considering Participation of Electric Vehicles in Flexible Regulation During Cold Wave Weather

  JIAO Zhijie, XU Yin, LIU Zhao, WANG Xiaojun, HE Jinghan, SI Fangyuan

  Available online:December 30, 2024  DOI: 10.7500/AEPS20240519001

  Abstract(65) HTML(156) PDF(3.06 M)( 819)

  Abstract:The arrival of a cold wave triggers a sudden drop in weather temperature, at this time, the energy consumption of the load increases, the output of renewable energy sharply decreases, the system backup and the power supply capacity of the higher-level power grid are insufficient, resulting in a significant power shortage problem within the power grid in a short period of time. With the increasing number of electric vehicles on the load side and the improvement in the responsiveness of flexible resources, the load-side flexible resources are adjusted to compensate compensate for the power shortage caused by the cold wave. This paper first clarifies that the flexibility of resources varies with changing scenarios, explores and constructs models of flexible resources such as electric vehicles during cold wave events. Secondly, considering that electric vehicles in flexible resources need to participate in grid scheduling through aggregation, a method for aggregating electric vehicles with uncertain connection times is developed. Subsequently, based on the flexible resources during cold wave events, non-residential loads, and residential loads, a rolling optimal scheduling method for the power grid during cold wave weather is proposed with the objective of minimizing social losses. The electricity adjustment of various resources in the grid during cold wave weather is determined. Finally, through case studies, the proposed method is shown to effectively address power shortages in the grid during cold wave events.

• Identification Method for Tree-touching Grounding Fault of Medium-voltage Line Based on Long-term Variation Features of Zero-sequence Current

  WANG Pengwei, XU Bingyin, LIANG Dong, WANG Lianhui, WANG Chao, ZOU Guofeng

  Available online:October 31, 2024  DOI: 10.7500/AEPS20240315002

  Abstract(71) HTML(56) PDF(6.79 M)( 157)

  Abstract:Distinguishing whether faults in medium voltage distribution lines are caused by lines touching trees is of great significance for clarifying the causes of forest fires and preventing line faults from causing forest fires. The zero-sequence currents of various high-impedance grounding faults are obtained through prototype experiments in the paper, and the long-term variation features of the zero-sequence current waveforms of high-impedance grounding faults are analyzed. Analysis shows that there are significant differences in the fluctuation, monotonicity, and sharpness of the waveforms of the effective value of the zero-sequence currents for line touching trees grounding faults compared to other high-impedance grounding faults. A multi feature fusion parameter set including standard deviation, discrete coefficient, kurtosis, skewness of the zero-sequence current effective value curve is designed, and a ientification method for tree-touching grounding fault of medium-voltage line based on support vector machine is constructed. The results showed that the proposed method achieved a fault recognition accuracy of 98%.

• Frequency Safety Assessment of Power Systems Based on Multivariable-sample Convolution and Interaction Network

  LIU Jie, SHI Fang, SONG Xuemeng, TIAN Shuoshuo, NIE Liqiang

  Available online:October 17, 2024  DOI: 10.7500/AEPS20231101004

  Abstract(61) HTML(58) PDF(2.34 M)( 252)

  Abstract:The existing intelligent assessment methods for transient frequency in power systems do not adequately consider the temporal characteristics of input data. Therefore, a frequency safety assessment method for power systems based on intelligent prediction of transient frequency response curves is proposed in the paper. A multivariate sample convolutional interactive network is designed to fully exploit the temporal characteristics of power system measurement data, thereby improving the prediction accuracy of power system transient frequency response curves; Key indicators such as maximum frequency deviation, occurrence time of maximum frequency deviation, and the metastability frequency are calculated based on the predicted frequency response curves, and the frequency safety of the system is comprehensively assessed. Simulation tests are conducted on frequency stability standard examples, and the results showed that the proposed method effectively improved the accuracies of frequency response curve prediction and system frequency safety assessment compared to classical methods such as deep learning.

• Selectivity Enhancement Scheme for Zero-sequence Current Protection Based on Distance Relay for High-resistance Grounding Fault

  LI Yong, LI Yinhong, LIU Huanzhang, LIU Yang

  Available online:October 09, 2024  DOI: 10.7500/AEPS20240228008

  Abstract(39) HTML(26) PDF(2.76 M)( 173)

  Abstract:The last section of zero-sequence current protection of AC line adopts 300 A, which has the risk of disordered tripping.Therefore, a new principle of high-resistance grounding distance relay based on zero-sequence reactance line and non-fault phase polarization is proposed. The relay adopts the technical route of phase selection before measurement. The phase selection element combines zero-sequence reactance line and non-fault phase polarization method to form a variety of combined criteria to complete the phase selection. Due to the phase difference between the zero-sequence current at the protection installation and the zero-sequence current at the fault point, the zero-sequence reactance lines of the single-phase grounding fault phase and the advance phase of the inter-phase grounding fault have aliasing region when the fault point is near the setting point. The large variation of the operation voltage of the non-fault phase is not conducive to distinguishing the two types of faults in the aliasing region, and thus the phase selection element is divided into low-resistance module and high-resistance module. The low-resistance module adopts the zero-sequence reactance line with the downward bias, which is used to identify the near end and low-resistance short circuit. With the assistance of the low-resistance module, the high-resistance module only needs to deal with the faults near the set point, which reduces the difficulty of distinguishing the two types of faults . After phase selection, the operation voltage before fault is obtained by non-fault phase polarization method, so as to determine the operation characteristics of the relay. The ability of high-resistance distance relay to withstand the transition resistance is far beyond the requirements of the regulations, which improves the selectivity of ground backup protection to high-resistance faults.

• High-resistance Grounding Fault Detection Method for Distribution Network Considering Capacitor Switching Disturbance

  HAN Zhaoru, SHI Fang, ZHANG Hengxu, JIN Zongshuai, YUN Zhihao

  Available online:September 29, 2024  DOI: 10.7500/AEPS20240116008

  Abstract(26) HTML(98) PDF(4.03 M)( 468)

  Abstract:The accurate and reliable detection of high-impedance grounding fault (HIGF) is a challenging issue in fault handling for distribution networks, and normal capacitor switching operations can cause interference. Addressing this problem, a disturbance-resistant detection method for HIGFs based on zero-sequence Lissajous curve analysis is proposed in this paper. Firstly, the zero-sequence electrical quantities of HIGFs and capacitor switching disturbances are theoretically derived. There is no regular difference in the traditional time-frequency domain feature aspect between the two, thereby clarifying the cause of the interference. Further, the zero-sequence current and voltage waveforms are reconstructed into zero-sequence Lissajous curves. A quantitative index for the distortion complexity of the Lissajous curve trajectory shape based on the mathematical morphology theory is proposed, and an adaptive starting criterion is designed in combination with the probability distribution law of the zero-sequence Lissajous curve area. The disturbance-resistant detection algorithm for high-impedance grounding faults in the noise scenario is presented. Finally, the effectiveness and reliability of the proposed method are verified through electromagnetic transient simulation examples and real fault tests in the distribution network.

• Development and Application of Controllable Line Commutated Converter

  HE Zhiyuan, GAO Chong, YE Hongbo, YANG Jun, WANG Chenghao, SHENG Caiwang

  Available online:August 29, 2024  DOI: 10.7500/AEPS20240506007

  Abstract(16) HTML(6) PDF(2.88 M)( 171)

  Abstract:Controllable line-commutated converter(CLCC) is a new type of DC converter equipment proposed to solve the commutation failure problem of conventional DC transmission converter. In June 2023, the world"s first set of controllable phase converter valves for the ±500kV/1200MW Gezhouba to Shanghai Nanqiao high-voltage DC transmission system renovation project (hereinafter referred to as "Genan renovation project") was successfully put into operation. The technical requirements and principles of CLCC are analyzed, as well as its technical and economic benefits. Combined with the input conditions of the Genan renovation project system, the electrical parameters and structural design scheme of the controllable line-commutated converter valve were proposed, and the equipment development was completed. The type test scheme and test parameters were proposed, and the type test assessment was completed. According to the technical characteristics of the controllable line-commutated converter valve, field tests such as low-voltage test, open line test, and artificial short-circuit test were carried out to ensure the smooth operation of the project. The operation performance since the project put into operation was introduced, and the correctness of the control sequence during the AC fault was analyzed based on the field recording. Finally, the application prospects of the controllable? line-commutated converter valve in UHVDC projects, provincial AC liaison line renovation and other scenarios were prospected and analyzed, providing a reference for the further promotion and application of the controllable commutation converter valve.

• Ultra-short-term Regional Load Forecast Based on Spatio-Temporal Graph Attention Network

  ZHAO Ziyu, CHEN Yuanrui, CHEN Tingwei, LIU Junfeng, ZENG Jun

  Available online:April 28, 2024  DOI: 10.7500/AEPS20230914003

  Abstract(66) HTML(40) PDF(3.29 M)( 185)

  Abstract:A regional-level ultra-short-term load forecasting model based on a spatio-temporal graph attention network is proposed in this paper. Firstly, based on the existing regional-level load, cell partitioning is carried out to construct a graph topology that considers cell correlation. Secondly, effective features are extracted from the spatial, feature, and temporal dimensions through the graph attention network, one dimensional convolutional network and gated recurrent unit, connecting the fully connected layers to output the results. Finally, simulation validation is conducted based on real power load data from the New England region of the United States, and model attention weights are extracted to analyze spatial dependencies between cells. The results show that, compared with traditional models, the proposed model provides higher accuracy and stability with different prediction steps, effectively exploiting the spatial dependence of regional spatial load.

• Pricing Strategy of Charging Station in Power-Transportation Coupling Network Considering User Travel Cost Budget

  XIE Longtao, XIE Shiwei, CHEN Kaiyue, ZHANG Yachao, CHEN Zhidong

  Available online:January 04, 2024  DOI: 10.7500/AEPS20230628010

  Abstract(61) HTML(158) PDF(2.23 M)( 209)

  Abstract:With the large-scale development of electric vehicles, it is of great significance to study how to effectively consider the travel behavior mechanism of users and formulate rational charging prices for charging stations for the collaborative optimization and scheduling of power-transportation networks. To solve this problem, this paper proposes a pricing strategy for charging stations in the power-transportation coupling network considering the user travel cost budget. Firstly, a transportation user equilibrium model considering the travel cost budget is established, and the equilibrium state is equivalently described through variational inequalities, so as to characterize the travel demands and charging behaviors of electric vehicles. Secondly, a second-order cone optimization model for distribution networks considering power reduction is constructed. The charging station pricing problem has been transformed into an optimization problem with variational inequality constraints, and an alternating iteration algorithm combined with an extra-gradient algorithm is designed to solve the problem. Finally, the effectiveness of the proposed model and methods is verified through a case, and the results show the necessity of considering the travel cost budget for charging pricing in coupled networks.

• Non-Intrusive Real-Time Monitoring System for Electric Bicycle Based on Wavelet Detection and Feature Graph Decision

  LI Xiang, Liu Yuhang, ZHANG Qi, WU Xin

  Available online:September 12, 2023  DOI: 10.7500/AEPS20230425001

  Abstract(67) HTML(70) PDF(2.53 M)( 195)

  Abstract:The illegal charging behavior of electric bicycles (EBs) in households has temporal randomness and spatial concealment,which poses significant safety hazards and is difficult to effectively manage. A non-intrusive real-time monitoring system for EB charging based on wavelet detection and feature graph decision is proposed, utilizing the characteristics of real-time autonomous execution and easy promotion of non-invasive monitoring systems. Considering the physical structure and charging characteristics of EB loads, the typical common characteristics of EB loads are analyzed from both transient and steady-state perspectives. The EB proprietary feature map with strong distinguishability and universality is constructed in advance to realize consistent and structured expression of EB steady-state common features. In the actual monitoring process, in order to reduce the computational power demand and data transmission pressure of the system, EB specific transient phenomena with high-frequency components are accurately located based on wavelet transform to complete EB like event detection. Finally, extract event waveforms and train efficient classifiers through graphs for load identification and real-time upload. By monitoring actual users, the effectiveness of the monitoring system has been verified, which can effectively solve the problem of charging EBs in buildings and households.

• Spatial Feature Based Detection of False Data Injection Attack on Synchronous Grid Measurements

  ZHENG Yao, ZHANG Jie, YAO Wenxuan, QIU Wei, TANG Sihao

  Available online:March 16, 2023  DOI: 10.7500/AEPS20220813001

  Abstract(42) HTML(202) PDF(2.51 M)( 49)

  Abstract:As the power system gradually moves toward a new ecosystem of energy interconnection and the deep coupling of the network layer and physical layer, the threat of network attacks on the power system continues to rise. The source identity (ID) spoofing attack, as a new and complex, strong stealthy false data injection attack, can cause the grid control system to misjudge and cause system paralysis. To address this problem, a spatial feature-based method is proposed for detecting false data injection attacks on synchronized measurements of power grids. It has extracted different spatial features of the synchronized measurement devices at different locations by variational modal decomposition (VMD) and improved discrete orthonormal Stockwell transform (IDOST), so as to extract the authentication information of the measurement data without losing the spatial features of the measurements. Combined with light convolutional neural network (LCNN) to evaluate the likelihood of measurement data being attacked by source ID to enhance the speed of detection response. The effectiveness of the method is verified by the detection results of actual multi-point synchronous measurement data.

• Short-term Wind Power Interval Prediction Based on Combined Mode Decomposition and Deep Learning

  XIAO Bai, ZHANG Bo, WANG Xinwei, GAO Ningyuan

  Available online:February 20, 2023  DOI: 10.7500/AEPS20220807002

  Abstract(236) HTML(618) PDF(3.51 M)( 167)

  Abstract:Wind power prediction is very important for the economic dispatch of power systems containing wind power. Aiming at the problem that point prediction is difficult to describe the uncertainty of wind power, a short-term wind power interval prediction method based on combined mode decomposition and deep learning is proposed. Firstly, the original wind power sequence is decomposed into multiple modal components by using the improved complete ensemble empirical mode decomposition with adaptive noise, and the high-frequency strong non-stationary components are decomposed again by using the variational mode decomposition. On this basis, the sample entropy is used to calculate the complexity of each component and reconstruct them into trend components, oscillation components and random components. Then, the three components are input into the Bayesian optimization bidirectional long short-term memory neural network to establish their respective prediction models, and the point prediction values of the three components are obtained. The mixed kernel density estimation method is used to estimate the error distribution of the prediction results of oscillation components and random components, and the overall interval prediction results are obtained by combining the point prediction values. Finally, the actual examples show that this method has higher prediction accuracy than other models.

• Named Entity Recognition for Electric Power Industry Based on Enhanced Text Features

  LIU Wensong, HU Zhuqing, ZHANG Jinhui, LIU Xuejing, LIN Feng, YU Jun

  Available online:September 27, 2022  DOI: 10.7500/AEPS20210323003

  Abstract(40) HTML(42) PDF(1.43 M)( 54)

  Abstract:Considering the characteristics of small scale, nested entities and abbreviated entities for electric corpus, the named entity recognition (NER) based on enhanced vectors of text features is proposed. Firstly, by the way of the low grain word segment and the preset dictionary, the semantic information in Chinese words is properly utilized, and the transmission errors caused by word segment are decreased. Secondly, the features of inner structure of a single Chinese word is learned by the word-level bidirectional gated recurrent unit (Word BiGRU). Together with the features of the part of speech for words and word length, the enhanced word vector is built by concatenating these features vectors with word vectors. Finally, the NER model is designed with BiGRU, attention mechanism and conditional random field (CRF). The proposed method is verified using electric corpus and F1 measurement reaches 87.02%, which proves the effectiveness of NER for electric power industry.

• Identification of Abnormal Operation Condition for Environmental Protection in Enterprises Based on Power Quality Monitoring Data

  ZHANG Yi, YAO Wenxu, SHAO Zhenguo, ZHANG Liangyu

  Available online:August 18, 2022  DOI: 10.7500/AEPS20211203007

  Abstract(88) HTML(136) PDF(4.68 M)( 219)

  Abstract:Aiming at the problems of abnormal operation condition monitoring for environmental protection in polluting enterprises at present, such as difficult implementation, large identification errors and easy tampering with the results, this paper proposes an identification method of abnormal operation conditions for environmental protection based on power quality monitoring data. The multi-dimensional power quality data obtained from non-invasive load monitoring at the public power entrance of enterprise equipment are used to train the model of condition classification, to realize abnormal condition identification, which is different from the existing scheme of power consumption monitoring with a separate meter installed for each device. First, the time series change-point detection and the clustering calculation for the characteristic data representing the production conditions are carried out to realize the division of production operation conditions of enterprises. Then, combined with the operation of environmental protection equipment, the categories of environmental protection operation conditions for classification are obtained. Furthermore, the operation condition scenarios related to environmental protection are classified and learned by the Stacking learning model. Finally, the trained classification model is used to identify the abnormal operation conditions for environmental protection in the enterprise. The effectiveness of the proposed method is verified by the simulation test data and the actual enterprise data.

• Causes and Suppression Measures of Resonant Overvoltage in Offshore Wind Power AC Transmission System

  YANG Daye, SONG Ruihua, XIANG Zutao, LIU Dong, CHAO Wujie, YAN Yuesheng

  Available online:May 26, 2022  DOI: 10.7500/AEPS20220126001

  Abstract(32) HTML(18) PDF(3.69 M)( 44)

  Abstract:Since the capacitance per unit length of cable is more than 20 times that of overhead lines with the same voltage level, more and more offshore wind power is connected to the grid through AC cable, which reduces the natural resonant frequency of the transmission system and increases the resonant risk of the system. Aiming at the resonant overvoltage phenomenon in the process of grid connection of an offshore wind farm, the fault recording data are analyzed. Based on the impedance model of the transmission system, the mechanism is analyzed, and through the electromagnetic transient simulation, it is verified that the natural resonant frequency near the double frequency of the transmission system is the fundamental reason for the resonant overvoltage caused by the operation of no-load transformer and no-load line in the wind farm. Combined with the engineering practice, the joint suppression measures of changing the operation mode of the transmission system, optimizing the control and protection system of the static var generator and increasing the access load are proposed. Finally, the effectiveness of the proposed measures is verified by simulation and field tests.

• Calculation of Power Supply Reliability for Distribution Network Based on Neo4j Graph Database

  JIANG Wei, WANG Minghua, CHEN Jinming, LIU Jiangdong, PU Shi, XU Zhiqi

  Available online:May 13, 2022  DOI: 10.7500/AEPS20211031001

  Abstract(118) HTML(214) PDF(1.74 M)( 101)

  Abstract:In order to improve the efficiency of power supply reliability calculation for the complex distribution system, a reliability calculation method based on the Neo4j graph database is proposed. Firstly, the topology structure of the distribution network is stored in vertex-edge form through the graph database. Meanwhile, the feeder classification and load partitioning in the complex distribution system is completed by using the characteristics of different types of edges in the Neo4j graph database, and the distribution network diagram model based on the graph database is built. Secondly, the subgraph division of the distribution network diagram model is combined with path search, and the model simplification is completed based on each subgraph. Finally, the power supply reliability analysis of the distribution system is realized based on the minimal path reliability algorithm combined with the efficient shortest path query and other functions of the Neo4j graph database. The effectiveness of the proposed method is verified by comparing the Roy Billinton test system and an actual 10 kV distribution network in China for algorithm verification.

• Flexible Ring Network Controller Without Interface Transformer for Medium Voltage Distribution Network

  LI Zheng, CHEN Wu, HOU Kai, SHI Mingming, MOU Xiaochun, ZHU Jinsong

  Available online:April 26, 2022  DOI: 10.7500/AEPS20210806005

  Abstract(137) HTML(172) PDF(2.92 M)( 102)

  Abstract:When the flexible ring network controller cancels the interface transformer, the transmission of the zero-sequence voltage component cannot be prevented when the AC side fails, thus increasing the fault range. Therefore, this paper uses classical circuit analysis and positive and negative sequence analysis algorithms to explain the basic principles of the formation and transmission of zero-sequence voltage components. A topology of the flexible ring network controller without the interface transformer is proposed. The AC side converters are all modular multilevel converters with traditional half-bridge sub-modules. And the full-bridge sub-module valve strings are connected in series on the positive and negative polarity busbars. Utilizing the ability of the full-bridge sub-module to output positive and negative voltages, the DC side voltage fluctuations are suppressed, and the fault range is prevented from expanding. By using the MATLAB/Simulink software, the characteristics of zero-sequence voltage suppressed during the fault are simulated and analyzed. The simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed topology.

• Electricity Theft Detection Based on Multiple Different Learners Fusion by Stacking Ensemble Learning

  YOU Wenxia, LI Qingqing, YANG Nan, SHEN Kun, LI Wenwu, WU Zeli

  Available online:March 30, 2022  DOI: 10.7500/AEPS20210731001

  Abstract(238) HTML(154) PDF(1.38 M)( 116)

  Abstract:Aiming at the problems that the consumer power consumption data categories are unbalanced in electricity theft detection, and the ensemble learning method using voting as a combination strategy can not give full play to the advantages of multiple different learners, a model using Stacking ensemble learning to fuse multiple different learners is proposed and applied to electricity theft detection. Firstly, starting from the factors affecting electricity metering, six electricity theft behavior modes are simulated according to five common electricity theft methods; Secondly, synthetic minority oversampling technique (SMOTE) is used to process the unbalanced power consumption data, and k-fold cross-validation method is used to divide the balanced training sets to alleviate the overfitting caused by repeated learning; Then, the evaluation indicators and diversity metrics are employed to optimize different primary learners and meta-learners of the model, and a Stacking combination learning electricity theft detection model integrating the advantages and differences of different learners is constructed; Finally, the comparative analysis results of examples show that the proposed electricity theft detection model can effectively solve the imbalance of power consumption data categories, give full play to the advantages of different learners, and the evaluation index is good.

• Topology Optimization of Power Systems Combining Deep Reinforcement Learning and Domain Knowledge

  YAN Ziming, XU Yan

  Available online:December 01, 2021  DOI: 10.7500/AEPS20210510001

  Abstract(92) HTML(68) PDF(1.43 M)( 186)

  Abstract:While the flexibility of power systems operation can be improved by topology optimization, the dimension of system-level discrete decision variables, including the connections of lines and substation busbars, is prohibitively high. Thus, the topology optimization problem of power systems can hardly be solved by the conventional mixed-integer optimization method. Aiming at this problem, a reinforcement learning based method is proposed combining asynchronous advantage actor-critic and power system domain knowledge, which transfers the computational burden of online optimization to the offline agent training stage. The defined reward function is adopted to minimize the violations of power transmission line flow limits. Forced constraints verification is employed to reduce the searching space and improve the efficiency of the reinforcement learning. The fast computation of the topological structure optimization of power system operation is realized,and the operation security of power systems is enhanced. The effectiveness of the proposed method is validated by simulation testing results.

• A Rapid Fault Tracing Method for Large-Scale Complex Distribution Networks Based on Dynamic Graph ProjectionZHANG Yujia1^, YUAN Ye1^, ZHOU Suyang2^, ZHU Hong3^, ZHOU Aihua3^, CHEN Qingquan⁴

  Zhang Yujia, YUAN Ye, ZHOU Suyang, ZHU Hong, ZHOU Aihua, CHEN Qingquan

  Available online:  DOI: 10.7500/AEPS20240112003

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  Abstract:With the rapid growth of the distribution network and the high penetration of distributed resources, the topology of distribution networks has become increasingly complex, posing significant challenges to fault location analysis. When applying matrix algorithms and intelligent optimization algorithms to fault location, it is necessary to construct network matrices or establish optimization models based on changing topology information. This greatly increases the computational burden and complexity, leading to low efficiency in data processing and computation. Therefore, this paper first constructs a graph data model for the distribution network topology. Utilizing graph projection techniques, it extracts optimized subgraphs tailored for fault tracing scenarios from the panoramic power grid graph. On this basis, the Yen"s shortest path search algorithm is employed to find potential fault paths from the power source to the abnormal nodes. By traversing the line nodes and assessing their overcurrent information, the fault section is identified, thereby resolving the issues of accurate representation and rapid search of the power grid topology. This enables quick and precise fault localization in large-scale complex distribution networks, greatly enhancing fault search efficiency while ensuring the accuracy of fault tracing.

• New Transmission Test System Generation Method Based on the Concept of Grid Evolution

  LI Bo, WEI Guangrui, ZHONG Haiwang, LIU Hui

  Available online:  DOI: 10.7500/AEPS20240409003

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  Abstract:The IEEE test case has been widely used for simulation testing in various fields such as power system planning and operation. However, due to data privacy concerns, it isn"t easy to access publicly available datasets of actual power system generation and network structures. To address the issue, based on the evolution concept of three-generation power grids, a new transmission test system generation method is proposed to build test cases that reflect actual power system network structures. Firstly, a transmission expansion planning model considering N-1 security constraints is established. Secondly, the optimization objectives and constraints are proposed according to the characteristics of different stages of network development to simulate the evolution process of the electricity network. To enhance the solving efficiency of the model, a binary representation method of transmission corridors is introduced to reduce the number of 0-1 variables. Finally, the provincial power grid is used as an example to verify the effectiveness of the proposed open-source power system dataset, according to the statistical characteristics of complex networks. The proposed dataset is also applied to optimal transmission switching for further verification.

• Identification Method of Multi-perspective Time-frequency Characteristic Synthesis for Transformer Excitation Inrush Current

  Chen Chun, ZHAN Luxin, CAO Bozhong, CAO Yijia, Li Yong, LIU Junle

  Available online:  DOI: 10.7500/AEPS20240723005

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  Abstract:The continuous integration of power electronic devices in distribution networks has led to an increasing level of harmonic currents, posing challenges for traditional transformer secondary harmonic restraint differential protection. Simultaneously, single-feature identification methods are influenced by distributed energy resource types and closing angles, making it difficult to accurately distinguish fault currents and excitation inrush currents in different scenarios. To enhance the accuracy of excitation inrush current identification, this paper proposes a multi-angle time-frequency analysis method that comprehensively integrates time-domain, frequency-domain, and time-frequency-domain features. It utilizes Bayesian optimization of XGBoost (extreme gradient boosting) classification parameters to improve model generalization, enabling accurate identification of fault currents and excitation inrush currents under various capacities and types of distributed energy resource integration. The SHAP (shapley additive explanations) value analysis method is employed to reveal the contribution of each feature value in the identification model. The proposed method was verified through PSCAD/EMTDC simulation data and field measured data. Within the data samples provided in this article, the Bayesian-XGBoost algorithm under multi-angle time-frequency analysis has an accuracy of identification of excitation inrush current close to 100%, which is better than several common classification algorithms compared in this paper.

• Construction Scheme of Voltage Supported Renewable Energy Station Based on Distributed Synchronous Condenser——(II) Coordinate Control Strategy and Test Verification

  LIANG Hao, QIN Chuan, XIE Huan, LIANG Beihua, WU Tao, WANG Xuanyuan, WU Long

  Available online:  DOI:

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  Abstract:It is an effective measure to improve the whole process voltage support ability of the power supply side to deploy the distributed synchronous condenser (SC) in renewable energy station. However, the current "reactive outer loop + voltage inner loop" cascade strategy is adopted to integrate the SC to the automatic voltage control system (AVC) of the station to restrict its low-frequency voltage source characteristics. This paper first describes the existing problems of SC access to the AVC of the station, and proposes the requirement of constant voltage in the whole process of SC. Then, based on the topology of the renewable energy station with SC, the reactive voltage conversion coefficient and the reactive power shunt influence factor are analyzed. Based on this, a new scheme of "constant voltage + reactive power shunt suppression" for integrating the SC to the AVC is proposed. The program was developed in a domestic mainstream manufacturer's equipment model, and the effectiveness of the scheme was verified by in-loop simulation of the SC and AVC dual controller at the station. Finally, the engineering application was completed in an actual new energy station. The field operation results show that the scheme realizes the steady-state regulation of AVC multi-type reactive power equipment of the station, effectively reduces the voltage fluctuation amplitude of PCC bus of the station, gives full play to the whole process voltage control of the SC, and guarantees the voltage stability margin of the system.

• A New Single-Ended Fault Location Algorithm for Grounding Electrode Lines Based on Broadband Excitation Injection and Short-Time Matrix Pencil Method

  TIAN Xincui, CHEN Kaiwen, SHAN Jieshan, ZHANG Yining, YU Jinyun, LI Qiang

  Available online:  DOI: 10.7500/AEPS20240904005

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  Abstract:The fault information in grounding electrode lines is weak and well-hidden, making detection and fault location very difficult. Based on this, a new single-ended fault location algorithm for grounding electrode lines is proposed, utilizing broadband excitation injection and the short-time matrix pencil method (STMPM). First, a Gaussian signal excitation with an “oscillatory decay characteristic” is injected into the grounding electrode line in differential mode, ensuring that the injected excitation does not leak into the DC system side through the neutral bus and minimizing waveform distortion during the propagation of the signal along the grounding electrode line, thereby improving the detection efficiency of fault traveling waves. Secondly, sliding short-time windows are used to perform singular value decomposition (SVD) on the fault traveling waves. The eigenvalues obtained from the decomposition are used to distinguish between interference signals and fault signals, effectively amplifying the weak fault signals while suppressing the interference signals. Finally, the damping factor of the fault traveling wave within the short window is determined, establishing a one-to-one mapping relationship between the zero-crossing moment of the damping factor and the arrival time of the fault traveling wave, and the fault distance is then determined. Extensive simulations show that the distance measurement algorithm can effectively detect fault traveling waves and achieve high fault location accuracy.