A distributed integrated household energy storage device charge and discharge management control system

Abstract

The present invention discloses a charging and discharging management control system for a distributed household energy storage integrated machine, which relates to the technical field of energy storage control and is used to solve the problem of resource waste caused by the inability to dynamically adjust each charging and discharging area. By collecting 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 the battery charging energy efficiency, a data analysis model is established to obtain a regional evaluation coefficient, which is compared and analyzed with a preset regional threshold to determine the adjustment results of each area. According to the power supply coverage area of each current battery energy storage module and the initial power supply area of the household, the vacant area of the household equipment is obtained, and a set of fuzzy rules is formulated based on the overlapping area of the adjacent power supply areas of the vacant area and the power supply area that can be covered by the redundant capacity of the adjacent power supply areas, so as to obtain the supplementary result of the vacant area and improve the power supply balance of the household area.

Description

Technical Field

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

[0002] With the rapid development of renewable energy and the popularization of energy Internet technology, household energy storage systems have played an important role in improving energy utilization efficiency, optimizing electricity costs, and supporting the development of green energy. Especially in the scenario of distributed energy access, a household energy storage integrated machine can combine energy storage devices with distributed energy (such as photovoltaic power generation) to provide reliable power protection for household users.

[0003] The existing technology has the following deficiencies:

[0004] Currently, the charging and discharging strategies of traditional household energy storage systems are often in a fixed mode, usually simple schedule control or manual operation. For complex electricity consumption scenarios, even if the charging and discharging plans can be dynamically adjusted, dynamic adjustment cannot be achieved for each charging and discharging area, resulting in resource waste, increasing the over-usage rate of the battery, and shortening the service life of the energy storage battery. Therefore, a charging and discharging management control system for a distributed household energy storage integrated machine is proposed.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a charging and discharging management control system for a distributed household energy storage integrated machine, which solves the problems raised in the above background art by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solution: A charging and discharging management control system for a distributed household energy storage integrated machine, including an energy storage acquisition module, a data processing module, a regional change module, and a vacancy supplement module; the modules are signal-connected to each other;

[0008] The energy storage acquisition module is used to collect the power supply area characteristic information and the energy storage module charging characteristic information of the energy storage module, and through normalization processing, obtain 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, and send them to the data processing module;

[0009] The data processing module is used to obtain the power supply area adjustment frequency, the difference between the maximum power consumption of the devices in the power supply area and the predicted power generation, the distance difference of all devices in the power supply area, and the maximum efficiency of the battery charging energy efficiency, and establish a data analysis model to obtain the area evaluation coefficient, and send it to the area change module;

[0010] The area change module is used to obtain the area evaluation coefficient, compare and analyze it with the preset area threshold, determine the adjustment results of each area according to the comparison result, and perform addition and subtraction calculations on the corresponding areas based on the current power supply coverage area of each battery energy storage module and the initial power supply area, to obtain the area of the home device vacancy range, and send it to the vacancy supplement module;

[0011] The vacancy supplement module is used to obtain the area of the home device vacancy range, collect the overlapping area of the 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 areas, and substitute them into fuzzy logic for fuzzy reasoning to obtain the vacancy range supplement result.

[0012] In a preferred embodiment, the power supply area characteristic information of the energy storage module includes the power supply area adjustment frequency, the difference between the maximum power consumption of the devices in the power supply area and the predicted power generation, and the distance difference of all devices in the power supply area; the charging characteristic information of the energy storage module includes the maximum efficiency of the battery charging energy efficiency;

[0013] By presetting the length of the time stamp, according to the number of area adjustments corresponding to the battery energy storage module within the time stamp, calculate the ratio of the number of area adjustments to the length of the time stamp to obtain the power supply area adjustment frequency ; where i is the i-th time stamp;

[0014] By calculating the sum of the peak power demand of all devices within the current time stamp in the power supply area minus the total possible power generation of distributed energy calculated by the preset prediction model within the current time stamp length, the difference between the maximum power consumption of the devices in the power supply area and the predicted power generation is obtained ;

[0015] 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 within the current time stamp into the distance formula to obtain the distance deviation of each device from the battery energy storage module and perform summation calculation, the distance difference of all devices in the power supply area is obtained ;

[0016] The battery energy conversion efficiency is obtained by the ratio of the effective energy stored in the battery during the charging process to the input electric energy. Then, within the set time stamp, multiple instantaneous battery energy conversion efficiencies are obtained, and the change curve of the energy efficiency with the charging current, voltage, and temperature is plotted, and the maximum value is selected as the maximum efficiency of the battery charging energy efficiency .

[0017] In a preferred embodiment, 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 the equipment in the power supply area, and the maximum efficiency of the battery charging energy efficiency are obtained to establish a data analysis model, and a regional evaluation coefficient is generated. , and the formula is:

[0018] ;

[0019] In the formula, is the regional evaluation coefficient, , , and are the preset proportional coefficients of 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 the equipment in the power supply area, and the maximum efficiency of the battery charging energy efficiency, respectively, and , , and are all greater than 0.

[0020] In a preferred embodiment, after obtaining the regional evaluation coefficient, the regional evaluation coefficient is compared and analyzed with the continuously iterated regional threshold;

[0021] 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 area reduction, and a reduction signal is generated;

[0022] 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 area increase, and an increase signal is generated.

[0023] In a preferred embodiment, the regional evaluation coefficient greater than or equal to the regional threshold is divided by the regional threshold to determine how many times the regional evaluation coefficient is of the regional threshold, and the range value marked as area reduction is set according to the multiple of the regional threshold. On the contrary, the regional threshold is divided by the regional evaluation coefficient less than the regional threshold to determine how many times the regional threshold is of the regional evaluation coefficient, and the range marked as area increase is set according to the multiple of the regional evaluation coefficient.

[0024] In a preferred embodiment, according to the reduction and increase range values, the adjustment results of the power supply areas corresponding to all the battery energy storage modules are obtained, and through mathematical area calculation, the power supply coverage area of each current battery energy storage module is obtained;

[0025] According to the initialized power supply area set by the home energy storage integrated machine, mathematical area calculation will be carried out to obtain the area of the initialized power supply area;

[0026] Perform numerical calculation on the initialized power supply area and the power supply coverage area of each current battery energy storage module. If the power supply coverage area of each current battery energy storage module is greater than or equal to the initialized power supply area, it indicates that the current home energy supply is satisfied, and an end signal is generated. Otherwise, subtraction calculation is performed to determine the area of the home equipment vacancy range.

[0027] In a preferred embodiment, determine the power supply area adjacent to the home equipment vacancy range area based on the home equipment vacancy range area;

[0028] Using the smart home map, clarify the geometric boundary of the vacant area to form a polygon or rectangular area. Obtain the boundary geometric area of all adjacent power supply areas, calculate the intersection area with each corresponding adjacent power supply area, and perform statistical calculation on the intersection areas to obtain the overlapping area of the power supply areas adjacent to the vacancy range area;

[0029] 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 total current power consumption to obtain the regional power supply capacity redundancy value. 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 redundancy capacity of the adjacent power supply area.

[0030] In a preferred embodiment, define the overlapping area of the power supply areas adjacent to the vacancy range area and the power supply area that can be covered by the redundancy capacity of the adjacent power supply area as input variables, and divide them into different fuzzy sets respectively;

[0031] Define the result of filling the vacancy range as the output variable and divide it into a fuzzy set;

[0032] Formulate fuzzy rules to describe the influence of the overlapping area of the power supply areas adjacent to the vacancy range area and the power supply area that can be covered by the redundancy capacity of the adjacent power supply area on the result of filling the vacancy range;

[0033] Perform fuzzy reasoning according to the fuzzy rules to determine the filling scheme for the vacancy range.

[0034] The technical effects and advantages of the present invention:

[0035] 1. The present invention collects 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 the equipment in the power supply area, and the maximum efficiency of the battery charging energy efficiency, establishes a data analysis model, 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 based on the current power supply coverage area of each battery energy storage module and the initialized power supply area, the corresponding area addition and subtraction calculations are performed to obtain the vacant area of the household equipment. For each charging and discharging area, dynamic adjustment is carried out to reduce resource waste, reduce the overuse rate of the battery, and improve the service life of the energy storage battery.

[0036] 2. The present invention obtains the vacant area of the household equipment, collects the overlapping area of the adjacent power supply areas of the vacant area and the power supply area that can be covered by the redundant capacity of the adjacent power supply areas, formulates a set of fuzzy rules for fuzzy reasoning, and obtains the supplementary result of the vacant area, so as to improve the power supply balance of the household area and improve the reliability and stability of the system power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the modules of a distributed household energy storage integrated machine charge and discharge management control system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0039] A distributed household energy storage integrated machine charge and discharge management control system of the present disclosure uses distributed installation for the energy storage points that collect electric energy. For example, energy storage battery modules are usually installed near the photovoltaic panels on the roof for quick collection of electric energy and release to the designated area, etc. Specifically, the household energy storage integrated machine is placed in the household living area and serves as a control center to control the charge and discharge strategies of the distributed energy storage battery modules distributed around the household and the dynamic adjustment of the discharge area respectively;

[0040] The present invention collects the power supply area characteristic information 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 based on 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 the 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 up an electric energy supplement mechanism to determine the electric energy supplement of the vacant area;

[0041] Embodiment 1: The present invention discloses a charge and discharge management control system for a distributed household energy storage integrated machine. As Figure 1 shown, it includes an energy storage acquisition module, a data processing module, a regional change module, and a vacancy supplement module; the modules are signal-connected to each other;

[0042] The energy storage acquisition module is used to collect the power supply area characteristic information and the energy storage module charging characteristic information of the energy storage module. Through normalization processing, 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 are obtained and sent to the data processing module;

[0043] Among them, normalization processing 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 influence of each feature on subsequent analysis or model training is more balanced. In the analysis of electronic scale data, normalization processing helps to eliminate the dimensional difference between different features and ensure the balanced contribution of each feature to the model;

[0044] Specifically, the normalization method is through Z-score standardization, and the specific formula is expressed as:

[0045]

[0046] In the formula, is the original data after normalization, is the original data, is the mean value of the original data, is the standard deviation of the original data;

[0047] Among them, the expression of the original data 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, the distance difference of all equipment in the power supply area, and the maximum efficiency of the battery charging energy efficiency, and the above formula is used for normalization processing and sent to the data processing module, which will not be elaborated here;

[0048] Specifically, the power supply area characteristic information of the energy storage module 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 the battery charging energy efficiency;

[0049] Among them, the power supply area adjustment frequency refers to the initialized power supply area set by the battery energy storage module through the household energy storage integrated machine. As time, the battery charging efficiency, and the equipment in the initialized power supply area change, the frequency of the area corresponding to the battery energy storage module is adjusted. Its acquisition logic is through a preset timestamp length. According to the number of area adjustments corresponding to the battery energy storage module within the timestamp, the ratio of the number of area adjustments to the timestamp length is calculated to obtain the power supply area adjustment frequency. ; where i is the i-th timestamp;

[0050] Among them, the preset timestamp length and the dynamic changes of adjacent timestamps are both set by the experimenter based on the average daily energy storage power and the average daily power supply of the energy storage battery module, which will not be elaborated here;

[0051] Specifically, the household energy storage integrated machine determines the area 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 area adjustments. Therefore, the energy storage acquisition module obtains the number of area adjustments corresponding to the battery energy storage module through the recorded information of the household energy storage integrated machine;

[0052] The difference between the maximum power consumption of the power supply area equipment and the predicted power generation refers to the difference between the maximum power consumption of all equipment in the timestamp in the area corresponding to the current battery energy storage module and the predicted power generation within the current timestamp length. Its acquisition logic is by calculating the sum of the peak power demand of all equipment within the current timestamp in the power supply area minus the sum of the possible power generation of distributed energy calculated through a preset prediction model within the current timestamp length, to obtain the difference between the maximum power consumption of the power supply area equipment and the predicted power generation. ;

[0053] 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 obtained by screening is not limited, but is obtained by the experimenter according to the specific implementation situation, which will not be elaborated here;

[0054] The distance difference of all equipment in the power supply area refers to the total distance difference between all equipment in the power supply area and the current battery energy storage module within the current timestamp. Its acquisition logic is by substituting the geometric positions of all equipment and the battery energy storage module in the power supply area of the battery energy storage module within the current timestamp into the distance formula to obtain the distance deviation of each equipment to the battery energy storage module and performing a summation calculation to obtain the distance difference of all equipment in the power supply area. ;

[0055] Specifically, let the set of equipment in the power supply area be denoted as , let the position of the battery energy storage module within the current timestamp be denoted as S;

[0056] Among them, the formula for obtaining the distance deviation of each device from the battery energy storage module is:

[0057]

[0058] In the formula, is the distance deviation of the j-th device from the battery energy storage module, , and are respectively the position points of the j-th device in the three-dimensional coordinates, , and are respectively the position points of the battery energy storage module in the three-dimensional coordinates;

[0059] The sum of the distance deviations of each device from the battery energy storage module is used to obtain the distance difference of all devices in the power supply area. The specific formula is as follows:

[0060]

[0061] In the formula, is the distance difference of all devices in the power supply area;

[0062] 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 through the wire length and the resistance per unit length. In this embodiment, expressing it in geometric distance can reflect the mobility of the device and lay a foundation for the subsequent analysis of inter-region adjustment; the electrical distance is more suitable for devices directly connected to the battery energy storage module and can more accurately analyze the redundant energy consumed in transmitting electric energy. Therefore, the calculation method of the distance difference of all devices in the power supply area is not specifically limited, but is implemented by the experimenter according to actual applications. For example, when the devices directly connected to the battery energy storage module in the power supply area are greater than 82%, the electrical distance is calculated, etc., which will not be elaborated here;

[0063] The maximum battery charging energy efficiency refers to the maximum value reached by the energy conversion efficiency of the battery system in the battery energy storage module during the charging process within the set timestamp. Its acquisition logic is to obtain the battery energy conversion efficiency through the ratio of the effective energy stored in the battery during the charging process to the input electric energy, and then, within the set timestamp, obtain multiple instantaneous battery energy conversion efficiencies and plot the curve of the energy efficiency varying with the charging current, voltage, and temperature, and select the maximum value as the maximum battery charging energy efficiency ;

[0064] Among them, the calculation formula for the battery energy conversion efficiency is as follows:

[0065]

[0066] In the formula, is the battery energy conversion efficiency, is the energy actually stored in the battery, is the instantaneous current measured currently, is the instantaneous voltage measured currently;

[0067] Specifically, the curves drawn are different at each timestamp. Due to factors such as the battery operation duration and the battery health state score, there are relative differences in the energy conversion efficiency of the battery. Therefore, the charging state of the current battery and the operation state of the photovoltaic panel can be analyzed from the drawn graph, etc., which will not be elaborated here;

[0068] The data processing module is used to obtain 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, establish a data analysis model, obtain the area evaluation coefficient, and send it to the area change module;

[0069] Among them, the data analysis model refers to a weighted analysis model, and the area evaluation coefficient is generated through weighted calculation;

[0070] Obtain 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, establish a data analysis model, and generate the area evaluation coefficient , and the formula based on is:

[0071]

[0072] In the formula, is the area evaluation coefficient, , , and are respectively the preset proportional coefficients of 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 , and , , and are all greater than 0;

[0073] Among them, 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 are all digital manifestations directly expressing whether the power supply area corresponding to the current battery energy storage module is adjusted;

[0074] 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.

[0075] 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;

[0076] 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.

[0077] 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;

[0078] After obtaining the regional assessment coefficient, the regional assessment coefficient is compared and analyzed with the continuously iterated regional threshold;

[0079] 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;

[0080] 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;

[0081] 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;

[0082] Calculate the ratio of the area evaluation coefficient greater than or equal to the area threshold to the area threshold to determine how many times the area evaluation coefficient is of the area threshold. Set the range value marked as area reduction according to the multiple of the area threshold. Conversely, calculate the ratio of the area threshold to the area evaluation coefficient less than the area threshold to determine how many times the area threshold is of the area evaluation coefficient, and set the range value marked as area increase according to the multiple of the area evaluation coefficient;

[0083] For example, calculate the ratio of the area evaluation coefficient greater than or equal to the area threshold to the area threshold, and get that the area evaluation coefficient is 1.5 times of the area threshold. Then, the power supply area corresponding to the battery energy storage module is reduced by 1.5 times or increased by 0.75 times of the current area reduction range. 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 will not be elaborated here;

[0084] According to the reduction and increase range values, obtain the adjustment results of the power supply areas corresponding to all battery energy storage modules. Through mathematical area calculation, obtain the power supply coverage area of each current battery energy storage module;

[0085] Perform mathematical area calculation on the initialized power supply area set according to the household energy storage integrated machine to obtain the initialized power supply area;

[0086] 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;

[0087] Perform numerical calculation on the initialized power supply area and the power supply coverage area of each current battery energy storage module. If the power supply coverage area of each current battery energy storage module is greater than or equal to the initialized power supply area, it means that the current household energy supply is satisfied, and an end signal is generated. Conversely, perform subtraction calculation to determine the area of the vacant range of household equipment;

[0088] The present invention collects 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, establishes a data analysis model, obtains the area evaluation coefficient, and compares and analyzes it with the preset area threshold. According to the comparison result, determines the adjustment results of each area, and according to the power supply coverage area of each current battery energy storage module and the initialized power supply area, and performs addition and subtraction calculations on the corresponding areas to obtain the area of the vacant range of household equipment, dynamically adjusts each charging and discharging area, reduces resource waste, reduces the over-usage rate of the battery, and improves the service life of the energy storage battery.

[0089] Embodiment 2: In Embodiment 1 of the present invention, 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 the equipment in the power supply area, and the maximum efficiency of the battery charging energy efficiency are mainly exemplified. A data analysis model is established to obtain the area evaluation coefficient, which is compared and analyzed with the preset area threshold. According to the comparison result, the adjustment result of each area is determined, and based on the current power supply coverage area of each battery energy storage module and the initialized power supply area area, the addition and subtraction calculations of the corresponding areas are performed to obtain the operation strategy for the area where the household equipment vacancy range area; however, in Embodiment 1, only the power supply coverage range of the battery energy storage module is analyzed, without considering the situation of the existing vacant power supply area. 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; for the above problems, Embodiment 2 of the present invention is further refined;

[0090] The vacancy supplement module is used to obtain the area of the household equipment vacancy range, collect the overlapping area of the 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 areas, and substitute them into the fuzzy logic for fuzzy reasoning to obtain the vacancy range supplement result;

[0091] Specifically, the power supply area adjacent to the household equipment vacancy range area is determined through the household equipment vacancy range area, where the power supply area refers to the power supply coverage area corresponding to the battery energy storage module;

[0092] The overlapping area of the adjacent power supply areas of the vacancy range area refers to the area size of the intersection part between the vacancy area and its adjacent existing power supply areas. Its acquisition logic is to use the smart home map to clarify the geometric boundary of the vacancy area, form a polygon or rectangular area, obtain the boundary geometric areas of all adjacent power supply areas, calculate the intersection area with the corresponding adjacent power supply area for each adjacent power supply area, and perform statistical calculations on the intersection areas to obtain the overlapping area of the adjacent power supply areas of the vacancy range area;

[0093] It should be noted that the overlapping area represents the area of the intersection of the power supply area near the geometric boundary of the vacancy area and the rest of the power supply areas. This part of the area indicates that the electric energy is abundant and can be divided into the vacancy area to supplement the electric energy;

[0094] The power supply area that can be covered by the redundancy capacity of adjacent power supply areas refers to the remaining available power supply capacity in the current power supply area after the energy storage module meets the maximum power consumption requirements of all connected devices. The acquisition logic is to obtain the current output capacity of the energy storage module and calculate the total sum based on the real-time power consumption of all devices in the power supply area, and then subtract the total current power consumption from the current output capacity to obtain the redundancy value of the regional power supply capacity. Divide the redundancy value of the regional power supply capacity by the power demand density of the vacant area to obtain the power supply area that can be covered by the redundancy capacity of adjacent power supply areas;

[0095] Specifically, the current output capacity is obtained based on the real-time state of the battery, including the current battery level and output efficiency. At the same time, the power demand density of the vacant area is obtained by dividing the total power demand of the devices in the vacant area by the area of the vacant area, which will not be elaborated here;

[0096] Among them, if the area of the vacant range, the overlapping area of adjacent power supply areas, and the power supply area that can be covered by the redundancy capacity of adjacent power supply areas are all less than or equal to 0, an alarm signal will be sent to the visualization port of the home energy storage integrated machine to prompt the user to supplement the external power grid to meet the power demand of the vacant area;

[0097] For example, "High", "Low", "Medium" for the area of the vacant range, the overlapping area of adjacent power supply areas, and the power supply area that can be covered by the redundancy capacity of adjacent power supply areas;

[0098] Formulate a set of fuzzy rules to describe the influence of different input variables on the output variable. The definition of the rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0099] Mark the area of the vacant range and the overlapping area of adjacent power supply areas as X, the power supply area that can be covered by the redundancy capacity of adjacent power supply areas as U, and the supplementary result of the vacant range as C_Public;

[0100] Then it can be defined as:

[0101] Rule 1: IF (X is High) AND (U is High) THEN (C_Public is High)

[0102] Rule 2: IF (U is Low) AND (U is Low) THEN (C_Public is Low) ...

[0103] Conduct fuzzy reasoning according to the fuzzy rules to determine the supplementary plan for the vacant range;

[0104] It should be noted that the division of the fuzzy sets can be adjusted according to the actual situation. For example, although three fuzzy sets are taken as an example in this embodiment, in fact, the power supply area covered by the overlapping area of the adjacent power supply areas of the vacant range area and the redundant capacity of the adjacent power supply areas can be divided into more than three sets to facilitate more accurate adjustment according to different signature algorithms.

[0105] Furthermore, for the high, medium, and low judgments of the power supply area covered by the overlapping area of the adjacent power supply areas of the vacant range area and the redundant capacity of the adjacent power supply areas, thresholds can be set according to the actual situation for judgment. For example, when the overlapping area of the adjacent power supply areas of the vacant range area exceeds 80%, it is labeled as "High", and when the power supply area covered by the redundant capacity of the adjacent power supply areas is less than 74%, it is labeled as "Low", etc., which will not be elaborated here;

[0106] The present invention obtains the vacant range area of the household equipment, collects the overlapping area of the adjacent power supply areas of the vacant range area and the power supply area covered by the redundant capacity of the adjacent power supply areas, 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 enhancing the reliability and stability of the system power supply.

[0107] The above formulas are all calculated by taking the numerical values after dimensionless. The formulas are obtained by software simulation of a large amount of collected data to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0109] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0110] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0111] 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 foregoing method embodiments, and will not be described herein again.

[0112] In 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 merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0113] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0115] 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, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0116] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to 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 ​​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; The characteristic information of the power supply area of ​​the energy storage module 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, and the distance difference between 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. ; 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.

2. A distributed household energy storage integrated device charging and discharging management and control system according to claim 1, 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.

3. A distributed household energy storage integrated device charge and discharge management and control system according to claim 2, 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.

4. A distributed household energy storage integrated device charge and discharge management and control system according to claim 3, 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.

5. A distributed household energy storage integrated device charging and discharging management and control system according to claim 4, 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 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.

6. A distributed household energy storage integrated device charge and discharge management and control system according to claim 5, 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.

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