A new energy fluctuation smoothing effect modeling method considering spatial correlation

Abstract

This invention discloses a modeling method for smoothing effects of new energy fluctuations that takes into account spatial correlation. It relates to the field of fluctuation modeling technology and addresses the problem of accurately identifying the fluctuation mitigation capabilities and smoothing effects formed during the actual operation of new energy clusters. By continuously monitoring the grid connection frequency of each new energy power station and combining this with geographical coordinates to identify the arrival status of disturbances, a disturbance sweep coefficient is constructed to characterize the continuity and arrival order of disturbance propagation, thus achieving a temporal characterization of the disturbance propagation path. Based on the disturbance sweep coefficient, the power stations are ranked, and the response relay trend is analyzed in conjunction with compensation start-up information. Collaborative response units are formed through clustering. A complementary release index is constructed by introducing the power flow migration degree of transmission sections and the power exchange volume of tie lines. Furthermore, a fluctuation smoothing effect model is established by combining the ramp events in aggregated power, thereby classifying and labeling the smoothing capabilities of collaborative response units.

Description

Technical Field

[0001] This invention relates to the field of fluctuation modeling technology, and more specifically, to a method for modeling the smoothing effect of new energy fluctuations that takes into account spatial correlation. Background Technology

[0002] With the continuous increase in the installed capacity of new energy sources, a large number of wind farms and photovoltaic power stations are connected to the grid in clusters. Since the output of new energy sources is affected by meteorological conditions, terrain environment and resource distribution differences, different power stations often exhibit obvious spatiotemporal differences. When meteorological disturbances, grid disturbances or power fluctuations occur in the region, the power stations do not respond simultaneously, but rather exhibit a sequential propagation process, thus forming a dynamic coupling response phenomenon across regions and multiple power stations.

[0003] The existing technology has the following shortcomings: Currently, most existing technologies treat renewable energy power plants as independent analytical objects for fluctuation assessment, lacking the ability to model the correlation between disturbance propagation paths, power plant collaborative response processes, and the power exchange effect of transmission networks. This makes it difficult to accurately identify the fluctuation mitigation capabilities and smoothing effects formed during the actual operation of renewable energy clusters, thus affecting the accuracy of the analysis of the operating characteristics of renewable energy clusters. Therefore, a new method for modeling the smoothing effect of renewable energy fluctuations that takes into account spatial correlation is proposed.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a new energy fluctuation smoothing effect modeling method that takes into account spatial correlation. This method solves the problems mentioned in the background art by employing a collaborative response analysis mechanism based on disturbance propagation path characterization and a fluctuation smoothing effect modeling mechanism that takes into account the complementary characteristics of power flow.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for modeling the smoothing effect of new energy fluctuations considering spatial correlation, comprising the following steps: Step S1: Monitor the grid connection point frequency data of each new energy power station in the new energy cluster area, obtain the geographical coordinates of each new energy power station, assess the disturbance arrival status in combination with the grid connection point frequency data, and calculate the disturbance sweep coefficient based on the disturbance arrival status. Step S2: Sort the new energy power stations using the perturbation sweep coefficient, collect the compensation start-up information of each new energy power station, evaluate the response relay trend of the new energy power stations based on the sorting results, perform cluster analysis based on the response relay trend and generate different collaborative response units. Step S3: Detect the power flow data of the transmission section of the coordinated response unit and calculate the power flow mobility. Collect the power exchange volume of the tie line of the coordinated response unit and generate the complementary release index by combining the power exchange volume of the tie line and the power flow mobility. Step S4: Obtain aggregated power data of the collaborative response units, identify the ramping events of the collaborative response units and count the ramping trigger frequency, combine the complementary release index to model the smoothing effect of the aggregated power data and classify and label the collaborative response units.

[0007] In a preferred embodiment, in step S1, the grid connection point frequency data of each new energy power station is collected. The grid connection point frequency data is the discrete frequency measurement value output by the synchronous phasor measurement device set at the grid connection point of each new energy power station under a unified sampling period. At the same time, the geographic coordinate data of each new energy power station is obtained. The geographic coordinate data is the horizontal and vertical position coordinates of the new energy power station in the geographic space. The frequency offset is obtained by calculating the difference between the measured frequency at the grid connection point at each sampling time and the preset rated frequency. When the absolute value of the frequency offset is greater than or equal to the preset frequency disturbance judgment threshold, the new energy power station is determined to be in the disturbance arrival state at the corresponding sampling time, and the state value is assigned to 1. When the absolute value of the frequency offset is less than the preset frequency disturbance judgment threshold, it is determined that the new energy power station is not in the disturbance arrival state at the corresponding sampling time, and the state value is assigned to 0.

[0008] In a preferred embodiment, in step S1, the time point when the state value of each new energy power station first changes from 0 to 1 is extracted and defined as the disturbance arrival time; Calculate the Euclidean distance between any two renewable energy power stations, retrieve the line impedance between each renewable energy power station in the power grid dispatch system, and calculate the equivalent electrical distance between the renewable energy power stations. After normalizing the Euclidean distance and the equivalent electrical distance, a weighted sum is performed to calculate the adjacency determination coefficient. When the adjacency determination coefficient is less than the preset adjacency determination threshold, it is determined that there is a spatial adjacency relationship between the corresponding new energy power stations; otherwise, it is determined that there is no spatial adjacency relationship.

[0009] In a preferred embodiment, in step S1, a judgment analysis is performed based on spatial adjacency and disturbance arrival time to identify effective disturbance propagation links and perform cumulative statistics to obtain the number of consecutive disturbance propagation for each new energy power station. The minimum arrival time of disturbances at all new energy power stations is used as the global disturbance start time benchmark. The difference between the arrival time of disturbances at each new energy power station and the global disturbance start time benchmark is calculated to obtain the disturbance propagation time span. The perturbation sweep coefficient is obtained by calculating the ratio of the number of consecutive perturbation propagations to the perturbation propagation time span.

[0010] In a preferred embodiment, in step S2, all new energy power stations are sorted in descending order based on the perturbation sweep coefficient of each new energy power station to form a perturbation sweep coefficient sorting sequence. Collect compensation start-up information from each new energy power station. The compensation start-up information is a Boolean value. When the reactive power compensation device is in the start-up state, the compensation start-up information is assigned a value of 1. When the reactive power compensation device is in the non-start-up state, the compensation start-up information is assigned a value of 0. Based on the compensation start-up information, the moment when the compensation start-up information of each new energy power station is first 1 is extracted to obtain the compensation start-up moment; Extract the position index of each new energy power station in the perturbation sweep coefficient sorting sequence; The location index, compensation start time and disturbance sweep coefficient of each new energy power station are used to construct a feature vector to represent the response relay trend. The feature vector is then normalized to obtain a standardized feature vector. Based on standardized feature vectors, the K-means clustering algorithm is used to divide the new energy power stations into different collaborative response units.

[0011] In a preferred embodiment, in step S3, the power flow data of the transmission section is the active power time series of the transmission section; The start time of a voltage disturbance event is determined when the voltage of the first new energy power station in the new energy cluster area exceeds the preset disturbance judgment boundary, and the end time is determined when the voltage of all new energy power stations returns to within the preset disturbance judgment boundary. Based on the start time of the voltage disturbance event, the observation time before the disturbance is traced back to the preset time, and the average value of the power flow data of the transmission section within the preset time is used as the reference power flow of the section before the disturbance. Based on the end time of the voltage disturbance event, the observation time after the disturbance is extended by a preset duration, and the average value of the power flow data of the transmission section within the preset observation time is taken as the power flow of the section after the disturbance. The difference between the power flow at the transmission section after the disturbance and the reference power flow at the transmission section before the disturbance is taken as the change in power flow at the transmission section. The ratio of the power flow rate of the coordinated response unit to the power flow mobility is calculated by using the reference power flow rate of the pre-disturbance section as the denominator and the absolute value of the power flow rate change of the transmission section as the numerator.

[0012] In a preferred embodiment, in step S3, for each communication line of the coordinated response unit, the instantaneous value of the active power of the communication line during the voltage disturbance event is collected, and the instantaneous value of the active power of the communication line at the start of the disturbance event is used as the steady-state exchange reference of the communication line. The difference between the instantaneous value of the active power of the tie line during the voltage disturbance event and the steady-state exchange reference of the tie line is taken as the power exchange quantity of the tie line. The total power exchange of the interconnecting lines is calculated based on the power exchange volume of the interconnecting lines. The complementary release index is calculated by combining the power flow migration degree and the total power exchange of the tie line.

[0013] In a preferred embodiment, in step S4, the instantaneous active power values ​​at the grid connection points of all new energy power plants within the collaborative response unit are sampled by a power measurement device, the instantaneous active power values ​​at the grid connection points at the same sampling time are summed, and the summation results are combined in chronological order to form aggregated power data. Using the preset slope recognition window length as the sliding window width, the data is slid sequentially along the aggregated power data. Within each sliding window, the difference between the corresponding values ​​at the start and end times of the window is calculated and divided by the window length to obtain the aggregated power decrease rate. Compare the rate of decrease in polymerization power with a preset ramp-up threshold: When the rate of decrease of the aggregation power is greater than or equal to the preset ramping threshold, a ramping event is determined to have occurred within the window, and the start time of the window is recorded in the ramping event triggering time set. Conversely, the time of the hill climb event is not included in the set of times it is triggered. The total number of elements in the set of times when the climbing event is triggered is taken as the climbing trigger frequency.

[0014] In a preferred embodiment, in step S4, the average of the squares of the differences between each value in the polymerization power data and the arithmetic mean of the polymerization power data is calculated to obtain the polymerization power fluctuation variance. The complementary release index corresponding to each collaborative response unit is retrieved, and the smoothing effect level value is calculated by combining the variance of aggregate power fluctuation. The smoothing effect level value is compared with the preset high-risk threshold and the preset low-risk threshold: When the smoothing effect level value is greater than the preset high-risk threshold, the corresponding collaborative response unit is marked as a fluctuation output type; When the smoothing effect level value is greater than the preset low-risk threshold and less than or equal to the preset high-risk threshold, the corresponding collaborative response unit is marked as a coordinated intermediate type. When the smoothing effect level value is less than or equal to the preset low-risk threshold, the corresponding collaborative response unit is marked as a volatility-absorbing type. Output the geographic coordinate range corresponding to each of the fluctuation output type collaborative response unit, the coordination intermediate type collaborative response unit, and the fluctuation absorption type collaborative response unit.

[0015] The technical effects and advantages of this invention are as follows: This invention continuously monitors the frequency of grid connection points of various new energy power plants and identifies the arrival status of disturbances by combining geographical coordinates. It quantifies the continuity of disturbance propagation and the order of arrival by constructing a disturbance sweep coefficient, thereby achieving a time-series characterization of the disturbance propagation path. Furthermore, based on the disturbance sweep coefficient, the power plants are sorted, and the response relay trend of the power plants is analyzed by integrating compensation start-up information. By clustering, collaborative response units with response consistency are formed, transforming the dispersed power plants into analysis objects with internal collaborative characteristics. On this basis, the power flow migration degree of transmission sections and the power exchange volume of tie lines are introduced for collaborative response units. A complementary release index is constructed, and a fluctuation smoothing effect model is established by combining the ramp event characteristics in the aggregated power data. This enables the classification and labeling of the smoothing capabilities of collaborative response units, thereby providing a basis for the optimization of new energy cluster scheduling and energy storage configuration. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of a new energy fluctuation smoothing effect modeling method that takes into account spatial correlation, according to the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the steps of a new energy fluctuation smoothing effect modeling method that takes into account spatial correlation, according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention continuously monitors the frequency of grid connection points of various new energy power plants and identifies the arrival status of disturbances by combining geographical coordinates. It quantifies the continuity of disturbance propagation and the order of arrival by constructing a disturbance sweep coefficient, thereby realizing the time-series characterization of disturbance propagation paths. Furthermore, based on the disturbance sweep coefficient, the power plants are sorted and the response relay trend of the power plants is analyzed by integrating compensation start-up information. By clustering, collaborative response units with response consistency are formed, transforming dispersed power plants into analytical objects with internal collaborative characteristics. On this basis, the power flow migration degree of transmission sections and the power exchange volume of tie lines are introduced for collaborative response units. A complementary release index is constructed and a fluctuation smoothing effect model is established by combining the ramp event characteristics in aggregated power data, thereby realizing the classification and labeling of the smoothing capabilities of collaborative response units.

[0020] Example 1, such as Figures 1 to 2 As shown, a method for modeling the smoothing effect of new energy fluctuations that takes into account spatial correlation includes the following steps: Step S1: Monitor the grid connection point frequency data of each new energy power station in the new energy cluster area, obtain the geographical coordinates of each new energy power station, assess the disturbance arrival status in combination with the grid connection point frequency data, and calculate the disturbance sweep coefficient based on the disturbance arrival status. Step S2: Sort the new energy power stations using the perturbation sweep coefficient, collect the compensation start-up information of each new energy power station, evaluate the response relay trend of the new energy power stations based on the sorting results, perform cluster analysis based on the response relay trend and generate different collaborative response units. Step S3: Detect the power flow data of the transmission section of the coordinated response unit and calculate the power flow mobility. Collect the power exchange volume of the tie line of the coordinated response unit and generate the complementary release index by combining the power exchange volume of the tie line and the power flow mobility. Step S4: Obtain aggregated power data of the collaborative response units, identify the ramping events of the collaborative response units and count the ramping trigger frequency, combine the complementary release index to model the smoothing effect of the aggregated power data and classify and label the collaborative response units.

[0021] The specific implementation is as follows: In step S1, the grid connection point frequency data of each new energy power station in the new energy cluster area is monitored, and the geographical coordinate data of each new energy power station is acquired simultaneously. The arrival status of the disturbance is determined based on the coupling relationship between frequency offset time series and spatial distance, and the disturbance sweep coefficient is calculated accordingly.

[0022] First, the grid connection point frequency data of each new energy power station is collected. The grid connection point frequency data is the discrete frequency measurement value output by the synchronous phasor measurement device set at the grid connection point of each new energy power station under a unified sampling period. The frequency measurement value is calculated from the phase angle change rate of the voltage phasor at the grid connection point. The grid connection point is the electrical connection boundary node between the new energy power station and the public power grid. At each preset sampling time, the voltage phasor data of the corresponding new energy power station grid connection point is measured and obtained. The voltage phasor data consists of voltage amplitude information and phase angle information. The phase angle information represents the instantaneous offset of the voltage at the grid connection point relative to the reference phase. The rate of change of the phase angle information at adjacent sampling times is calculated to obtain the corresponding grid connection point frequency measurement value.

[0023] It should be noted that the synchronous phasor measurement device refers to a wide-area measurement device deployed at the grid connection point of a new energy power station for synchronous measurement of voltage, current phasors and system frequency.

[0024] At the same time, the geographic coordinate data of each new energy power station is obtained. The geographic coordinate data comes from the GIS spatial information system and is represented as two-dimensional spatial coordinates, that is, the horizontal and vertical position coordinates of the new energy power station in the geographic space.

[0025] Frequency offset calculation is performed on the grid connection point frequency data. The difference between the measured grid connection point frequency value at each sampling time and the preset rated frequency is calculated to obtain the frequency offset. The frequency offset is used to represent the degree of deviation of the new energy power station from the reference frequency at the sampling time. The larger the value, the more obvious the deviation of the power balance state of the power station.

[0026] It should be noted that a GIS spatial information system refers to an information processing platform used to store, manage, analyze, and display geospatial data, and is used to provide spatial location distribution information of new energy power stations; the preset rated frequency refers to the standard operating frequency benchmark value specified by the power system where the new energy power station is located, and its value is determined by the dispatching and operation specifications for the new energy power station to connect to the power grid.

[0027] Subsequently, the frequency offset is used to determine the disturbance state. A frequency disturbance determination threshold is set, which is derived from the statistical distribution of the absolute value of the frequency offset under historical steady-state operating conditions, and is used to characterize the upper limit of normal operation fluctuations. When the absolute value of the frequency offset is greater than or equal to the frequency disturbance determination threshold, the new energy power station at the corresponding sampling time is determined to be in a disturbance arrival state, and the state value is assigned to 1; when the absolute value of the frequency offset is less than the frequency disturbance determination threshold, the new energy power station at the corresponding sampling time is determined not to be in a disturbance arrival state, and the state value is assigned to 0, thus forming a disturbance state sequence.

[0028] The arrival time of a disturbance is defined as the point in time when the state value of each renewable energy power station first changes from 0 to 1 by sequentially scanning the disturbance state sequence. The arrival time of a disturbance represents the order in which the disturbance arrives at different renewable energy power stations during its spatial propagation; the smaller the value, the earlier the power station is affected by the disturbance.

[0029] Based on the introduction of geographic coordinates, spatial weighting correction is performed on the arrival time of disturbances. First, the Euclidean distance between any two new energy power stations is calculated to characterize the physical spatial distribution relationship between the new energy power stations.

[0030] Simultaneously, the electrical connection topology data between each new energy power station in the power grid dispatch system is retrieved, including the line impedance connected to each power station. Based on the electrical connection topology data, the equivalent electrical distance between any two new energy power stations is calculated. The equivalent electrical distance is defined as the sum of the absolute values ​​of the line impedances of each segment on the shortest electrical path between the two new energy power stations.

[0031] Furthermore, the Euclidean distance and the equivalent electrical distance are normalized and then weighted and summed to calculate the adjacency determination coefficient.

[0032] A threshold for adjacency determination is set. When the adjacency determination coefficient is less than the threshold, it is determined that there is a spatial adjacency relationship between the corresponding new energy power stations; otherwise, it is determined that there is no spatial adjacency relationship.

[0033] It should be noted that the adjacency determination threshold is set as follows: the adjacency determination coefficient distribution corresponding to the station pairs whose effective disturbance propagation links were actually observed in historical disturbance events is statistically analyzed, and the 75th percentile value of the adjacency determination coefficient in the effective propagation samples is taken as the adjacency determination threshold; for newly commissioned clusters that lack historical samples, the initial value can be obtained by simulation calculation based on the topological impedance in the power grid planning stage, and then corrected after actual disturbance events are accumulated.

[0034] Taking any new energy power station as the reference node, and traversing all new energy power stations, if the reference node has a spatial adjacency with another new energy power station, and the arrival time of the disturbance at the reference node is earlier than the arrival time of the disturbance at the other new energy power station, then it is determined that there is a valid disturbance propagation link from the reference node to the other new energy power station.

[0035] The effective disturbance propagation links of each new energy power station are cumulatively statistically analyzed to obtain the number of consecutive disturbance propagations for each new energy power station. The larger the value, the more subsequent adjacent new energy power stations can be guided to respond during the disturbance propagation process, indicating that it has a stronger leading propagation capability or propagation hub characteristics in the disturbance propagation link.

[0036] Simultaneously, a disturbance propagation time span is defined for each renewable energy power station to characterize its time delay relative to the global disturbance initiation time. Specifically, the minimum disturbance arrival time among all renewable energy power stations is used as the global disturbance initiation time benchmark. The disturbance arrival time of each renewable energy power station is then calculated by subtracting the global disturbance initiation time benchmark to obtain the disturbance propagation time span. The disturbance propagation time span characterizes the response delay of the corresponding renewable energy power station relative to the initiation node; a larger value indicates that the corresponding renewable energy power station participates in the overall disturbance propagation process later, placing it in a later stage of the propagation in the time series.

[0037] Finally, the perturbation sweep coefficient is obtained by calculating the ratio of the number of consecutive perturbation propagations to the perturbation propagation time span. The perturbation sweep coefficient characterizes the intensity of the propagation contribution of new energy power plants per unit time in the perturbation spatial propagation process. The larger the value, the more significant the spatial continuity role and the higher the propagation participation density of the power plant in the perturbation propagation link. Therefore, it can be used as a basic input variable for subsequent power plant ranking and cluster analysis.

[0038] In step S2, the new energy power stations are sorted using the perturbation sweep coefficient, the compensation start-up information of each new energy power station is collected, and the response relay trend of the new energy power stations is analyzed based on the sorting results. Then, cluster analysis is performed to generate collaborative response units.

[0039] Based on the disturbance sweep coefficients of each new energy power station, all new energy power stations are sorted in descending order to form a disturbance sweep coefficient sorting sequence, which represents the relative spatial propagation priority in the disturbance propagation link. The earlier the sorting position, the earlier it participates in the spatial continuation process of the disturbance propagation link.

[0040] Furthermore, compensation start-up information of each new energy power station is collected. The compensation start-up information is a Boolean value, which comes from the operating status record data of the reactive power compensation device. When the reactive power compensation device is in the start-up state, the compensation start-up information is assigned a value of 1, and when the reactive power compensation device is in the non-start-up state, the compensation start-up information is assigned a value of 0.

[0041] It should be noted that reactive power compensation devices refer to power electronic compensation equipment installed inside new energy power plants to regulate the voltage level and reactive power output capacity at the grid connection point.

[0042] Based on the compensation start-up information, the moment when the compensation start-up information of each new energy power station is first 1 is extracted to obtain the compensation start-up moment. The smaller the value, the earlier the corresponding new energy power station starts responding to the disturbance.

[0043] Extract the position index of each new energy power station in the perturbation sweep coefficient sorting sequence, that is, the serial number position of the corresponding new energy power station in the perturbation sweep coefficient sorting result. The smaller the value, the earlier the spatial connection stage of the power station is in the perturbation propagation link.

[0044] The location index, compensation start time, and disturbance sweep coefficient of each new energy power station are used to construct a feature vector to represent the response relay trend. The feature vector is then normalized to obtain a standardized feature vector.

[0045] The feature vector, through the three-dimensional fusion of location index, compensation start time, and disturbance sweep coefficient, jointly characterizes the response relay trend of new energy power plants in the disturbance propagation process from three dimensions: spatial propagation order, temporal response lead, and disturbance propagation contribution intensity. Among them, the location index reflects the spatial priority of the power plant in the disturbance propagation link, the compensation start time characterizes the responsiveness of the power plant's reactive power compensation device to the disturbance, and the disturbance sweep coefficient quantifies the unit-time succession contribution of the power plant in the spatial propagation of the disturbance. The combination of the three makes the power plants in the same coordinated response unit exhibit a high degree of consistency in space, time, and propagation intensity, thus providing a response relay trend characterization with clear physical meaning for subsequent cluster analysis.

[0046] The new energy power stations are divided using the K-means clustering algorithm based on standardized feature vectors. The number of collaborative response units is preset and the corresponding number of initial cluster centers are randomly selected and iterated. In each iteration, the Euclidean distance between each new energy power station and each initial cluster center is calculated, and the new energy power station is assigned to the initial cluster center with the smallest distance. After the affiliation is completed, the position of each cluster center is recalculated and iteratively updated. The calculation stops when the change in cluster center is less than the preset convergence threshold during two consecutive iterations, and different collaborative response units are formed. This makes the new energy power stations within the same collaborative response unit have similar disturbance propagation characteristics and response timing characteristics, while there are obvious differences between different collaborative response units.

[0047] It should be noted that the K-means clustering algorithm is a data clustering method based on sample similarity, used to automatically group new energy power plants with similar characteristics into the same category. The preset convergence threshold is used to determine whether the K-means clustering algorithm has reached a stable clustering state. The specific setting process is as follows: First, retrieve historical new energy cluster disturbance event samples and construct a response relay trend feature vector. Perform K-means clustering calculation on the historical samples. In each iteration, record the change in cluster centers before and after the update, and statistically analyze the distribution of cluster center changes for all historical samples during the clustering convergence stage. Then, calculate the preset quantile value of the distribution of cluster center changes as the preset convergence threshold.

[0048] In step S3, the transmission section is a set of physical channels in the power grid consisting of several parallel transmission lines, which are used to undertake the task of sending power outward by the coordinated response unit; the power flow data of the transmission section is the active power time series collected by the power measurement devices deployed at both ends of each line of the corresponding transmission section and uploaded to the power grid dispatching system. During the collection process, the instantaneous value of active power of each line is synchronously recorded according to the preset sampling period, and then the power grid dispatching system summarizes and stores it according to the transmission section.

[0049] A power measurement device is an electrical quantity acquisition device installed at both ends of the busbars of each line in a power transmission section, used to measure the active power of the line in real time online.

[0050] A voltage disturbance event refers to a disturbance process in which the voltage of the grid connection point of a new energy power station continuously falls below or deviates from the normal operating range within a short period of time in a new energy cluster area. The start time is determined by the moment when the voltage of the first grid connection point of the power station exceeds the preset disturbance judgment boundary, and the end time is determined by the moment when the voltage of all grid connection points of the power stations recovers to within the preset disturbance judgment boundary.

[0051] Based on the start time of the voltage disturbance event, the observation time before the disturbance is traced back to the preset time, and the average value of the power flow data of the transmission section within the preset time is used as the reference power flow of the section before the disturbance. Based on the end time of the voltage disturbance event, a preset observation period after the disturbance is extended, and the average value of the power flow data of the transmission section within the preset observation period after the disturbance is taken as the power flow of the section after the disturbance.

[0052] The difference between the power flow at the transmission section after the disturbance and the reference power flow at the transmission section before the disturbance is taken as the change in power flow at the transmission section.

[0053] It should be added that the preset observation duration before the disturbance is used to establish a steady-state power flow reference when the disturbance event has not occurred, and the lower limit value can be taken based on the statistical results of the steady-state operation fluctuation amplitude of the power grid to which it belongs; the preset observation duration after the disturbance is used to capture the power flow redistribution process caused by the disturbance event, and the upper limit value can be taken based on the longest compensation duration of the energy storage device in the coordinated response unit.

[0054] The ratio of the power flow rate of the coordinated response unit to the power flow mobility is calculated by using the reference power flow rate of the pre-disturbance section as the denominator and the absolute value of the power flow rate change of the transmission section as the numerator.

[0055] The power flow migration reflects the magnitude of the power flow deviation of the external transmission channel of the cooperative response unit relative to its own steady-state operating level under the action of voltage disturbance events. The larger the value, the more the disturbance event has caused a large proportion of the power flow to be transferred to other areas through the external transmission channel, and the more active the power flow support channel of the cooperative response unit is. For each tie line of the coordinated response unit, the instantaneous value of the active power of the tie line recorded by the power measurement devices at both ends of the tie line during the voltage disturbance event is collected, and the instantaneous value of the active power of the tie line at the beginning of the disturbance event is used as the steady-state exchange reference of the tie line. A connecting line is a power transmission line that connects the current coordinated response unit with the adjacent external area and undertakes the power exchange between the areas. The difference between the instantaneous value of the active power of the tie line during the voltage disturbance event and the steady-state exchange reference of the tie line is taken as the power exchange quantity of the tie line. The absolute value of the power exchange of the tie line is taken and accumulated in the order of sampling time to obtain the cumulative value of the power exchange increment of the tie line during the disturbance event. The cumulative value of the exchange increment of all interconnecting lines in the coordinated response unit is summed to obtain the total power exchange of the interconnecting lines.

[0056] Based on the total power exchange and power flow migration of the integrated interconnection lines, the complementary release index of the coordinated response unit is calculated using the following formula: ; in, For complementary release index, For the total power exchange of the interconnecting lines, To disturb the reference current flow at the front section. For the degree of trend migration, The preset exchange weighting coefficient is T, where T is the duration of the voltage disturbance event.

[0057] The complementary release index reflects the overall strength of the inter-regional power support behavior of the coordinated response unit during continuous voltage disturbance events relative to its own steady-state transmission level. The larger the complementary release index value, the larger the scale of power support completed between regions during the disturbance, the more active the transmission channel, and the stronger the spatial complementarity capability of the coordinated response unit.

[0058] It should be added that the preset exchange weight coefficient is determined as follows: Before the voltage disturbance event occurs, several inter-regional power support events recorded in the historical dispatch records of the power grid to which the new energy cluster area belongs are statistically analyzed. The ratio of the total power exchange of the tie lines to the corresponding pre-disturbance cross-section reference power flow and the value of the power flow migration degree are calculated for each event. The explanatory weight of the two types of indicators for the spatial complementary behavior of the coordinated response unit in the historical events is compared. The median value of the proportion of the total power exchange of the tie lines to the pre-disturbance cross-section reference power flow in the two types of indicators is taken as the preset exchange weight coefficient.

[0059] In step S4, the time interval consisting of the voltage disturbance event and the preset observation duration before and after it is used as the sampling interval for aggregated power data. The instantaneous active power values ​​of all new energy power plants in the coordinated response unit are synchronously sampled at fixed sampling intervals using a power measurement device. The instantaneous active power values ​​of the grid-connected power plants at the same sampling time are summed, and the summation results are combined in chronological order to form aggregated power data.

[0060] Using the preset slope recognition window length as the sliding window width, the data is slid sequentially along the aggregated power data. Within each sliding window, the difference between the corresponding values ​​at the start and end times of the window is calculated and divided by the window length to obtain the aggregated power decrease rate. Compare the rate of decrease in polymerization power with a preset ramp-up threshold: When the rate of decrease of the aggregation power is greater than or equal to the preset ramping threshold, it is determined that a ramping event has occurred within the window, and the start time of the window is recorded in the ramping event triggering time set. Conversely, if no climbing event occurs within the window, it is determined that no climbing event has occurred and is not included in the set of climbing event trigger times.

[0061] The total number of elements in the set of times when the climbing event is triggered is taken as the climbing trigger frequency.

[0062] It should be noted that the preset ramp threshold is determined as follows: Before a voltage disturbance event occurs, the power decline rate during normal operation in the aggregated power data of the new energy cluster area is statistically analyzed, and its average value and standard deviation are calculated. The sum of the average value and three times the standard deviation is used as the preset ramp threshold. The preset ramp identification window length can be set according to the response and resolution capability of the power grid dispatch automation system to short-term power events.

[0063] The variance of the aggregation power fluctuation is obtained by averaging the squares of the differences between each value in the aggregation power data and the arithmetic mean of the aggregation power data.

[0064] Retrieve the complementary release index corresponding to each collaborative response unit, and calculate the smoothing effect level value of the collaborative response unit according to the following formula: ; in, The smoothing effect level value, The variance of polymerization power fluctuation. For hill climbing trigger frequency, For complementary release index, The preset complementary sensitivity coefficient is used.

[0065] It should be noted that the preset complementary sensitivity coefficient is determined as follows: In several consecutive voltage disturbance events recorded after the new energy cluster area was put into operation, the ramp-up triggering frequency of each coordinated response unit under each event is used as the dependent variable, and the complementary release index is used as the independent variable for linear regression fitting; since the complementary release index is negatively correlated with the ramp-up triggering frequency, the absolute value of the fitted regression coefficient is taken as the preset complementary sensitivity coefficient to ensure that the smoothing effect level value decreases accordingly when the complementary release index increases.

[0066] The smoothing effect level value is compared with the preset high-risk threshold and the preset low-risk threshold: When the smoothing effect level value is greater than the preset high-risk threshold, the corresponding collaborative response unit is marked as a fluctuation output type; When the smoothing effect level value is greater than the preset low-risk threshold and less than or equal to the preset high-risk threshold, the corresponding collaborative response unit is marked as a coordinated intermediate type. When the smoothing effect level value is less than or equal to the preset low-risk threshold, the corresponding collaborative response unit is marked as a fluctuation absorption type.

[0067] Among them, the fluctuation output type refers to the coordinated response unit that experiences a large drop in aggregated power, frequent ramp-up events, and insufficient inter-regional power compensation capability during voltage disturbance events, making it the main source of fluctuations in the entire network; the coordinated intermediate type refers to the coordinated response unit that bears a certain output loss during fluctuations and also has a certain inter-regional complementary capability, being in a transitional state between fluctuation generation and fluctuation absorption; the fluctuation absorption type refers to the coordinated response unit that experiences small fluctuations in aggregated power or sufficient inter-regional power support during voltage disturbance events, possessing strong fluctuation smoothing capability, and is the main unit for absorbing fluctuations in the entire network.

[0068] It should be noted that the preset high-risk threshold and preset low-risk threshold are determined as follows: For several consecutive voltage disturbance events recorded in the historical operation data of the new energy cluster area, the smoothing effect level value of each collaborative response unit under each event is calculated according to the above formula. The obtained smoothing effect level values ​​are sorted in ascending order, and the upper quarter quantile value is taken as the preset high-risk threshold, and the lower quarter quantile value is taken as the preset low-risk threshold, so as to ensure that the judgment interval between the high-risk threshold and the low-risk threshold can cover most of the medium-fluctuation collaborative response units.

[0069] Ultimately, the geographical coordinate ranges corresponding to the fluctuation output type collaborative response unit, the coordinated intermediate type collaborative response unit, and the fluctuation absorption type collaborative response unit will be output as the result of smoothing the distribution of new energy potential, which will be used to support the selection of new energy base expansion locations, energy storage capacity configuration, and cross-regional power transmission channel planning.

[0070] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0071] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0072] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0073] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0074] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for modeling the smoothing effect of new energy fluctuations considering spatial correlation, characterized in that: Includes the following steps: Step S1: Monitor the grid connection point frequency data of each new energy power station in the new energy cluster area, obtain the geographical coordinates of each new energy power station, assess the disturbance arrival status in combination with the grid connection point frequency data, and calculate the disturbance sweep coefficient based on the disturbance arrival status. Step S2: Sort the new energy power stations using the perturbation sweep coefficient, collect the compensation start-up information of each new energy power station, evaluate the response relay trend of the new energy power stations based on the sorting results, perform cluster analysis based on the response relay trend and generate different collaborative response units. Step S3: Detect the power flow data of the transmission section of the coordinated response unit and calculate the power flow mobility. Collect the power exchange volume of the tie line of the coordinated response unit and generate the complementary release index by combining the power exchange volume of the tie line and the power flow mobility. Step S4: Obtain aggregated power data of the collaborative response units, identify the ramping events of the collaborative response units and count the ramping trigger frequency, combine the complementary release index to model the smoothing effect of the aggregated power data and classify and label the collaborative response units.

2. The method for modeling the smoothing effect of new energy fluctuations considering spatial correlation according to claim 1, characterized in that: In step S1, the grid connection point frequency data of each new energy power station is collected. The grid connection point frequency data is the discrete frequency measurement value output by the synchronous phasor measurement device set at the grid connection point of each new energy power station under a unified sampling period. At the same time, the geographic coordinate data of each new energy power station is obtained. The geographic coordinate data is the horizontal and vertical position coordinates of the new energy power station in the geographic space. The frequency offset is obtained by calculating the difference between the measured frequency at the grid connection point at each sampling time and the preset rated frequency. When the absolute value of the frequency offset is greater than or equal to the preset frequency disturbance judgment threshold, the new energy power station is determined to be in the disturbance arrival state at the corresponding sampling time, and the state value is assigned to 1. When the absolute value of the frequency offset is less than the preset frequency disturbance judgment threshold, it is determined that the new energy power station is not in the disturbance arrival state at the corresponding sampling time, and the state value is assigned to 0.

3. The method for modeling the smoothing effect of new energy fluctuations considering spatial correlation according to claim 2, characterized in that: In step S1, the time point when the state value of each new energy power station first changes from 0 to 1 is extracted and defined as the disturbance arrival time; Calculate the Euclidean distance between any two renewable energy power stations, retrieve the line impedance between each renewable energy power station in the power grid dispatch system, and calculate the equivalent electrical distance between the renewable energy power stations. After normalizing the Euclidean distance and the equivalent electrical distance, a weighted sum is performed to calculate the adjacency determination coefficient. When the adjacency determination coefficient is less than the preset adjacency determination threshold, it is determined that there is a spatial adjacency relationship between the corresponding new energy power stations; otherwise, it is determined that there is no spatial adjacency relationship.

4. The method for modeling the smoothing effect of new energy fluctuations considering spatial correlation according to claim 3, characterized in that: In step S1, a judgment analysis is performed based on spatial adjacency and disturbance arrival time to identify effective disturbance propagation links and accumulate statistics to obtain the number of consecutive disturbance propagation times for each new energy power station. The minimum arrival time of disturbances at all new energy power stations is used as the global disturbance start time benchmark. The difference between the arrival time of disturbances at each new energy power station and the global disturbance start time benchmark is calculated to obtain the disturbance propagation time span. The perturbation sweep coefficient is obtained by calculating the ratio of the number of consecutive perturbation propagations to the perturbation propagation time span.

5. A method for modeling the smoothing effect of new energy fluctuations considering spatial correlation, as described in claim 4, is characterized in that: In step S2, based on the disturbance sweep coefficients of each new energy power station, all new energy power stations are sorted in descending order to form a disturbance sweep coefficient sorting sequence. Collect compensation start-up information from each new energy power station. The compensation start-up information is a Boolean value. When the reactive power compensation device is in the start-up state, the compensation start-up information is assigned a value of 1. When the reactive power compensation device is in the non-start-up state, the compensation start-up information is assigned a value of 0. Based on the compensation start-up information, the moment when the compensation start-up information of each new energy power station is first 1 is extracted to obtain the compensation start-up moment; Extract the position index of each new energy power station in the perturbation sweep coefficient sorting sequence; The location index, compensation start time and disturbance sweep coefficient of each new energy power station are used to construct a feature vector to represent the response relay trend. The feature vector is then normalized to obtain a standardized feature vector. Based on standardized feature vectors, the K-means clustering algorithm is used to divide the new energy power stations into different collaborative response units.

6. The method for modeling the smoothing effect of new energy fluctuations considering spatial correlation according to claim 1, characterized in that: In step S3, the power flow data of the transmission section is the active power time series of the transmission section; The start time of a voltage disturbance event is determined when the voltage of the first new energy power station in the new energy cluster area exceeds the preset disturbance judgment boundary, and the end time is determined when the voltage of all new energy power stations returns to within the preset disturbance judgment boundary. Based on the start time of the voltage disturbance event, the observation time before the disturbance is traced back to the preset time, and the average value of the power flow data of the transmission section within the preset time is used as the reference power flow of the section before the disturbance. Based on the end time of the voltage disturbance event, the observation time after the disturbance is extended by a preset duration, and the average value of the power flow data of the transmission section within the preset observation time is taken as the power flow of the section after the disturbance. The difference between the power flow at the transmission section after the disturbance and the reference power flow at the transmission section before the disturbance is taken as the change in power flow at the transmission section. The ratio of the power flow rate of the coordinated response unit to the power flow mobility is calculated by using the reference power flow rate of the pre-disturbance section as the denominator and the absolute value of the power flow rate change of the transmission section as the numerator.

7. A method for modeling the smoothing effect of new energy fluctuations considering spatial correlation, as described in claim 6, is characterized in that: In step S3, for each tie line of the coordinated response unit, the instantaneous value of the active power of the tie line during the voltage disturbance event is collected, and the instantaneous value of the active power of the tie line at the beginning of the disturbance event is used as the steady-state exchange reference of the tie line. The difference between the instantaneous value of the active power of the tie line during the voltage disturbance event and the steady-state exchange reference of the tie line is taken as the power exchange quantity of the tie line. The total power exchange of the interconnecting lines is calculated based on the power exchange volume of the interconnecting lines. The complementary release index is calculated by combining the power flow migration degree and the total power exchange of the tie line.

8. A method for modeling the smoothing effect of new energy fluctuations considering spatial correlation, as described in claim 1, is characterized in that: In step S4, the instantaneous active power values ​​at the grid connection points of all new energy power plants within the collaborative response unit are sampled using a power measurement device. The instantaneous active power values ​​at the grid connection points at the same sampling time are summed, and the summation results are combined in chronological order to form aggregated power data. Using the preset slope recognition window length as the sliding window width, the data is slid sequentially along the aggregated power data. Within each sliding window, the difference between the corresponding values ​​at the start and end times of the window is calculated and divided by the window length to obtain the aggregated power decrease rate. Compare the rate of decrease in polymerization power with a preset ramp-up threshold: When the rate of decrease of the aggregation power is greater than or equal to the preset ramping threshold, a ramping event is determined to have occurred within the window, and the start time of the window is recorded in the ramping event triggering time set. Conversely, the time of the hill climb event is not included in the set of times it is triggered. The total number of elements in the set of times when the climbing event is triggered is taken as the climbing trigger frequency.

9. A method for modeling the smoothing effect of new energy fluctuations considering spatial correlation, as described in claim 8, is characterized in that: In step S4, the average of the squares of the differences between each value in the polymerization power data and the arithmetic mean of the polymerization power data is calculated to obtain the polymerization power fluctuation variance. The complementary release index corresponding to each collaborative response unit is retrieved, and the smoothing effect level value is calculated by combining the variance of aggregate power fluctuation. The smoothing effect level value is compared with the preset high-risk threshold and the preset low-risk threshold: When the smoothing effect level value is greater than the preset high-risk threshold, the corresponding collaborative response unit is marked as a fluctuation output type; When the smoothing effect level value is greater than the preset low-risk threshold and less than or equal to the preset high-risk threshold, the corresponding collaborative response unit is marked as a coordinated intermediate type. When the smoothing effect level value is less than or equal to the preset low-risk threshold, the corresponding collaborative response unit is marked as a volatility-absorbing type. Output the geographic coordinate range corresponding to each of the fluctuation output type collaborative response unit, the coordination intermediate type collaborative response unit, and the fluctuation absorption type collaborative response unit.

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