Distributed household energy storage all-in-one machine charging and discharging management control system

Abstract

The invention discloses a distributed household energy storage all-in-one machine charging and discharging management control system, relates to the technical field of energy storage control, and is used for solving the problem of resource waste caused by the fact that each charging and discharging area cannot be dynamically adjusted. Establishing a data analysis model according to the distance difference of all equipment in the power supply area and the maximum efficiency of the battery charging energy efficiency, obtaining an area evaluation coefficient, performing comparative analysis on the area evaluation coefficient and a preset area threshold value, determining an adjustment result of each area, and determining the adjustment result of each area according to the power supply coverage area of each current battery energy storage module and the initialized power supply area area. And according to the overlapping area of the adjacent power supply areas of the vacancy range area and the power supply area which can be covered by the redundancy capability of the adjacent power supply areas, a group of fuzzy rules is formulated, a vacancy range supplementing result is obtained, and the power supply balance of the household areas is improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage control technology, and more specifically, to a distributed household energy storage integrated machine charging and discharging management and control system. Background Art

[0002] With the rapid development of renewable energy and the popularization of energy Internet technology, home energy storage systems have played an important role in improving energy efficiency, optimizing electricity costs and supporting the development of green energy. Especially in the distributed energy access scenario, home energy storage devices can provide reliable power guarantee for home users by combining energy storage equipment with distributed energy (such as photovoltaic power generation).

[0003] The prior art has the following deficiencies: At present, the charging and discharging strategies of traditional home energy storage systems are often fixed modes, usually simple schedule control or manual operation. For complex power usage scenarios, even if the charging and discharging plans can be adjusted dynamically, it is impossible to dynamically adjust the charging and discharging areas, resulting in resource waste, increasing battery overuse rate, and shortening the service life of energy storage batteries. Therefore, a distributed home energy storage integrated charging and discharging management and control system is proposed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a distributed household energy storage integrated machine charging and discharging management and control system, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0006] To achieve the above object, the present invention provides the following technical solution: a distributed household energy storage integrated machine charge and discharge management and control system, including an energy storage acquisition module, a data processing module, a regional change module and a vacancy supplement module; signal connections between the modules; The energy storage acquisition module is used to collect the characteristic information of the power supply area of ​​the energy storage module and the charging characteristic information of the energy storage module. Through normalization processing, the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area and the maximum efficiency of battery charging energy efficiency are obtained and sent to the data processing module; The data processing module is used to obtain the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency, and establish a data analysis model to obtain the regional evaluation coefficient and send it to the regional change module; The regional change module is used to obtain the regional assessment coefficient and compare and analyze it with the preset regional threshold. According to the comparison results, the adjustment results of each region are determined. According to the current power supply coverage area of ​​each battery energy storage module and the initial power supply area, the addition and subtraction calculations of the corresponding areas are performed to obtain the vacant area of ​​the household equipment and send it to the vacancy supplement module; The vacancy supplement module is used to obtain the vacancy range area of ​​household equipment, collect the overlapping area of ​​adjacent power supply areas of the vacancy range area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area, and substitute it into fuzzy logic for fuzzy reasoning to obtain the vacancy range supplement result.

[0007] In a preferred embodiment, the energy storage module power supply area characteristic information includes the power supply area adjustment frequency, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, and the distance difference of all equipment in the power supply area; the energy storage module charging characteristic information includes the maximum efficiency of battery charging energy efficiency; Through the preset timestamp length, according to the regional adjustment times corresponding to the battery energy storage module in the timestamp, the ratio of the regional adjustment times to the timestamp length is calculated to obtain the power supply area adjustment frequency. ; Where i is the i-th timestamp; The difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation is obtained by calculating the sum of the peak power demands of all devices in the current timestamp and subtracting the sum of the possible power generation of distributed energy calculated by the preset prediction model within the current timestamp length. ; By substituting the geometric positions of all devices and the battery energy storage module in the power supply area of ​​the battery energy storage module at the current timestamp into the distance formula, the distance deviation from each device to the battery energy storage module is obtained and summed up to obtain the distance difference of all devices in the power supply area. ; The battery energy conversion efficiency is obtained by the ratio of the effective energy stored in the battery to the input electrical energy during the charging process. Then, multiple instantaneous battery energy conversion efficiencies are obtained according to the set timestamp, and the energy efficiency curve with charging current, voltage, and temperature is plotted. The maximum value is selected as the maximum battery charging energy efficiency. .

[0008] In a preferred embodiment, the power supply area adjustment frequency, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency are obtained to establish a data analysis model and generate a regional evaluation coefficient. , based on the formula: ; In the formula, is the regional assessment coefficient, , , as well as Adjust frequency for each power supply area , the difference between the maximum power consumption of equipment in the power supply area and the predicted power generation , the distance between all devices in the power supply area is poor And the maximum efficiency of battery charging energy efficiency The preset scaling factor of , , as well as Both are greater than 0.

[0009] In a preferred embodiment, after obtaining the regional evaluation coefficient, the regional evaluation coefficient is compared and analyzed with the continuously iterated regional threshold value; If the regional evaluation coefficient is greater than or equal to the regional threshold, the power supply area corresponding to the current battery energy storage module is marked as a reduced area, and a reduction signal is generated; If the regional evaluation coefficient is less than the regional threshold, the power supply area corresponding to the current battery energy storage module is marked as regional increase, and an increase signal is generated.

[0010] In a preferred embodiment, the ratio of the regional assessment coefficient greater than or equal to the regional threshold is calculated with the regional threshold to determine how many multiples of the regional threshold the regional assessment coefficient is, and the range value marked as the region reduction is set according to the multiples of the regional threshold. Conversely, the regional threshold is calculated with the regional assessment coefficient less than the regional threshold to determine how many multiples of the regional threshold the regional assessment coefficient is, and the range marked as the region increase is set according to the multiples of the regional assessment coefficient.

[0011] In a preferred embodiment, the adjustment results of the power supply areas corresponding to all battery energy storage modules are obtained according to the reduced and increased range values, and the power supply coverage area of ​​each battery energy storage module is obtained by mathematical area calculation; Perform mathematical area calculation based on the initial power supply area set by the household energy storage device to obtain the area of ​​the initial power supply area; The initialization power supply area and the current power supply coverage area of ​​each battery energy storage module are numerically calculated. If the current power supply coverage area of ​​each battery energy storage module is greater than or equal to the initialization power supply area, it means that the current household energy supply is met and an end signal is generated. Otherwise, a subtraction calculation is performed to determine the vacant area of ​​household equipment.

[0012] In a preferred embodiment, the power supply area adjacent to the household equipment vacancy range area is determined by the household equipment vacancy range area; Using the smart home map, the geometric boundaries of the vacant area are clarified to form a polygonal or rectangular area, and the boundary geometric areas of all adjacent power supply areas are obtained. For each adjacent power supply area, the intersection area with the corresponding adjacent power supply area is calculated, and the intersection area is statistically calculated to obtain the overlapping area of ​​the vacant area and the adjacent power supply area; By obtaining the current output capacity of the energy storage module and calculating the total according to the real-time power consumption of all equipment in the power supply area, the current output capacity is subtracted from the current power consumption sum to obtain the regional power supply capacity redundancy value, and the regional power supply capacity redundancy value is calculated by ratio with the power demand density of the vacant area to obtain the power supply area that can be covered by the redundant capacity of the adjacent power supply area.

[0013] In a preferred embodiment, the overlapping area of ​​the vacant range area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area are defined as input variables, and they are divided into different fuzzy sets respectively; The result of filling the vacancy range is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of the overlapping area of ​​adjacent power supply areas in the vacant area and the power supply area that can be covered by the redundant capacity of adjacent power supply areas on the results of filling the vacant area; Perform fuzzy reasoning based on fuzzy rules to determine the solution for filling the vacancy scope.

[0014] Technical effects and advantages of the present invention: 1. The present invention establishes a data analysis model by collecting the power supply area adjustment frequency, the difference between the maximum power consumption of the power supply area equipment and the predicted power generation, the distance difference of all equipment in the power supply area and the maximum efficiency of battery charging energy efficiency, obtains the regional evaluation coefficient, and compares and analyzes it with the preset regional threshold. According to the comparison result, the adjustment result of each area is determined, and according to the current power supply coverage area of ​​each battery energy storage module and the initialization power supply area, the addition and subtraction calculations of the corresponding areas are performed to obtain the vacant range area of ​​the household equipment, and dynamic adjustments are made to each charging and discharging area to reduce resource waste, reduce the battery overuse rate, and increase the service life of the energy storage battery.

[0015] 2. The present invention obtains the vacant range area of ​​household equipment, collects the overlapping area of ​​the vacant range area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area, formulates a set of fuzzy rules for fuzzy reasoning, and obtains the vacant range supplement result, thereby improving the power supply balance of the household area and improving the reliability and stability of the system power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a module schematic diagram of a distributed household energy storage integrated machine charging and discharging management and control system of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The present invention discloses a distributed household energy storage integrated machine charge and discharge management and control system, which adopts distributed loading for energy storage points for collecting electric energy. For example, energy storage battery modules are usually installed near photovoltaic panels placed on the roof to quickly complete the collection of electric energy and release to a designated area, etc. Specifically, the household energy storage integrated machine is placed in the area where the family lives, and serves as a control center to control the charge and discharge strategies of distributed energy storage battery modules distributed around the family and the dynamic adjustment of the discharge area. The present invention collects characteristic information of the power supply area of ​​the energy storage module and the charging characteristic information of the energy storage module, analyzes the relationship between the power supply area of ​​the energy storage module and the charging energy efficiency according to the power supply area adjustment frequency, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area and the maximum efficiency of the battery charging energy efficiency, obtains the dynamic range trend of each energy supply area, and then sets the power replenishment mechanism to determine the power replenishment of the vacant area; Embodiment 1: The present invention discloses a distributed household energy storage integrated device charging and discharging management and control system, such as Figure 1 As shown, it includes energy storage acquisition module, data processing module, area change module and vacancy supplement module; signal connection between each module; The energy storage acquisition module is used to collect the characteristic information of the power supply area of ​​the energy storage module and the charging characteristic information of the energy storage module. Through normalization processing, the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area and the maximum efficiency of battery charging energy efficiency are obtained and sent to the data processing module; Among them, normalization is a common method of data preprocessing, which is used to convert data of different scales or ranges to a unified scale, so that the impact of each feature on subsequent analysis or model training is more balanced. In the analysis of electronic scale data, normalization helps to eliminate the dimensional differences between different features and ensure that each feature contributes to the model in a balanced manner; Specifically, the normalization method is through Z-score standardization, and the specific formula is expressed as:

[0019] In the formula, is the normalized original data, is the original data, is the mean of the original data, is the standard deviation of the original data; The original data includes the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency, and is sent to the data processing module for normalization processing using the above formula, which will not be elaborated here; Specifically, the energy storage module power supply area characteristic information includes the power supply area adjustment frequency, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, and the distance difference of all equipment in the power supply area; the energy storage module charging characteristic information includes the maximum efficiency of battery charging energy efficiency; The power supply area adjustment frequency refers to the initial power supply area set by the battery energy storage module through the home energy storage integrated machine. As time, battery charging efficiency and equipment in the initial power supply area change, the frequency of regional adjustment corresponding to the battery energy storage module is obtained by calculating the ratio of the regional adjustment number to the timestamp length according to the number of regional adjustments corresponding to the battery energy storage module in the timestamp through the pre-set timestamp length. ; Where i is the i-th timestamp; Among them, the pre-set timestamp length and the dynamic changes of adjacent timestamps are set by the experimenters according to the average daily energy storage capacity and the average daily power supply of the energy storage battery module, and will not be elaborated here; Specifically, the household energy storage integrated machine determines the regional adjustment corresponding to the current battery energy storage module according to the user's preset threshold or the result of machine learning, and records the number of regional adjustments. Therefore, the energy storage acquisition module obtains the number of regional adjustments corresponding to the battery energy storage module through the recorded information of the household energy storage integrated machine; The difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation refers to the difference between the maximum power consumption of all devices in the timestamp in the area corresponding to the current battery energy storage module and the power generation predicted within the current timestamp length. The acquisition logic is to calculate the sum of the peak power demands of all devices in the power supply area at the current timestamp minus the sum of the possible power generation of distributed energy calculated by the preset prediction model within the current timestamp length, and obtain the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation. ; Among them, the preset prediction model refers to a general machine learning model, which can be an LSTM model or a random forest model to improve the accuracy of power generation prediction. The experimenter can screen the model according to the characteristics of the collected data. The specific prediction model screened is not limited, but is obtained by the experimenter according to the specific implementation situation, which will not be elaborated here; The distance difference of all devices in the power supply area refers to the total distance difference between all devices in the power supply area and the current battery energy storage module at the current timestamp. The acquisition logic is to substitute the geometric positions of all devices and the battery energy storage module in the power supply area of ​​the battery energy storage module at the current timestamp into the distance formula to obtain the distance deviation from each device to the battery energy storage module and sum them up to obtain the distance difference of all devices in the power supply area. ; Specifically, let the set of devices in the power supply area be , let the battery energy storage module position at the current timestamp be recorded as S; Among them, the distance deviation formula from each device to the battery energy storage module is obtained as follows:

[0020] In the formula, is the distance deviation from the jth device to the battery energy storage module, , as well as are the position points of the jth device in three-dimensional coordinates, , as well as They are the location points of the battery energy storage module on the three-dimensional coordinates; The distance deviation from each device to the battery energy storage module is summed to obtain the distance difference of all devices in the power supply area. The specific formula is as follows:

[0021] In the formula, The distance difference between all devices in the power supply area; It should be noted that the distance expression between the device and the battery energy storage module also includes the electrical distance, which is obtained by the wire length and the resistance per unit length. In this embodiment, the geometric distance expression can reflect the mobility of the device and pave the way for the analysis of subsequent inter-regional adjustments; the electrical distance is more suitable for devices directly connected to the battery energy storage module, and can more accurately analyze the excess energy consumed by the transmission of electric energy. In this regard, the calculation method of the distance difference of all devices in the power supply area is not specifically limited, but is implemented by the experimenters according to actual applications. For example, when the device is directly connected to the battery energy storage module in the power supply area, the power distance calculation is performed, etc., which will not be repeated here; The maximum efficiency of battery charging energy efficiency refers to the maximum value of the energy conversion efficiency of the battery system in the battery energy storage module during the charging process within the set timestamp. The acquisition logic is to obtain the battery energy conversion efficiency through the ratio of the effective energy stored in the battery to the input electrical energy during the charging process, and then obtain multiple instantaneous battery energy conversion efficiencies within the set timestamp, and draw a curve of energy efficiency changing with charging current, voltage, and temperature, and select the maximum value as the maximum efficiency of battery charging energy efficiency. ; Among them, the calculation formula for battery energy conversion efficiency is as follows:

[0022] In the formula, is the battery energy conversion efficiency, is the energy actually stored in the battery, is the instantaneous current measured at present, is the instantaneous voltage currently measured; Specifically, the drawn curves are different in each timestamp. Since factors such as battery operation time and battery health status score will lead to relative differences in battery energy conversion efficiency, the current battery charging status and photovoltaic panel operation status can be analyzed in the drawing, which will not be elaborated here. The data processing module is used to obtain the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency, and establish a data analysis model to obtain the regional evaluation coefficient and send it to the regional change module; Among them, the data analysis model refers to a weighted analysis model, which generates regional assessment coefficients through weighted calculation; Obtain the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency to establish a data analysis model and generate a regional evaluation coefficient , based on the formula:

[0023] In the formula, is the regional assessment coefficient, , , as well as Adjust frequency for each power supply area , the difference between the maximum power consumption of equipment in the power supply area and the predicted power generation , the distance between all devices in the power supply area is poor And the maximum efficiency of battery charging energy efficiency The preset scaling factor of , , as well as All are greater than 0; Among them, the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency are all data that directly express whether the power supply area corresponding to the current battery energy storage module is adjusted; It can be seen from the formula that when the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation is greater than 0, the higher the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, and the distance difference of all equipment in the power supply area are, it means that the power supply area corresponding to the current battery energy storage module needs to be reduced, and the regional evaluation coefficient is higher. Conversely, when the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation is less than 0, the higher the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation and the maximum efficiency of the battery charging energy efficiency are, it means that the power supply area corresponding to the current battery energy storage module needs to be expanded, and the regional evaluation coefficient is lower. The regional change module is used to obtain the regional assessment coefficient and compare and analyze it with the preset regional threshold. According to the comparison results, the adjustment results of each region are determined. According to the current power supply coverage area of ​​each battery energy storage module and the initial power supply area, the addition and subtraction calculations of the corresponding areas are performed to obtain the vacant area of ​​the household equipment and send it to the vacancy supplement module; The logic for obtaining the regional threshold is to collect the power supply area adjustment set corresponding to the historical battery energy storage module, divide the data set into a training set and a test set, set the evaluation index and clustering algorithm, and in each round of cross-validation, train the model on the training set and evaluate the model performance on the test set. Then, adjust the regional threshold according to the performance of the validation set. Therefore, the regional threshold is constantly updated. In the present invention, clustering algorithm is a kind of unsupervised learning algorithm, which is used to divide the adjustment range values ​​in the data set into marked groups or clusters; a common one is K-means clustering, which divides the weighing data in the data set into K clusters, so that the distance between each adjustment range value and the center point (center of mass) of the cluster to which it belongs is minimized, and finally the difference between the adjustment range values ​​is measured by the Euclidean distance, so as to set the regional threshold; After obtaining the regional assessment coefficient, the regional assessment coefficient is compared and analyzed with the continuously iterated regional threshold; If the regional evaluation coefficient is greater than or equal to the regional threshold, the power supply area corresponding to the current battery energy storage module is marked as a reduced area, and a reduction signal is generated; If the regional evaluation coefficient is less than the regional threshold, the power supply area corresponding to the current battery energy storage module is marked as regional increase, and an increase signal is generated; Among them, the specific increase or decrease range value of the power supply area corresponding to the battery energy storage module marked as area reduction and area increase can be set according to the specific value of the area evaluation coefficient; The regional evaluation coefficient greater than or equal to the regional threshold is calculated by ratio with the regional threshold, and the regional evaluation coefficient is determined to be a multiple of the regional threshold, and the range value marked as the region reduction is set according to the multiple of the regional threshold; conversely, the regional threshold is calculated by ratio with the regional evaluation coefficient less than the regional threshold, and the regional threshold is determined to be a multiple of the regional evaluation coefficient, and the range value marked as the region increase is set according to the multiple of the regional evaluation coefficient; For example, according to the ratio calculation of the regional assessment coefficient greater than or equal to the regional threshold and the regional threshold, the regional assessment coefficient is obtained to be 1.5 times the regional threshold, then the power supply area corresponding to the battery energy storage module is reduced by 1.5 times or 0.75 times the current area. The specific reduction or increase range is set by the experimenter according to the power supply area corresponding to the specific battery energy storage module, which is not described here; According to the reduced and increased range values, the adjustment results of the power supply areas corresponding to all battery energy storage modules are obtained, and the current power supply coverage area of ​​each battery energy storage module is obtained through mathematical area calculation; Perform mathematical area calculation based on the initial power supply area set by the household energy storage device to obtain the area of ​​the initial power supply area; It should be noted that the number of initialized power supply areas is always a fixed value and is consistent with the number of power supply areas corresponding to all battery energy storage modules; Numerical calculation is performed on the initialization power supply area and the current power supply coverage area of ​​each battery energy storage module. If the current power supply coverage area of ​​each battery energy storage module is greater than or equal to the initialization power supply area, it means that the current household energy supply is satisfied and an end signal is generated. Otherwise, a subtraction calculation is performed to determine the vacant area of ​​household equipment; The present invention establishes a data analysis model by collecting the power supply area adjustment frequency, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all the equipment in the power supply area and the maximum efficiency of the battery charging energy efficiency, obtains the regional evaluation coefficient, and compares and analyzes with the preset regional threshold. According to the comparison result, the adjustment result of each area is determined, and according to the current power supply coverage area of ​​each battery energy storage module and the initialization power supply area, the addition and subtraction calculations of the corresponding areas are performed to obtain the vacant range area of ​​the household equipment, and each charging and discharging area is dynamically adjusted to reduce resource waste, reduce the battery overuse rate, and increase the service life of the energy storage battery.

[0024] Embodiment 2: Embodiment 1 of the present invention focuses on examples of collecting the power supply area adjustment frequency, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area and the maximum efficiency of the battery charging energy efficiency, establishing a data analysis model, obtaining the regional evaluation coefficient, and comparing and analyzing it with the preset regional threshold. According to the comparison result, the adjustment result of each area is determined, and according to the current power supply coverage area of ​​each battery energy storage module and the initialization power supply area, the addition and subtraction calculation of the corresponding areas are performed to obtain the operation strategy of the vacant range area of ​​the household equipment; however, in Embodiment 1, only the power supply coverage area of ​​the battery energy storage module is analyzed, and the situation of the vacant power supply area is not taken into account. Obviously, this will cause the electrical equipment in some areas to be unable to obtain effective power supply, reducing the reliability and stability of the system power supply; in view of the above problems, Embodiment 2 of the present invention is further refined; The vacancy supplement module is used to obtain the vacancy range area of ​​household equipment, collect the overlapping area of ​​the adjacent power supply area of ​​the vacancy range area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area, and substitute it into the fuzzy logic for fuzzy reasoning to obtain the vacancy range supplement result; Specifically, the power supply area adjacent to the vacant area of ​​the household equipment is determined by the vacant area of ​​the household equipment, wherein the power supply area refers to the power supply coverage area corresponding to the battery energy storage module; The overlapping area of ​​the vacant area and the adjacent power supply area refers to the size of the intersection between the vacant area and its adjacent existing power supply area. The acquisition logic is to use the smart home map to clarify the geometric boundary of the vacant area, form a polygonal or rectangular area, obtain the boundary geometric area of ​​all adjacent power supply areas, calculate the intersection area of ​​each adjacent power supply area with the corresponding adjacent power supply area, and perform statistical calculations on the intersection area to obtain the overlapping area of ​​the vacant area and the adjacent power supply area; It should be noted that the overlapping area expresses the area of ​​the intersection of the power supply area near the geometric boundary of the vacant area and the remaining power supply areas. This area indicates that there is sufficient electric energy and can be divided into the vacant area to supplement the electric energy; The power supply area that can be covered by the redundant capacity of the adjacent power supply area refers to the remaining available power supply capacity in the current power supply area after the energy storage module meets the maximum power demand of all connected devices. The acquisition logic is to obtain the current output capacity of the energy storage module, and calculate the sum according to the real-time power consumption of all devices in the power supply area, subtract the current output capacity from the current power consumption sum to obtain the regional power supply capacity redundancy value, and calculate the ratio of the regional power supply capacity redundancy value to the power demand density of the vacant area to obtain the power supply area that can be covered by the redundant capacity of the adjacent power supply area; Specifically, the current output capacity is obtained based on the real-time status of the battery, including the current power and output efficiency. At the same time, the power demand density of the vacant area is obtained by the ratio of the total power demand of the equipment in the vacant area to the area of ​​the vacant area, which will not be described in detail here. If the overlapping area of ​​the vacant area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area are both less than or equal to 0, an alarm signal is sent to the visualization port of the home energy storage device to prompt the user to supplement the external power grid to meet the power demand of the vacant area; For example, "High", "Low", and "Medium" are for the overlapping area of ​​the vacant range and the power supply area that can be covered by the redundant capacity of the adjacent power supply area; Formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example: The overlapping area of ​​the adjacent power supply areas in the vacant range is marked as X, the power supply area that can be covered by the redundant capacity of the adjacent power supply areas is marked as U, and the vacant range supplement result is marked as C_Public; Then we can define: Rule 1: IF (X is High) AND (U is High) THEN (C_Public is High) Rule 2: IF (U is Low) AND (U is Low) THEN (C_Public is Low) ... Carry out fuzzy reasoning based on fuzzy rules to determine the supplementary plan for the vacancy range; It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although the present embodiment takes three fuzzy sets as examples, in fact, the vacant range area, the overlapping area of ​​adjacent power supply areas, and the power supply area that can be covered by the redundant capacity of adjacent power supply areas can be divided into more than three sets to facilitate better precise adjustment according to different signature algorithms.

[0025] Further, for the high, medium and low judgment of the overlapping area of ​​the vacant range area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area, a threshold can be set according to the actual situation for judgment. For example, when the overlapping area of ​​the vacant range area and the adjacent power supply area exceeds 80%, it is marked as "High", and when the power supply area that can be covered by the redundant capacity of the adjacent power supply area is less than 74%, it is marked as "Low", etc., which will not be elaborated here; The present invention obtains the vacant range area of ​​household equipment, collects the overlapping area of ​​the vacant range area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area, formulates a set of fuzzy rules for fuzzy reasoning, obtains the vacant range supplement result, improves the power supply balance of the household area, and improves the reliability and stability of the system power supply.

[0026] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0027] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0028] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0029] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0030] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0031] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0032] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0033] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0034] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0035] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed household energy storage integrated device charging and discharging management and control system, characterized by: It includes energy storage acquisition module, data processing module, area change module and vacancy supplement module; signal connection between each module; The energy storage acquisition module is used to collect the characteristic information of the power supply area of ​​the energy storage module and the charging characteristic information of the energy storage module. Through normalization processing, the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area and the maximum efficiency of battery charging energy efficiency are obtained and sent to the data processing module; The data processing module is used to obtain the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency, and establish a data analysis model to obtain the regional evaluation coefficient and send it to the regional change module; The regional change module is used to obtain the regional assessment coefficient and compare and analyze it with the preset regional threshold. According to the comparison results, the adjustment results of each region are determined. According to the current power supply coverage area of ​​each battery energy storage module and the initial power supply area, the addition and subtraction calculations of the corresponding areas are performed to obtain the vacant area of ​​the household equipment and send it to the vacancy supplement module; The vacancy supplement module is used to obtain the vacancy range area of ​​household equipment, collect the overlapping area of ​​adjacent power supply areas of the vacancy range area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area, and substitute it into fuzzy logic for fuzzy reasoning to obtain the vacancy range supplement result.

2. A distributed household energy storage integrated device charging and discharging management and control system according to claim 1, characterized in that: The energy storage module power supply area characteristic information includes the power supply area adjustment frequency, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, and the distance difference of all equipment in the power supply area; the energy storage module charging characteristic information includes the maximum efficiency of battery charging energy efficiency; Through the preset timestamp length, according to the regional adjustment times corresponding to the battery energy storage module in the timestamp, the ratio of the regional adjustment times to the timestamp length is calculated to obtain the power supply area adjustment frequency. ; Where i is the i-th timestamp; The difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation is obtained by calculating the sum of the peak power demands of all devices in the current timestamp and subtracting the sum of the possible power generation of distributed energy calculated by the preset prediction model within the current timestamp length. ; By substituting the geometric positions of all devices and the battery energy storage module in the power supply area of ​​the battery energy storage module at the current timestamp into the distance formula, the distance deviation from each device to the battery energy storage module is obtained and summed up to obtain the distance difference of all devices in the power supply area. ; The battery energy conversion efficiency is obtained by the ratio of the effective energy stored in the battery to the input electrical energy during the charging process. Then, multiple instantaneous battery energy conversion efficiencies are obtained according to the set timestamp, and the energy efficiency curve with charging current, voltage, and temperature is plotted. The maximum value is selected as the maximum battery charging energy efficiency. .

3. A distributed household energy storage integrated device charging and discharging management and control system according to claim 2, characterized in that: Obtain the adjustment frequency of the power supply area, the difference between the maximum power consumption of the equipment in the power supply area and the predicted power generation, the distance difference of all equipment in the power supply area, and the maximum efficiency of battery charging energy efficiency to establish a data analysis model and generate a regional evaluation coefficient , based on the formula: ； In the formula, is the regional assessment coefficient, , , as well as Adjust frequency for each power supply area , the difference between the maximum power consumption of equipment in the power supply area and the predicted power generation , the distance between all devices in the power supply area is poor And the maximum efficiency of battery charging energy efficiency The preset scaling factor of , , as well as Both are greater than 0.

4. A distributed household energy storage integrated device charge and discharge management and control system according to claim 3, characterized in that: After obtaining the regional assessment coefficient, the regional assessment coefficient is compared and analyzed with the continuously iterated regional threshold; If the regional evaluation coefficient is greater than or equal to the regional threshold, the power supply area corresponding to the current battery energy storage module is marked as a reduced area, and a reduction signal is generated; If the regional evaluation coefficient is less than the regional threshold, the power supply area corresponding to the current battery energy storage module is marked as regional increase, and an increase signal is generated.

5. A distributed household energy storage integrated device charging and discharging management and control system according to claim 4, characterized in that: The ratio of the regional assessment coefficient that is greater than or equal to the regional threshold is calculated with the regional threshold to determine how many times the regional assessment coefficient is the regional threshold, and the range value marked as the reduction of the region is set according to the multiple of the regional threshold. Conversely, the regional threshold is calculated with the regional assessment coefficient that is less than the regional threshold to determine how many times the regional threshold is the regional assessment coefficient, and the range value marked as the increase of the region is set according to the multiple of the regional assessment coefficient.

6. A distributed household energy storage integrated device charge and discharge management and control system according to claim 5, characterized in that: According to the reduced and increased range values, the adjustment results of the power supply areas corresponding to all battery energy storage modules are obtained, and the current power supply coverage area of ​​each battery energy storage module is obtained through mathematical area calculation; Perform mathematical area calculation based on the initial power supply area set by the household energy storage device to obtain the area of ​​the initial power supply area; The initialization power supply area and the current power supply coverage area of ​​each battery energy storage module are numerically calculated. If the current power supply coverage area of ​​each battery energy storage module is greater than or equal to the initialization power supply area, it means that the current household energy supply is met and an end signal is generated. Otherwise, a subtraction calculation is performed to determine the vacant area of ​​household equipment.

7. A distributed household energy storage integrated device charge and discharge management and control system according to claim 6, characterized in that: Determine the power supply area adjacent to the vacant area of ​​the household equipment through the vacant area of ​​the household equipment; Using the smart home map, the geometric boundaries of the vacant area are clarified to form a polygonal or rectangular area, and the boundary geometric areas of all adjacent power supply areas are obtained. For each adjacent power supply area, the intersection area with the corresponding adjacent power supply area is calculated, and the intersection area is statistically calculated to obtain the overlapping area of ​​the vacant area and the adjacent power supply area; By obtaining the current output capacity of the energy storage module and calculating the total according to the real-time power consumption of all equipment in the power supply area, the current output capacity is subtracted from the current power consumption sum to obtain the regional power supply capacity redundancy value, and the regional power supply capacity redundancy value is calculated by ratio with the power demand density of the vacant area to obtain the power supply area that can be covered by the redundant capacity of the adjacent power supply area.

8. A distributed household energy storage integrated device charge and discharge management and control system according to claim 7, characterized in that: The overlapping area of ​​the vacant area and the adjacent power supply area and the power supply area that can be covered by the redundant capacity of the adjacent power supply area are defined as input variables, and they are divided into different fuzzy sets respectively; The result of filling the vacancy range is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of the overlapping area of ​​adjacent power supply areas in the vacant area and the power supply area that can be covered by the redundant capacity of adjacent power supply areas on the results of filling the vacant area; Perform fuzzy reasoning based on fuzzy rules to determine the plan for filling the vacancy range.

Images