Energy storage low-frequency low-voltage to discharge control method and system

Abstract

The invention relates to the technical field of electric power control, in particular to an energy storage low-frequency and low-voltage to discharge control method and system, and the method comprises the steps: obtaining an initial load data sequence of a power grid in at least two continuous historical time periods before a target time period; acquiring a data fitting weight of each historical time period for the target time period; according to the data fitting weight and the initial load data sequence, constructing a load data change prediction curve of the target time period, dividing the load data change prediction curve into at least two sub-curves, and obtaining load change trend factors of the sub-curves; and for any sampling moment in the target time period, according to the actual load data at any sampling moment and the load change trend factor of the sub-curve to which the actual load data belong, obtaining a power grid load pressure index so as to carry out real-time transfer and discharge control on the energy storage equipment of the power grid, and through combination of the predicted load data and the actual load data, carrying out real-time transfer and discharge control on the energy storage equipment of the power grid. And the accuracy of charging and discharging the power grid by the energy storage equipment is improved.

Description

Technical Field

[0001] The present invention relates to the field of power control technology, and in particular to a method and system for controlling energy storage low-frequency and low-voltage conversion and discharge. Background Art

[0002] The low-frequency and low-voltage energy storage discharge and conversion control can store excess electric energy when the grid load is low and electric energy obtained from other sources through technologies such as battery packs, and provide electric energy replenishment during peak grid load periods, avoiding power outages caused by excessive grid load or energy waste caused by insufficient energy utilization due to low grid load, so as to balance the grid load and reduce grid pressure, thereby ensuring electricity safety and improving the economic benefits and energy efficiency management level of the energy storage system.

[0003] The current energy storage transfer and discharge control usually judges the current load ratio in the power grid. When the load ratio meets the set load ratio threshold, the power grid is supplied with electric energy or the electric energy in the power grid is recovered and stored. For example, if the current load reaches more than 70% of the maximum load that the power grid can bear, the stored electric energy is released to the power grid to cope with the power supply shortage in the power grid. If the current load reaches less than 50% of the maximum load that the power grid can bear, the excess electric energy in the power grid is recovered and stored. However, this method is relatively inactive and lacks the ability to predict possible problems in the future. It is easy to miss the best response time, resulting in insufficient power compensation or untimely power storage, resulting in waste, etc., which is not conducive to the accurate transfer and release control of power by the energy storage system.

[0004] Therefore, how to improve the accurate control of power transfer by energy storage systems has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and system for controlling low-frequency and low-voltage transfer and discharge of energy storage to solve the problem of how to improve the accurate transfer and discharge control of the energy storage system on electricity.

[0006] In a first aspect, an embodiment of the present invention provides a method for controlling low-frequency and low-voltage energy storage discharge, the method comprising the following steps:

[0007] Respectively obtain initial load data sequences of the power grid in at least two consecutive historical time periods before the target time period, and respectively perform adaptive smoothing processing on each of the initial load data sequences to obtain corresponding smoothed data sequences;

[0008] Constructing a load data change curve for each of the smoothed data sequences, wherein the horizontal axis of the load data change curve is time and the vertical axis is load data, and obtaining a data fitting weight of each of the historical time periods for the target time period according to the period interval between each of the historical time periods and the target time period, and each of the load data change curves;

[0009] According to the data fitting weight of each of the historical time periods for the target time period, a load data change prediction curve for the target time period is constructed; according to the difference between adjacent data points in the load data change prediction curve, the load data change prediction curve is divided into at least two sub-curves; according to the data difference in each of the sub-curves, a load change trend factor of the corresponding sub-curve is obtained;

[0010] For any sampling moment within the target time period, the grid load pressure index at any sampling moment is obtained based on the actual load data at any sampling moment and the load change trend factor of the sub-curve to which the sampling moment belongs, and the energy storage equipment of the grid is controlled in real time according to the grid load pressure index at any sampling moment.

[0011] In a second aspect, an embodiment of the present invention provides an energy storage low-frequency and low-voltage conversion and discharge control system, comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the above-mentioned energy storage low-frequency and low-voltage conversion and discharge control method.

[0012] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0013] The present invention obtains the initial load data sequence of the power grid in at least two continuous historical time periods before the target time period, performs adaptive smoothing processing on each of the initial load data sequence, and obtains the corresponding smoothed data sequence; constructs a load data change curve of each smoothed data sequence, wherein the horizontal axis of the load data change curve is time and the vertical axis is load data; obtains the data fitting weight of each historical time period for the target time period according to the period interval between each historical time period and the target time period, and each load data change curve; constructs the load data change prediction curve of the target time period according to the data fitting weight of each historical time period for the target time period, divides the load data change prediction curve into at least two sub-curves according to the difference between adjacent data points in the load data change prediction curve, and obtains the load change trend factor of the corresponding sub-curve according to the data difference in each sub-curve; for any sampling moment in the target time period, obtains the power grid load pressure index at any sampling moment according to the actual load data at any sampling moment and the load change trend factor of the sub-curve to which the any sampling moment belongs, and performs real-time switching and discharging control on the energy storage device of the power grid according to the power grid load pressure index at any sampling moment. Among them, by analyzing the changing rules and power consumption patterns of load data in multiple historical time periods to predict the load data in the target time period, a load data change prediction curve in the target time period can be obtained. In order to perform real-time grid load pressure analysis at each sampling moment in the target time period, the load data change prediction curve is divided into multiple sub-curves with different changes according to the fluctuations of the load data change prediction curve, and then the load change trend factor of each sub-curve is combined with the actual load data at each sampling moment in the target time period, so as to obtain a grid load pressure indicator that is more in line with reality, and then the charging and discharging strategy of the energy storage equipment in the grid is adjusted according to the grid load pressure indicator, so as to improve the accuracy of the energy storage equipment charging and discharging of the grid, achieve the purpose of peak shaving and valley filling of the power load in the grid, maintain the stable operation of the grid, and improve the utilization rate of the grid energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 It is a method flow chart of a method for controlling low-frequency and low-voltage energy storage discharge and conversion provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0017] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0018] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0019] See also Figure 1 , is a method flow chart of a method for controlling energy storage low-frequency and low-voltage discharge conversion provided by Embodiment 1 of the present invention, such as Figure 1 As shown, the method may include:

[0020] Step S101 , respectively obtaining initial load data sequences of the power grid in at least two consecutive historical time periods before a target time period, and respectively performing adaptive smoothing processing on each initial load data sequence to obtain a corresponding smoothed data sequence.

[0021] The present invention is mainly aimed at obtaining more accurate control decisions when energy storage devices in the power grid control the charging and discharging of the power grid. Therefore, in the embodiment of the present invention, current and voltage acquisition modules are used to collect load data in the power grid, and one day is used as a time period. Assuming that the target time period is the i-th day, each day of the past month before the i-th day is used as a historical time period to obtain the load data within the month before the i-th day, and then obtain the initial load data sequence corresponding to each day in the past month. It is worth noting that the sampling frequency of the load data is once per second, which is not limited here and can be set according to the implementation scenario.

[0022] Since the changes in load data have certain volatility in time series, it is not conducive to the observation and analysis of the overall change trend. Therefore, in order to more accurately understand the change rules and patterns of the initial load data series of each day in the past month, the mean filtering algorithm is used to smooth the initial load data series of each day in the past month to improve the accuracy of historical data.

[0023] Since the load data may fluctuate differently in different time periods, the filter windows at different positions may also be different when using mean filtering. By determining the size of the adaptive filtering window, the overall smoothness of the load data can be improved to highlight its overall change pattern over time. Therefore, for the initial load data sequence of any day, the initial load data sequence is processed by adaptive mean filtering to obtain the corresponding smooth data sequence. The specific process is as follows:

[0024] For any load data in any initial load data sequence, taking the any load data as the center, obtaining a preset number of adjacent load data in the any initial load data sequence, forming a target data sequence with the any load data, performing first-order difference processing on the target data sequence to obtain a first-order difference sequence, and calculating the coefficient of variation of the first-order difference sequence;

[0025] The negative number of the coefficient of variation is used as an independent variable of a preset exponential function to obtain a corresponding exponential function value, a subtraction value between a constant 1 and the exponential function value is obtained, an odd integer is taken upward for the product of the subtraction value and a preset hyperparameter to obtain a corresponding odd integer, and the odd integer is used as an adaptive filtering window when performing mean filtering on any load data;

[0026] Obtain an adaptive filtering window corresponding to each load data in any of the initial load data sequences, and perform mean filtering on any of the initial load data sequences according to the adaptive filtering window corresponding to each load data in any of the initial load data sequences to obtain a corresponding smoothed data sequence.

[0027] In one embodiment, taking the jth load data in the ath initial load data sequence as an example, 10 load data before and after the jth load data are selected in the ath initial load data sequence to form a target data sequence of the jth load data, and the target data sequence is subjected to first-order difference processing to obtain a corresponding differential sequence. The coefficient of variation (CV) is an indicator to measure the relative variability of data, and is usually used to compare the degree of dispersion between different data sets. It is the ratio of the standard deviation to the mean, usually expressed as a percentage. Therefore, by calculating the coefficient of variation of the differential sequence, the adaptive filtering window when the mean filtering is performed on the jth load data is obtained, and the calculation expression of the size of the adaptive filtering window is:

[0028]

[0029] Among them, N aj represents the size of the adaptive filtering window of the jth load data in the ath initial load data sequence, represents the sign of an odd integer upwards, e represents a natural constant; Yaj represents the coefficient of variation of the j-th load data in the a-th initial load data sequence, L represents the hyperparameter, and its value is 20.

[0030] It should be noted that the larger the coefficient of variation of the j-th load data is, the less smooth the local data of the j-th load data is and the greater the volatility is. Therefore, the larger the adaptive filtering window of the j-th load data should be so that more valid data can be referred to during smoothing filtering.

[0031] Similarly, the adaptive filtering window of each load data in the ath initial load data sequence is obtained. After the adaptive filtering window is determined, the ath initial load data sequence is mean filtered to obtain the ath initial load data sequence after filtering, which is recorded as the ath smoothed data sequence. Thus, the smoothed data sequence corresponding to the initial load data sequence of each day in the past month before the i-th day can be obtained, which improves the smoothness of the power grid load data.

[0032] It is worth noting that the coefficient of variation, mean filtering and first-order difference processing all belong to the existing technology and will not be described in detail here.

[0033] Step S102, construct a load data change curve for each smooth data sequence, where the horizontal axis of the load data change curve is time and the vertical axis is load data, and based on the period interval between each historical time period and the target time period, and each load data change curve, obtain the data fitting weight of each historical time period for the target time period.

[0034] After obtaining the smoothed data sequence corresponding to each day in the past month before the i-th day, considering that the rules and patterns of load changes in the power grid are related to the user's living habits and work and rest schedules, and have certain regular characteristics, the load data of the power grid on the i-th day can be predicted through the historical load data before the i-th day (that is, the smoothed data sequence of each day in the past month), which is used to improve the accuracy of charging and discharging control of energy storage equipment in combination with the actual load conditions of the power grid on the i-th day.

[0035] First, for each smooth data sequence, with time as the horizontal axis and load data as the vertical axis, a load data change curve corresponding to each smooth data sequence is constructed to obtain multiple load data change curves. Then, according to the load change law presented by all load data change curves, the weight of the load data of each day in the past month when predicting the load data change curve of the i-th day is analyzed, that is, the data fitting weight of each day in the past month relative to the i-th day, the data fitting weight. The larger the data fitting weight, the higher the importance of the load data prediction for the i-th day. Therefore, in the embodiment of the present invention, according to the period interval between each historical time period and the target time period, and each load data change curve, the data fitting weight of each historical time period for the target time period is obtained, and the specific acquisition method is as follows:

[0036] (1) respectively obtaining the DTW distance between each two of the load data change curves, and respectively obtaining the data reference degree of each of the historical time periods according to all the DTW distances and according to the period interval between each of the historical time periods and the target time period.

[0037] The step of respectively obtaining the data reference degree of each historical time period according to all DTW distances and according to the period interval between each historical time period and the target time period includes:

[0038] For any historical time period, according to the DTW distance between the load data change curve corresponding to any historical time period and the load data change curve corresponding to each other historical time period, a preset Gaussian kernel function is used to obtain the local density value of the load data change curve corresponding to any historical time period;

[0039] The period interval between any one of the historical time periods and the target time period is obtained, and the data reference degree of any one of the historical time periods is obtained according to the product of the inverse of the period interval and the local density value.

[0040] In one embodiment, the DTW algorithm is first used to calculate the DTW distance between each two load data change curves to characterize the change similarity between the load data change curves. Then, a Gaussian kernel function with a kernel bandwidth parameter of 2 is obtained. The Gaussian kernel function is used to calculate the local density value of each load data change curve in all load data change curves. The higher the local density value, the closer the overall change law of the load data change curve is to other load data change curves, and the more it can characterize the daily load change law and pattern in the power grid. On the contrary, the smaller the local density value, the load usage of the load data change curve on the corresponding day has changed to a certain extent. Therefore, in order to more accurately describe the prediction accuracy of the load data of the past month for the load data of the i-th day, the time interval between the corresponding day of each load data change curve and the i-th day is further combined. The closer the time interval, the greater the reference of the load data. It is worth noting that the DTW algorithm and the Gaussian kernel function are both existing technologies and will not be described in detail here.

[0041] The calculation expression for the data reference degree of the ijth day in the past month is:

[0042]

[0043] Among them, μ i-b Indicates the data reference degree of the ibth day in the past month, ρ i-b Represents the local density value of the load data change curve of the ibth day in the past month, t i-b It represents the number of days between the ib-th day and the i-th day in the past month, that is, the period interval of the time period, and 1 represents a constant.

[0044] Similarly, the data reference level for each day in the past month can be obtained.

[0045] (2) Obtaining the data reference level of each of the historical time periods, obtaining the sum of the data reference levels, and for any historical time period, calculating the ratio of the data reference level of any historical time period to the sum of the data reference levels, to obtain the data fitting weight of any historical time period for the target time period.

[0046] In one embodiment, the calculation expression of the data fitting weight of the ibth day relative to the ith day in the past month is:

[0047]

[0048] Among them, q i-b represents the data fitting weight of the ibth day relative to the ith day in the past month, μ i-bIt represents the data reference level of the ibth day in the past month, and m represents the number of days in the past month, that is, the total number of periods in the historical time period.

[0049] It should be noted that the greater the degree of data reference, the greater the weight of the load data of the corresponding day when predicting the load data of the i-th day.

[0050] At this point, the data fitting weights for each day in the past month relative to the i-th day can be obtained.

[0051] Step S103, constructing a load data change prediction curve for the target time period according to the data fitting weight of each historical time period for the target time period, dividing the load data change prediction curve into at least two sub-curves according to the difference between adjacent data points in the load data change prediction curve, and obtaining the load change trend factor of the corresponding sub-curve according to the data difference in each sub-curve.

[0052] After determining the data fitting weight of each day in the past month relative to the i-th day, the load change on the i-th day can be predicted by combining the smoothed data sequence of each day in the past month, and the corresponding load data change prediction curve on the i-th day can be obtained. The method for obtaining the load data change prediction curve on the i-th day is:

[0053] The data fitting weight of each historical time period for the target time period is used as the weight of each load data in the corresponding smoothed data sequence. For any sampling moment within the target time period, the load data corresponding to the same sampling moment as the any sampling moment is obtained from the smoothed data sequence corresponding to each historical time period to form a historical load data sequence at any sampling moment. The historical load data sequence is weighted averaged to obtain a load forecast value at any sampling moment.

[0054] The load forecast value at each sampling moment within the target time period is obtained, and a load data change prediction curve composed of the load forecast values ​​at each sampling moment within the target time period is constructed, wherein the horizontal axis of the load data change prediction curve is time and the vertical axis is the load forecast value.

[0055] In one embodiment, taking the kth sampling moment in the i-th day as an example, in the smoothed data sequence corresponding to each day in the past month, the load data corresponding to the kth sampling moment in each day is respectively obtained to form a historical load data sequence at the kth sampling moment in the i-th day, and at the same time, the data fitting weights of each day in the past month relative to the i-th day are respectively used as the weights of each load data in the corresponding smoothed data sequence, and then the weighted summation of the historical load data sequence at the kth sampling moment in the i-th day is performed, and then the mean is calculated, and the mean is used as the load prediction value at the kth sampling moment in the i-th day. At this time, the load prediction for the kth sampling moment in the i-th day is completed. Similarly, the load prediction value at each sampling moment in the i-th day is obtained, and then a load data change prediction curve with time as the horizontal axis and load prediction value as the vertical axis is formed.

[0056] After predicting the grid load usage on the i-th day, that is, after obtaining the load data change prediction curve for the i-th day, the change law and trend of the load data change prediction curve are analyzed. Specifically, first, according to the inflection point in the load data change prediction curve, the load data change prediction curve is divided into multiple sub-curves, one sub-curve represents a load change trend, and then, the load change trend factor of each sub-curve is further analyzed in detail.

[0057] The method of dividing the load data change prediction curve into a plurality of sub-curves according to the inflection point in the load data change prediction curve includes:

[0058] For any data point on the load data change prediction curve, K data points on the left side of the any data point are obtained on the load data change prediction curve to form a left adjacent data sequence, and the left adjacent data sequence is processed by first-order difference to obtain a corresponding mean of the difference values ​​in the first-order difference sequence, which is recorded as the left difference value mean;

[0059] Obtaining K data points on the right side of any data point on the load data change prediction curve to form a right adjacent data sequence, performing first-order difference processing on the right adjacent data sequence, and correspondingly obtaining a mean of the difference values ​​in the first-order difference sequence, which is recorded as the right difference value mean;

[0060] If the left difference value mean or the right difference value mean does not exist for any of the data points, the confidence that the any of the data points is an inflection point is set to 0; if the left difference value mean and the right difference value mean exist for any of the data points, the absolute value of the difference between the left difference value mean and the right difference value mean is calculated as the confidence that the any of the data points is an inflection point;

[0061] Obtaining the confidence that the left adjacent data point of any data point is an inflection point and the confidence that the right adjacent data point is an inflection point, if the confidence that the any data point is an inflection point is greater than both the confidence that the left adjacent data point is an inflection point and the confidence that the right adjacent data point is an inflection point, marking the any data point as an inflection point;

[0062] At least one inflection point on the load data change prediction curve is obtained, and the inflection point is used as a curve segmentation point to divide the load data change prediction curve into at least two sub-curves.

[0063] In one embodiment, K=10 is set, and the xth data point on the load data change prediction curve is taken as an example. On the load data change prediction curve, the 10 data points before the xth data point are obtained to form a left adjacent data sequence, and the left adjacent data sequence is subjected to a first-order difference process, and the corresponding difference value mean in the first-order difference sequence is obtained, which is recorded as the left difference value mean of the xth data point. Similarly, the 10 data points after the xth data point are obtained to form a right adjacent data sequence, and the right adjacent data sequence is subjected to a first-order difference process, and the corresponding difference value mean in the first-order difference sequence is obtained, which is recorded as the right difference value mean of the xth data point. Then, according to the left difference value mean and the right difference value mean of the xth data point, the confidence that the xth data point is an inflection point is obtained, and the calculation expression of the confidence is:

[0064]

[0065] Among them, P i Indicates the confidence that the xth data point on the load data change prediction curve is an inflection point. represents the mean of the right difference values ​​of the x-th data point, represents the mean of the left difference value of the x-th data point, and || represents the absolute value sign.

[0066] It should be noted that The larger the value of , the greater the difference in differential data between the selected data points on the left and right sides of the x-th data point, indicating that the greater the difference in curve changes on both sides of the x-th data point, the greater the confidence that the x-th data point is an inflection point.

[0067] Considering that not every data point on the load data change prediction curve has a left adjacent data sequence and a right adjacent data sequence, and such data points are generally the first few data points or the last few data points on the load data change prediction curve, and are generally the data points where the load data change prediction curve is in a stable stage, therefore, for data points that only have a left adjacent data sequence or only have a right adjacent data sequence, the confidence level of these data points belonging to the inflection point is directly set to 0.

[0068] After determining the confidence that each data point on the load data change prediction curve is an inflection point, the confidence that the xth data point is an inflection point is compared with the confidence that the x-1th data point is an inflection point and the confidence that the x+1th data point is an inflection point. If the confidence that the xth data point is an inflection point is greater than the confidence that the x-1th data point is an inflection point, and is also greater than the confidence that the x+1th data point is an inflection point, then the xth data point is determined to be an inflection point. According to the method for determining whether the xth data point is an inflection point, the same traversal is performed on each data point on the load data change prediction curve to obtain at least one inflection point on the load data change prediction curve. Furthermore, the inflection point is used as a curve segmentation point to divide the load data change prediction curve into at least two sub-curves.

[0069] Among them, the method for analyzing the load change trend factor of each sub-curve in detail is:

[0070] For any sub-curve, the difference between each data point on the sub-curve and its left adjacent data point is obtained on the load data change prediction curve, and each difference is used as an independent variable of a preset exponential function to obtain the corresponding exponential function result. According to the mean of all exponential function results, the load change trend factor of the sub-curve is obtained.

[0071] In one implementation, taking the yth sub-curve as an example, the expression for calculating the load change trend factor of the yth sub-curve is:

[0072]

[0073] Among them, X y Indicates the load change trend factor of the y-th sub-curve, n y represents the total number of data points on the y-th sub-curve, exp() represents an exponential function with a natural constant as the base, and F y(s+1) represents the load forecast value corresponding to the s+1th data point on the yth sub-curve, F ys Represents the load forecast value corresponding to the sth data point on the yth sub-curve.

[0074] It should be noted that the change trend of the yth sub-curve is analyzed by calculating the difference in load forecast value between the next data point and the previous data point on the yth sub-curve, corresponding to If the value of is greater than 1, it means that the y-th sub-curve is on an upward trend, that is, the load pressure of the y-th sub-curve in the time period corresponding to the i-th day is relatively large; otherwise, If the value of is less than 1, it means that the y-th sub-curve shows a downward trend, and the load pressure of the y-th sub-curve in the time period corresponding to the i-th day is relatively small.

[0075] At this point, the load change trend factor of each sub-curve in the load data change prediction curve of the i-th day can be obtained. The larger the load change trend factor, the greater the grid load pressure in the corresponding time period.

[0076] Step S104, for any sampling moment within the target time period, according to the actual load data at any sampling moment and the load change trend factor of the sub-curve to which any sampling moment belongs, obtain the grid load pressure index at any sampling moment, and perform real-time switching and discharging control on the grid energy storage device according to the grid load pressure index at any sampling moment.

[0077] When the actual load data at any sampling moment of the i-th day is collected, the load data change prediction curve obtained by combining the load forecast of the i-th day with the historical load data is used to analyze the power grid load pressure index at the sampling moment. The specific method is: obtain the maximum load bearing capacity of the power grid, calculate the ratio between the actual load data at any sampling moment and the maximum load bearing capacity, obtain the product between the ratio and the load change trend factor of the sub-curve to which the sampling moment belongs, normalize the product, and obtain the power grid load pressure index at any sampling moment.

[0078] In one embodiment, the calculation expression of the power grid load pressure index at any sampling time of the i-th day is:

[0079]

[0080] Among them, Y0 represents the grid load pressure index at any sampling time of the i-th day, norm() represents the normalization function, F0 represents the actual load data at any sampling time of the i-th day, and F max represents the maximum load capacity of the power grid, and X0 represents the load change trend factor of the sub-curve at any sampling time of the i-th day.

[0081] It should be noted that It represents the ratio between the actual grid load at the current sampling moment and the current maximum load that the grid can bear. The larger the ratio is, the greater the grid load pressure is at this moment. Similarly, the larger the load change trend factor of the sub-curve corresponding to the current sampling moment is, the greater the load pressure of the grid will be in the future. Therefore, the grid load pressure index at the current sampling moment is greater.

[0082] After determining the grid load pressure index at any sampling time of the i-th day, the energy storage equipment in the grid can be controlled in real time. The specific control method is:

[0083] Obtain the preset first load pressure index threshold and second load pressure index threshold, and the first load pressure index threshold is less than the second load pressure index threshold. If the grid load pressure index at any sampling moment is less than the first load pressure index threshold, charge the energy storage device of the grid. If the grid load pressure index at any sampling moment is greater than the second load pressure index threshold, discharge the energy storage device of the grid.

[0084] In an embodiment, set the first load pressure index threshold T1 to 0.3 and the second load pressure index threshold T2 to 0.65. When Y0 < T1, it indicates that the current sampling moment does not belong to the peak period of the grid load. The excess electric energy in the grid can be recovered and stored, that is, charge the energy storage device of the grid. When Y0 > T2, it indicates that the current sampling moment belongs to the peak period of the grid load, and electric energy supply is required to avoid phenomena such as power interruption caused by excessive grid load or waste of electric energy caused by insufficient utilization of electric energy due to small grid load. Therefore, discharge the energy storage device of the grid to balance the grid load and reduce the grid pressure. When T1 ≤ Y0 ≤ T2, keep the grid operation state unchanged, and neither charge nor discharge the energy storage device of the grid.

[0085] Thus, through the above method for obtaining the grid load pressure index, it is possible to judge the charging and discharging of the grid at each sampling moment of the ith day respectively, so as to improve the response ability of the energy storage device of the grid, ensure the safety of power consumption, and improve the economic benefits and energy efficiency management level of the energy storage system.

[0086] Based on the same inventive concept as the above method, an embodiment of the present invention also provides an energy storage low-frequency and low-voltage conversion discharge control system, including a memory and a processor. The processor executes the computer program stored in the memory to implement the steps of any one of the above methods for an energy storage low-frequency and low-voltage conversion discharge control method.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for controlling energy storage low-frequency and low-voltage discharge, characterized in that: The method comprises: Respectively obtain initial load data sequences of the power grid in at least two consecutive historical time periods before the target time period, and respectively perform adaptive smoothing processing on each of the initial load data sequences to obtain corresponding smoothed data sequences; Constructing a load data change curve for each of the smoothed data sequences, wherein the horizontal axis of the load data change curve is time and the vertical axis is load data, and obtaining a data fitting weight of each of the historical time periods for the target time period according to the period interval between each of the historical time periods and the target time period, and each of the load data change curves; According to the data fitting weight of each of the historical time periods for the target time period, a load data change prediction curve for the target time period is constructed; according to the difference between adjacent data points in the load data change prediction curve, the load data change prediction curve is divided into at least two sub-curves; according to the data difference in each of the sub-curves, a load change trend factor of the corresponding sub-curve is obtained; For any sampling moment within the target time period, the grid load pressure index at any sampling moment is obtained based on the actual load data at any sampling moment and the load change trend factor of the sub-curve to which the sampling moment belongs, and the energy storage equipment of the grid is controlled in real time according to the grid load pressure index at any sampling moment.

2. The energy storage low-frequency and low-voltage discharge control method according to claim 1 is characterized in that: The step of performing adaptive smoothing processing on each of the initial load data sequences to obtain a corresponding smoothed data sequence comprises: For any load data in any initial load data sequence, taking the any load data as the center, obtaining a preset number of adjacent load data in the any initial load data sequence, forming a target data sequence with the any load data, performing first-order difference processing on the target data sequence to obtain a first-order difference sequence, and calculating the coefficient of variation of the first-order difference sequence; The negative number of the coefficient of variation is used as an independent variable of a preset exponential function to obtain a corresponding exponential function value, a subtraction value between a constant 1 and the exponential function value is obtained, an odd integer is taken upward for the product of the subtraction value and a preset hyperparameter to obtain a corresponding odd integer, and the odd integer is used as an adaptive filtering window when performing mean filtering on any load data; Obtain an adaptive filtering window corresponding to each load data in any of the initial load data sequences, and perform mean filtering on any of the initial load data sequences according to the adaptive filtering window corresponding to each load data in any of the initial load data sequences to obtain a corresponding smoothed data sequence.

3. The energy storage low-frequency and low-voltage discharge control method according to claim 1 is characterized in that: The step of obtaining the data fitting weight of each of the historical time periods for the target time period according to the period interval between each of the historical time periods and the target time period and each of the load data change curves includes: respectively obtaining the DTW distance between each two of the load data change curves, and respectively obtaining the data reference degree of each of the historical time periods according to all the DTW distances and according to the period interval between each of the historical time periods and the target time period; Obtain the data reference degree of each of the historical time periods, obtain the sum of the data reference degrees, and for any historical time period, calculate the ratio of the data reference degree of any historical time period to the sum of the data reference degrees to obtain the data fitting weight of any historical time period for the target time period.

4. The energy storage low-frequency and low-voltage discharge control method according to claim 3 is characterized in that: The step of respectively acquiring the data reference degree of each historical time period according to all DTW distances and according to the period interval between each historical time period and the target time period comprises: For any historical time period, according to the DTW distance between the load data change curve corresponding to any historical time period and the load data change curve corresponding to each other historical time period, a preset Gaussian kernel function is used to obtain the local density value of the load data change curve corresponding to any historical time period; The period interval between any one of the historical time periods and the target time period is obtained, and the data reference degree of any one of the historical time periods is obtained according to the product of the inverse of the period interval and the local density value.

5. The energy storage low-frequency and low-voltage discharge control method according to claim 1 is characterized in that: The step of constructing a load data change prediction curve for the target time period according to the data fitting weight of each historical time period for the target time period includes: The data fitting weight of each historical time period for the target time period is used as the weight of each load data in the corresponding smoothed data sequence. For any sampling moment within the target time period, the load data corresponding to the same sampling moment as the any sampling moment is obtained in the smoothed data sequence corresponding to each historical time period to form a historical load data sequence at any sampling moment. The historical load data sequence is subjected to weighted mean processing to obtain a load forecast value at any sampling moment. The load forecast value at each sampling moment within the target time period is obtained, and a load data change prediction curve composed of the load forecast values ​​at each sampling moment within the target time period is constructed, wherein the horizontal axis of the load data change prediction curve is time and the vertical axis is the load forecast value.

6. The energy storage low-frequency and low-voltage discharge control method according to claim 1 is characterized in that: The step of dividing the load data change prediction curve into at least two sub-curves according to differences between adjacent data points in the load data change prediction curve comprises: For any data point on the load data change prediction curve, K data points on the left side of the any data point are obtained on the load data change prediction curve to form a left adjacent data sequence, and the left adjacent data sequence is processed by first-order difference to obtain a corresponding mean of the difference values ​​in the first-order difference sequence, which is recorded as the left difference value mean; Obtaining K data points on the right side of any data point on the load data change prediction curve to form a right adjacent data sequence, performing first-order difference processing on the right adjacent data sequence, and correspondingly obtaining a mean of the difference values ​​in the first-order difference sequence, which is recorded as the right difference value mean; If the left difference value mean or the right difference value mean does not exist for any of the data points, the confidence that the any of the data points is an inflection point is set to 0; if the left difference value mean and the right difference value mean exist for any of the data points, the absolute value of the difference between the left difference value mean and the right difference value mean is calculated as the confidence that the any of the data points is an inflection point; Obtaining the confidence that the left adjacent data point of any data point is an inflection point and the confidence that the right adjacent data point is an inflection point, if the confidence that the any data point is an inflection point is greater than both the confidence that the left adjacent data point is an inflection point and the confidence that the right adjacent data point is an inflection point, marking the any data point as an inflection point; At least one inflection point on the load data change prediction curve is obtained, and the inflection point is used as a curve segmentation point to divide the load data change prediction curve into at least two sub-curves.

7. The energy storage low-frequency and low-voltage discharge control method according to claim 1 is characterized in that: The step of obtaining the load change trend factor of the corresponding sub-curve according to the data difference in each sub-curve includes: For any sub-curve, the difference between each data point on the sub-curve and its left adjacent data point is obtained on the load data change prediction curve, and each difference is used as an independent variable of a preset exponential function to obtain the corresponding exponential function result. According to the mean of all exponential function results, the load change trend factor of the sub-curve is obtained.

8. The energy storage low-frequency and low-voltage discharge control method according to claim 1 is characterized in that: The obtaining of the grid load pressure index at any sampling moment according to the actual load data at any sampling moment and the load change trend factor of the sub-curve to which the sampling moment belongs includes: The maximum load bearing capacity of the power grid is obtained, the ratio between the actual load data at any sampling moment and the maximum load bearing capacity is calculated, the product between the ratio and the load change trend factor of the sub-curve to which the sampling moment belongs is obtained, the product is normalized, and the power grid load pressure index at any sampling moment is obtained.

9. The energy storage low-frequency and low-voltage discharge control method according to claim 1 is characterized in that: The real-time switching and discharging control of the energy storage device of the power grid according to the power grid load pressure index at any sampling moment includes: A preset first load pressure index threshold and a second load pressure index threshold are obtained, and the first load pressure index threshold is less than the second load pressure index threshold. If the load pressure index of the power grid at any sampling moment is less than the first load pressure index threshold, the energy storage device of the power grid is charged; if the load pressure index of the power grid at any sampling moment is greater than the second load pressure index threshold, the energy storage device of the power grid is discharged.

10. A low-frequency and low-voltage energy storage discharge control system, comprising a memory and a processor, characterized in that: The processor executes the computer program stored in the memory to implement a method for controlling the low-frequency and low-voltage energy storage discharge and conversion as described in any one of claims 1 to 9.

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