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      • YANG Ming, LI Menglin, WANG Bo, LIU Chun, YU Yixiao, WANG Chuanqi

        Available online:November 04, 2024  DOI: 10.7500/AEPS20240227001

        Abstract:The main task of the new power system is to build a high proportion of renewable energy supply and consumption system, the operation law of its source, network, load and storage is closely related to meteorological changes. The operation and scheduling, planning and design, disaster prevention and reduction, and other aspects of the power grid all need to consider the influence of weather. The integration and development of the two disciplines of electricity and meteorology have become inevitable. However, currently, there is little literature that systematically reviews the key technologies and applications of numerical weather prediction of new power systems. This paper first summarizes the key technologies of numerical weather prediction from four aspects: meteorological observation, quality control, data assimilation, and numerical modeling; Secondly, typical application scenarios and methods of numerical meteorology are summarized from three aspects: source load power prediction, wind and solar resource assessment, and power grid disaster warning; Finally, the unique requirements of the new power system for the accuracy, timeliness, and precision of numerical weather prediction are analyzed. The shortcomings of numerical weather prediction technology in electricity demand are discussed, as well as the contradictions and promoting relationships between key technology improvements driven by demand. The key research directions for the integration and development of electricity and meteorology disciplines in the future are discussed in terms of data, mode, and platform construction, in order to provide reference for relevant theoretical research and practical applications.

      • CHEN Hehou, FU Linbo, ZHANG Rufeng, JIANG Tao, LI Xue

        Available online:November 04, 2024  DOI: 10.7500/AEPS20240330002

        Abstract:In high penetration-distributed generator microgrid (HP-DGMG), the uncertainty of distributed generator (DG) can have an impact on bidding revenues and even increase the operational risk of HP-DGMG and distribution network. Considering the uncertainty of distributed photovoltaic in HP-DGMG, an active and reactive power bidding transaction strategy of microgrid based on distributionally robust chance constraint (DRCC) is proposed in this paper. First, considering the power sale and power purchase transaction characteristics of HP-DGMG, the bidding and trading framework of HP-DGMG in the distribution market environment is constructed, and the bi-layer bidding model of HP-DGMG active and reactive power transactions in the distribution market is further established. Secondly, DRCC is introduced to deal with the uncertainty of distributed photovoltaic power generation in microgrid, and the optimization model of HP-DGMG active-reactive power bidding transaction based on the moment information is constructed. By using conditional value-at-risk theory and duality theory, the distributionally robust model of HP-DGMG bidding transaction is transformed into second-order cone programming form. Then, a single-layer mathematical programs with equilibrium constraint (MPCE) model of HP-DGMG bidding in distribution market environment considering photovoltaic uncertainty is proposed by using primal-dual counterpart method, and it is converted into mixed integer second-order cone programming problem for solving. Finally, the HP-DGMG located in 7-node distribution network and 33-node distribution network is analyzed and verified, and the effectiveness of the proposed HP-DGMG bidding transaction strategy is verified.

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

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

        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%.

      • WANG Wei, LI Binbin, SONG Xinzhe, YANG Dongmei, ZHOU Shaoze, WEI Zheng

        Available online:October 31, 2024  DOI: 10.7500/AEPS20230924003

        Abstract:With a three-level half-bridge structure, the three-level series resonant converter (TL-SRC) has the advantage of significantly reducing the number of power modules and thus reducing the cost, so it is widely used in the field of DC transformers. However, when the TL-SRC operates in the power reverse transmission mode, the inconsistent charging current of the junction capacitors of the inner and outer switches will cause a voltage imbalance issue of the switching devices, which brings the risk of overvoltage damage to the devices. To solve this problem, this paper analyzes the mechanism of voltage imbalance of the three-level inner and outer switches in the power reverse transmission mode. Based on the mechanism, a passive voltage balancing method with parallel auxiliary capacitors at both ends of the inner switch is proposed, and the parameter design method of auxiliary capacitors is given. Further, an active voltage balancing method for controlling the timing of the inner and outer switches is proposed, and the basis for designing the timing of the inner and outer switches is derived. Finally, the correctness of the theoretical analysis and the feasibility of the proposed method are verified on a 75 kW experimental platform.

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

        Available online:October 31, 2024  DOI: 10.7500/AEPS20240326005

        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 balance 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 effect of the electric-hydrogen conversion process. Therefore, a two-stage stochastic optimization 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. In stage 1, an adaptive time division method based on deep neural network is proposed. In stage 2, with the goal of minimizing the system operation costs and combining with time-segment opportunity constraints, the stochastic optimization 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.

      • YANG Tianxin, HUANG Yunhui, HE Zhenyu, WANG Dong, TANG Jinrui, XIE Changjun

        Available online:October 31, 2024  DOI: 10.7500/AEPS20240320002

        Abstract:Aiming at the regulation demand of high proportion of renewable energy systems on grid-side energy storage, an optimal configuration method of siting and sizing for grid-forming battery energy storage station based on multi-timescale regulation is proposed. Firstly, in order to describe the volatility and uncertainty of the net load, an improved iterative self-organized clustering and a multi-timescale scenario set generation method combining with Gaussian mixture model are proposed. On this basis, a multi-scenario and multi-timescale joint optimal operation model is established to deal with the volatility and uncertainty of net load, and optimize the configuration of energy storage capacity. Then, the relationship between inertia constant, cycle life of energy storage battery and its reserve capacity is analyzed, and a multi-objective optimization method of siting and sizing for grid-forming battery energy storage station is proposed which can improve the inertia of power system and cycle life of energy storage battery. Finally, a case is analyzed based on the actual system from a region in central China, and the results show that the method can accurately configure the optimal capacity of energy storage that meets at least 90% of the scenarios for multi-timescale regulation demand. At the same time, after the multi-objective model is used to correct the energy storage siting and sizing, the system inertia distribution index and the cycle life of the energy storage battery are increased by 28.9% and 27.2%, respectively.

      • XUE Yusheng, YANG Mingyu, CAI Bin, XUE Feng

        Available online:October 30, 2024  DOI: 10.7500/AEPS20240416001

        Abstract:The evolution pathways of carbon emission reduction and carbon sink increment are two critical components of the overall goals of “carbon emission peak and carbon neutrality” revolution. To integrate the holistic view of complex systems with the mechanistic view of multi-level subsystems, the whole-reductionism thinking is used to map the evolution pathways of carbon emission reduction and carbon sink increment in the electric power industry, as well as the overall revolution pathway of of carbon emission peak and carbon neutrality, onto a decision-making plane composed of carbon sink increment and carbon emission reduction quantities. On this two-dimensional pathway plane, it is feasible to cluster all reasonable evolution pathways into single-digit number of candidate pathways. Based on the trajectory dynamics thought, the dynamic evolution relationship is modeled. During the process of tracking a designated candidate pathway, the complex high-dimensional system is linearized in short intervals according to the trajectory values. This allows for the high-dimensional linear algorithm to be applied to optimize the minimum opportunity cost required to track each pathway segment within the constraints of various relevant fields. After accumulating the opportunity costs corresponding to the candidate pathways, the optimal pathway can be selected from the candidate pathways. The effectiveness of the proposed whole-reductionism method for the revolution pathway of carbon emission peak and carbon neutrality is demonstrated through a sand-table simulation for the evolution pathways of carbon emission reduction and carbon sink increment in electric power industries of China under the revolution of carbon emission peak and carbon neutrality.

      • CHEN Zhong, PAN Jundi, CAI Rong, NI Chunyi, TIAN Jiang, WANG Yi

        Available online:October 29, 2024  DOI: 10.7500/AEPS20240102010

        Abstract:In the context of power cyber-physical systems, the communication links in distribution networks are more vulnerable to cyber-attacks compared to transmission networks. Among the existing state estimation methods considering cyber-attacks, the detection-correction method overly relies on attack detection, while the robust estimation method roughly regards cyber-attacks as quantitative outliers. The performance of state estimation under cyber-attacks needs further improvement. Therefore, a forecasting-aided state estimation method for distribution networks based on event-triggering encryption is proposed to enhance the active defense capability of distribution network state estimation against cyber-attacks and ensure the performance of state estimation. Firstly, an event-triggering encryption transmission framework is constructed to enhance the active defense capability of distribution network state estimation against cyber-attacks. Secondly, addressing the uncertainty in the measurement error distribution introduced by the event-triggering encryption transmission framework, an enhanced cauchy-kernel-based maximum correntropy cubature Kalman filter is designed to utilize the state prediction values to achieve accurate state estimation under unknown measurement noise distribution. Finally, simulation analyses are conducted in improved IEEE 33-bus and IEEE 118-bus distribution network systems to validate the effectiveness of the proposed algorithm.

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      Volume 48,2024 Issue 20

        >Active Control and Cooperative Dispatch for Distribution Networks with High-penetration Distributed Energy Resources
      • WU Wenchuan, LIN Chenhui, SUN Hongbin, WANG Bin, LIU Haotian, WU Guannan, LI Penghua, SUN Shumin, LU Jiangang

        2024,48(20):2-11, DOI: 10.7500/AEPS20240510007

        Abstract:With the large-scale integration of distributed energy resources (DERs) and flexible power loads, the distribution networks are transforming into active distribution networks (ADNs). This transformation poses significant challenges to the energy management and operation control: 1) The integration of massive DERs requires additional scheduling capacity, which necessitates the active control of DERs to enhance system support capabilities. The variability of these sources also significantly increases the operation risk of ADNs. 2) The complexity and frequent changes in DERs make timely maintenance impractical, and the accuracy of the distribution network model is poor. The engineering application of the precise modeling based operation control and optimal scheduling technology is difficult. To address these challenges, this paper introduces the theory and methods based on machine learning, proposes an energy management and operation control technology system for ADNs that integrates “measurement-identification-control”, and realizes operation control and optimal scheduling with minimal/no model maintenance. Meanwhile, the following key techniques are analyzed: 1) the real-time scheduling technology for distribution networks with weak models or without models, achieving autonomous optimization; 2) the adaptive dynamic control technology for DER clusters, enabling proactive support for the power grid; 3) the probability optimization scheduling method for risk quantification, achieving a balance between risk and economy. Finally, the architecture of the energy management and operation control system suitable for ADNs with a extremely high proportion of DERs is briefly introduced.

      • LI Zhiyi, LI Bihuan, JU Ping

        2024,48(20):12-24, DOI: 10.7500/AEPS20240124002

        Abstract:The generalized load dispersed in the new distribution system has a certain regulation potential, which provides an effective way to deal with the source-load rebalancing caused by the rising penetration of renewable energy. The observability of generalized load characteristics is the premise for the load side to participate in the regulation. This paper discusses the connotation, assessment and improvement of generalized load observability. Firstly, the concept and connotation of observability are clarified, and three levels of observability research are proposed based on generalized load characteristics and power grid regulation requirements, namely, component identification, proportion estimation and situation analysis around typical components of generalized load. On this basis, the characteristics of typical load components are analyzed and the selection principle of observability assessment method is proposed. Then, the measurement configuration scheme is discussed for the purpose of improving observability, and the ideas for value mining of measurement data are sorted out. Finally, the research on generalized load observability in new distribution systems is prospected, including the overall framework, assessment and improvement.

      • ZHAO Bo, TANG Yajie, XU Hao, LIU Nian, GONG Diyang

        2024,48(20):25-35, DOI: 10.7500/AEPS20240227009

        Abstract:With the increasing penetration of distributed photovoltaic (DPV), the proportion of traditional energy units continues to decline, resulting in a reduction of inertia and primary frequency regulation capabilities in power systems. Therefore, it is crucial to explore the frequency support capabilities of DPV in low-inertia systems. On this basis, first, this paper proposes an improved frequency active support method for voltage-type DPV-virtual synchronous generator (DPV-VSG) with variable reserve rate to enhance the system inertia, reduce the response delay, and improve the ability of DPV for active participation in system frequency support. Then, an optimal selection model for setting the power and frequency parameters based on the transient search optimization (TSO) algorithm is proposed to solve the optimal initial reserve power and best frequency regulation parameters of DPV, which improves the limitation of fixed photovoltaic initial reserve power. Finally, the proposed model is applied in a single-generator model and an actual distribution station area model with high penetration of DPV to verify its effectiveness.

      • GUO Fanghong, FENG Xiurong, YANG Hao, TANG Yajie, WANG Lei

        2024,48(20):36-47, DOI: 10.7500/AEPS20240514016

        Abstract:Aiming at energy optimal scheduling problems in newly established microgrids (MGs) with the data scarcity and the uncertainty of source and load, this paper proposes a dual-data-model-driven distributionally robust optimization (DRO) framework for MGs. Firstly, the accuracy and robustness of the scenario generation using historical meteorological data are enhanced by the integration of neural networks with photovoltaic physical generation models to address the problem of data scarcity. Secondly, by the introduction of the DRO strategy and linear decision rules based on the Wasserstein distance, the energy optimization scheduling problem of MGs considering the uncertainty of source and load is transformed from a complex semi-infinite programming (SIP) problem to a mixed-integer linear programming (MILP) problem that is easy to be solved. The proposed DRO-based energy scheduling framework can realize the balance between low operation costs and high reliability, and can adapt to the real-time changes in photovoltaic generation power and other factors. Finally, the experimental comparison results under three typical weather conditions verify the effectiveness of the proposed method.

      • JU Yuntao, KANG Xiaofan, LIU Wenwu, ZHANG Jinqi

        2024,48(20):48-58, DOI: 10.7500/AEPS20231102003

        Abstract:Microgrid is an important form to accommodate renewable energy and improve the reliability of distribution network, which usually has an independent energy management system. Distribution networks with multiple microgrids need to cooperate with each microgrid to meet the constraints of safe and economic operation. It is necessary to adopt the distributed coordinated method considering the uncertainty of renewable energy and the characteristics of discrete adjusting equipment in the coordinated scheduling process between distribution networks and microgrids. To meet the above requirements, the renewable energy uncertainty is modeled by using the idea of the distributionally robust optimization modeling. And the discrete adjusting equipment is processed based on the simplicial decomposition method-nonlinear block Gauss-Seidel method-augmented Lagrange method (SDM-GS-ALM). The algorithm approximates integer variables by convex hull, and theoretically ensures that every computation problem is convex. On the basis of convex problem, column-and-constraint generation (C&CG) as the outer algorithm and SDM-GS-ALM as the inner algorithm are embedded in each stage of C&CG, which realizes the construction of distributed coordinated computing framework for distribution networks and microgrids. Finally, based on a real 240-bus engineering system in North America, the aggregate equivalent method of line operation state is used to verify the effectiveness of the proposed method.

      • LIU Yunxin, YAO Liangzhong, ZHAO Bo, XU Jian, LIAO Siyang, PANG Xuanpei

        2024,48(20):59-68, DOI: 10.7500/AEPS20230713007

        Abstract:The coordinated operation of microgrid cluster with a high proportion of distributed renewable energy is one of the important technical means to improve the local renewable energy accommodation capacity and reduce the operation and carbon emission costs. In order to make the microgrid cluster adapt to the fluctuation and randomness of renewable energy and the continuous changes in the operating environment, in this paper, the sub-microgrids are flexibly formed into various forms of microgrid clusters. A Stackelberg game model of low-carbon economic dispatch of distribution network-microgrid clusters is established, the optimization goal of which is to minimize the carbon emissions and total operation cost of the distribution network-microgrid cluster system. On this basis, according to the characteristics of flexible clustering, an emerging income allocation method based on the value of mutual power is proposed. The effectiveness of the proposed method is verified by the case simulation of an IEEE 33-bus distribution system-actual microgrid cluster.

      • CHEN Houhe, GAO Kangjing, ZHANG Rufeng, JIANG Tao, YAN Kefei

        2024,48(20):69-80, DOI: 10.7500/AEPS20240330006

        Abstract:A large number of renewable energy are connected to the electricity-gas distribution network (EGDN) and the pressure changes of the pipeline network during gas transportation may increase the voltage fluctuation of the distribution network. In this paper, a distributed active-reactive power coordinated optimization control method for EGDN considering asynchronous communication is proposed. Firstly, the active-reactive power characteristics of each component in the EGDN are studied. Secondly, according to the characteristics of reactive power demand in the process of EGDN energy transportation, the supply and demand model of reactive power in the EGDN is established. Then, considering the EGDN reactive power balance and network operation constraints, the reactive power and voltage optimization control model for EGDN is established to minimize the power grid voltage fluctuation, network loss and electricity/gas purchasing cost of EDGN. Considering the difference in information collection and transmission between the distribution network and the gas distribution network, there may be communication delay and information packet loss. In order to ensure the security and privacy of the information of each energy entity in the EGDN, the asynchronous alternating direction method of multipliers is used to solve the model. The simulation of EGDN system composed of improved IEEE 33-node system and 7-node gas distribution network show that the proposed model can effectively alleviate EGDN voltage fluctuation, reduce network loss and reduce the total operation cost of the system

      • ZHANG Xiao, WU Zhi, ZHENG Shu, GU Wei, HU Bo, DONG Jichao

        2024,48(20):81-90, DOI: 10.7500/AEPS20240416006

        Abstract:The access of multiple distributed sources and loads leads to enhanced voltage volatility in the distribution network. Meanwhile, the uncertainty fluctuation in the voltage of the upper main grid also affect the voltage characteristics of the distribution network. In order to effectively deal with the voltage fluctuations of the main grid and the distribution network, this paper proposes a multi-timescale voltage control framework for active distribution networks based on the combination of data-driven and model solving. In the slow time scale, considering the voltage fluctuation of the main grid, a multiple-feeder environment with a non-infinity system of the upper main grid is constructed, and the voltage control problem in this environment is modeled as an adversarial Markov process. During the training process, the voltage of the main grid is perturbed with a projected gradient descent algorithm. The Bayesian deep Q network algorithm is utilized to sense the voltage fluctuation of the upper main grid and realize the fast control of taps of the on-load tap changer. In the fast time scale, the reactive power output of the photovoltaic inverter is controlled based on the traditional second-order cone optimization method. The case results show that the method can accurately sense the voltage fluctuation of the upper main grid, realize model-free voltage control of the distribution network in a very short time, and ensure that the voltage of each node is maintained within the safety range.

      • WU Junyong, WANG Yi

        2024,48(20):91-99, DOI: 10.7500/AEPS20231110002

        Abstract:The integration of high proportion of distributed photovoltaic (PV) into distribution networks can lead to a series of problems such as reverse power flow and voltage violations, posing a serious threat to the safe operation of distribution networks. This paper introduces the theory of moment difference analysis for distributed PV distribution networks, transforming the problem of distributed PV accommodation in distribution networks into a balance problem of restoring and maintaining moment difference equations. For a given distribution network, when the maximum node voltage reaches the specified voltage upper-limit, the moment difference between PV moment and load moment, termed as critical moment difference, approximates to a constant. The critical moment difference represents the ability limit of a distribution network in accommodating distributed PV, where PV moments are the entities that need to be accommodated in distribution networks, and load moments serve as resources for accommodating PV moments. Based on the moment difference analysis theory, the PV curtailment minimizing decision for distributed PV in distribution networks is proposed and applied to the analysis and calculation of a 10 kV single-branch distribution line, and a 12.66 kV multi-branch distribution network in the IEEE 33-node system. Results of the case studies show that the proposed PV curtailment minimizing decision improves the optimization speed by 2 877 times under the condition of an error of no more than 1.2%, verifying the correctness and rapidity of the method.

      • ZHU Yongqi, LIU Youbo, TANG Zhiyuan, XU Zirong, GAO Hongjun, LIU Junyong

        2024,48(20):100-108, DOI: 10.7500/AEPS20240409006

        Abstract:The integration of a high proportion of distributed photovoltaic into distribution networks exacerbates the system uncertainty. Moreover, it is difficult to accurately acquire data such as the network topology and line parameters of distribution networks, rendering traditional control methods for distribution networks based on precise physical modeling ineffective. With the widespread application of measurement devices in distribution networks, it becomes increasingly easier to obtain operation data of distribution networks. This paper proposes a model-free voltage control method for active distribution networks based on measurement data of distribution networks. Firstly, a Hankel matrix is constructed based on the historical data of the distribution network to establish the relationship between the node voltages of the network and the output power of energy storage. Secondly, using local measurement data and considering uncertain disturbance factors and the attenuation model of the energy storage lifespan, an optimization framework for distribution network voltage under data-enabled predictive control is constructed to achieve the rolling optimization of distribution network voltage within the control cycle. Finally, the effectiveness and superiority of the proposed method are verified through simulations using the IEEE 34-bus standard case and the modified IEEE 123-bus case.

      • >Review·Perspective
      • LI Zhuohao, SHI Qionglin, WANG Kangli, JIANG Kai

        2024,48(20):109-129, DOI: 10.7500/AEPS20231221006

        Abstract:As an important energy storage battery, lithium-ion batteries have gradually matured and been widely used in various industrial fields in recent years, effectively alleviating the pressure of energy transition and environmental pollution. To ensure the safe and efficient long-term service of lithium-ion batteries and reduce operation costs, it is especially important to accurately estimate the state of health (SOH) of batteries in real time. In this paper, the current development of SOH estimation methods for lithium-ion batteries is reviewed. Firstly, the aging mechanism of lithium-ion batteries and related concepts of SOH are introduced. Secondly, traditional SOH estimation methods, including test-based methods, model-based methods, data-driven methods, and hybrid methods, are introduced. Additionally, new SOH estimation methods based on advanced sensing technologies are presented, demonstrating the improvement processes of various methods. A brief overview of SOH estimation methods for lithium-ion battery modules in energy storage systems is also presented. The emerging advanced sensing methods involve perceiving internal information of batteries, offering broad prospects for applications. Then, the advantages, disadvantages and improvement perspectives of these methods are analyzed and compared to provide a reference for choosing the appropriate method when facing different problems. Finally, to promote the practical application of SOH estimation methods for lithium-ion batteries, the challenges faced by the field are presented and future research directions in the field are prospected.

      • >Basic Research
      • WU Yunyi, WANG Sen, SUN Yonghui, ZHANG Wenjie

        2024,48(20):130-139, DOI: 10.7500/AEPS20231201001

        Abstract:With the proposal of the “carbon peaking and carbon neutrality” goals, the penetration rate of photovoltaic power generation in the power grid continues to increase. However, photovoltaic power generation may be affected by various environmental factors. Among them, local obstruction caused by photovoltaic panel pollution is an important factor that causes power loss and affects the efficiency of photovoltaic power generation. In response to the traditional pollution detection relying on the construction of large datasets, and the problems of low forecasting accuracy and single data form in loss forecasting, a forecasting method of photovoltaic output loss based on image correction and reconstruction is proposed, which uses image correction and reconstruction to detect photovoltaic panel pollution and estimate power loss. This method first detects pollution through image correction and image reconstruction, and converts image data into text data. Then, features are extracted from the corrected and reconstructed image data. Finally, multi-modal feature data containing temporal information is constructed for loss forecasting. The test results show that the proposed method has improved performance compared with traditional methods.

      • SHEN Jiakai, MA Shiying, XIE Yan, TANG Xiaojun, ZHU Shaoxuan, HUO Qidi

        2024,48(20):140-148, DOI: 10.7500/AEPS20231007002

        Abstract:The high proportion of renewable energy has affected the generation of thermal units and brought strong volatility. Thermal units are often at deep regulation state and are susceptible to disturbances, resulting in poor boiler heat storage levels and insufficient frequency response capabilities, posing a threat to system frequency stability. This paper focuses on the impact of dynamic changes in heat storage of thermal power units. Based on DC power flow and classical boiler models, a transient analysis model for the frequency of interconnected systems considering heat storage dynamics of boilers is constructed. By means of model equivalence aggregation, equivalent transformation, solution loop and approximate substitution, the order of the model has been reduced. Furthermore, based on the characteristics of the model, modal decomposition is carried out to derive a simple closed-form solution to the two-machine equivalent model. Based on the closed-form solution, the impact of the parameters on the heat storage model of the thermal power boiler on the transient frequency process of the interconnected system is analyzed. The calculation example shows that the established model and the proposed analytical method can accurately take into account the impact of heat storage of boiler on interconnected systems in frequency transient calculations, and the results can provide references for enhancing the frequency support capability of thermal power units.

      • LI Shichun, ZHANG Yeli, LIU Songkai, SHI Mingda, ZHANG Ye, LI Zhenxing

        2024,48(20):149-158, DOI: 10.7500/AEPS20240108002

        Abstract:The virtual inertia constant of grid-following energy storage has time-varying characteristic and the equivalent inertia constant of the system cannot be aggregated and solved, which leads to the failure of constructing a complete system frequency response (SFR) model in the SFR modeling application of the new power system including the virtual inertia control of grid-following energy storage. On this basis, the constantized calculation method of the virtual inertia constant for grid-following electrochemical energy storage is proposed, which can aggregate and solve the equivalent inertia constant of the system by calculating the constantized virtual inertia constant with the equivalent inertia constant-energy support effect. The proposed method establishes an objective function with the closest energy change to solve the constantized virtual inertia constant in sections according to the constraints of the key nodes and dynamic process of energy change, and the two-stage different characteristics of the virtual inertia response of grid-following energy storage. Finally, the accuracy of the calculation results is verified by establishing the SFR model which combines the time-domain simulation method and relevant evaluation indices.

      • YI Boyu, CHEN Yiguang, ZHANG Shaofan, WANG Shouxiang, ZHAO Qianyu, HUANG Yijun

        2024,48(20):159-170, DOI: 10.7500/AEPS20231114004

        Abstract:Extreme disasters can cause varying degrees of damage to the distribution and transmission networks, affecting the normal operation of the power system. After a failure occurs, a reasonable recovery strategy is beneficial for rapidly restoring power supply and reducing outage losses. Aimed at the scenarios where extreme disasters cause simultaneous multiple failures in distribution and transmission networks, this paper proposes a collaborative post-disaster recovery strategy for distribution and transmission networks based on multi-period Anderson acceleration, which coordinates the resources of both networks. The strategy targets minimizing operation costs during the recovery process, considers the operating status of generating units in the transmission network and the fault repair sequence , and also incorporates the flexible resource response of the distribution network, the fault repair sequence, network reconfiguration, and islanding strategies. A fully parallel analytical target cascading method is adopted to solve the model. To address the issue of poor convergence in the inner loop of the analytical target cascading method caused by the changes in network topology, an extended multi-period Anderson acceleration method is applied in the solving process. The T30D2 and T57D8 test cases demonstrate that the proposed method can effectively reduce the power outage losses during the post-disaster recovery process and significantly improve computation speed, showing good adaptability for the large-scale network with the integration of multiple active distribution networks.

      • GAO Xueqian, LIU Chang, LIU Wenxia

        2024,48(20):171-181, DOI: 10.7500/AEPS20240120002

        Abstract:In the “Three North” regions of China, wind resources are abundant but system flexibility resources are scarce. During the heating period, the proportion of electric output of thermoelectric unit is high, affecting wind power integration and posing severe challenges to the safe and economic operation of the system. To improve the economy of wind power accommodation, a collaborative robust planning method for electric and thermal flexibility resources considering reserve optimization is proposed. First, the peak shaving operation mechanism of promoting thermoelectric decoupling and its collaborative planning mechanism through various resources has been studied. On this basis, a min-max-min three-layer two-stage light robust planning model is established. The main problem aims to minimize the sum of the planned annual incremental investment cost, operation cost, and risk cost of insufficient reserve, optimizes all kinds of resource investment schemes and day-ahead deterministic optimal scheduling. Taking into account the uncertainty of wind power based on day-ahead scheduling results,the sub-problem minimizes the risk of insufficient reserve in the worst scenario, reschedule the equipment within days, searches for the worst scenario, and assesses the risk of insufficient reserve. The main problem and sub-problems are solved iteratively based on the column-and-constraint generation algorithm and the strong duality theory. Finally, the validity of the model is verified by a numerical case, and the robustness and risk of the model are analyzed.

      • ZHOU Baorong, LI Jiangnan, LYU Yifan, CAI Xipeng, MAO Tian, XU Yinliang

        2024,48(20):182-190, DOI: 10.7500/AEPS20240210001

        Abstract:In recent years, the proposal of goals of “carbon emission peak and carbon neutrality” has promoted the low-carbon transformation in the field of electric energy. In the new power system, in addition to the power generation side, the user side should also bear part of the responsibility for carbon emissions. To fill the research gap on the allocation of carbon emission responsibilities on the user side and carbon features of existing user profiles, this paper proposes a construction method for the user electricity-carbon profile based on the improved self-organizing map (ISOM). Firstly, a power flow model based on load data is built and the carbon emission flow is analyzed. Secondly, based on the carbon emission flow analysis, the load dynamic dispatching model combining carbon emission reduction potential is constructed, and the multi-dimensional electricity-carbon features are obtained. Then, based on the sparrow search algorithm (SSA) and the self-organizing map (SOM) of triangular-topological neighborhoods, the multi-dimensional electricity-carbon features are clustered to form the user electricity-carbon profile. Finally, actual load data of power grid users are tested in different dispatching scenarios and compared with existing methods. The experimental results verify the effectiveness and practicality of the proposed method.

      • XIANG Liyu, ZHOU Yibo, SU Sheng, LAI Zhiqiang, FENG Xiaofei, LI Bin

        2024,48(20):191-199, DOI: 10.7500/AEPS20240124001

        Abstract:In the low-voltage distribution network, the residual current in the distribution station area is closely related to the consumption behavior of users with wiring errors of neutral lines and grounding lines, while the normal users have little influence on the residual current in the distribution station area. In this paper, an identification method for users with wiring errors and leakage current based on correlation analysis of residual current in distribution station area is proposed, which identifies the users with wiring errors according to the internal relationship between the load current of the users with wiring errors and the total residual current in the distribution station area. First, the theoretical defects of abnormal user identification based on current amplitude regression analysis is pointed out. Then, the state equation and measurement equation are constructed by combining the real part time series data of the residual current and the user load current in the distribution station area. Kalman filter is used to estimate the state, and the correlation coefficient between the load current of each user and the residual current in the distribution station area is calculated to identify the users with wiring errors and leakage current. Finally, the validity of the proposed method is verified based on the verified actual data of the distribution station area with wiring errors and leakage current.