The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators...This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes.A dynamic layered sorting allocation method is also proposed.The proposed evaluation method considers the power-limiting degree of the last cycle,the adjustment margin,and volatility.It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time,and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results.A case study of a large-scale wind power base in Northwest China was conducted.The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods.The results show that the three scoring trends are the same,and that the proposed evaluation method is closer to the average level of the latter two,demonstrating higher accuracy.The proposed allocation method can reduce the number of adjustments made to wind farms,which is significant for the allocation and evaluation of wind power clusters.展开更多
Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia ar...Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.展开更多
To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power ...To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.展开更多
Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow.This paper uses polynomial normal transformation me...Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow.This paper uses polynomial normal transformation method to deal with non-normal random variable correlation,and solves probabilistic load flow based on Kriging method.This method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear combination.Compared with traditional approaches which need a greater number of calculation times,long simulation time,and large memory space,Kriging method can rapidly estimate node state variables and branch current power distribution situation.As one of the generator nodes in the western Yunnan power grid,a certain wind farm is chosen for empirical analysis.Results are used to compare with those by Monte Carlo-based accurate solution,which proves the validity and veracity of the model in wind farm power modeling as output of the actual turbine through PSD-BPA.展开更多
We develop a theoretical formulation for the large-scale dynamics of galaxy clusters involving two spherical 'isothermal fluids' coupled by their mutual gravity and derive asymptotic similarity solutions analy...We develop a theoretical formulation for the large-scale dynamics of galaxy clusters involving two spherical 'isothermal fluids' coupled by their mutual gravity and derive asymptotic similarity solutions analytically. One of the fluids roughly approximates the massive dark matter halo, while the other describes the hot gas, the relatively small mass contribution from the galaxies being subsumed in the gas. By properly choosing the self-similar variables, it is possible to consistently transform the set of time-dependent two-fluid equations of spherical symmetry with self-gravity into a set of coupled nonlinear ordinary differential equations (ODEs). We focus on the analytical analysis and discuss applications of the solutions to galaxy clusters.展开更多
Santos Basin contains the major hub of oil and gas exploration in Brazil. Consequently, knowledge of ocean surface winds in this area is very important for operational and planning activities. In addition, the importa...Santos Basin contains the major hub of oil and gas exploration in Brazil. Consequently, knowledge of ocean surface winds in this area is very important for operational and planning activities. In addition, the importance of renewable energies is nowadays unquestionable, specifically in the case of the wind energy. In this paper, a data clustering technique is applied in order to obtain representative local wind patterns in Santos Basin. Reanalysis data from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) have been used in this study.展开更多
The homogeneity of groups of 16-dimensional wind direction roses (obtained by hierarchical clustering in a previous report) is discussed through the application of Andrews’ Curves. Principal Component Analysis (PCA) ...The homogeneity of groups of 16-dimensional wind direction roses (obtained by hierarchical clustering in a previous report) is discussed through the application of Andrews’ Curves. Principal Component Analysis (PCA) is employed to reduce dimensionality and to provide an ordering of the variables to compute Andrews’ Curves. Our results suggest that Andrews’ Curves greatly facilitate the visualization of homogeneity as well as reveal information that allows improving the clusters’ arrangement. A combined analysis employing Andrews’ Curves and Calinkski and Harabasz’ approach (a method for determining the optimal number of groups) helps to assess the strength of the group structure of the data as well as to detect anomalies such as misclassified objects or atypical values. Furthermore, it allows finding out that the 24 original seasonal hourly roses (representing the “day”) become better represented by 6 groups (rather than by 5 as proposed in the previous report). The new group arrangement was consistent with the dendogram for another cut-off distance. As a result the wind occurrences are now represented by a more detailed and smooth pattern: there is a decrease in northern wind between midday and twilight while eastern winds become more important towards the evening. The methodology proposed is a subject to be considered to become part of an automated system.展开更多
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
基金supported by the National Natural Science Foundation of China(Grant No.52076038,U22B20112,No.52106238)the Fundamental Research Funds for Central Universities(No.423162,B230201051).
文摘This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes.A dynamic layered sorting allocation method is also proposed.The proposed evaluation method considers the power-limiting degree of the last cycle,the adjustment margin,and volatility.It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time,and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results.A case study of a large-scale wind power base in Northwest China was conducted.The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods.The results show that the three scoring trends are the same,and that the proposed evaluation method is closer to the average level of the latter two,demonstrating higher accuracy.The proposed allocation method can reduce the number of adjustments made to wind farms,which is significant for the allocation and evaluation of wind power clusters.
基金supported by the Natural Science Foundation of China under contact(61233007)
文摘Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.
基金supported by the State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023035).
文摘To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.
文摘Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow.This paper uses polynomial normal transformation method to deal with non-normal random variable correlation,and solves probabilistic load flow based on Kriging method.This method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear combination.Compared with traditional approaches which need a greater number of calculation times,long simulation time,and large memory space,Kriging method can rapidly estimate node state variables and branch current power distribution situation.As one of the generator nodes in the western Yunnan power grid,a certain wind farm is chosen for empirical analysis.Results are used to compare with those by Monte Carlo-based accurate solution,which proves the validity and veracity of the model in wind farm power modeling as output of the actual turbine through PSD-BPA.
文摘We develop a theoretical formulation for the large-scale dynamics of galaxy clusters involving two spherical 'isothermal fluids' coupled by their mutual gravity and derive asymptotic similarity solutions analytically. One of the fluids roughly approximates the massive dark matter halo, while the other describes the hot gas, the relatively small mass contribution from the galaxies being subsumed in the gas. By properly choosing the self-similar variables, it is possible to consistently transform the set of time-dependent two-fluid equations of spherical symmetry with self-gravity into a set of coupled nonlinear ordinary differential equations (ODEs). We focus on the analytical analysis and discuss applications of the solutions to galaxy clusters.
文摘Santos Basin contains the major hub of oil and gas exploration in Brazil. Consequently, knowledge of ocean surface winds in this area is very important for operational and planning activities. In addition, the importance of renewable energies is nowadays unquestionable, specifically in the case of the wind energy. In this paper, a data clustering technique is applied in order to obtain representative local wind patterns in Santos Basin. Reanalysis data from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) have been used in this study.
文摘The homogeneity of groups of 16-dimensional wind direction roses (obtained by hierarchical clustering in a previous report) is discussed through the application of Andrews’ Curves. Principal Component Analysis (PCA) is employed to reduce dimensionality and to provide an ordering of the variables to compute Andrews’ Curves. Our results suggest that Andrews’ Curves greatly facilitate the visualization of homogeneity as well as reveal information that allows improving the clusters’ arrangement. A combined analysis employing Andrews’ Curves and Calinkski and Harabasz’ approach (a method for determining the optimal number of groups) helps to assess the strength of the group structure of the data as well as to detect anomalies such as misclassified objects or atypical values. Furthermore, it allows finding out that the 24 original seasonal hourly roses (representing the “day”) become better represented by 6 groups (rather than by 5 as proposed in the previous report). The new group arrangement was consistent with the dendogram for another cut-off distance. As a result the wind occurrences are now represented by a more detailed and smooth pattern: there is a decrease in northern wind between midday and twilight while eastern winds become more important towards the evening. The methodology proposed is a subject to be considered to become part of an automated system.
