Net following type distributed energy storage system

Abstract

The invention relates to the technical field of electric power energy storage, and discloses a network following type distributed energy storage system, which comprises an acquisition module, a determination module, a judgment module and a setting module. The acquisition module is used for deploying a plurality of distributed energy storage units, each energy storage unit comprises at least one energy storage device, power data of corresponding power devices are acquired based on the energy storage units, and the power data comprise voltage power data and current power data; the determination module is used for analyzing the power data and determining a power energy storage correlation value between the power devices; the judgment module is used for judging whether energy storage is carried out on all the power equipment or not based on the power energy storage correlation value and the power energy storage correlation threshold value; the setting module is used for setting the next sampling period based on the electric power energy storage value when it is judged that energy storage is not carried out on all the electric power equipment, intelligent distributed energy storage can be achieved, the power grid load can be balanced, the energy utilization efficiency can be improved, and the system operation cost can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage, and in particular to a grid-connected distributed energy storage system. Background Art

[0002] With the transformation of the global energy structure and the increasing maturity of renewable energy technology, the proportion of clean energy such as solar energy and wind energy in the power system has increased year by year. These renewable energy sources have the advantages of environmental protection and sustainability, but their intermittent and unstable nature poses new challenges to the stability, security and reliability of the power grid. In order to effectively utilize these clean energy sources and ensure the efficient and stable operation of the power grid, energy storage systems have become one of the key technologies.

[0003] In the prior art, each energy storage unit usually uses batteries, supercapacitors or flywheels as energy storage media. These units are designed to be modular and can be quickly deployed and expanded according to actual needs. However, this traditional centralized energy storage system usually has a slow response speed and cannot adapt to the rapid changes in grid load in real time. Although the existing distributed energy storage system has been improved, it still has problems such as inflexible scheduling and untimely response. In addition, the existing distributed energy storage system can only determine whether to store energy for one power device, and cannot cooperate with other power devices. It is impossible to determine whether to store energy from all power devices, and the energy storage judgment is relatively simple. Summary of the invention

[0004] The embodiment of the present invention provides a grid-following distributed energy storage system to solve the technical problems in the prior art that it is impossible to realize intelligent power energy storage, balance the grid load, improve energy utilization efficiency, and reduce system operating costs.

[0005] In order to achieve the above object, the present invention provides a grid-connected distributed energy storage system, comprising:

[0006] An acquisition module is used to deploy multiple distributed energy storage units, each of which includes at least one energy storage device, and acquire power data of corresponding power devices based on the energy storage unit, wherein the power data includes voltage power data and current power data;

[0007] A determination module, used to analyze the power data and determine the power storage related values ​​between each power device;

[0008] A judgment module, used for judging whether to store energy for all electric devices based on the electric energy storage related value and the electric energy storage related threshold value;

[0009] The setting module is used to set the next sampling period based on the power storage value when it is determined that energy storage is not performed on all power equipment.

[0010] Furthermore, the acquisition module is used to:

[0011] The acquisition module is used to monitor the voltage and current parameters in the power equipment in real time using at least one sensor device;

[0012] The acquisition module is used to convert the monitored voltage and current parameters into digital signals through an analog-to-digital converter;

[0013] The acquisition module is used to filter the digital signal based on Kalman filtering to remove noise and interference;

[0014] The acquisition module is used to perform a data analysis algorithm on the filtered digital signal to extract key performance indicators of the power system;

[0015] The acquisition module is used to store the key performance indicator data obtained through analysis in a database;

[0016] The acquisition module is used to further screen and integrate the data in the database according to preset conditions and rules to generate voltage power data and current power data.

[0017] Furthermore, the determining module is used to:

[0018] The determination module is used to generate a sub-data packet for the power data corresponding to each power device, and generate a power device sequence A according to all the sub-data packets, wherein A={a1, a2, a3, ..., an}, n is the number of sub-data packets, an={Δbn, Δen}, Δbn is the current power data corresponding to the sub-data packet an, and Δen is the voltage power data corresponding to the sub-data packet an;

[0019] The determination module is used to match each sub-data packet in the power equipment sequence A with the relevant database to obtain a first correlation factor, and calculate and sum to obtain a first correlation factor and value;

[0020] The determination module is used to extract the voltage power data of the sub-data packet a1, extract the current power data of the sub-data packet a2, determine the second correlation factor based on the extracted voltage power data and current power data, sequentially extract the voltage power data and current power data of all remaining adjacent sub-data packets, calculate the corresponding correlation factors, and construct a second correlation factor set based on the calculated correlation factors and the second correlation factors;

[0021] The determination module is used to calculate a comprehensive correlation factor and value based on the second set of correlation factors;

[0022] The determination module is used to calculate the power storage correlation value between each power device according to the first correlation factor and value and the comprehensive correlation factor and value.

[0023] Furthermore, the determining module is used to:

[0024] The determination module is used to calculate the power storage related value between each power device according to the following formula:

[0025] X = y(p1) + y(p2);

[0026] Wherein, X is the power storage correlation value between each power device, y(p1) is a calculation function determined based on the first correlation factor and value, and y(p2) is a calculation function determined based on the comprehensive correlation factor and value.

[0027] Furthermore, the determining module is used to:

[0028] The determination module is used to obtain a preset first preset correlation factor and a preset second preset correlation factor;

[0029] The determining module is used to classify the correlation factors in the second correlation factor set that are less than or equal to the first preset correlation factor into a low correlation factor set;

[0030] The determining module is used to classify the correlation factors in the second correlation factor set that are greater than the first preset correlation factor and less than the second preset correlation factor into a medium correlation factor set;

[0031] The determining module is used to divide the correlation factors in the second correlation factor set that are greater than or equal to the second preset correlation factor into a high correlation factor set;

[0032] The determination module is used to calculate the comprehensive correlation factor and value according to the low correlation factor set, the medium correlation factor set and the high correlation factor set.

[0033] Furthermore, the determining module is used to:

[0034] The determination module is used to count the number of low correlation factors in the low correlation factor set, the number of medium correlation factors in the medium correlation factor set, and the number of high correlation factors in the high correlation factor set;

[0035] The determination module is used to calculate the comprehensive correlation factor and value according to the number of low correlation factors, the number of medium correlation factors and the number of high correlation factors:

[0036] The determination module is used to calculate the comprehensive correlation factor and value according to the following formula:

[0037]

[0038] Among them, G is the comprehensive correlation factor and value, Q1 is the number of low correlation factors, Q2 is the number of medium correlation factors, Q3 is the number of high correlation factors, and h is the optimization coefficient of the comprehensive correlation factor and value.

[0039] Furthermore, the determining module is used to:

[0040] The determination module is used to determine the optimization coefficient h of the comprehensive correlation factor and value according to the following steps:

[0041] The determination module is used to calculate the number and value of the low correlation factors and the high correlation factors;

[0042] The determination module is used to pre-set a first preset optimization coefficient h1, a second preset optimization coefficient h2 and a third preset optimization coefficient h3;

[0043] The determination module is used for, when identifying the first preset condition, using the first preset optimization coefficient h1 as the optimization coefficient of the comprehensive correlation factor and value, that is, h=h1;

[0044] The determination module is used for, when identifying the second preset condition, using the second preset optimization coefficient h2 as the optimization coefficient of the comprehensive correlation factor and value, that is, h=h2;

[0045] The determination module is used to use the third preset optimization coefficient h3 as the optimization coefficient of the comprehensive correlation factor and value when the third preset condition is identified, that is, h=h3; wherein,

[0046] The first preset condition is that the number of the correlation factors is less than the number and value, the second preset condition is that the number of the correlation factors is equal to the number and value, and the third preset condition is that the number of the correlation factors is greater than the number and value.

[0047] Furthermore, the judging module is used for:

[0048] The judgment module is used to judge not to store energy for all power equipment when the power storage related value is less than the power storage related threshold;

[0049] The judgment module is used to judge whether to store energy for all power equipment when the power storage related value is greater than or equal to the power storage related threshold.

[0050] Furthermore, the setting module is used to:

[0051] Acquire the application direction of each electric device, and acquire the first weight of the electric device according to the application direction-weight mapping table;

[0052] Obtaining a second weight of the electric equipment based on an electric energy storage related value-weight mapping table;

[0053] Setting a next sampling period based on the first weight and the second weight;

[0054] Set the next sampling period according to the following formula:

[0055]

[0056] Where T is the next sampling period, m is the number of power devices, r1 j 2 is the first weight of the jth power device, r2j 2 is the second weight of the j-th power device.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention discloses a distributed energy storage system with a grid connection, including: an acquisition module, a determination module, a judgment module and a setting module. The acquisition module is used to deploy multiple distributed energy storage units, each of which includes at least one energy storage device, and acquires power data of corresponding power equipment based on the energy storage unit, wherein the power data includes voltage power data and current power data; the determination module is used to analyze the power data and determine the power storage related values ​​between each power equipment; the judgment module is used to determine whether to store energy for all power equipment based on the power storage related values ​​and the power storage related thresholds; the setting module is used to set the next sampling period based on the power storage value when it is determined that energy is not stored for all power equipment. The present invention can realize intelligent distributed energy storage, balance the load of the power grid, improve energy utilization efficiency, and reduce system operation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0060] Figure 1 A schematic structural diagram of a grid-connected distributed energy storage system in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0061] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0062] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0063] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0064] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0065] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0066] like Figure 1 As shown, an embodiment of the present invention discloses a grid-following distributed energy storage system, comprising:

[0067] An acquisition module is used to deploy multiple distributed energy storage units, each of which includes at least one energy storage device, and acquire power data of corresponding power devices based on the energy storage unit, wherein the power data includes voltage power data and current power data;

[0068] A determination module, used to analyze the power data and determine the power storage related values ​​between each power device;

[0069] A judgment module, used for judging whether to store energy for all electric devices based on the electric energy storage related value and the electric energy storage related threshold value;

[0070] The setting module is used to set the next sampling period based on the power storage value when it is determined that energy storage is not performed on all power equipment.

[0071] The beneficial effects of the above technical solution are: the present invention can realize intelligent distributed energy storage, balance the grid load, improve energy utilization efficiency, and reduce system operating costs.

[0072] In some embodiments of the present application, the acquisition module is used to:

[0073] The acquisition module is used to monitor the voltage and current parameters in the power equipment in real time using at least one sensor device;

[0074] The acquisition module is used to convert the monitored voltage and current parameters into digital signals through an analog-to-digital converter;

[0075] The acquisition module is used to filter the digital signal based on Kalman filtering to remove noise and interference;

[0076] The acquisition module is used to perform a data analysis algorithm on the filtered digital signal to extract key performance indicators of the power system;

[0077] The acquisition module is used to store the key performance indicator data obtained through analysis in a database;

[0078] The acquisition module is used to further screen and integrate the data in the database according to preset conditions and rules to generate voltage power data and current power data.

[0079] In this embodiment, the sensor device includes a voltage sensor and a current sensor.

[0080] The beneficial effect of the above technical solution is that the present invention further screens and integrates the data in the database according to preset conditions and rules to generate voltage power data and current power data, which can lay the foundation for distributed energy storage of all power equipment.

[0081] In some embodiments of the present application, the determining module is used to:

[0082] The determination module is used to generate a sub-data packet for the power data corresponding to each power device, and generate a power device sequence A according to all the sub-data packets, wherein A={a1, a2, a3, ..., an}, n is the number of sub-data packets, an={Δbn, Δen}, Δbn is the current power data corresponding to the sub-data packet an, and Δen is the voltage power data corresponding to the sub-data packet an;

[0083] The determination module is used to match each sub-data packet in the power equipment sequence A with the relevant database to obtain a first correlation factor, and calculate and sum to obtain a first correlation factor and value;

[0084] The determination module is used to extract the voltage power data of the sub-data packet a1, extract the current power data of the sub-data packet a2, determine the second correlation factor based on the extracted voltage power data and current power data, sequentially extract the voltage power data and current power data of all remaining adjacent sub-data packets, calculate the corresponding correlation factors, and construct a second correlation factor set based on the calculated correlation factors and the second correlation factors;

[0085] The determination module is used to calculate a comprehensive correlation factor and value based on the second set of correlation factors;

[0086] The determination module is used to calculate the power storage correlation value between each power device according to the first correlation factor and value and the comprehensive correlation factor and value.

[0087] In this embodiment, each power device has a corresponding sub-data packet, and each sub-data packet includes voltage power data and current power data.

[0088] In this embodiment, each sub-data packet corresponds to a first correlation factor.

[0089] In this embodiment, each voltage power data corresponds to a correlation factor, and each current power data also corresponds to a correlation factor, and the sum of these two correlation factors is used as the second correlation factor.

[0090] The beneficial effect of the above technical solution is that the present invention calculates the power storage correlation value between each power device according to the first correlation factor and value and the comprehensive correlation factor and value, thereby laying the foundation for distributed energy storage and avoiding the singleness of energy storage methods existing in the prior art.

[0091] In some embodiments of the present application, the determining module is used to:

[0092] The determination module is used to calculate the power storage related value between each power device according to the following formula:

[0093] X = y(p1) + y(p2);

[0094] Wherein, X is the power storage correlation value between each power device, y(p1) is a calculation function determined based on the first correlation factor and value, and y(p2) is a calculation function determined based on the comprehensive correlation factor and value.

[0095] In some embodiments of the present application, the determining module is used to:

[0096] The determination module is used to obtain a preset first preset correlation factor and a preset second preset correlation factor;

[0097] The determining module is used to classify the correlation factors in the second correlation factor set that are less than or equal to the first preset correlation factor into a low correlation factor set;

[0098] The determining module is used to classify the correlation factors in the second correlation factor set that are greater than the first preset correlation factor and less than the second preset correlation factor into a medium correlation factor set;

[0099] The determining module is used to divide the correlation factors in the second correlation factor set that are greater than or equal to the second preset correlation factor into a high correlation factor set;

[0100] The determination module is used to calculate the comprehensive correlation factor and value according to the low correlation factor set, the medium correlation factor set and the high correlation factor set.

[0101] In this embodiment, the first preset correlation factor is smaller than the second preset correlation factor.

[0102] The beneficial effect of the above technical solution is that the present invention calculates comprehensive correlation factors and values ​​according to a low correlation factor set, a medium correlation factor set and a high correlation factor set, thereby providing data support for the calculation of power storage related values, ensuring data calculation accuracy and avoiding large errors.

[0103] In some embodiments of the present application, the determining module is used to:

[0104] The determination module is used to count the number of low correlation factors in the low correlation factor set, the number of medium correlation factors in the medium correlation factor set, and the number of high correlation factors in the high correlation factor set;

[0105] The determination module is used to calculate the comprehensive correlation factor and value according to the number of low correlation factors, the number of medium correlation factors and the number of high correlation factors:

[0106] The determination module is used to calculate the comprehensive correlation factor and value according to the following formula:

[0107]

[0108] Among them, G is the comprehensive correlation factor and value, Q1 is the number of low correlation factors, Q2 is the number of medium correlation factors, Q3 is the number of high correlation factors, and h is the optimization coefficient of the comprehensive correlation factor and value.

[0109] In some embodiments of the present application, the determining module is used to:

[0110] The determination module is used to determine the optimization coefficient h of the comprehensive correlation factor and value according to the following steps:

[0111] The determination module is used to calculate the number and value of the low correlation factors and the high correlation factors;

[0112] The determination module is used to pre-set a first preset optimization coefficient h1, a second preset optimization coefficient h2 and a third preset optimization coefficient h3;

[0113] The determination module is used for, when identifying the first preset condition, using the first preset optimization coefficient h1 as the optimization coefficient of the comprehensive correlation factor and value, that is, h=h1;

[0114] The determination module is used for, when identifying the second preset condition, using the second preset optimization coefficient h2 as the optimization coefficient of the comprehensive correlation factor and value, that is, h=h2;

[0115] The determination module is used to use the third preset optimization coefficient h3 as the optimization coefficient of the comprehensive correlation factor and value when the third preset condition is identified, that is, h=h3; wherein,

[0116] The first preset condition is that the number of the correlation factors is less than the number and value, the second preset condition is that the number of the correlation factors is equal to the number and value, and the third preset condition is that the number of the correlation factors is greater than the number and value.

[0117] In this embodiment, 0.8<first preset optimization coefficient h1<second preset optimization coefficient h2<third preset optimization coefficient h3<1.2;

[0118] The beneficial effect of the above technical solution is that the present invention selects the corresponding optimization coefficient according to the relationship between the number and value of the correlation factors, thereby realizing the dynamic adjustment of the comprehensive correlation factors and values, and further ensuring the calculation accuracy.

[0119] In some embodiments of the present application, the judgment module is used to:

[0120] The judgment module is used to judge not to store energy for all power equipment when the power storage related value is less than the power storage related threshold;

[0121] The judgment module is used to judge whether to store energy for all power equipment when the power storage related value is greater than or equal to the power storage related threshold.

[0122] The beneficial effect of the above technical solution is that the present invention determines whether to store energy for all power equipment based on power storage related values ​​and power storage related thresholds, thereby achieving accurate judgment and avoiding the singleness of distributed energy storage.

[0123] In some embodiments of the present application, an application direction of each power device is obtained, and a first weight of the power device is obtained according to an application direction-weight mapping table;

[0124] Obtaining a second weight of the electric equipment based on an electric energy storage related value-weight mapping table;

[0125] Setting a next sampling period based on the first weight and the second weight;

[0126] Set the next sampling period according to the following formula:

[0127]

[0128] Where T is the next sampling period, m is the number of power devices, r1 j 2 is the first weight of the jth power device, r2j 2 is the second weight of the j-th power device.

[0129] In this embodiment, the application directions include high-voltage transmission lines, transformers, circuit breakers, distribution boxes, switches, protection devices, etc.

[0130] In this embodiment, the application direction-weight mapping table and the power storage related value-weight mapping table are preset based on historical data.

[0131] The beneficial effect of the above technical solution is that the present invention sets the next sampling period based on the first weight and the second weight, which can ensure the timeliness and real-time performance of the distributed energy storage of the power equipment and avoid large delays.

[0132] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0133] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0134] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A grid-connected distributed energy storage system, characterized in that: include: An acquisition module is used to deploy multiple distributed energy storage units, each of which includes at least one energy storage device, and acquire power data of corresponding power devices based on the energy storage unit, wherein the power data includes voltage power data and current power data; A determination module, used to analyze the power data and determine the power storage related values ​​between each power device; A judgment module, used for judging whether to store energy for all electric devices based on the electric energy storage related value and the electric energy storage related threshold value; The setting module is used to set the next sampling period based on the power storage value when it is determined that energy storage is not performed on all power equipment.

2. The grid-connected distributed energy storage system according to claim 1, characterized in that: The acquisition module is used for: The acquisition module is used to monitor the voltage and current parameters in the power equipment in real time using at least one sensor device; The acquisition module is used to convert the monitored voltage and current parameters into digital signals through an analog-to-digital converter; The acquisition module is used to filter the digital signal based on Kalman filtering to remove noise and interference; The acquisition module is used to perform a data analysis algorithm on the filtered digital signal to extract key performance indicators of the power system; The acquisition module is used to store the key performance indicator data obtained through analysis in a database; The acquisition module is used to further screen and integrate the data in the database according to preset conditions and rules to generate voltage power data and current power data.

3. The grid-connected distributed energy storage system according to claim 1, characterized in that: The determination module is used for: The determination module is used to generate a sub-data packet for the power data corresponding to each power device, and generate a power device sequence A according to all the sub-data packets, wherein A={a1, a2, a3, ..., an}, n is the number of sub-data packets, an={Δbn, Δen}, Δbn is the current power data corresponding to the sub-data packet an, and Δen is the voltage power data corresponding to the sub-data packet an; The determination module is used to match each sub-data packet in the power equipment sequence A with the relevant database to obtain a first correlation factor, and calculate and sum to obtain a first correlation factor and value; The determination module is used to extract the voltage power data of the sub-data packet a1, extract the current power data of the sub-data packet a2, determine the second correlation factor based on the extracted voltage power data and current power data, sequentially extract the voltage power data and current power data of all remaining adjacent sub-data packets, calculate the corresponding correlation factors, and construct a second correlation factor set based on the calculated correlation factors and the second correlation factors; The determination module is used to calculate a comprehensive correlation factor and value based on the second set of correlation factors; The determination module is used to calculate the power storage correlation value between each power device according to the first correlation factor and value and the comprehensive correlation factor and value.

4. The grid-connected distributed energy storage system according to claim 3 is characterized in that: The determination module is used for: The determination module is used to calculate the power storage related value between each power device according to the following formula: X = y(p1) + y(p2); Wherein, X is the power storage correlation value between each power device, y(p1) is a calculation function determined based on the first correlation factor and value, and y(p2) is a calculation function determined based on the comprehensive correlation factor and value.

5. The grid-connected distributed energy storage system according to claim 3, characterized in that: The determination module is used for: The determination module is used to obtain a preset first preset correlation factor and a preset second preset correlation factor; The determining module is used to classify the correlation factors in the second correlation factor set that are less than or equal to the first preset correlation factor into a low correlation factor set; The determining module is used to classify the correlation factors in the second correlation factor set that are greater than the first preset correlation factor and less than the second preset correlation factor into a medium correlation factor set; The determining module is used to divide the correlation factors in the second correlation factor set that are greater than or equal to the second preset correlation factor into a high correlation factor set; The determination module is used to calculate the comprehensive correlation factor and value according to the low correlation factor set, the medium correlation factor set and the high correlation factor set.

6. The grid-connected distributed energy storage system according to claim 5, characterized in that: The determination module is used for: The determination module is used to count the number of low correlation factors in the low correlation factor set, the number of medium correlation factors in the medium correlation factor set, and the number of high correlation factors in the high correlation factor set; The determination module is used to calculate the comprehensive correlation factor and value according to the number of low correlation factors, the number of medium correlation factors and the number of high correlation factors: The determination module is used to calculate the comprehensive correlation factor and value according to the following formula: Among them, G is the comprehensive correlation factor and value, Q1 is the number of low correlation factors, Q2 is the number of medium correlation factors, Q3 is the number of high correlation factors, and h is the optimization coefficient of the comprehensive correlation factor and value.

7. The grid-connected distributed energy storage system according to claim 6, characterized in that: The determination module is used for: The determination module is used to determine the optimization coefficient h of the comprehensive correlation factor and value according to the following steps: The determination module is used to calculate the number and value of the low correlation factors and the high correlation factors; The determination module is used to pre-set a first preset optimization coefficient h1, a second preset optimization coefficient h2 and a third preset optimization coefficient h3; The determination module is used for, when identifying the first preset condition, using the first preset optimization coefficient h1 as the optimization coefficient of the comprehensive correlation factor and value, that is, h=h1; The determination module is used for, when identifying the second preset condition, using the second preset optimization coefficient h2 as the optimization coefficient of the comprehensive correlation factor and value, that is, h=h2; The determination module is used to use the third preset optimization coefficient h3 as the optimization coefficient of the comprehensive correlation factor and value when the third preset condition is identified, that is, h=h3; wherein, The first preset condition is that the number of the correlation factors is less than the number and value, the second preset condition is that the number of the correlation factors is equal to the number and value, and the third preset condition is that the number of the correlation factors is greater than the number and value.

8. The grid-connected distributed energy storage system according to claim 1, characterized in that: The judging module is used for: The judgment module is used to judge not to store energy for all power equipment when the power storage related value is less than the power storage related threshold; The judgment module is used to judge whether to store energy for all power equipment when the power storage related value is greater than or equal to the power storage related threshold.

9. The grid-connected distributed energy storage system according to claim 1, characterized in that: The setting module is used for: Acquire the application direction of each electric device, and acquire the first weight of the electric device according to the application direction-weight mapping table; Obtaining a second weight of the electric equipment based on an electric energy storage related value-weight mapping table; Setting a next sampling period based on the first weight and the second weight; Set the next sampling period according to the following formula: Where T is the next sampling period, m is the number of power devices, r1 j 2 is the first weight of the jth power device, r2j 2 is the second weight of the j-th power device.

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