Rapid expansion and reconstruction method of modular energy storage system

Abstract

The invention belongs to the technical field of power grid energy storage management, and provides a rapid capacity expansion and reconstruction method of a modular energy storage system. Comprising the following steps: obtaining participation records of each modular group in the modular energy storage system on power grid side peak regulation in a plurality of power grid historical operation stages, performing power grid side peak regulation participation analysis and power grid side peak regulation demand trend analysis, and identifying a module group to be expanded; based on the capacity data of each modular group and the maximum charging and discharging rate data, correlation analysis is carried out, and a modular group capacity-charging rate coupling model and a modular group capacity-discharging rate coupling model are constructed; and obtaining charge and discharge rate data of the to-be-expanded module group when participating in power grid side peak regulation for multiple times, carrying out volatility analysis, predicting a target charge rate and a target discharge rate, and obtaining an expansion upper limit of the to-be-expanded module group by combining the modular group capacity-charge rate coupling model and the modular group capacity-discharge rate coupling model. And accurate capacity expansion of the modular energy storage system is realized.

Description

Technical Field

[0001] This invention belongs to the field of power grid energy storage management technology, specifically a method for rapid expansion and reconfiguration of modular energy storage systems. Background Technology

[0002] Against the backdrop of accelerated energy structure transformation, energy storage systems are playing an increasingly important role in mitigating grid load fluctuations and enhancing the absorption capacity of new energy sources. Among them, modular energy storage systems, with their advantages of flexible deployment, convenient maintenance, and on-demand expansion, have become the mainstream choice for grid-side peak shaving applications.

[0003] Grid-side peak shaving scenarios place extremely high demands on the charging and discharging speeds of energy storage systems, requiring rapid charging during off-peak hours and rapid discharging during peak hours to ensure grid stability. However, existing expansion schemes often lack precise consideration of the matching relationship between charging and discharging performance and capacity. Blindly expanding can easily lead to a decline in the overall charging and discharging performance of the system, resulting in the problem of "expansion without efficiency." This not only fails to meet the stringent rate requirements of peak shaving but may also waste equipment resources and investment. How to balance capacity increase and stable charging and discharging performance during the expansion process has become the core challenge facing the development of modular energy storage systems.

[0004] To address this, the present invention provides a method for rapid expansion and reconfiguration of modular energy storage systems. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a method for rapid expansion and reconfiguration of modular energy storage systems, comprising the following steps:

[0007] Step 1: Obtain the participation records of each modular group in the modular energy storage system in grid-side peak shaving during multiple historical grid operation phases, and conduct grid-side peak shaving participation analysis and grid-side peak shaving demand trend analysis to identify the modular groups to be expanded.

[0008] Step 2: Based on the capacity data and maximum charge / discharge rate data of each modular group, perform correlation analysis to construct a modular group capacity-charging rate coupling model and a modular group capacity-discharge rate coupling model;

[0009] Step 3: Based on the module group to be expanded, obtain the charging and discharging rate data of the module group to be expanded when participating in grid-side peak shaving multiple times and perform volatility analysis to predict the target charging rate and target discharging rate;

[0010] Step 4: By combining the target charging rate, target discharging rate, modular group capacity-charging rate coupling model, and modular group capacity-discharging rate coupling model, the upper limit of the expansion capacity of the module group to be expanded is obtained by comparison.

[0011] As a further aspect of the present invention: the process of performing grid-side peak shaving participation analysis is as follows:

[0012] The number of times the modular group participated in grid-side peak shaving in multiple historical power grid operation phases and the number of times the modular group participated in grid-side peak shaving in each historical power grid operation phase were obtained and processed and analyzed to obtain the average frequency value and peak shaving participation stability value of the modular group.

[0013] The deviation between the average frequency value and the stable value of peak shaving participation is calculated to obtain the peak shaving participation value of the modular group. The stability of the modular group's participation in grid-side peak shaving is judged based on the peak shaving participation value.

[0014] As a further aspect of the present invention: the method for obtaining the peak-shaving participation average frequency value and the peak-shaving participation stability value is as follows:

[0015] The average number of times the modular group participated in grid-side peak shaving in multiple historical power grid operation phases was averaged and then proportionally calculated with the total number of grid-side peak shavings in multiple historical power grid operation phases to obtain the average frequency value of peak shaving participation.

[0016] Among them, the total number of peak shaving operations on the grid side within multiple historical operation phases of the power grid is the sum of the number of peak shaving operations on the grid side within each historical operation phase of the power grid;

[0017] The coefficient of variation was calculated for the number of times the modular group participated in grid-side peak shaving in each historical operation phase of the power grid, and the peak shaving participation stability value was obtained.

[0018] As a further aspect of the present invention: the process of performing grid-side peak-shaving demand trend analysis is as follows:

[0019] The number of peak shaving events on the grid side during each historical operation phase of the power grid is obtained and integrated into a peak shaving event sequence according to the time sequence of the historical operation phase of the power grid. The least squares method is used to perform linear fitting on the peak shaving event sequence to obtain the linear fitting slope. If the linear fitting slope is positive, it indicates that the peak shaving demand on the grid side is showing an increasing trend. Conversely, if the linear fitting slope is negative or 0, it indicates that the peak shaving demand on the grid side is showing a non-increasing trend.

[0020] As a further aspect of the present invention: the process of identifying the module group to be expanded is as follows:

[0021] If a modular group participates in grid-side peak shaving stability and grid-side peak shaving demand shows an increasing trend, then the modular group will be marked as a module group to be expanded.

[0022] As a further aspect of the present invention: the process of performing correlation analysis is as follows:

[0023] The capacity data of each modular group is integrated to obtain the modular group capacity sequence. The maximum charging rate and maximum discharging rate of each modular group are then integrated to obtain the maximum charging rate sequence and the maximum discharging rate sequence.

[0024] The Pearson correlation coefficients between the modular group capacity sequence and the maximum charging rate sequence and the maximum discharging rate sequence are calculated and their absolute values ​​are processed to obtain the correlation type judgment coefficients. The correlation type judgment coefficients include the capacity charging correlation coefficient and the capacity discharging correlation coefficient.

[0025] The correlation type between the modular group capacity and the maximum charging rate and the maximum discharging rate is determined based on the capacity charging correlation coefficient and the capacity discharging correlation coefficient.

[0026] As a further aspect of the present invention: the process of constructing the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model is as follows:

[0027] If it is a linear correlation type, the least squares method is used to perform linear fitting on the modular group capacity sequence and the maximum charging rate sequence, as well as the modular group capacity sequence and the maximum discharging rate sequence, respectively, to obtain the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model.

[0028] If the correlation is nonlinear, then nonlinear fitting is performed on the modular group capacity sequence and the maximum charging rate sequence, and on the modular group capacity sequence and the maximum discharging rate sequence, respectively. After performing nonlinear fitting, the fitting model with the highest goodness of fit is selected as the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model, respectively.

[0029] As a further aspect of the present invention: the process of performing volatility analysis is as follows:

[0030] The charging rate and discharging rate of the module group to be expanded during multiple grid-side peak shaving operations are integrated into a charging rate group and a discharging rate group, respectively.

[0031] The coefficients of variation of the charging rate group and the discharging rate group are calculated separately to obtain the charging and discharging rate fluctuation values, which include the charging rate fluctuation value and the discharging rate fluctuation value.

[0032] The stability of the charging and discharging rates can be determined by the fluctuation values ​​of the charging and discharging rates.

[0033] As a further aspect of the present invention: the process of predicting the target charging rate and the target discharging rate is as follows:

[0034] If stable, the charging rate group and the discharging rate group are averaged respectively to obtain the target charging rate and the target discharging rate.

[0035] If the rate is unstable, select the maximum value from the charging rate group and the discharging rate group respectively as the target charging rate and the target discharging rate.

[0036] As a further aspect of the present invention: the method for obtaining the expansion limit of the module group to be expanded is as follows:

[0037] Substituting the target charging rate into the modular group capacity-charging rate coupling model, the output yields the upper limit of capacity expansion under the target charging rate constraint. Substituting the target discharging rate into the modular group capacity-discharging rate coupling model, the output yields the upper limit of capacity expansion under the target discharging rate constraint.

[0038] If the expansion limit under the target charging rate limit is higher than the expansion limit under the target discharging rate limit, then the expansion limit under the target discharging rate limit is selected as the expansion limit of the module group to be expanded; otherwise, the expansion limit under the target charging rate limit is selected as the expansion limit of the module group to be expanded.

[0039] The beneficial effects of this invention are as follows: First, by deeply analyzing historical power grid operation data, the modular groups to be expanded are accurately selected from numerous modular groups, avoiding blind expansion. Next, through scientific correlation analysis, a coupled model of modular group capacity and charge / discharge rate is constructed, quantifying the intrinsic relationship between the two. Then, based on the fluctuation of the charge / discharge rate of the modular groups to be expanded, the target charging rate and target discharging rate are reasonably predicted, providing reliable parameters for subsequent calculations. Finally, by substituting the target rates into the coupled model and comparing them, the upper limit of the expansion capacity of the modular groups to be expanded is obtained. Through this series of steps, precise expansion of the modular energy storage system is achieved. The expanded modular groups can fully meet the peak-shaving rate requirements of the power grid, charging and discharging in a timely manner during peak and off-peak periods, effectively smoothing power grid load fluctuations and ensuring stable power grid operation; at the same time, it avoids resource waste caused by excessive expansion. Attached Figure Description

[0040] The invention will now be further described with reference to the accompanying drawings.

[0041] Figure 1 This is a flowchart illustrating the steps of the rapid expansion and reconfiguration method for the modular energy storage system described in this embodiment of the invention.

[0042] Figure 2This is a schematic diagram illustrating the logic of determining the upper limit of the expansion of the module group to be expanded in the rapid expansion and reconfiguration method of the modular energy storage system described in this embodiment of the invention. Detailed Implementation

[0043] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0044] Example: Please refer to Figure 1 As shown in the embodiment of the present invention, the method for rapid expansion and reconfiguration of a modular energy storage system specifically includes the following steps:

[0045] Step 1: Obtain the participation records of each modular group in the modular energy storage system in grid-side peak shaving during multiple historical grid operation phases, and conduct grid-side peak shaving participation analysis and grid-side peak shaving demand trend analysis. Based on the analysis results, identify the modular groups to be expanded in the modular groups.

[0046] Understandably, the load on the power grid varies significantly at different times. To ensure the stable operation of the grid, energy storage systems are needed for peak shaving, charging during off-peak hours and discharging during peak hours. When the demand for peak shaving on the grid increases, it is necessary to expand the capacity of energy storage systems.

[0047] In step one, the historical operation phase of the power grid refers to the periodic time between the start time of each operation and the current time.

[0048] In step one, the process of performing grid-side peak shaving participation analysis is as follows:

[0049] Based on any modular group;

[0050] The number of times the modular group participated in grid-side peak shaving in multiple historical power grid operation phases was obtained and averaged to obtain the average number of times it participated in grid-side peak shaving. The average number of times it participated in grid-side peak shaving was then proportionally calculated with the total number of times it participated in grid-side peak shaving in multiple historical power grid operation phases to obtain the average frequency value of peak shaving participation of the modular group.

[0051] Among them, the total number of peak shaving operations on the grid side within multiple historical operation phases of the power grid is the sum of the number of peak shaving operations on the grid side within each historical operation phase of the power grid;

[0052] The number of times each modular group participated in grid-side peak shaving in each historical operation phase of the power grid was obtained, and then integrated into a peak shaving participation sequence according to the time sequence of the historical operation phase of the power grid.

[0053] Calculate the coefficient of variation of the peak-shaving participation sequence to obtain the peak-shaving participation stability value of the modular group;

[0054] The deviation between the average frequency value of peak shaving participation and the stable value of peak shaving participation is calculated to obtain the peak shaving participation value of the modular group.

[0055] It is understandable that the physical meaning of the peak shaving participation value is as follows: the peak shaving participation value is calculated through the average frequency value of peak shaving participation and the stable value of peak shaving participation. The average frequency value of peak shaving participation reflects the average proportion of times the modular group participates in grid-side peak shaving. The higher the average proportion, the more frequently the modular group participates in grid-side peak shaving. The stable value of peak shaving participation reflects the fluctuation of the number of times the modular group participates in grid-side peak shaving in each historical operation phase of the power grid. The smaller the fluctuation, the more stable the modular group's participation in grid-side peak shaving.

[0056] The stability of modular group participation in grid-side peak shaving is determined based on the peak shaving participation value. In some embodiments, the peak shaving participation value and the peak shaving participation threshold are compared.

[0057] If the peak shaving participation value is greater than or equal to the peak shaving participation threshold, it indicates that the modular group participates in grid-side peak shaving stability.

[0058] If the peak shaving participation value is less than the peak shaving participation threshold, it indicates that the modular group's participation in grid-side peak shaving is unstable.

[0059] In step one, the process of performing grid-side peak-shaving demand trend analysis is as follows:

[0060] Obtain the number of peak shaving events on the grid side during each historical operation phase of the power grid, and integrate them into a peak shaving event sequence according to the time sequence of the historical operation phase of the power grid.

[0061] The least squares method is used to perform linear fitting on the peak shaving frequency sequence to obtain the linear fitting slope. If the linear fitting slope is positive, it indicates that the peak shaving demand on the grid side is showing an increasing trend.

[0062] Conversely, if the slope of the linear fit is negative or 0, it indicates that the peak-shaving demand on the grid side shows a non-growing trend.

[0063] In step one, the process of identifying the module group to be expanded is as follows:

[0064] Based on any modular group;

[0065] If the modular group participates in grid-side peak shaving stability and the grid-side peak shaving demand shows an increasing trend, then the modular group will be marked as a module group to be expanded.

[0066] Conversely, if the modular group's participation in grid-side peak shaving is unstable or the grid-side peak shaving demand shows a non-growing trend, the modular group will be marked as a non-expansion module group.

[0067] Understandably, the significance of step one lies in the following: This step, by calculating the average frequency, stable value, and peak-shaving participation value, filters out the modular groups that participate in stable peak-shaving; simultaneously, it combines the slope of the linear fitting of peak-shaving demand to determine the demand growth trend. Ultimately, it accurately identifies the modular groups to be expanded that are "stable in participation and experiencing demand growth," providing a clear target for subsequent analysis focused solely on these groups, avoiding meaningless comprehensive screening, and improving the efficiency of expansion planning.

[0068] Step 2: Based on the capacity data and maximum charge / discharge rate data of each modular group, conduct correlation analysis, and construct a modular group capacity-charging rate coupling model and a modular group capacity-discharge rate coupling model based on the correlation analysis results.

[0069] In step two, the modular group capacity data represents the maximum capacity of the modular group, and the maximum charge / discharge rate data includes the maximum charging rate and maximum discharging rate of the modular group when performing grid-side peak shaving.

[0070] It should be noted that the capacity data and maximum charge / discharge rate data of each modular group can be obtained through the energy storage system management platform or the equipment manual of the modular group.

[0071] In step two, the correlation analysis based on the capacity data and maximum charge / discharge rate data of each modular group is performed as follows:

[0072] By integrating the capacity data of each modular group, a modular group capacity sequence is obtained. Similarly, by integrating the maximum charging rate and maximum discharging rate of each modular group, a maximum charging rate sequence and a maximum discharging rate sequence are obtained.

[0073] The Pearson correlation coefficients between the modular group capacity sequence and the maximum charging rate sequence and the maximum discharging rate sequence are calculated and their absolute values ​​are processed to obtain the correlation type judgment coefficients. The correlation type judgment coefficients include the capacity charging correlation coefficient and the capacity discharging correlation coefficient.

[0074] It should be noted that the capacity charging correlation coefficient corresponds to the modular group capacity sequence and the maximum charging rate sequence, while the capacity discharging correlation coefficient corresponds to the modular group capacity sequence and the maximum discharging rate sequence.

[0075] The correlation between the modular group capacity and the maximum charging rate and the maximum discharging rate are determined based on the capacity charging correlation coefficient and the capacity discharging correlation coefficient, respectively. Specifically:

[0076] If the capacity-charging correlation coefficient is greater than or equal to the capacity-charging correlation coefficient threshold, it indicates that the modular group capacity and the maximum charging rate are linearly correlated; otherwise, it is a non-linear correlation.

[0077] If the capacity-discharge correlation coefficient is greater than or equal to the capacity-discharge correlation coefficient threshold, it indicates that the modular group capacity and the maximum discharge rate are linearly correlated; otherwise, it is a non-linear correlation.

[0078] In step two, the process of constructing the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model is as follows:

[0079] If it is a linear correlation type, the least squares method is used to perform linear fitting on the modular group capacity sequence and the maximum charging rate sequence to obtain the modular group capacity-charging rate coupling model. The modular group capacity sequence and the maximum discharging rate sequence are then linearly fitted to obtain the modular group capacity-discharging rate coupling model.

[0080] If it is a nonlinear correlation type, then nonlinear fitting is performed on the modular group capacity sequence and the maximum charging rate sequence, and the modular group capacity sequence and the maximum discharging rate sequence, respectively. After performing nonlinear fitting, the fitting model with the highest fitting goodness is selected as the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model, respectively.

[0081] Among them, nonlinear fitting methods include, but are not limited to, quadratic function fitting, exponential function fitting, and power function fitting;

[0082] Understandably, the significance of step two lies in determining the type of correlation between capacity and charge / discharge rate by calculating the Pearson correlation coefficient, and then selectively choosing linear or nonlinear fitting to construct a coupling model. This model quantifies the intrinsic relationship between module group capacity and charge / discharge rate, providing a mathematical basis for subsequently substituting the target rate to deduce the upper limit of capacity, and is the core tool for realizing capacity expansion analysis.

[0083] Step 3: Based on the charge and discharge rate data of the module group to be expanded during multiple participations in grid-side peak shaving, perform volatility analysis, and predict the target charging rate and target discharging rate based on the volatility analysis results;

[0084] In step three, the volatility analysis process is as follows:

[0085] The charging rate and discharging rate of the module group to be expanded during multiple grid-side peak shaving operations are integrated into a charging rate group and a discharging rate group, respectively.

[0086] The coefficients of variation of the charging rate group and the discharging rate group are calculated separately to obtain the charging and discharging rate fluctuation values, which include the charging rate fluctuation value and the discharging rate fluctuation value.

[0087] If the charging rate fluctuation value is greater than or equal to the charging rate fluctuation threshold, it indicates that the charging rate is unstable; conversely, if it is less than the charging rate fluctuation threshold, it indicates that the charging rate is stable.

[0088] If the discharge rate fluctuation value is greater than or equal to the discharge rate fluctuation threshold, it indicates that the discharge rate is unstable; conversely, if it is less than the discharge rate fluctuation threshold, it indicates that the discharge rate is stable.

[0089] In step three, the process of predicting the target charging rate and the target discharging rate is as follows:

[0090] If stable, the charging rate group and the discharging rate group are averaged respectively to obtain the target charging rate and the target discharging rate.

[0091] If it is unstable, select the maximum value from the charging rate group and the discharging rate group respectively as the target charging rate and the target discharging rate.

[0092] It should be noted that the maximum value of the charging rate group is used as the target charging rate, and the maximum value of the discharging rate group is used as the target discharging rate.

[0093] Understandably, the significance of step three lies in calculating the fluctuation value based on the historical charge and discharge data of the module group to be expanded, and determining the target rate using either the mean or the maximum value according to stability. This step provides accurate input parameters—the target charge and discharge rate—for the subsequent coupled model, ensuring that the expansion limit calculated by the model matches the actual operating rate requirements of the module group, and avoiding deviations in the expansion scheme due to inaccurate parameters.

[0094] Step 4: By combining the target charging rate, target discharging rate, modular group capacity-charging rate coupling model, and modular group capacity-discharging rate coupling model, the upper limit of the expansion capacity of the module group to be expanded is obtained by comparing the outputs.

[0095] In step four, please refer to Figure 2 As shown, the method for obtaining the expansion limit of the module group to be expanded is as follows:

[0096] Substituting the target charging rate into the modular group capacity-charging rate coupling model, the output yields the upper limit of capacity expansion A1 under the target charging rate limit. Substituting the target discharging rate into the modular group capacity-discharging rate coupling model, the output yields the upper limit of capacity expansion A2 under the target discharging rate limit.

[0097] Compare the capacity expansion limit A1 under the target charging rate limit and the capacity expansion limit A2 under the target discharging rate limit;

[0098] If the expansion limit A1 under the target charging rate limit is higher than the expansion limit A2 under the target discharging rate limit, then the expansion limit A2 under the target discharging rate limit is selected as the expansion limit of the module group to be expanded.

[0099] If the expansion limit A1 under the target charging rate limit is lower than the expansion limit A2 under the target discharging rate limit, then the expansion limit A1 under the target charging rate limit is selected as the expansion limit of the module group to be expanded.

[0100] If the expansion limit A1 under the target charging rate limit is equal to the expansion limit A2 under the target discharging rate limit, then either the expansion limit A1 under the target charging rate limit or the expansion limit A2 under the target discharging rate limit is selected as the expansion limit of the module group to be expanded.

[0101] Understandably, the significance of step four lies in substituting the target rate into the coupled model to obtain the expansion upper limit under two rate constraints, and taking the higher value as the final upper limit. This step directly outputs the specific capacity expansion threshold of the module group to be expanded, providing a clear quantitative indicator for actual expansion projects, ensuring that the expanded module group can meet the peak-shaving rate requirements without wasting resources due to excessive expansion.

[0102] The principle of this invention is as follows: The core of the rapid expansion and reconfiguration method for modular energy storage systems is to achieve efficient system expansion by accurately identifying the objects to be expanded, constructing a quantitative model, and determining a reasonable expansion threshold. First, based on historical power grid operation data, the peak-shaving participation of each modular group is analyzed: the average frequency value (participation frequency percentage) and stable value (frequency fluctuation) of peak-shaving participation are calculated to obtain the peak-shaving participation value, and module groups with stable participation are selected. Simultaneously, the growth trend of peak-shaving demand is determined by the linear fitting slope of the peak-shaving frequency sequence, marking the module groups to be expanded that are "stable in participation and have increasing demand." Second, using the capacity and maximum charge / discharge rate data of each module group, the correlation type (linear / nonlinear) is determined by the Pearson correlation coefficient, and capacity-charging rate and capacity-discharging rate coupling models are constructed to quantify the intrinsic relationship between capacity and rate. Next, the volatility of the historical charge / discharge rates of the module groups to be expanded is analyzed; the average value is taken when stable and the maximum value is taken when unstable, providing accurate input for the model. Finally, the target rate is substituted into the coupled model to obtain the expansion upper limit under the two rate constraints. The higher value is taken as the final expansion threshold to ensure that the expansion can meet the peak shaving demand and avoid resource waste, thus realizing the scientific expansion and reconfiguration of the modular energy storage system.

[0103] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for rapid expansion and reconfiguration of modular energy storage systems, characterized by: Includes the following steps: Step 1: Obtain the participation records of each modular group in the modular energy storage system in grid-side peak shaving during multiple historical grid operation phases, and conduct grid-side peak shaving participation analysis and grid-side peak shaving demand trend analysis to identify the modular groups to be expanded. Step 2: Based on the capacity data and maximum charge / discharge rate data of each modular group, perform correlation analysis to construct a modular group capacity-charging rate coupling model and a modular group capacity-discharge rate coupling model; Step 3: Based on the module group to be expanded, obtain the charging and discharging rate data of the module group to be expanded when participating in grid-side peak shaving multiple times and perform volatility analysis to predict the target charging rate and target discharging rate; Step 4: By combining the target charging rate, target discharging rate, modular group capacity-charging rate coupling model, and modular group capacity-discharging rate coupling model, the upper limit of the expansion capacity of the module group to be expanded is obtained by comparison.

2. The method for rapid expansion and reconfiguration of a modular energy storage system according to claim 1, characterized in that: The process of participating in the analysis of grid-side peak shaving is as follows: The number of times the modular group participated in grid-side peak shaving in multiple historical power grid operation phases and the number of times the modular group participated in grid-side peak shaving in each historical power grid operation phase were obtained and processed and analyzed to obtain the average frequency value and peak shaving participation stability value of the modular group. The deviation between the average frequency value and the stable value of peak shaving participation is calculated to obtain the peak shaving participation value of the modular group. The stability of the modular group's participation in grid-side peak shaving is judged based on the peak shaving participation value.

3. The method for rapid expansion and reconfiguration of a modular energy storage system according to claim 2, characterized in that: The methods for obtaining the average frequency value and the stable value of peak shaving participation are as follows: The average number of times the modular group participated in grid-side peak shaving in multiple historical power grid operation phases was averaged and then proportionally calculated with the total number of grid-side peak shavings in multiple historical power grid operation phases to obtain the average frequency value of peak shaving participation. Among them, the total number of peak shaving operations on the grid side within multiple historical operation phases of the power grid is the sum of the number of peak shaving operations on the grid side within each historical operation phase of the power grid; The coefficient of variation was calculated for the number of times the modular group participated in grid-side peak shaving in each historical operation phase of the power grid, and the peak shaving participation stability value was obtained.

4. The rapid expansion and reconfiguration method for a modular energy storage system according to claim 3, characterized in that: The process of conducting grid-side peak-shaving demand trend analysis is as follows: The number of peak shaving events on the grid side during each historical operation phase of the power grid is obtained and integrated into a peak shaving event sequence according to the time sequence of the historical operation phase of the power grid. The least squares method is used to perform linear fitting on the peak shaving event sequence to obtain the linear fitting slope. If the linear fitting slope is positive, it indicates that the peak shaving demand on the grid side is showing an increasing trend. Conversely, if the linear fitting slope is negative or 0, it indicates that the peak shaving demand on the grid side is showing a non-increasing trend.

5. The rapid expansion and reconfiguration method for a modular energy storage system according to claim 4, characterized in that: The process of identifying the module group to be expanded is as follows: If a modular group participates in grid-side peak shaving stability and grid-side peak shaving demand shows an increasing trend, then the modular group will be marked as a module group to be expanded.

6. The method for rapid expansion and reconfiguration of a modular energy storage system according to claim 1, characterized in that: The process of performing correlation analysis is as follows: The capacity data of each modular group is integrated to obtain the modular group capacity sequence. The maximum charging rate and maximum discharging rate of each modular group are then integrated to obtain the maximum charging rate sequence and the maximum discharging rate sequence. The Pearson correlation coefficients between the modular group capacity sequence and the maximum charging rate sequence and the maximum discharging rate sequence are calculated and their absolute values ​​are processed to obtain the correlation type judgment coefficients, which include the capacity charging correlation coefficient and the capacity discharging correlation coefficient. The correlation type between the modular group capacity and the maximum charging rate and the maximum discharging rate is determined based on the capacity charging correlation coefficient and the capacity discharging correlation coefficient.

7. The method for rapid expansion and reconfiguration of a modular energy storage system according to claim 6, characterized in that: The process of constructing the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model is as follows: If it is a linear correlation type, the least squares method is used to perform linear fitting on the modular group capacity sequence and the maximum charging rate sequence, as well as the modular group capacity sequence and the maximum discharging rate sequence, respectively, to obtain the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model. If the correlation is nonlinear, then nonlinear fitting is performed on the modular group capacity sequence and the maximum charging rate sequence, and on the modular group capacity sequence and the maximum discharging rate sequence, respectively. After performing nonlinear fitting, the fitting model with the highest goodness of fit is selected as the modular group capacity-charging rate coupling model and the modular group capacity-discharging rate coupling model, respectively.

8. The method for rapid expansion and reconfiguration of a modular energy storage system according to claim 7, characterized in that: The process of performing volatility analysis is as follows: The charging rate and discharging rate of the module group to be expanded during multiple grid-side peak shaving operations are integrated into a charging rate group and a discharging rate group, respectively. The coefficients of variation of the charging rate group and the discharging rate group are calculated separately to obtain the charging and discharging rate fluctuation values, which include the charging rate fluctuation value and the discharging rate fluctuation value. The stability of the charging and discharging rates can be determined by the fluctuation values ​​of the charging and discharging rates.

9. The method for rapid expansion and reconfiguration of a modular energy storage system according to claim 8, characterized in that: The process of predicting the target charging rate and target discharging rate is as follows: If stable, the charging rate group and the discharging rate group are averaged respectively to obtain the target charging rate and the target discharging rate. If the rate is unstable, select the maximum value from the charging rate group and the discharging rate group respectively as the target charging rate and the target discharging rate.

10. The method for rapid expansion and reconfiguration of a modular energy storage system according to claim 1, characterized in that: The method for obtaining the expansion limit of the module group to be expanded is as follows: Substituting the target charging rate into the modular group capacity-charging rate coupling model, the output yields the upper limit of capacity expansion under the target charging rate constraint. Substituting the target discharging rate into the modular group capacity-discharging rate coupling model, the output yields the upper limit of capacity expansion under the target discharging rate constraint. If the expansion limit under the target charging rate is higher than the expansion limit under the target discharging rate, then the expansion limit under the target discharging rate is selected as the expansion limit of the module group to be expanded; otherwise, the expansion limit under the target charging rate is selected as the expansion limit of the module group to be expanded.

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