Active supporting method for transient voltage of regional weak power grid participated by composite energy storage system

Abstract

The invention relates to the technical field of electric energy, in particular to a composite energy storage system participated regional weak power grid transient voltage active supporting method. Obtaining topological data of each line and operation parameter time sequence data under a voltage jump event (including active and inactive support) in a target power grid region; the line cluster of the power grid line is obtained based on the topological data, so that differentiation analysis is facilitated; the energy storage system can provide transient voltage support, and in each line cluster, the value, fluctuation and event number characteristics of operating parameters under an active support voltage event are synthesized to determine the active support demand degree value of each line; the emergency degree value is quantitatively adjusted by comparing operation parameter change difference characteristics under different voltage sudden change events; and finally, adjusting a preset droop coefficient based on the active support demand degree value and the adjustment emergency degree value to obtain an adaptive droop coefficient, so that the energy storage system can participate in transient voltage support more flexibly and efficiently, and the stability and reliability of a regional power grid are improved.

Description

Technical Field

[0001] This invention relates to the field of electrical energy technology, specifically to a method for active support of transient voltage in regional weak power grids using a composite energy storage system. Background Technology

[0002] Regionally weak power grids typically feature relatively simple grid structures, limited transmission capacity, and weak anti-interference capabilities, making them highly susceptible to transient voltage instability when faced with various internal and external disturbances. Transient voltage instability not only affects the normal operation of power equipment and reduces power quality, but in severe cases can even lead to large-scale power outages, causing significant losses to social production and daily life. Energy storage systems, with their advantages of rapid charging and discharging and fast response, can quickly provide or absorb reactive power during grid voltage fluctuations, providing transient voltage support for the grid. Therefore, the application of energy storage systems in power grids is becoming increasingly widespread.

[0003] Currently, the main method for analyzing transient voltage stability in power grid systems is the QV control strategy with a fixed droop coefficient. However, in actual power grid systems, the operating status and data conditions of the power grid may change at any time. Therefore, the active support requirements and emergency situations required by different power grid lines will vary. This means that a fixed droop coefficient usually cannot be flexibly adapted to the current power grid status, making it difficult to achieve efficient active support for transient voltage, thereby affecting the overall stability and reliability of weak regional power grids. Summary of the Invention

[0004] To address the challenge that in real-world power grid systems, the operating status and data conditions of the grid can change at any time, leading to varying active support requirements and emergency situations for different grid lines. This makes it difficult for fixed droop coefficients to flexibly adapt to the current grid conditions, hindering efficient active transient voltage support and impacting the overall stability and reliability of regional weak power grids. The present invention aims to provide a method for using a composite energy storage system to actively support transient voltages in regional weak power grids. The specific technical solution adopted is as follows: Within the target power grid area, topology data for each power grid line and timing data of operating parameters for each power grid line under voltage surge events are acquired; wherein, the voltage surge events are divided into active voltage support events and inactive voltage support events. Cluster analysis is performed on the power grid lines based on their topology data to obtain line clusters. Within each line cluster, the degree of active support requirement for each power grid line is determined based on the numerical and fluctuation characteristics of the time series data of the operating parameters of the power grid lines under active support voltage events, as well as the number of active support voltage events. For any given power grid line, compare the differences in the time-series data of the operating parameters of that power grid line under different types of voltage surge events to determine the adjustment urgency value for each power grid line. Based on the active support demand value and adjustment urgency value corresponding to each power grid line, the preset droop coefficient of each power grid line is adjusted to obtain an adaptive droop coefficient for active support of transient voltage.

[0005] Furthermore, the method for obtaining the line clusters includes: Within the target power grid area, for any two power grid lines, analyze the differences in topology data between the power grid lines to obtain the topology difference factor between these two power grid lines. Within the target power grid area, the topological difference factor between power grid lines is used as a distance metric. The K-means clustering algorithm and a preset K value are used to perform cluster analysis on all power grid lines to obtain line clusters.

[0006] Furthermore, the method for obtaining the topological difference factor includes: The topology data includes at least the line length, conductor type, line impedance, and transformer turns ratio; The various topological data of each power grid line are used to construct a topological feature vector; In the target power grid region, for any two power grid lines, the Euclidean distance between the topological feature vectors of these two power grid lines is used as the topological difference factor between the two power grid lines.

[0007] Furthermore, the method for obtaining the proactive support demand level value includes: In each active support voltage event of each power grid line, the first active support demand factor of each power grid line at the time of each active support event is determined based on the numerical characteristics of the operating parameters. In each active support voltage event of each power grid line, the fluctuation characteristics of the time series data of the operating parameters are analyzed to determine the second active support demand factor of each power grid line at the time of each active support voltage event. The normalized value of the product of the first active support demand factor and the second active support demand factor for each power grid line under each active support voltage event is used as the active support demand characteristic value for each power grid line under each active support voltage event. The mean of the active support demand characteristic value of each power grid line under all active support voltage events is multiplied by the proportion of the number of active support voltage events of each power grid line in the total number of active support voltage events in its respective line cluster, and the normalized value of the product is used as the active support demand level value of each power grid line.

[0008] Furthermore, the method for obtaining the first active support demand factor includes: The types of operating parameters include voltage, current, and reactive power; In each active support voltage event of each power grid line, the proportion of reactive power of each power grid line at the time of the active support voltage event to the preset reactive power is used as the first active support demand factor of each power grid line at the time of the active support voltage event.

[0009] Furthermore, the method for obtaining the second active support demand factor includes: In each active support voltage event of each power grid line, the normalized value of the difference between the maximum and minimum values ​​in the current time series data is used as the second active support demand factor for each power grid line at the time of each active support voltage event.

[0010] Furthermore, the method for obtaining the adjustment urgency value includes: For any power grid line, analyze the numerical fluctuation characteristics of the voltage time series data of the power grid line under each voltage sudden event, and construct the voltage feature vector of the power grid line under each voltage sudden event. By pairwise combining the active support voltage events and non-active support voltage events of the power grid line, all unique event combinations are obtained. Under each event combination, the Euclidean distance between voltage feature vectors is calculated as a distance factor. The mean of the distance factors corresponding to all event combinations is negatively correlated and normalized, and the result is used as the adjustment urgency value of the power grid line.

[0011] Furthermore, the method for constructing the voltage feature vector includes: For any power grid line, in the voltage time series data of the power grid line under each voltage sudden event, the variance of all voltage values ​​is used as the fluctuation factor, the mean of all voltage values ​​is used as the mean feature value, and the fluctuation factor and the mean feature value are used as the voltage feature vector of the power grid line under each voltage sudden event.

[0012] Furthermore, the method for obtaining the adaptive droop coefficient includes: The normalized product of the active support demand level and the adjustment urgency level of each power grid line, and the sum of this product with a preset constant, are used as the adaptive adjustment value for each power grid line. The product of the adaptive adjustment value and the preset droop coefficient for each power grid line is used as the adaptive droop coefficient for each power grid line.

[0013] Furthermore, the method for obtaining the preset K value includes: The optimal K value is obtained based on the profile coefficient method, and the optimal K value is used as the preset K value.

[0014] The present invention has the following beneficial effects: First, topological data and time-series data of operating parameters under voltage surge events (divided into active and inactive voltage support events) are comprehensively acquired for each power grid line in the target power grid area. This provides a rich and accurate data foundation for subsequent precise analysis of power grid line characteristics and operating status. Based on the topological data of the power grid lines, cluster analysis is performed to obtain line clusters. At this point, the topological data of the power grid lines in each line cluster are highly similar, facilitating differentiated analysis based on the characteristics of different types of power grid lines. This makes the subsequent evaluation and treatment of each power grid line more targeted and reasonable. When regulating voltage, the energy storage system can quickly provide or absorb reactive power during grid voltage fluctuations, providing transient voltage support to the grid. Therefore, within each line cluster, the active support requirement of each power grid line can be determined by comprehensively considering the numerical characteristics, fluctuation characteristics, and quantity characteristics of the operating parameter time-series data under active voltage support events, as well as the active voltage support events themselves. This comprehensively and accurately reflects the actual needs of each line in terms of transient voltage stability. Furthermore, for any given power grid line, the urgency level is quantified by comparing the differences in the time-series data of its operating parameters under different types of voltage surge events. This allows the energy storage system to provide greater support to lines with high urgency levels, improving the ability of the regional weak power grid to cope with sudden voltage problems. Therefore, in each line cluster, a preset droop coefficient is adjusted based on the active support demand value and the urgency level adjustment value corresponding to each power grid line, resulting in an adaptive droop coefficient for active support against transient voltage. This adaptive adjustment method can dynamically change the control strategy of the energy storage system according to the actual needs and emergency situations of the power grid lines, enabling the energy storage system to participate more flexibly and efficiently in supporting transient voltage in the regional weak power grid, significantly improving the overall stability and reliability of the regional weak power grid. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1A flowchart illustrating a method for actively supporting transient voltage in a regional weak power grid using a composite energy storage system, as provided in one embodiment of the present invention. Figure 2 A flowchart illustrating a method for obtaining an active support demand level value according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating a method for obtaining an adjustment urgency value, provided as an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for actively supporting transient voltage in a regional weak power grid using a composite energy storage system based on the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a composite energy storage system to actively support transient voltage in a regional weak power grid, as provided by this invention.

[0020] Please see Figure 1 The diagram illustrates a method flowchart for a composite energy storage system to actively support transient voltage in a regional weak power grid, according to an embodiment of the present invention. The method includes the following steps: Step S1: In the target power grid area, acquire the topology data of each power grid line and the timing data of the operating parameters of each power grid line under voltage mutation events; wherein, voltage mutation events are divided into active support voltage events and non-active support voltage events.

[0021] A composite energy storage system mainly consists of supercapacitor cells (SC), power conversion systems (PCS), and energy management systems (EMS). The supercapacitor cells (SC) possess extremely high power density and rapid response capabilities, responsible for handling large power surges at the initial stage of transient events, absorbing or releasing instantaneous high power. The power conversion system (PCS) includes an independent DC converter (for controlling the charging and discharging of the supercapacitor) and a shared DC / AC inverter (for grid connection), used to precisely control the output according to instructions from the EMS. The energy management system (EMS) is responsible for executing control commands issued from higher levels and coordinating the power distribution between the batteries and supercapacitors according to internal strategies. The supercapacitor cells (SC) are charged by connecting to renewable energy sources (such as photovoltaic and wind power) and then adapted through equipment such as converters.

[0022] In the target power grid area, power time-series data is acquired. In this embodiment of the invention, the types of power time-series data include voltage, current, and reactive power. The voltage acquisition method includes: deploying a high-precision synchronous phasor measurement unit (PMU) on each power grid line in the target power grid area (the power grid area to be monitored) to collect voltage time-series data in real time. The current acquisition method includes: deploying a supercapacitor monitoring unit in each supercapacitor module of the supercapacitor unit (SC) to collect the current time-series data of each supercapacitor module in real time. The reactive power acquisition method includes: deploying a system-level monitoring unit in the power transformation system (PCS) to collect the reactive power time-series data output by the power transformation system. In this embodiment of the invention, the power time-series data acquisition frequency is set to 25Hz, and the acquisition period is set to 120 minutes of historical data starting from the current moment. The specific acquisition frequency and period settings can be adjusted according to the implementation scenario and are not limited here.

[0023] At this point, all power time-series data in the target power grid area can be obtained. Then, for the voltage time-series data of each power grid line, voltage surge events are detected using existing mathematical models (such as minimizing errors, similarity change methods, etc., which are well-known technologies and will not be elaborated here). Based on existing transient stability methods (well-known technologies and will not be elaborated here), voltage surge events are classified into active voltage support events and non-active voltage support events. In this embodiment of the invention, the occurrence of voltage surge events usually corresponds to a single moment, but the data characteristics before and after a single moment are also valuable for analysis. Therefore, taking the moment of occurrence of each voltage surge event as the center, a 1-minute segment is extracted in the time series as the occurrence period corresponding to each voltage surge event. Then, for each voltage surge event of each power grid line, all types of power time-series data under the corresponding occurrence period are used as the operating parameter time-series data of each power grid line under each voltage surge time. Similar to the types of power data, the types of operating parameters are also voltage, current, and reactive power. Among them, voltage is obtained by measuring the power grid line, while current and reactive power are obtained by measuring the composite energy storage system.

[0024] Furthermore, given that the topological characteristics of power grid lines also affect the stability of transient voltage, the topological data of each power grid line in the target power grid area is exported through the power grid GIS system. In this embodiment of the invention, the topological data includes at least the line length, conductor type, line impedance, and transformer turns ratio.

[0025] Step S2: Perform cluster analysis on the power grid lines based on the topology data of the power grid lines to obtain line clusters; in each line cluster, determine the active support demand level value of each power grid line based on the numerical and fluctuation characteristics of the time series data of the operating parameters of the power grid lines under active support voltage events, as well as the quantitative characteristics of active support voltage events.

[0026] The topology data of power grid lines has a significant impact on their electrical performance and voltage stability. Lines with different topological characteristics may exhibit different behaviors and requirements when facing voltage surges. For example, lines with longer lengths and higher impedance may be more difficult to recover from voltage drops, requiring stronger active support; while lines with thicker conductors and suitable transformer turns ratios may have better voltage regulation capabilities and relatively lower requirements for active support. Therefore, clustering analysis of all power grid lines based on topology data can group lines with similar electrical characteristics and voltage stability requirements into one category, laying the foundation for subsequent accurate assessment and analysis.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining line clusters includes: Since topology data includes at least line length, conductor type, line impedance, and transformer turns ratio, multiple topology data of each power grid line are used to construct a topology feature vector. Then, in the target power grid area, for any two power grid lines, the Euclidean distance between the topology feature vectors of these two power grid lines is used as the topology difference factor between these two power grid lines. The smaller the topology difference factor, the more likely the electrical characteristics of these two power grid lines are to have similar performance and characteristics.

[0028] Therefore, within the target power grid area, the topological difference factor between power grid lines is used as a distance metric. K-means clustering algorithm and a preset K value are used to perform cluster analysis on all power grid lines, thereby obtaining line clusters. Specifically, the optimal K value is obtained based on the silhouette coefficient method and is used as the preset K value.

[0029] Thus, the aforementioned process can be used to perform cluster analysis on all power grid lines within the target power grid area, resulting in high similarity in the topological data of the power grid lines in each line cluster.

[0030] It should be noted that the K-means clustering algorithm and the silhouette coefficient method are both well-known techniques, and the specific process will not be elaborated here.

[0031] The numerical characteristics of the time-series data of the operating parameters of power grid lines under active voltage support events directly reflect the real-time electrical state of the lines during voltage surge events. The fluctuation characteristics of the operating parameters reflect the stability of the integrated energy storage system during active voltage support events; considering these fluctuation characteristics allows for a more comprehensive assessment of the line's stability requirements, which are closely related to the active support requirements of the grid lines. Simultaneously, the quantitative characteristics of active voltage support events reflect the frequency of voltage surge problems faced by the power grid lines, allowing for an assessment of the lines' active support requirements from the perspective of event frequency. Therefore, within each line cluster, based on the numerical and fluctuation characteristics of the time-series data of the operating parameters of the power grid lines under active voltage support events, as well as the quantitative characteristics of active voltage support events, the active support requirement level for each power grid line was determined.

[0032] Preferably, in one embodiment of the present invention, the method for obtaining the degree of proactive support demand includes: Please see Figure 2 The diagram illustrates a method flowchart for obtaining the degree of proactive support demand in one embodiment of the present invention. The method includes the following steps: Step S201: In each active support voltage event of each power grid line, based on the numerical characteristics of the operating parameters, determine the first active support demand factor of each power grid line at the time of each active support event.

[0033] The types of operating parameters include voltage, current, and reactive power; In power grid operation, reactive power plays a crucial role in maintaining voltage stability. When there is an excess of reactive power in the power grid, a composite energy storage system needs to absorb the excess reactive power in order to maintain grid stability. Conversely, when there is a shortage of reactive power in the power grid, a composite energy storage system needs to provide power in order to maintain grid stability. Therefore, the greater the reactive power of the power grid line during an active support event, the greater the role of the energy storage system under that active support event, and the greater the support required.

[0034] Therefore, in each active support voltage event of each power grid line, the proportion of reactive power of each power grid line at the time of the active support voltage event to the preset reactive power is used as the first active support demand factor of each power grid line at the time of the active support voltage event. Based on the above analysis, it can be seen that the larger the first active support demand factor is, the stronger the role of the composite energy storage system is considered to be under the active support voltage event, and thus the higher the degree of active support demand.

[0035] It should be noted that the preset reactive power in this embodiment of the present invention is the maximum reactive power that the composite energy storage system can provide, and the specific value can be determined according to the implementation scenario.

[0036] Step S202: In each active support voltage event of each power grid line, analyze the fluctuation characteristics of the time series data of the operating parameters, and determine the second active support demand factor of each power grid line when each active support voltage event occurs.

[0037] The fluctuation characteristics of current time-series data in the operating parameter time-series data can intuitively reflect the stability of the support effect of the composite energy storage system in each active support voltage event. If the current fluctuation is large, it indicates that the support effect of the line is unstable when a voltage surge occurs, and stronger active support is needed to maintain its stable operation. Conversely, if the current fluctuation is small, it indicates that the line may have a certain self-regulating capability, and the demand for active support is relatively low. Therefore, in each active support voltage event of each grid line, the difference between the maximum and minimum values ​​in the current time-series data is calculated. The smaller the difference, the more stable the output of the composite system is at this time, which can also be regarded as the weaker demand for the active support effect of the composite system during grid operation. Therefore, the normalized value of this difference is used as the second active support demand factor for each grid line in each active support voltage event. Based on the above analysis, it can be seen that the larger the second active support demand factor, the more unstable the output of the composite energy storage system is in the active support voltage event, the weaker the support effect, and the higher the active support demand of the grid line. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0038] Step S203: Combine the first active support demand factor and the second active support demand factor of each power grid line under each active support voltage event to obtain the support demand characteristic value of each power grid line under each active support voltage event.

[0039] Based on the analysis in steps S201 and S202 above, it is known that the first active support demand factor and the second active support demand factor of each grid line under each active support voltage event are positively correlated with the degree of active support demand for the composite energy storage system. Therefore, the normalized value of the product of the first active support demand factor and the second active support demand factor of each grid line under each active support voltage event is taken as the active support demand characteristic value of each grid line under each active support voltage event. The larger the active support demand characteristic value, the higher the active support demand intensity of the grid line in each active support voltage event. Normalization is a well-known technique in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0040] Step S204: Combine the support demand characteristic value of each power grid line under all active support voltage events with the number of occurrences of the active support voltage events of each power grid line in its respective line cluster to obtain the active support demand level value of each power grid line.

[0041] If a power grid line frequently experiences active voltage support events, it indicates that the grid environment is complex or the line itself has potential problems, requiring a higher level of active support to cope with frequent voltage surges. Conversely, lines with lower event frequencies have a relatively lower demand for active support. Therefore, the proportion of active voltage support events for each power grid line in the total number of active voltage support events within its respective line cluster was calculated. A higher proportion indicates more frequent anomalies in power grid lines with similar topology data, thus requiring higher active support. The mean of the active support demand characteristic value for each power grid line under all active voltage support events was then calculated and multiplied by the aforementioned proportion. The normalized product was then used as the active support demand level value for each power grid line. A higher active support demand level value indicates a higher demand for the composite energy storage system during voltage surge events. Normalization is a technique well-known to those skilled in the art; the normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0042] At this point, we can obtain the active support requirement value for each power grid line in each line cluster.

[0043] Step S3: For any power grid line, compare the differences in the time series data of the operating parameters of the power grid line under different types of voltage change events, and determine the adjustment urgency value of each power grid line.

[0044] Active voltage support events and inactive voltage support events represent two different states in power grid operation. Active voltage support events typically occur when the power grid takes proactive measures (such as deploying energy storage devices) to stabilize voltage when voltage anomalies occur. Inactive voltage support events, on the other hand, occur when the power grid experiences voltage fluctuations during natural operation without any specific proactive support measures. By comparing the differences in the time-series data of operating parameters under these two types of events, we can gain a deeper understanding of the specific performance of power lines in different scenarios, thereby accurately quantifying the urgency of adjustment for each power grid line.

[0045] Preferably, in one embodiment of the present invention, the method for obtaining the urgency level value includes: Please see Figure 3 The diagram illustrates a method flowchart for adjusting the urgency value in one embodiment of the present invention. The method includes the following steps: Step S301: For any power grid line, analyze the numerical fluctuation characteristics of the voltage time series data of the power grid line under each voltage change event, and construct the voltage feature vector of the power grid line under each voltage change event.

[0046] Variance is an important statistic for measuring the dispersion of data. It accurately reflects the magnitude of voltage fluctuations around the mean. Therefore, for any power grid line, in the voltage time series data of that line under each voltage surge event (including active and inactive voltage surge events), the variance of all voltage values ​​is used as the fluctuation factor. The mean of the voltage values ​​represents the average voltage level over a period of time, reflecting the approximate location of the power grid line voltage under the voltage surge event; therefore, the mean of all voltage values ​​is used as the mean characteristic value. Then, the fluctuation factor and the mean characteristic value are used as the voltage characteristic vector of the power grid line under each voltage surge event. The voltage characteristic vector combines two characteristics of voltage, representing the fluctuation amplitude and the average level, respectively, thus providing richer information for subsequent difference feature calculations.

[0047] Step S302: Combine the active support voltage events and non-active support voltage events of the power grid line in pairs to obtain all unique event combinations.

[0048] Active voltage support events and inactive voltage support events reflect different modes of power grid operation. Active support refers to proactive measures taken by the power grid to address voltage issues, while inactive support represents the natural operating state. Comparing the voltage characteristics of lines under these two types of events allows us to understand the actual improvement effect of active support measures on line voltage, as well as the problems existing in the lines under natural operating conditions. Therefore, we can pairwise combine the active and inactive voltage support events of this power grid circuit to obtain all unique event combinations.

[0049] For example, there are three active support voltage events, denoted as events 1, 2, and 3, and two non-active support events, denoted as events 4 and 5. Then there will be a total of 6 event combinations, namely (event 1, event 4), (event 1, event 5), (event 2, event 4), (event 2, event 5), (event 3, event 4), and (event 3, event 5).

[0050] Step S303: Under the event combination, calculate the difference characteristics between voltage feature vectors to obtain the adjustment urgency value of the power grid line.

[0051] Under each event combination, the Euclidean distance between voltage characteristic vectors is calculated as a distance factor. A smaller distance factor indicates that the voltage change characteristics under active and inactive voltage support events in that event combination are more consistent, suggesting a weaker active support effect. To avoid serious impact on subsequent grid operation, the urgency level of the grid line should be increased. Therefore, the mean of the distance factors corresponding to all event combinations for that grid line is negatively correlated and normalized to correct the logical relationship, thus obtaining the adjustment urgency value for that grid line. This negative correlation mapping and normalization can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0052] At this point, the adjustment urgency value for each power grid line can be obtained.

[0053] Step S4: Based on the active support demand value and adjustment urgency value corresponding to each power grid line, adjust the preset droop coefficient of each power grid line to obtain an adaptive droop coefficient for active support of transient voltage.

[0054] In the monitoring of transient voltage stability of the power grid, when disturbances occur in the grid, the QV model (existing technology) is used to quickly adjust the reactive power of the grid to maintain grid stability. The QV model typically employs droop control during the control process. However, based on a fixed droop coefficient, energy storage control may not fully utilize the reactive power potential of energy storage under varying operating conditions, or may exhibit insufficient response flexibility. Therefore, in this embodiment of the invention, the preset droop coefficient of the power grid line can be adaptively adjusted using the active support demand value and adjustment urgency value of each power grid line in the aforementioned steps, resulting in an adaptive droop coefficient that conforms to the power characteristics of the power grid line itself.

[0055] Preferably, in one embodiment of the present invention, the method for obtaining the adaptive droop coefficient includes: Neither the active support demand value nor the adjustment urgency value alone can comprehensively and accurately describe the line's state under transient voltage scenarios. The active support demand value focuses on the line's subjective need for support, while the adjustment urgency value focuses on the objective urgency of the line's current voltage state. Multiplying the two together can comprehensively consider both subjective needs and objective conditions, more accurately reflecting the actual situation of the line during transient voltage events.

[0056] Therefore, the active support demand value and adjustment urgency value of each power grid line are multiplied together. The larger the product, the greater the demand for voltage support and the higher the adjustment urgency of the power grid line, thus requiring a higher droop coefficient to provide support. The normalized value of the resulting product is then summed with a preset constant to serve as the adaptive adjustment value for each power grid line. In this embodiment of the invention, the preset constant is 1. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0057] Finally, the product of the adaptive adjustment value and the preset droop coefficient for each power grid line is used as the adaptive droop coefficient for each power grid line.

[0058] At this point, the adaptive droop coefficient of each power grid line at the current moment can be obtained. Then, the QV model can be used to adopt an adaptive QV control strategy based on the composite energy storage system to actively support the transient voltage of the regional weak power grid, while effectively improving the stability of the power grid transient voltage and the system reliability.

[0059] It should be noted that the preset droop coefficient can be set according to the implementation scenario, and the value is generally between 0.001 and 0.01. No specific limit is made here.

[0060] In this embodiment of the invention, all numerical values ​​involved in the calculation have undergone data preprocessing to eliminate the influence of dimensions. The specific means of eliminating the influence of dimensions are well known to those skilled in the art and will not be limited or described in detail here.

[0061] In summary, firstly, topological data of each power grid line and time-series data of operating parameters under voltage surge events (divided into active and non-active voltage support events) are comprehensively acquired in the target power grid area. This provides a rich and accurate data foundation for subsequent precise analysis of power grid line characteristics and operating status. Cluster analysis based on the power grid line topological data yields line clusters. At this point, the topological data similarity of power grid lines within each cluster is relatively high, facilitating differentiated analysis based on the characteristics of different line types. This makes subsequent evaluation and treatment of each line more targeted and reasonable. When regulating voltage, energy storage systems can rapidly provide or absorb reactive power during grid voltage fluctuations, providing transient voltage support to the grid. Therefore, within each line cluster, the active support requirement of each power grid line can be determined by comprehensively considering the numerical characteristics, fluctuation characteristics, and quantity characteristics of the operating parameter time-series data under active voltage support events, as well as the number of active voltage support events. This comprehensively and accurately reflects the actual needs of each line in terms of transient voltage stability. Furthermore, for any given power grid line, the urgency level is quantified by comparing the differences in the time-series data of its operating parameters under different types of voltage surge events. This allows the energy storage system to provide greater support to lines with high urgency levels, improving the ability of the regional weak power grid to cope with sudden voltage problems. Therefore, in each line cluster, a preset droop coefficient is adjusted based on the active support demand value and the urgency level adjustment value corresponding to each power grid line, resulting in an adaptive droop coefficient for active support against transient voltage. This adaptive adjustment method can dynamically change the control strategy of the energy storage system according to the actual needs and emergency situations of the power grid lines, enabling the energy storage system to participate more flexibly and efficiently in supporting transient voltage in the regional weak power grid, significantly improving the overall stability and reliability of the regional weak power grid.

[0062] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for a composite energy storage system to participate in active support of transient voltage of a weak regional power grid, characterized in that, The method comprises: In the target power grid area, obtain the topological data of each power grid line and the operation parameter time series data of each power grid line under a voltage mutation event; wherein the voltage mutation event is divided into an active support voltage event and a non-active support voltage event; Based on the topological data of the power grid line, perform cluster analysis on the power grid line to obtain a line cluster; in each line cluster, based on the numerical characteristics and fluctuation characteristics of the operation parameter time series data of the power grid line under the active support voltage event, and the quantity characteristics of the active support voltage event, determine the active support demand degree value of each power grid line; For any one power grid line, compare the change difference characteristics of the operation parameter time series data of the power grid line under different types of voltage mutation events to determine the adjustment emergency degree value of each power grid line; Based on the active support demand degree value and the adjustment emergency degree value of each power grid line, adjust the preset droop coefficient of each power grid line to obtain an adaptive droop coefficient for active support of transient voltage.

2. The method of claim 1, wherein the method further comprises: The method for obtaining the line cluster comprises: In the target power grid area, for any two power grid lines, analyze the difference characteristics of the topological data between the power grid lines to obtain a topological difference factor between the two power grid lines; In the target power grid area, use the topological difference factor between the power grid lines as a distance measure, and use the K-means clustering algorithm and a preset K value to perform cluster analysis on all power grid lines to obtain the line cluster.

3. The method of claim 2, wherein the method further comprises: The method for obtaining the topological difference factor comprises: The topological data at least includes line length, conductor type, line impedance and transformer ratio; Construct a topological feature vector from multiple topological data of each power grid line; In the target power grid area, for any two power grid lines, use the Euclidean distance of the topological feature vectors between the two power grid lines as the topological difference factor between the two power grid lines.

4. The method of claim 1, wherein the method further comprises: The method for obtaining the active support demand degree value comprises: In each active support voltage event of each power grid line, based on the numerical characteristics of the operation parameters, determine a first active support demand factor of each power grid line when each active support event occurs; In each active support voltage event of each power grid line, analyze the fluctuation characteristics of the operation parameter time series data to determine a second active support demand factor of each power grid line when each active support voltage event occurs; The product of the first active support demand factor and the second active support demand factor of each power grid line under each active support voltage event is normalized to obtain an active support demand characteristic value of each power grid line under each active support voltage event; Multiply the mean value of the active support demand characteristic values of each power grid line under all active support voltage events by the proportion of the number of active support voltage events of each power grid line in the total number of active support voltage events in the line cluster to which the power grid line belongs, and normalize the product to obtain the active support demand degree value of each power grid line.

5. The method of claim 4, wherein the method further comprises: The method for obtaining the first active support demand factor comprises: The types of the operating parameters include voltage, current, and reactive power; In each active support voltage event of each power grid line, a proportion of the reactive power of each power grid line at a time when the active support voltage event occurs in the preset reactive power is taken as a first active support demand factor of each power grid line at the time when each active support voltage event occurs.

6. The method of claim 5, wherein the method further comprises: The method for obtaining the second active support demand factor comprises: In each active support voltage event of each power grid line, a normalized value of a difference between a maximum value and a minimum value in the current time series data is taken as a second active support demand factor of each power grid line at the time when each active support voltage event occurs.

7. The method of claim 5, wherein the method further comprises: The method for obtaining the adjustment emergency degree value comprises: For any power grid line, a numerical fluctuation characteristic in the voltage time series data of the power grid line at each voltage mutation event is analyzed to construct a voltage characteristic vector of the power grid line at each voltage mutation event; The active support voltage events and the non-active support voltage events of the power grid line are combined two by two to obtain all non-repeated event combinations; Under each event combination, an Euclidean distance between the voltage characteristic vectors is calculated as a distance factor, and a value obtained by negatively correlating and normalizing a mean value of the distance factors corresponding to all event combinations is taken as the adjustment emergency degree value of the power grid line.

8. The method of claim 7, wherein the method further comprises: The method for constructing the voltage characteristic vector comprises: For any power grid line, in the voltage time series data of the power grid line at each voltage mutation event, a variance of all voltage values is taken as a fluctuation factor, a mean value of all voltage values is taken as a mean characteristic value, and the fluctuation factor and the mean characteristic value are taken as the voltage characteristic vector of the power grid line at each voltage mutation event.

9. The method of claim 1, wherein the method further comprises: The method for obtaining the adaptive droop coefficient comprises: A product of the active support demand degree value and the adjustment emergency degree value of each power grid line is normalized, and a sum of the normalized product and a preset constant is taken as an adaptive adjustment value of each power grid line; A product of the adaptive adjustment value of each power grid line and a preset droop coefficient is taken as an adaptive droop coefficient of each power grid line.

10. The method of claim 2, wherein the method further comprises: The method for obtaining the preset K value comprises: An optimal K value is obtained based on a contour coefficient method, and the optimal K value is taken as the preset K value.

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