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      • WEI Xiangyu, XIANG Yue, SHEN Xiaodong, YANG Jingxian, LIU Junyong

        Available online:January 27, 2021  DOI: 10.7500/AEPS20201005003

        Abstract:The reasonable cognition of the influence of typhoons on regional wind speed forecasting is crucial to the maximum utilization of regional wind power in the future. Based on the numerical weather forecasting information of multi-model ensembled typhoon, a multi-step wind speed forecasting model considering the influence of typhoons is proposed. In view of the noise of wind speed data during typhoons, empirical wavelet transform (EWT) is used to deconstruct the historical data of wind speed and eliminated the noise disturbance. Then the multi-step prediction of the reconstructed wind speed series is carried out by using gated circulation unit (GRU) network to obtain the prediction information of wind speed without considering the influence of typhoons. For the lack of data during typhoons, deep belief network (DBN) is introduced to improve the accuracy of wind speed forecasting considering the influence of typhoons. Finally, a case study is carried out based on the actual data of a weather station in southern China and compared with the fundamental case without considering numerical forecasting information of typhoons. The result shows that compared with the baseline model without considering the influence of typhoons, the proposed model can effectively reduce the forecasting errors of wind speed.

      • SHI Yusong, XU Qingshan, ZHENG Jian

        Available online:January 27, 2021  DOI: 10.7500/AEPS20200804005

        Abstract:In order to monitor the illegal parking and charging behavior of electric bicycles from the grid side and reduce fire accidents caused by electric bicycle charging, on the basis of non-intrusive load identification, a charging identification method for electric bicycles is proposed based on feature selection and incremental learning. First, according to the measured current waveform of electric bicycle charging, this paper analyzes the load characteristics and lists 15 load features. The semi-supervised Fisher score and the maximal information coefficient are used to measure the feature discrimination and redundancy, and the greedy search algorithm is used to rank the importance of features. The feature subset with the highest identification accuracy is selected based on the ranking and identification results. Then, based on the incremental learning method for one-class support vector machine, the electric bicycle load identification and online learning of the classifier are realized. Finally, the experiment is carried out with actual measured data, and the results show that the method proposed can accurately identify the charging behavior of electric bicycles, which verifies the effectiveness of the algorithm.

      • LIU Yonghui, ZHANG Xian, XIE Kai, FENG Heng, SUN Hongyan, Shao Ping

        Available online:January 27, 2021  DOI: 10.7500/AEPS20200721003

        Abstract:The new situation of gradually liberalizing the power generation and consumption plan, accelerating the construction of the spot market, and the rapid development of the Energy Internet puts forward new requirements for the electricity trading system. Starting from the overall design of the new-generation of electricity trading platform, combined with the idea of Energy Internet construction, this paper analyzes the key requirements of medium and long-term power curve trading, horizontal and vertical integration, two-level market collaborative operation, and high-performance settlement, puts forward the overall architecture system based on “cloud platform + micro service”, reshapes six business applications, and designs optimized clearing, intelligent registration, and full service. Finally, the core supporting ability of the platform construction is discussesed, and the preliminary application realized.

      • WANG Can, WU Yaowen, SUN Jianjun, ZHA Xiaoming, DING Kai, LI Wei

        Available online:January 27, 2021  DOI: 10.7500/AEPS20200714006

        Abstract:With a large number of distributed generators (DGs) integrating into the network, the uncertainty and randomness of the output of the supply and load ends in the distribution network gradually increase. The application of flexible multi-state switch (FMS) enables the interconnection between supply areas no longer subject to the voltage levels and the phases. Therefore, the joint operation optimization of multi-supply areas in the active distribution network (ADN) will become the new normal, which provides the foundation for the improvement of the load unbalance condition and the voltage level. By using flexible interconnection characteristics of the FMS, taking the weighted minimum of the supply area and feeder load balance indices as the objective function, the double-layer embedded load balancing model for ADN is established, and a hybrid optimization algorithm consisting of improved particle swarm optimization (PSO) and second-order cone programming is used to solve the problem of network reconfiguration topology and FMS output. First, the closed-loop distribution network is simplified and equivalent to decrease the particle scale during the network reconfiguration of PSO algorithm in the outer layer of the whole method to speed up the calculation. In the inner layer, the objective function is linearized using optimal equidistant piecewise linear approximation algorithm (OEPLAA), by which the problem of output optimization of FMS can be transformed into a second-order cone programming problem. Finally, the proposed double-layer load balancing method is analyzed and verified through an actual distribution network in a certain place.

      • WU Zhouyang, AI Xin, HU Junjie

        Available online:January 27, 2021  DOI: 10.7500/AEPS20200629008

        Abstract:Demand-side resources have the potential to mitigate short-term power imbalances in the system through their flexibility, but they also face the challenge of the electricity consumption uncertainty. This paper deals with the electricity consumption uncertainty and frequency regulation demand on user side. Taking an aggregator with electric vehicles and temperature-controlled loads as an example, a reserve optimization and real-time scheduling model participating in frequency regulation ancillary services is established. In the day-ahead market, the reporting stage evaluates the risk cost of real-time operation based on the power space, which realizes the joint optimization of energy and reserve. In the real-time control stage, the power of each user is allocated based on the power space, which improves the adjustment ability of aggregators to respond to system power while meeting power demand. The simulation analysis verifies the feasibility of combining different load aggregations for joint scheduling, and the effect of the load aggregation in power mutual support and improving overall operation efficiency.

      • LIU Lu, LI Linzhi, LU Tianqi, WU Hao

        Available online:January 27, 2021  DOI: 10.7500/AEPS20200611004

        Abstract:In the power system, the identification of critical links is of great significance for the propagation mechanism analysis of cascading failures, the formulation of preventive measures and the improvement of system reliability. In this paper, the stochastic approach for link-structure analysis (SALSA) using in the web link analysis is introduced into critical link analysis for cascading failures in the power system, and the concepts “propagative branch” and “vulnerable branch” are proposed. Based on massive cascading failure simulation data, the roles of branches in the failure propagation are identified, as well as the high-risk failure propagation relationships which will cause serious blackout consequences. The verification on the IEEE 39-bus system and an actual provincial power grid in China shows that the proposed method can effectively identify the critical links of the system, and the mitigation measures for critical links can effectively reduce the risk of cascading failure blackouts.

      • HUANG Wei, GUO Tianfei, GAO Yu, LIU Qihui

        Available online:January 15, 2021  DOI: 10.7500/AEPS20200729005

        Abstract:The reactive power control of wind turbines can greatly improve the flexibility of wind farms. Obtaining the safe operating domain of reactive power control accurately can not only provide a reliable boundary for the realization of various control strategies, but also reduce the investment of reactive power compensation devices. This paper takes the current mainstream model, i.e., doubly-fed induction generator (DFIG) as the research object and establishes its mathematical model in the dq rotating coordinate system. On this basis, combined with the directional method of vector control, three restriction factors of the rotor-side converter (RSC) capacity, RSC operating current and DFIG capacity are considered for the stator reactive power limit, and two restriction factors of the grid-side converter (GSC) capacity and maximum operating current are considered for the reactive power exchanged by GSC and grid. Also, an analytic formula of the reactive power limit considering the reactive power loss of the grid-side filter inductor is derived. Finally, the time-domain simulation verifies the correctness of the analytical formula. The simulation results based on the example parameters show that the maximum 8% error of the total reactive power can be avoided by taking into account the reactive power loss of the grid-side filter inductor and the DFIG reactive power limit calculation requires comprehensive consideration of multi restriction factors.

      • ZHAO Tianhui, ZHANG Yao, WANG Jianxue

        Available online:January 15, 2021  DOI: 10.7500/AEPS20200622003

        Abstract:For mass power load data, a method based on density-based spatial clustering and outlier boundaries is proposed to identify the load abnormal data. Firstly, the density-based spatial clustering method is used to classify load curves into normal and abnormal power consumption patterns. Also, the load curves with normal power consumption pattern are classified into different load levels. Then, the outlier boundaries are built using the confidence interval of load expected value and the inter-quartile range of the deviation between load sample and sample average under different load levels. Considering the contingencies of atypical power consumption behavior, the obtained outlier boundaries are corrected by time offset of load consumption and the outlier boundaries for abnormal power consumption patterns are built. Finally, the proposed method is tested in the example with the load data sets of residential and industrial users. Compared with the traditional method, the precision of the proposed method is improved by more than 10% on average, and the comprehensive evaluation index is improved by more than 4% on average.

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      Volume 45,2021 Issue 2

      • CHEN Yue, LIU Feng, WEI Wei, MEI Shengwei

        2021,45(2):1-11, DOI: 10.7500/AEPS20200506004

        Abstract:The prevalence of distributed renewable energy provides a new way to construct clean and efficient smart grids. Meanwhile, it also leads to some difficulties at the demand side such as more random behaviors of end users, more difficult energy management, and less efficient equipment utilization. To deal with these new challenges and fully invoke demand-side flexibility, energy sharing which is an innovative distributed operation paradigm emerges as a response. This paper presents the concept of demand-side energy sharing and analyzes its potential in smoothing uncertainty, enhancing operation economy and improving equipment utilization efficiency. The key points and basic requirements in designing energy sharing mechanism are elaborated. On this basis, the domestic and international research on energy sharing mechanism design is summarized into five categories from the perspectives of cooperative game and non-cooperative game. Current research progress and remaining problems are also concluded. Further, future research direction is previewed from two aspects, i.e. the internal design and external connection of the demand-side energy sharing market.

      • >Cyber Energy Systems
      • ZHANG Yimei, DONG Chaoyu, DONG Xiaohong, XIAO Qian, ZHANG Mingdong, JIA Hongjie

        2021,45(2):12-20, DOI: 10.7500/AEPS20200428005

        Abstract:The stability of the cyber-energy system with electric vehicle (EV) cluster participating in frequency regulation is studied. A spectral feature and stability assessment method is proposed for cyber-energy systems with frequency regulation of EV cluster. Firstly, the analysis model of cyber-energy systems with EV clusters is developed considering the power system (physical) and communication (information). Secondly, an infinitesimal generator is used to transform the spectrum of the frequency regulation system with physical-information links into the spectrum of the infinitesimal operator, removing the exponential terms. Thirdly, based on the users’ demands, simultaneously considering accuracy and speed, the Chebyshev discretization scheme is selected to discretize the infinitesimal operator, resulting in a finite-dimensional matrix approximant. By solution of the eigenvalues for the approximant matrix and Newton iterative correction, the critical eigenvalues and eigenvectors of the regulation system are obtained to evaluate and analyze the stability of the system in a specific state. Based on this, the stability region of the cyber-energy system with frequency regulation of the EV cluster is extracted, which is used to evaluate the effect of cyber delay on the system stability from multiple directions. Finally, the effectiveness of the proposed method is validated by case studies.

      • FU Jian, HU Bo, XIE Kaigui, NIU Tao, LI Chunyan, WANG Leibao

        2021,45(2):21-29, DOI: 10.7500/AEPS20200507001

        Abstract:With the development of Energy Internet and smart grid, the power system in China has developed into a complex cyber-physical system, and the risk of various malicious attacks increases greatly. In order to ensure safe and reliable operation of the power system, it is of great practical significance to model all kinds of malicious attacks and propose corresponding defense measures. In this paper, a defensive stochastic planning model is proposed according to coordinated cyber-physical attacks. The objective function of the model is to minimize the expected load shedding caused by attacks. The purpose of defense attacks is achieved by optimizing candidate generators and lines considering the impact of various coordinated attack schemes. First, the coordinated attack model is established from the perspective of attackers in modelling, and the method of generating coordinated attack scenarios is proposed. Then, based on the generated coordinated attack scenarios, a stochastic planning model of the power system is proposed to defend the attacks, and the operation situations of the planning system from perspectives of operators and planners are both considered after the attack. Finally, case studies on a modified IEEE RTS-79 system are performed to show the correctness and effectiveness of the proposed model.

      • HAN He, ZHANG Peichao, DU Wei, LIU Xuezhi, XU Boqiang, YAO Cheng

        2021,45(2):30-36, DOI: 10.7500/AEPS20200707003

        Abstract:Quantity regulation is a basic regulation mode of district heating networks. First, aiming at the characteristics of the quantity regulation that the flow is variable and in the turbulent resistance square area, a second-order cone relaxation method for pressure drop of hydraulic branches is proposed, and a penalty cost term is introduced to ensure exact relaxation. Second, according to the characteristics of the stable thermal condition of quantity regulation, a equivalent heat loss method of thermal branches is proposed. The heat loss of the pipeline is equivalent to the load to form a lossless pipeline, which realizes the independent analysis of hydraulic and thermal models. Then, combined with the branch flow model of the distribution network, a joint power flow model of the district heat-electric system is built. Furthermore, a joint optimal power flow model with the form of convex-concave programming for the district heat-electric system is established. Finally, the optimization model is transformed into a second-order cone programming problem and solved sequentially. Simulation cases show that compared with the non-convex accurate model, the proposed method retains a high solution accuracy, improves the solving speed significantly, and can obtain the global optimal solution.

      • >Basic Research
      • ZHANG Tiance, WANG Jianxiao, LI Gengyin, ZHOU Ming, WANG Xuanyuan, LIU Zhen

        2021,45(2):37-45, DOI: 10.7500/AEPS20200430026

        Abstract:With the increasing penetration of renewable energy in distribution networks, a series of reliability issues, e.g., voltage over-limit and power flow overloading, have become severe threats. Therefore, an perception method of voltage spatial-temporal distribution with high penetration of renewable energy is proposed. Due to the absence of power flow models of the distribution network in practice, a data-driven method is designed to make accurate short-term prediction for nodal voltage perception. The proposed method is composed of three sectors: numerical weather prediction (NWP) based distributed wind power and photovoltaic forecasting, in which the relationship between meteorological data and distributed energy output is developed; generalized regression neural network (GRNN) based learning mechanism for constructing voltage sensitivity matrix, in which data-driven nodal power-voltage mapping is developed without a power flow model of the distribution network; and the kernel density estimation (KDE) based GRNN sample amendment method for avoiding the forecasting errors caused by the local density deviation of the original sample. Case studies based on IEEE 33-bus and Venezuela 141-bus distribution systems demonstrate the effectiveness of the proposed method. Compared with similar methods, the proposed KDE-GRNN has a significant advantage in forecast precision and rate of convergence.

      • LIU Andi, LI Yan, XIE Wei, YANG Chenguang, WANG Shaorong, SHI Zhixiong

        2021,45(2):46-54, DOI: 10.7500/AEPS20200621001

        Abstract:Based on the micro-synchronous phasor measurement unit (μPMU) that can be deployed on a large scale, the characteristics of various measurement systems at this stage are analyzed, and a parameter estimation method of lines in distribution networks based on multiple source data and multiple time sections is proposed. In this method, the fast sampling speed of μPMU is used for the fusion of measurement data based on multiple time sections, and the precise time stamp of μPMU is used for the aligment of mixed measurement data at the same time section. The measurement equations of μPMU, supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) with multiple time sections are jointly established. If the equations are independent of each other, the least square method parameter estimation is performed. The parameter estimation model is constructed for the typical backbone/branch line mode of the distribution network. Through the IEEE 33-bus distribution network simulation example, the application of parameter estimation under various measurement configurations are analyzed to prove the effectiveness of the proposed method.

      • ZHANG Hong, MA Hongjun, YAN He, SHI Hua, ZHANG Qian

        2021,45(2):55-63, DOI: 10.7500/AEPS20200518002

        Abstract:In view of the high degree of autonomy of the microgrid and the uncertainty of renewable energy output, this paper proposes a two-stage day-ahead optimal scheduling framework of coordination between supply and demand from the perspective of grid-connected microgrid operators. First of all, a bi-level optimization model of supply and demand collaborative scheduling is established based on the master-slave Stackelberg game. The upper operator’s problem includes two stages: day-ahead scheduling and real-time control. In the real-time control stage, the worst-case conditional value at risk (WCVaR) is utilized to assess the cost risk caused by the uncertainty of renewable energy resources in the worst-case scenario. Then the Karush-Kuhn-Tucker condition,the Big-M method and strong duality theory of linear programming are used to transform the bi-level optimization problem into a mixed integer linear programming problem. Case analysis indicates that the proposed model can decide the optimal day-ahead scheduling scheme under the worst-case probability distribution of renewable energy output, simultaneously optimize the electricity price and load curve, and reduce the system operation cost and risk.

      • DING Yurong, CHEN Hongkun, WU Jun, LOU Qinghui, LIAO Jiaqi, LI Baolin

        2021,45(2):64-73, DOI: 10.7500/AEPS20200811002

        Abstract:A multi-objective optimal dispatch model of the integrated energy system (IES) and its solution method are proposed for fully tapping the energy use potential on the energy use side of the IES to further improve the energy utilization efficiency. Taking into account the coordinated optimization of the energy supply side and the energy use side, a comprehensive energy efficiency model considering integrated demand response is established and the optimization objectives are introduced. Aiming at the lowest system economic cost and the highest comprehensive energy efficiency, the multi-objective optimal dispatch model of the electricity-gas-heat IES is established. Charnes-Cooper transform is used to linearize the comprehensive energy efficiency objective, so as to solve the fractional programming problem accurately and quickly. And the improved ε-constraint method is used to obtain the Pareto frontier set. A coupling system with modified IEEE 24-bus power network, Belgian 20-bus natural gas network and the 14-bus heat network is used to analyze the system economic costs and comprehensive energy efficiency in four scenarios. Results show that the proposed method can guide power users and multi-energy users to choose comprehensive demand response behaviors, and can further improve the overall energy efficiency of IES through collaborative optimization on both sides of supply and demand. It also verifies the feasibility of optimal dispatch results for balancing the economic and efficient operation objectives of IES.

      • WANG Qingyan, SUN Yuanyuan, XIE Xiangmin, LI Yahui, XU Qingshen, ZHANG Yan

        2021,45(2):74-81, DOI: 10.7500/AEPS20200330001

        Abstract:With the large-scale integration of renewable energy power generation, the harmonics in the power system show more obvious uncertain characteristics such as randomness and variability. To study the uncertain harmonic level of the power system, this paper proposes a probabilistic harmonic power flow algorithm based on the improved multi-point estimate and maximum entropy distribution. First, according to the discrete approximation theory, multiple sampling points and weights of random variables with independent standard normal distribution are determined. According to the space transformation of random variables, the weights and sampling points of randomly distributed random variables such as power and harmonic current are obtained. Based on this, the statistical characteristics of output random variables such as harmonic voltage can be calculated. Then the probability distribution of the output variable based on the maximum entropy distribution is obtained. Compared with the traditional point estimate and series expansion method, the proposed method obtains higher efficiency and accuracy by using the improved multi-point estimate method. The fitting effect of probability density function is guaranteed by using the maximum entropy distribution. Finally, the modified IEEE 33-bus system is used to verify the performance of the proposed method in different analysis scenarios and its advantages compared with traditional algorithms. Meanwhile, the influence of the correlation of input random variables in the calculation of harmonic power flow on the harmonic level of the system is analyzed by using the proposed method.

      • QIAO Yanhui, HAN Shuang, XU Yanping, LIU Yongqian, MA Tiandong, CAI Qian

        2021,45(2):82-88, DOI: 10.7500/AEPS20200812006

        Abstract:The quantitative analysis method of the complementarity between wind power output and photovoltaic power output based on weather classification can scientifically guide the optimal dispatch of wind-photovoltaic complementary power generation systems. In order to overcome the shortcomings of the existing weather classification methods that principal component analysis cannot extract nonlinear features and t-distributed stochastic neighbor embedding (t-SNE) based algorithm does not consider the actual distribution of samples, a weather classification and complementarity analysis method for wind and photovoltaic power output based on the kernel principal component analysis (KPCA) and the self-organizing feature map (SOFM) neural network is proposed. Firstly, the KPCA is employed to extract the feature vectors based on numerical weather prediction data. Then, a weather pattern classification model based on the SOFM neural network is constructed by using the feature vectors as input conditions. Finally, Based on the evaluation indicators for complementary ratio of fluctuation and complementary ratio of ramp, the complementary degree and the optimal grid-connected capacity ratio of wind and photovoltaic power output under different weather patterns are quantitatively analyzed from two perspectives of fluctuation and ramp. The results demonstrate that the fluctuation complementarity and the optimal grid-connected capacity ratio of wind and photovoltaic power output have obvious difference under different weather patterns, which verifies the effectiveness of the proposed method.

      • SONG Junying, CUI Yiwei, LI Xinran, ZHONG Wei, LIU Taowen, LI Peiqiang

        2021,45(2):89-96, DOI: 10.7500/AEPS20200519004

        Abstract:Clustering analysis is the basic method for the load characteristic classification and synthesis. Aiming at the shortcomings in clustering quality and robustness of existing clustering methods applied to online load modeling based on the power grid big data platform, this paper proposes an improved piecewise linear representation (IPLR) method for daily load curve dimension reduction. Based on the advantages of IPLR for adaptive dimension reduction and reconstruction of data sets, combined with the characteristics of dynamic time warping (DTW) distance which is suitable for measuring the similarity between time series of unequal dimension, a daily load curve clustering method combining IPLR and DTW distance is constructed. Firstly, according to the variation of adjacent and interval sampling points of load curves, the characteristic points of load curves are extracted, and the curves are reconstructed by adaptive dimension reduction. Then, the DTW distance is taken as the similarity measurement index, and the clustering analysis of dimension reduction data is carried out by using Canopy based K-means (CK-means) algorithm. The method proposed is applied to the classification and synthesis of the daily load curves of typical consumers in a provincial power grid of China. The results show that the proposed dimension reduction method matches with the similarity measurement method, has good comprehensive performance, and is suitable for the analysis of the industrial composition ratio of synthesis load in substations.

      • DU Zhanghua, SU Sheng, LIU Zhengyi, XUE Yang, YANG Yining, LIU Sha

        2021,45(2):97-104, DOI: 10.7500/AEPS20200222002

        Abstract:Data-driven power theft detection methods mainly identify low power abnormalies according to power and derived indicators, which could result in high false positive due to interference. Taking the advantage of the characteristic that the indicies of production and operation status of industrial and commercial users are generally constant, a second inspection method for electricity theft based on identification of production and operation status is proposed. First, the daily three-phase power of the detected abnormal low-power users is used as the load characteristic to identify the power consumption behavior mode and the production and operation status of the day. Then, the daily load characteristics are clustered by affinitty propagation. When the load characteristics of the abnormal period of low power are clustered with the normal low-power production and operation status as the same group, it is considered that the abnormality caused by the normal transition of the state can be ruled out of the suspected electricity theft. Based on the experiments of actual electricity theft data, the proposed method can reduce the false positive rate.

      • YOU Wenxia, SHEN Kun, YANG Nan, LI Qingqing, WU Yonghua, LI Wenwu

        2021,45(2):105-113, DOI: 10.7500/AEPS20200411002

        Abstract:Aiming at the deficiency of the single classification method in traditional electricity theft detection, this paper proposes an electricity theft detection method based on Bagging heterogeneous ensemble learning. Considering the performance of different individual learners on the data sets and the diversity between various learners, an electricity theft detection model based on Bagging heterogeneous ensemble learning with combination of various individual learners is developed. The individual learners of the model include the k-nearest neighbors, the error back propagation network, the gradient boosting decision tree and random forest. The outputs of the individual learners are combined by an improved weighted voting strategy. The Irish smart meter data set is used to verify the feasibility of the algorithm. The results show that, compared with the traditional single model, the electricity theft detection based on Bagging ensemble learning has better performance in accuracy, true positive rate and false positive rate. The sensitivity analysis shows the validity of the electricity theft detection method based on Bagging heterogeneous ensemble learning.

      • >Application Research
      • ZHANG Yong, GUO Jun, LIU Jinbo, YANG Xiaoyu, GUO Lei, LIU Jia, MA Chunlei

        2021,45(2):114-121, DOI: 10.7500/AEPS20200322004

        Abstract:Dispatching and control cloud (DCC) is an important part of the “three clouds” planning of State Grid Corporation of China. Aiming at the characteristics of hierarchical deployment design, which combines unification and distribution of dispatching and control cloud, basic architecture of infrastructure as a service (IaaS) layer for dispatching and control cloud is designed, which is combined with the bi-level deployment system framework of leading nodes for national dispatching and control center-regional power grid dispatching and control center and provincial collaborative nodes, based on the concept of cloud computing, and oriented to power grid regulation business. The virtualization (sharing and dynamic deployment) of hardware resources, standardization of data and service-oriented application of dispatching and control cloud platform are realized. On the other hand, the technologies such as resource virtualization of servers and storages, domain name system, load balance, read-write separation etc. are introduced into IaaS layer architecture, which provides basic support for dispatching and control cloud nodes to adopt dual-site mode, and it realizes that each node of the cloud can provide external services and remote active-active application at the business level at the same time.

      • ZHANG Aijun, LI Dandan, ZHANG Qingbo, XING Huadong, SHI Peng

        2021,45(2):122-129, DOI: 10.7500/AEPS20200622004

        Abstract:In recent years, the continuous growth of the installed capacity of renewable energy generation has brought new problems to the stability of power systems. An analysis method based on the frequency response matrix is proposed to calculate the influence of wind power grid connection on the low-frequency oscillation mode in power systems. The result shows that the phase sum of some elements in the frequency response matrix of the synchronous grid and wind turbine system determines the influence of wind power at different access points on the low-frequency oscillation mode. Compared with the traditional analysis method, the vector margin method only needs to obtain the low-frequency oscillation mode and the transfer function matrix of the wind turbine system and synchronous grid system without the necessity of calculation of eigenvectors and residue. The process is simple and the result is intuitive. When multiple wind turbines are integrated to the system simultaneously, the influence of each wind turbine can be observed visually in a two-dimension complex plane. The effectiveness of the analysis results can be verified by the case of Inner Mongolia grid in China.

      • WANG Xiaohui, BAI Yu, ZHANG Yantao, GAO Feng, LIU Dong

        2021,45(2):130-138, DOI: 10.7500/AEPS20200606001

        Abstract:The cascading commutation failure is one of the new forms of cascading failures in AC/DC hybrid power grid. Based on the reflection and the participation of the cascading patterns in the typical phenomena relevant to commutation failures, this paper proposes a suppression strategy to reduce coupling by temporarily regulating the impedance of the critical transmission lines. The discussion is emphasized on the location selection to implement the proposed strategy. Based on the weighted graphic network model, the scope within which the commutation failures tend to cascade is firstly obtained by high voltage direct current (HVDC) clustering. After that, the critical path with the relatively-large influence on the HVDC coupling relationship in a given HVDC cluster is searched. Then the power flow betweenness is applied to compare the impacts on the system operation status in the case that the impedance of different lines is changed. Thereby taking measures on the important lines is avoided. Meanwhile, the voltage-time commutation area is employed to determine the exact HVDC cluster where the proposed strategy should be launched. Hence the differentiating suppression of the cascading commutation failures is implemented. Simulations on the modified IEEE 39-bus system and a regional grid of China are carried out in PSCAD/EMTDC, and the results validate the efficiency and feasibility of the proposed strategy of regulating the impedance on the critical lines to prevent the cascading commutation failures.

      • QUAN Xiangjun, ZHANG Congyue, WU Zaijun, TANG Chenghong, DOU Xiaobo, HU Qinran

        2021,45(2):139-147, DOI: 10.7500/AEPS20200417003

        Abstract:The voltage-controlled grid-connected inverter has received more and more attention due to its unique advantages, such as easy island operation, being able to break away from the phase-locked loop, and helping to improve the stability of power grid. Aiming to meet the demand of instantaneous power control for voltage-controlled grid-connected inverters, this paper proposes the active inertia support control and the droop control, the time constants and amplitudes of which can be independently adjusted. The feedforward control method and zero-pole cancellation technique are used to reduce the original second-order active power control system to a first-order system. Consequently, the dynamic performance of the power control is improved. Moreover, the introduction of the feed-forward method improves the control freedom of the system and makes the power regulation time and adjusted power-frequency amplitude can be independently designed. The experimental results show that the proposed control method can achieve inertia support and droop control with good dynamic performance.

      • DU Yan, WU Houbo, YANG Xiangzhen, JIANG Wei, SU Jianhui

        2021,45(2):148-156, DOI: 10.7500/AEPS20200225010

        Abstract:The real-time power grid impedance information obtained by grid-connected inverters can be used in power grid status monitoring, fault diagnosis, and grid-connected equipment stability control, to improve the intelligent level of grid-connected equipment and power grid regulation. Since the injected disturbance signal is often applied to the current control loop of the inverter, the impedance measurement frequency band is limited by the bandwidth of the controller. In order to improve the accuracy of high-frequency impedance measurement, this paper proposes a power grid impedance measurement method based on Sobol quasi-random pulse width modulation (SQRPWM) by taking advantages of the characteristic of the frequency spectrum shift of random pulse width modulation and the uniformity of Sobol quasi-random sequences. The frequency boundary of SQRPWM is optimized and designed according to the system bandwidth and stability constraints. The feasibility of the proposed impedance measurement scheme based on SQRPWM and the mixed impedance measurement scheme combined with the single pulse and SQRPWM is verified on the experimental platform of Starsim. The results show that the proposed impedance measurement scheme has the advantages of small disturbance and high measurement accuracy in a high frequency band. Moreover, if the proposed method is combined with the current-disturbance based impedance measurement method, the frequency band of the power grid impedance measurement can be further expanded and the broadband impedance information can be obtained.

      • XIAO Jian, GAN Ming, LIU Youzhi, ZI Hui, LIN Jikeng

        2021,45(2):157-163, DOI: 10.7500/AEPS20200422005

        Abstract:How to conduct the accurate equivalency of external grids has been a hot topic for many years. A practical online equivalent method for external grid based on measurement data is proposed. Firstly, an equivalent network for the external grid is built, which includes coupling branches between boundary nodes, and each boundary node is connected to an equivalent power source of the external grid through a branch. Furthermore, a parameter optimization solution model for the equivalent network is constructed based on online measurement data obtained by supervisory control and data acquisition (SCADA). The model takes the minimum sum of the mismatch between the measured value and the calculated value of the boundary node as the objective, and the parameters of all the equivalent branches within the reasonable range as the constraints. In order to ensure the stability and convergence of the equivalent parameter estimation, two strategies are proposed. One is to take the impedance value of the equivalent branch of Ward equivalent network in the maximum and minimum operation modes as the online equivalent network parameter range. The other is to obtain supplementary measurements based on historical data, thus avoiding the requirement of unplanned opening and closing operations of the internal grid and a series of following problems. Example results demonstrate the validity and effectiveness of the proposed model and method.

      • DENG Weisi, WU Yunliang, SUN Yujun, YIN Ziheng, LI Bao, LAI Xiaowen

        2021,45(2):164-172, DOI: 10.7500/AEPS20200426006

        Abstract:When the pre-clearing result of an electricity spot market could not meet all the security constraints due to inappropriate boundary condition settings or other reasons, the necessary security correction measures should be implemented. This paper proposes a generation scheduling correction method to eliminate security violations. The security correction is first applied to the non-market units to get the feasible solution which meets all the security constraints. Then the final clearing result is obtained by the optimization of market units. The theory of steady-state security region (SSR) is applied to the non-market unit correction, and the ramping constraints are included in the description of the SSR boundary. The optimal correcting direction could be revealed by the vector of steady-state security distance (SSD). And the effectiveness that each unit eliminates security violations could be evaluated based on the sensitivity of security distance and the regulating ability of the unit. Based on this, the generation scheduling correction decision-making method is proposed, and the influence of the correction in each time interval on the SSD of the adjacent time interval is considered during the calculation procedure. Thus, the effect of correction optimization is improved. Based on the IEEE 118-bus system, the correction results and the influence of security constrain limits and unit ramping ability on security correction are analyzed, which prove the effectiveness of the proposed method.

      • >Survey
      • HE Zhiyuan, LU Jingjing, LIU Tianqi, ZHANG Kai, YANG Xu, ZHAO Chengyong

        2021,45(2):173-183, DOI: 10.7500/AEPS20200417002

        Abstract:Energy crisis and reform bring great challenges to the traditional power transmission mode, and flexible DC power grid have become one of the new power transmission modes because of its flexibility, controllability, and high efficiency. However, there are still many problems to be solved in the key technologies and equipment. In this paper, the problem of fault current suppression in flexible DC power grids is taken as the research object. Firstly, the technical requirements of the fault current suppression in flexible DC power grids and the state of the art at home and abroad are analyzed. On this basis, the key technical problems to be solved and research ideas are proposed from the aspects of fault current distribution characteristics and evolution laws, fault current limiting principles and the multi-dimensional collaborative suppression, and digital physical dynamic simulation, etc. Finally, some important issues that still need to be solved in the research of flexible DC power grids are summarized and prospected.